From 11df0e822fa3fba2365ff10baf6f43ce4de2bc1f Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Wed, 19 Jun 2024 13:45:40 +0200 Subject: [PATCH] Remove /livepapers resource, since the livepaper API has been separated into its own repository at https://gitlab.ebrains.eu/live-papers/live-papers-api --- .../validation_service/data_models.py | 476 ----- .../validation_service/db.py | 17 - .../validation_service/examples.py | 1633 +---------------- .../validation_service/main.py | 3 +- .../resources/livepapers.py | 356 ---- .../validation_service/tests/fixtures.py | 50 - .../0433eafb-4044-4f58-9d20-7dad2c69d39a.json | 392 ---- .../04ac2988-b717-469d-88ce-d54d02036eb1.json | 86 - .../0b52fda9-bfe5-4479-9fb0-7b6af8384c31.json | 88 - .../0d6f04db-8886-4553-b950-a90fdf339b4a.json | 79 - .../15c1fb11-6239-4fef-b62c-e56bb065f100.json | 200 -- .../1c1f53e4-55d1-45a3-a63a-a16c491a07f4.json | 135 -- .../203d1466-8792-4b05-b546-09ee178387c3.json | 151 -- .../3dce6a30-b879-4393-9c38-2f5906f949ad.json | 101 - .../42a90bce-d52c-4e55-b6a8-3ec5c14a828f.json | 219 --- .../44f1d119-6233-44c4-9584-b2352a4c254e.json | 91 - .../5ec5bd1f-6e72-4c17-8c45-6a206c2c0c72.json | 790 -------- .../67806cc2-84e0-4bb3-ae52-8cc3e5abf738.json | 137 -- .../84b9eb9f-996c-4b03-b81b-fb7871424b62.json | 196 -- .../88f46758-bb66-4a0f-a1b2-e914d93d2978.json | 88 - .../93a5c03a-6995-47bc-af9f-4f0d85950d1d.json | 124 -- .../9c00022b-82be-435e-b23f-bf4ee4cacc28.json | 210 --- .../9d00321b-f927-4153-8c98-8f59e24bd5c6.json | 81 - .../9ef99ad2-233a-49d1-9499-6c1b6dd641f6.json | 81 - .../b11ca08c-3fad-4020-85ca-adb2fd58541b.json | 219 --- .../b3816c12-2d3a-430e-a6d4-139f0a132de7.json | 136 -- .../b6917332-e092-4bf3-bf31-3f0d212ff861.json | 145 -- .../bee280cc-8184-4380-a2cb-a74b131de611.json | 545 ------ .../c1573aeb-d139-42a2-a7fc-fd68319e428e.json | 1312 ------------- .../cb7e5f66-0984-4f0c-acad-6281be4bb5c9.json | 81 - .../cbafb007-ff16-4171-b3eb-d97de7069c76.json | 128 -- .../cf895d83-49b8-4c72-b1ac-8b974bbe4eb5.json | 118 -- .../d36d1804-5b02-4e9b-bfbd-04f0de59686b.json | 84 - .../f633acd5-cab7-4d33-a9bb-4d167524c861.json | 278 --- .../tests/test_data/livepapers/summary.json | 350 ---- .../tests/test_data_models.py | 80 +- .../tests/test_livepapers.py | 225 --- 37 files changed, 3 insertions(+), 9482 deletions(-) delete mode 100644 validation_service_api/validation_service/resources/livepapers.py delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/0433eafb-4044-4f58-9d20-7dad2c69d39a.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/04ac2988-b717-469d-88ce-d54d02036eb1.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/0b52fda9-bfe5-4479-9fb0-7b6af8384c31.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/0d6f04db-8886-4553-b950-a90fdf339b4a.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/15c1fb11-6239-4fef-b62c-e56bb065f100.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/1c1f53e4-55d1-45a3-a63a-a16c491a07f4.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/203d1466-8792-4b05-b546-09ee178387c3.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/3dce6a30-b879-4393-9c38-2f5906f949ad.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/42a90bce-d52c-4e55-b6a8-3ec5c14a828f.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/44f1d119-6233-44c4-9584-b2352a4c254e.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/5ec5bd1f-6e72-4c17-8c45-6a206c2c0c72.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/67806cc2-84e0-4bb3-ae52-8cc3e5abf738.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/84b9eb9f-996c-4b03-b81b-fb7871424b62.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/88f46758-bb66-4a0f-a1b2-e914d93d2978.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/93a5c03a-6995-47bc-af9f-4f0d85950d1d.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/9c00022b-82be-435e-b23f-bf4ee4cacc28.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/9d00321b-f927-4153-8c98-8f59e24bd5c6.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/9ef99ad2-233a-49d1-9499-6c1b6dd641f6.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/b11ca08c-3fad-4020-85ca-adb2fd58541b.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/b3816c12-2d3a-430e-a6d4-139f0a132de7.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/b6917332-e092-4bf3-bf31-3f0d212ff861.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/bee280cc-8184-4380-a2cb-a74b131de611.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/c1573aeb-d139-42a2-a7fc-fd68319e428e.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/cb7e5f66-0984-4f0c-acad-6281be4bb5c9.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/cbafb007-ff16-4171-b3eb-d97de7069c76.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/cf895d83-49b8-4c72-b1ac-8b974bbe4eb5.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/d36d1804-5b02-4e9b-bfbd-04f0de59686b.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/f633acd5-cab7-4d33-a9bb-4d167524c861.json delete mode 100644 validation_service_api/validation_service/tests/test_data/livepapers/summary.json delete mode 100644 validation_service_api/validation_service/tests/test_livepapers.py diff --git a/validation_service_api/validation_service/data_models.py b/validation_service_api/validation_service/data_models.py index 9cb3c984..62603a58 100644 --- a/validation_service_api/validation_service/data_models.py +++ b/validation_service_api/validation_service/data_models.py @@ -1754,107 +1754,6 @@ def __hash__(self): return hash(f"{self.lastname}, {self.firstname}") -class LivePaperDataItem(BaseModel): - url: HttpUrl - label: str - view_url: HttpUrl = None - type: str = None # todo: make this an Enum - identifier: UUID = None - - @classmethod - def from_kg_object(cls, data_item, kg_client): - if isinstance(data_item, KGProxy): - data_item = data_item.resolve(kg_client, scope="any") - service_links = as_list(omcore.ServiceLink.list(kg_client, scope="any", space=data_item.space, data_location=data_item)) - if service_links: - view_url = service_links[0].open_data_in.value - else: - view_url = None - if data_item.hosted_by is None: # or data_item.resource_type.name == "URL": - return cls( - url=data_item.iri.value, - label=data_item.name, - view_url=view_url, - type="URL", - identifier=data_item.uuid - ) - else: - resource_type = data_item.hosted_by.resolve(kg_client, scope="any") # todo: use scope='released' - return cls( - url=data_item.iri.value, - label=data_item.name, - type=resource_type.name, - identifier=data_item.uuid - ) - - def to_kg_objects(self, kg_live_paper_section, kg_live_paper, kg_client): - if self.identifier: - identifier = self.identifier - else: - namespace = UUID('6669a40d-9afd-4ec6-aa23-7893c3b0ded1') - identifier = uuid5(namespace, - (self.url + self.label + kg_live_paper_section.name - + kg_live_paper.name)) - id = kg_client.uri_from_uuid(identifier) - resource_item = ompub.LivePaperResourceItem( - id=str(id), - name=self.label, - iri=self.url, - #view_url=self.view_url, - hosted_by=term_cache["Organization"]["names"].get(self.type, None), # todo: filter the organizations to keep only those that represent repositories - is_part_of=kg_live_paper_section - ) - kg_objects = [resource_item] - if self.view_url: - service_link = omcore.ServiceLink( - display_label=self.label, - data_location=resource_item, - open_data_in=IRI(self.view_url), - service=lookup_service(self.view_url), - preview_image=None - ) - kg_objects.append(service_link) - return kg_objects - - -class LivePaperSection(BaseModel): - order: int - type: str # todo: make this an Enum - title: str - icon: str = None - description: str = None - data: List[LivePaperDataItem] - - @classmethod - def from_kg_object(cls, section, kg_client): - if isinstance(section, KGProxy): - section = section.resolve(kg_client, scope="any") - resource_items = ompub.LivePaperResourceItem.list(kg_client, size=1000, scope="any", space=section.space, is_part_of=section) - return cls( - order=int(section.order), - type=section.type, - title=section.name, - icon=None, # todo: generate based on section_type - description=section.description, - data=sorted([LivePaperDataItem.from_kg_object(item, kg_client) - for item in as_list(resource_items)], key=lambda item: item.label) - ) - - def to_kg_objects(self, kg_live_paper, kg_client): - section = ompub.LivePaperSection( - order=self.order, - type=self.type, - name=self.title, - description=self.description, - is_part_of=kg_live_paper) - data_items = sum([ - obj.to_kg_objects(kg_live_paper_section=section, - kg_live_paper=kg_live_paper, - kg_client=kg_client) - for obj in self.data], []) - return [section] + data_items - - def slugify(name): slug = "" for char in name.replace(" ", "-"): @@ -1863,371 +1762,6 @@ def slugify(name): return slug.lower() -class LivePaper(BaseModel): - lp_tool_version: str = "0.1" - id: UUID = None - alias: Slug = None - modified_date: datetime - version: str = None - authors: List[PersonWithAffiliation] - corresponding_author: List[PersonWithAffiliation] = None - created_author: List[PersonWithAffiliation] = None - approved_author: PersonWithAffiliation = None - year: date = None - live_paper_title: str - associated_paper_title: str = None - journal: str = None - url: HttpUrl = None - citation: str = None - doi: HttpUrl = None - associated_paper_doi: HttpUrl = None - associated_paper_volume: str = None - associated_paper_issue: str = None - associated_paper_pagination: str = None - abstract: str = None - license: str = None - resources_description: str = None - collab_id: str - resources: List[LivePaperSection] - - @classmethod - def from_kg_object(cls, lp, kg_client): - def get_people(obj, reference_date=None): - if obj is None: - return None - return [ - PersonWithAffiliation.from_kg_object(p, kg_client, reference_date=reference_date) - for p in as_list(obj) - ] - - def get_person(obj, reference_date=None): - if obj is None: - return None - return PersonWithAffiliation.from_kg_object(obj, kg_client, reference_date=reference_date) - - scope = lp.scope or "any" - lpv = as_list(lp.versions)[-1] # todo: sort by release_date and/or last_modified - lpv = lpv.resolve(kg_client, scope=scope) - - related_publication_identifiers = as_list(lpv.related_publications) - associated_paper_title = None - associated_paper_release_date = None - associated_paper_doi = None - associated_paper_url = None - associated_paper_abstract = None - associated_paper_citation = None - associated_paper_volume = None - associated_paper_issue = None - associated_paper_pagination = None - corresponding_author = None - journal_name = None - - if len(related_publication_identifiers) > 0: - related_publication_identifier = related_publication_identifiers[0].resolve(kg_client, scope=scope) #? or maybe need to check both scopes - # to do: generalise the following to chapter, book, ... - if isinstance(related_publication_identifier, ompub.ScholarlyArticle): - related_publication = related_publication_identifier - related_publications = [related_publication] - elif isinstance(related_publication_identifier, omcore.DOI): - related_publications = as_list(ompub.ScholarlyArticle.list(kg_client, scope=scope, - digital_identifier=related_publication_identifier)) - if len(related_publications) > 0: - related_publication = related_publications[0].resolve( - kg_client, - follow_links={ - "digital_identifier": {}, - "is_part_of": {"is_part_of": {}}, - "custodians": {} - }, - scope=scope - ) - else: - related_publication = None - else: - related_publication = None - related_publications = [] - if related_publication: - related_publication.resolve( - kg_client, - scope=scope, - follow_links={ - "digital_identifier": {}, - "is_part_of": {"is_part_of": {}}, - "custodians": {} - } - ) - associated_paper_title = related_publication.name - associated_paper_release_date = related_publication.publication_date - associated_paper_doi = related_publication.digital_identifier.identifier if related_publication.digital_identifier else None - associated_paper_url = related_publication.iri.value if related_publication.iri else None - associated_paper_abstract = related_publication.abstract - associated_paper_citation = related_publication.get_citation_string(kg_client) - associated_paper_pagination = related_publication.pagination - corresponding_author = get_people(related_publication.custodians, reference_date=associated_paper_release_date) - journal_info = related_publication.get_journal(kg_client, True, True) if related_publication.is_part_of else None - if journal_info: - (_journal, _volume, _issue) = journal_info - journal_name = _journal.name if _journal else None - associated_paper_volume = _volume.volume_number if _volume else None - associated_paper_issue = _issue.issue_number if _issue else None - if associated_paper_volume == "placeholder": - associated_paper_volume = None - else: - related_publications = [] - original_authors = [] - for rel_pub in related_publications: - original_authors.extend(get_people(rel_pub.authors, reference_date=associated_paper_release_date)) - # now remove possible duplicates - original_authors = list(dict.fromkeys(original_authors)) - - custodians = lpv.custodians or lp.custodians - sections = ompub.LivePaperSection.list(kg_client, size=1000, scope="any", space=lp.space, is_part_of=lpv) - if lp.space.startswith("collab-"): - collab_id = lp.space[7:] - else: - collab_id = lp.space - live_paper_doi = None - if lpv.digital_identifier: - try: - live_paper_doi = lpv.digital_identifier.resolve(kg_client, scope=scope).identifier - except ResolutionFailure as err: - logger.warn(str(err)) - return cls( - modified_date=lpv.modification_date, - alias=lp.alias, - version=lpv.version_identifier, - authors=original_authors, - corresponding_author=corresponding_author, - created_author=get_people(lp.authors, reference_date=associated_paper_release_date), - approved_author=get_person(as_list(custodians)[0], reference_date=associated_paper_release_date) if custodians else None, - year=associated_paper_release_date, # what if multiple related pubs - for now we assume only one - associated_paper_title=associated_paper_title, - live_paper_title=lpv.name or lp.name, - journal=journal_name, - associated_paper_volume=associated_paper_volume, - associated_paper_issue=associated_paper_issue, - associated_paper_pagination=associated_paper_pagination, - url=associated_paper_url, - citation=associated_paper_citation, - doi=live_paper_doi, - associated_paper_doi=associated_paper_doi, - abstract=associated_paper_abstract, - license=term_cache["License"]["ids"].get(lpv.license.id, None).name if lpv.license else None, - collab_id=collab_id, - resources_description=lpv.description or lp.description, - resources=sorted([LivePaperSection.from_kg_object(sec, kg_client) - for sec in as_list(sections)], - key=lambda sec: sec.order), - id=lp.uuid - ) - - def to_kg_objects(self, kg_client): - original_authors = [p.to_kg_object(kg_client) for p in self.authors] - if self.approved_author: - custodian = self.approved_author.to_kg_object(kg_client) - else: - custodian = None - live_paper_authors = [p.to_kg_object(kg_client) for p in as_list(self.created_author)] - - def get_journal_volume_issue(journal_name, volume, issue): - journal = ompub.Periodical.by_name(journal_name, kg_client, scope="any") # also check "released" - volume = ompub.PublicationVolume(is_part_of=journal, volume_number=volume or "placeholder") - if issue: - return ompub.PublicationIssue(is_part_of=volume, issue_number=issue) - else: - return volume - - if self.associated_paper_title: - date_published = None - journal_info = None - if self.year: - date_published = self.year - if self.journal: - journal_info = get_journal_volume_issue(self.journal, self.associated_paper_volume, self.associated_paper_issue) - - related_pub = ompub.ScholarlyArticle( - name=self.associated_paper_title, - iri=self.url, - authors=original_authors, - custodians=self.corresponding_author[0].to_kg_object(kg_client), - digital_identifier=omcore.DOI(identifier=self.associated_paper_doi) if self.associated_paper_doi else None, - is_part_of=journal_info, - pagination=self.associated_paper_pagination, - publication_date=date_published, - abstract=self.abstract - ) - else: - related_pub = None - alias = self.alias - if alias is None: - alias = slugify(self.live_paper_title) - version = self.version or "v0" - if related_pub: - if related_pub.digital_identifier: - associated_publication = related_pub.digital_identifier - else: - associated_publication = related_pub - else: - associated_publication = None - lpv = ompub.LivePaperVersion( - name=self.live_paper_title, - alias=f"{alias}-{version}", - modification_date=self.modified_date, - version_identifier=version, - related_publications=associated_publication, - license=term_cache["License"]["names"].get(self.license, None) - ) - lp = ompub.LivePaper( - name=self.live_paper_title, - alias=alias, - authors=live_paper_authors, - custodians=custodian, - description=self.resources_description, - digital_identifier=omcore.DOI(identifier=self.doi) if self.doi else None, - versions=[lpv] - ) - if self.id: - lp.id = lp.__class__.uri_from_uuid(self.id, kg_client) - sections = sum([section.to_kg_objects(lpv, kg_client) for section in self.resources], []) - people = {} - for person in original_authors + live_paper_authors + [custodian]: - people[person.full_name] = person - #breakpoint() - return { - "people": people.values(), - "sections": sections, - "paper": [related_pub, lp] if related_pub else [lp] - } - - -class LivePaperSummary(BaseModel): - id: UUID - detail_path: str - modified_date: datetime - citation: str = None - live_paper_title: str - associated_paper_title: str = None - year: date = None - collab_id: str = None - doi: str = None - alias: str = None - - @classmethod - def from_kg_object(cls, lp, kg_client): - scope = lp.scope or "any" - lpv = as_list(lp.versions)[-1] # todo: sort by release_date and/or last_modified - try: - lpv = lpv.resolve(kg_client, scope=scope) - except ResolutionFailure as err: - # this shouldn't happen in general, but sometimes occurs on staging - # when kg-ppd is in an inconsistent state - logger.warning(str(err)) - return None - - associated_paper_title = None - associated_paper_release_date = None - associated_paper_citation = None - related_publication_identifiers = as_list(lpv.related_publications) - if len(related_publication_identifiers) > 0: - try: - related_publication_identifier = related_publication_identifiers[0].resolve(kg_client, scope=scope) - except ResolutionFailure as err: - logger.warn(str(err)) - related_publication_identifier = None - # to do: generalise the following to chapter, book, ... - # also search in livepapers space if that's different from lp.space - related_publication = None - if related_publication_identifier: - if isinstance(related_publication_identifier, ompub.ScholarlyArticle): - related_publication = related_publication_identifier - elif isinstance(related_publication_identifier, omcore.DOI): - related_publications = as_list(ompub.ScholarlyArticle.list(kg_client, scope=scope, space=lp.space, - digital_identifier=related_publication_identifier)) - if len(related_publications) > 0: - related_publication = related_publications[0].resolve( - kg_client, - follow_links={ - "digital_identifier": {}, - "is_part_of": {"is_part_of": {}}, - "custodians": {} - }, - scope=scope - ) - else: - logger.warn(f"Can't handle {type(related_publication_identifier)} yet") - if related_publication: - associated_paper_title = related_publication.name - associated_paper_release_date = related_publication.publication_date - associated_paper_citation = related_publication.get_citation_string(kg_client) - if lpv.digital_identifier: - try: - lp_doi = lpv.digital_identifier.resolve(kg_client, scope=scope).identifier - except ResolutionFailure as err: - logger.warn(str(err)) - lp_doi = None - else: - lp_doi = None - if lp.space.startswith("collab-"): - collab_id = lp.space[7:] - else: - collab_id = lp.space - #breakpoint() - try: - obj = cls( - modified_date=lpv.modification_date, - live_paper_title=lpv.name or lp.name, - associated_paper_title=associated_paper_title, - citation=associated_paper_citation, - year=associated_paper_release_date, - collab_id=collab_id, - doi=lp_doi, - alias=lp.alias, - id=lp.uuid, - detail_path=f"/livepapers/{lp.uuid}" - ) - except ValidationError as err: - logger.error(f"Unable to return LivePaperSummary: {err}") - obj = None - return obj - - @classmethod - def from_kg_query(cls, item, client): - uuid=client.uuid_from_uri(item["id"]) - if item["versions"]: - versions = sorted(item["versions"], key=lambda ver: ver["modified_date"]) - lpv = versions[-1] - associated_paper_title = None - associated_paper_release_date = None - associated_paper_citation = None - if lpv["related_publication"]: - rel_pub = lpv["related_publication"]["citation_data"] - associated_paper_title = rel_pub["title"] - associated_paper_release_date = datetime.fromisoformat(rel_pub["publication_date"]) - associated_paper_citation = get_citation_string(rel_pub) - if item["space"].startswith("collab-"): - collab_id = item["space"][7:] - else: - collab_id = item["space"] - try: - obj = cls( - modified_date=lpv["modified_date"], - live_paper_title=lpv["name"] or item["name"], - associated_paper_title=associated_paper_title, - citation=associated_paper_citation, - year=associated_paper_release_date, - collab_id=collab_id, - doi=lpv.get("lp_doi", None), - alias=item["alias"], - id=uuid, - detail_path=f"/livepapers/{uuid}" - ) - except ValidationError as err: - logger.error(f"Unable to return LivePaperSummary for uuid {uuid}: {err}") - obj = None - else: - logger.error(f"Unable to return LivePaperSummary: no versions. Item name is {item['name']} with uuid {uuid}") - obj = None - return obj def get_citation_string(citation_data): @@ -2250,13 +1784,3 @@ def get_citation_string(citation_data): pagination = citation_data["pagination"] date_published = datetime.fromisoformat(citation_data["publication_date"]) return f"{author_str} ({date_published.year}). {title} {journal_name}, {volume_number}: {pagination}." - - -class AccessCode(BaseModel): - value: str - - @validator('value') - def min_length(cls, value): - if len(value) < 6: - raise ValueError("access code must contain at least six characters") - return value diff --git a/validation_service_api/validation_service/db.py b/validation_service_api/validation_service/db.py index 3b29db5b..b42a8a98 100644 --- a/validation_service_api/validation_service/db.py +++ b/validation_service_api/validation_service/db.py @@ -8,7 +8,6 @@ from fastapi import HTTPException, status from fairgraph.openminds.core import Model, ModelVersion, SoftwareVersion from fairgraph.openminds.computation import ValidationTest, ValidationTestVersion -from fairgraph.openminds.publications import LivePaper from . import settings RETRY_INTERVAL = 60 # seconds @@ -94,19 +93,3 @@ def _get_test_instance_by_id(instance_id, kg_client, scope): detail=f"Test instance with ID '{instance_id}' not found.", ) return test_instance - - -def _get_live_paper_by_id_or_alias(lp_id, kg_client, scope): - - if isinstance(lp_id, UUID): - identifier_type = "ID" - live_paper = LivePaper.from_uuid(str(lp_id), kg_client, scope=scope) - else: - identifier_type = "alias" - live_paper = LivePaper.from_alias(lp_id, kg_client, space=LivePaper.default_space, scope=scope) - if not live_paper: - raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, - detail=f"Live paper with {identifier_type} '{lp_id}' not found.", - ) - return live_paper diff --git a/validation_service_api/validation_service/examples.py b/validation_service_api/validation_service/examples.py index bfa7d141..0c017805 100644 --- a/validation_service_api/validation_service/examples.py +++ b/validation_service_api/validation_service/examples.py @@ -52,1635 +52,4 @@ }, ], }, - "LivePaper": { - "lp_tool_version": "0.1", - "id": "c1573aeb-d139-42a2-a7fc-fd68319e428e", - "alias": "2018-migliore-et-al", - "modified_date": "2021-08-10T09:26:55.917000+00:00", - "version": None, - "authors": [ - { - "firstname": "Rosanna", - "lastname": "Migliore", - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy" - }, - { - "firstname": "Carmen A.", - "lastname": "Lupascu", - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy" - }, - { - "firstname": "Luca L.", - "lastname": "Bologna", - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy" - }, - { - "firstname": "Armando", - "lastname": "Romani", - "affiliation": "Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland" - }, - { - "firstname": "Jean-Denis", - "lastname": "Courcol", - "affiliation": "Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland" - }, - { - "firstname": "Stefano", - "lastname": "Antonel", - "affiliation": "Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland" - }, - { - "firstname": "Werner A.H.", - "lastname": "Van Geit", - "affiliation": "Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland" - }, - { - "firstname": "Alex M.", - "lastname": "Thomson", - "affiliation": "UCL School of Pharmacy, University College London, London, UK" - }, - { - "firstname": "Audrey", - "lastname": "Mercer", - "affiliation": "UCL School of Pharmacy, University College London, London, UK" - }, - { - "firstname": "Sigrun", - "lastname": "Lange", - "affiliation": "UCL School of Pharmacy, University College London, London, UK; School of Life Sciences, University of Westminster, London, UK" - }, - { - "firstname": "Joanne", - "lastname": "Falck", - "affiliation": "UCL School of Pharmacy, University College London, London, UK" - }, - { - "firstname": "Christian A.", - "lastname": "Rössert", - "affiliation": "Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland" - }, - { - "firstname": "Ying", - "lastname": "Shi", - "affiliation": "Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland" - }, - { - "firstname": "Olivier", - "lastname": "Hagens", - "affiliation": "Laboratory of Neural Microcircuitry (LNMC), Brain Mind Institute, EPFL, Lausanne, Switzerland" - }, - { - "firstname": "Maurizio", - "lastname": "Pezzoli", - "affiliation": "Laboratory of Neural Microcircuitry (LNMC), Brain Mind Institute, EPFL, Lausanne, Switzerland" - }, - { - "firstname": "Tamas F.", - "lastname": "Freund", - "affiliation": "Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary" - }, - { - "firstname": "Szabolcs", - "lastname": "Kali", - "affiliation": "Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary" - }, - { - "firstname": "Eilif B.", - "lastname": "Muller", - "affiliation": "Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland" - }, - { - "firstname": "Felix", - "lastname": "Schürmann", - "affiliation": "Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland" - }, - { - "firstname": "Henry", - "lastname": "Markram", - "affiliation": None - }, - { - "firstname": "Michele", - "lastname": "Migliore", - "affiliation": None - } - ], - "corresponding_author": [ - { - "firstname": "Rosanna", - "lastname": "Migliore", - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy" - } - ], - "created_author": [ - { - "firstname": "Luca L.", - "lastname": "Bologna", - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy" - } - ], - "approved_author": { - "firstname": "Luca L.", - "lastname": "Bologna", - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy" - }, - "year": "2018-01-01", - "live_paper_title": "The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow", - "associated_paper_title": "The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow", - "journal": "PLOS Computational Biology", - "url": "https://doi.org/10.1371/journal.pcbi.1006423", - "citation": "Migliore R, Lupascu CA, Bologna LL, Romani A, Courcol J-D, Antonel S, et al. (2018) The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow. PLoS Comput Biol 14(9): e1006423.", - "doi": "https://doi.org/10.25493/EF9C-ZKU", - "associated_paper_doi": "https://doi.org/10.1371/journal.pcbi.1006423", - "abstract": "The peak conductance of many ion channel types measured in any given animal is highly variable across neurons, both within and between neuronal populations. The current view is that this occurs because a neuron needs to adapt its intrinsic electrophysiological properties either to maintain the same operative range in the presence of abnormal inputs or to compensate for the effects of pathological conditions. Limited experimental and modeling evidence suggests this might be implemented via the correlation and/or degeneracy in the function of multiple types of conductances. To study this mechanism in hippocampal CA1 neurons and interneurons, we systematically generated a set of morphologically and biophysically accurate models. We then analyzed the ensembles of peak conductance obtained for each model neuron. The results suggest that the set of conductances expressed in the various neuron types may be divided into two groups: one group is responsible for the major characteristics of the firing behavior in each population and the other more involved with degeneracy. These models provide experimentally testable predictions on the combination and relative proportion of the different conductance types that should be present in hippocampal CA1 pyramidal cells and interneurons.", - "license": None, - "resources_description": "Data and models: all data and models used in the paper are available at the links reported below, grouped into the following categories: ", - "collab_id": "live-paper-2018-migliore-et-al", - "resources": [ - { - "order": 0, - "type": "section_morphology", - "title": "Morphologies", - "icon": "settings_input_antenna", - "description": "Morphology files (in .asc format) used in the paper for each etype (see Table S5 of the Supplementary Material):", - "data": [ - { - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_migliore_et_al/morphologies/980120A.asc", - "label": "980120A", - "view_url": None, - "type": "URL", - "identifier": "5801c465-193a-5916-b810-fa991a9ef1a4" - }, - { - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_migliore_et_al/morphologies/mpg141208_B_idA.asc", - 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"type": "URL", - "identifier": "0438951b-6f3f-5856-aab8-0fae3db1f33f" - }, - { - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/Migliore_2018_CA1/models/CA1_int_cAC_031031AM1_20180201104443/CA1_int_cAC_031031AM1_20180201104443.zip", - "label": "CA1_int_cAC_031031AM1_20180201104443", - "view_url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2018-migliore-et-al/Model%20Catalog#model_id.b5ef2c2e-f0bf-4624-a579-7e1be74f8800", - "type": "URL", - "identifier": "20e1e460-303a-59fa-8b4a-213cab9a76a4" - } - ] - }, - { - "order": 2, - "type": "section_custom", - "title": "ModelDB link and test simulations", - "icon": "note_add", - "description": "

A reduced self-consistent set of files needed to reproduce Fig.4A of the paper is available on\n ModelDB.\n
\n
Use the BSP Neuron As A Service (NaaS) tool to do in silico experiments with the single cell models shown in Fig.4A:\n
\n > Start by clicking on any button below to run a simulation.\n
\n > After entering the NaaS page, click on the \"Simulation\" tab, adjust the current strength with the appropriate value, and run the simulation by clicking the \"Start simulation\" button.\n

\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n
\n
", - "data": [] - } - ] - } -} +} \ No newline at end of file diff --git a/validation_service_api/validation_service/main.py b/validation_service_api/validation_service/main.py index 823fccd9..591d2a1c 100644 --- a/validation_service_api/validation_service/main.py +++ b/validation_service_api/validation_service/main.py @@ -2,7 +2,7 @@ from starlette.middleware.sessions import SessionMiddleware from starlette.middleware.cors import CORSMiddleware -from .resources import models, tests, vocab, results, auth, livepapers +from .resources import models, tests, vocab, results, auth from . import settings @@ -50,5 +50,4 @@ app.include_router(tests.router, tags=["Validation Tests"]) app.include_router(results.router, tags=["Validation Results"]) #app.include_router(simulations.router, tags=["Simulations"]) -app.include_router(livepapers.router, tags=["Live Papers"]) app.include_router(vocab.router, tags=["Controlled vocabularies"]) diff --git a/validation_service_api/validation_service/resources/livepapers.py b/validation_service_api/validation_service/resources/livepapers.py deleted file mode 100644 index f5ada976..00000000 --- a/validation_service_api/validation_service/resources/livepapers.py +++ /dev/null @@ -1,356 +0,0 @@ - -import logging -from uuid import UUID -from typing import List, Union -from datetime import datetime - - -from fastapi import APIRouter, Depends, Header, Query, HTTPException, status -from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials -from ..auth import ( - get_kg_client_for_user_account, get_kg_client_for_service_account, - User -) -from ..data_models import LivePaper, LivePaperSummary, ConsistencyError, AccessCode, Slug -from ..db import _get_live_paper_by_id_or_alias, _check_service_status -import fairgraph.openminds.core as omcore -import fairgraph.openminds.publications as ompub -from fairgraph.utility import as_list - -LIVEPAPERS_SPACE = "livepapers" -logger = logging.getLogger("validation_service_api") - -auth = HTTPBearer() -router = APIRouter() - - -# todo: -# - handle KG objects as data items - - -def collab_id_from_space(space): - if space.startswith("collab-"): - return space[7:] - else: - return None - - -@router.get("/livepapers/", response_model=List[LivePaperSummary]) -async def query_live_papers( - title: str = Query(None, description="Live paper title to search for"), - editable: bool = False, - token: HTTPAuthorizationCredentials = Depends(auth) -): - user = User(token, allow_anonymous=False) - kg_user_client = get_kg_client_for_user_account(token) - kg_service_client = get_kg_client_for_service_account() - - filters = {} - if title: - filters["name"] = title - - query_label = "LP_LivePapers_summary" - query = kg_service_client.retrieve_query(query_label) - if query is None: - raise HTTPException( - status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail=f"Query '{query_label}' could not be retrieved", - ) - - lps = kg_user_client.query(query, filters, scope="any", id_key="id", use_stored_query=True).data - - if editable: - # include only those papers the user can edit - editable_collabs = await user.get_editable_collabs() - - accessible_lps = [ - lp for lp in lps - if lp["space"] == "myspace" or collab_id_from_space(lp["space"]) in editable_collabs - ] - # alternative implementation, profile these - # accessible_lps = [ - # lp for lp in lps - # if lp["space"] == "myspace" or await can_edit_collab(collab_id_from_space(lp["space"])), token.credentials) - # ] - else: - accessible_lps = lps - # todo: think about sorting - summaries = [] - for lp in accessible_lps: - summary = LivePaperSummary.from_kg_query(lp, kg_user_client) - if summary: - summaries.append(summary) - return sorted(summaries, key=lambda lp: lp.year or 1970) - - -@router.get("/livepapers-published/", response_model=List[LivePaperSummary]) -async def query_released_live_papers(): - # check - do we need service account, or will any user account get released instances? - kg_client = get_kg_client_for_service_account() - lps = ompub.LivePaper.list(kg_client, scope="released", size=1000, space=LIVEPAPERS_SPACE) - return sorted( - [ - LivePaperSummary.from_kg_object(lp, kg_client) - for lp in as_list(lps) - ], - key=lambda lp: lp.year - ) - - -@router.get("/livepapers/{lp_id}", response_model=LivePaper) -async def get_live_paper( - lp_id: Union[UUID, Slug], - token: HTTPAuthorizationCredentials = Depends(auth) -): - user = User(token, allow_anonymous=False) - kg_client = get_kg_client_for_user_account(token) - lp = _get_live_paper_by_id_or_alias(lp_id, kg_client, scope="any") - - def get_access_code(lp): # to implement - return None - - if lp: - if ( - token.credentials == get_access_code(lp) - or (lp.space.startswith("collab-") - and await user.can_view_collab(collab_id_from_space(lp.space))) - or await user.is_admin() - ): - try: - obj = LivePaper.from_kg_object(lp, kg_client) - except ConsistencyError as err: - raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(err)) - else: - raise HTTPException( - status_code=status.HTTP_403_FORBIDDEN, - detail=f"This account cannot edit Collab #{lp.space}", - ) - else: - raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, - detail=f"Live Paper {lp_id} not found, or you do not have access", - ) - return obj - - -@router.get("/livepapers-published/{lp_id}", response_model=LivePaper) -async def get_live_paper( - lp_id: Union[UUID, Slug] -): - kg_client = get_kg_client_for_service_account() - # check - do we need service account, or will any user account get released instances? - lp = _get_live_paper_by_id_or_alias(lp_id, kg_client, scope="released") - if lp: - try: - obj = LivePaper.from_kg_object(lp, kg_client) - except ConsistencyError as err: - raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(err)) - else: - raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, - detail=f"Live Paper {lp_id} not found.", - ) - return obj - - -@router.post("/livepapers/", response_model=LivePaperSummary, status_code=status.HTTP_201_CREATED) -async def create_live_paper( - live_paper: LivePaper, - token: HTTPAuthorizationCredentials = Depends(auth) -): - _check_service_status() - user = User(token, allow_anonymous=False) - logger.info("Beginning post live paper") - if live_paper.id: - raise HTTPException( - status_code=status.HTTP_400_BAD_REQUEST, - detail=f"Cannot provide id when creating a live paper. Use PUT to update an existing paper.", - ) - if not live_paper.collab_id: - raise HTTPException( - status_code=status.HTTP_400_BAD_REQUEST, - detail="Collab ID needs to be provided", - ) - - if not ( - live_paper.collab_id == "myspace" - or await user.can_edit_collab(live_paper.collab_id) - or await user.is_admin() - ): - raise HTTPException( - status_code=status.HTTP_403_FORBIDDEN, - detail=f"This account is not a member of Collab #{live_paper.collab_id}", - ) - - kg_user_client = get_kg_client_for_user_account(token) - kg_service_client = get_kg_client_for_service_account() - - kg_objects = live_paper.to_kg_objects(kg_user_client) - assert isinstance(kg_objects["paper"][-1], ompub.LivePaper) - - # use both service client (for checking curated spaces) and user client (for checking private spaces) - if kg_objects["paper"][-1].exists(kg_service_client) or kg_objects["paper"][-1].exists(kg_user_client): - raise HTTPException( - status_code=status.HTTP_409_CONFLICT, - detail=f"Another live paper with the same name already exists.", - ) - - if live_paper.collab_id in ("myspace", "livepapers"): - kg_space = live_paper.collab_id - else: - kg_space = f"collab-{live_paper.collab_id}" - if kg_space not in kg_user_client.spaces(names_only=True): - # configure space the first time it is used - types = [omcore.DOI, omcore.ISBN, omcore.ISSN, omcore.WebResource, omcore.ServiceLink, - omcore.Person, omcore.Organization] + ompub.list_kg_classes() - try: - kg_user_client.configure_space(kg_space, types) - except Exception as err: - # todo: more fine-grained error reporting. Check content of Exception, - # 403 may not be appropriate - raise HTTPException( - status_code=status.HTTP_403_FORBIDDEN, - detail=f"There was an error when trying to configure KG space {kg_space}: {err}", - ) - - for category in ("people", "paper", "sections"): # the order is important - for obj in kg_objects[category]: - obj.save(kg_user_client, space=obj.space or kg_space, recursive=True, ignore_auth_errors=True) - logger.info("Saved objects") - return LivePaperSummary.from_kg_object(kg_objects["paper"][-1], kg_user_client) - - -@router.put("/livepapers/{lp_id}", status_code=status.HTTP_200_OK) -async def update_live_paper( - lp_id: UUID, #todo: handle Slug - live_paper: LivePaper, - token: HTTPAuthorizationCredentials = Depends(auth) -): - _check_service_status() - logger.info("Beginning put live paper") - user = User(token, allow_anonymous=False) - if not ( - live_paper.collab_id == "myspace" - or await user.can_edit_collab(live_paper.collab_id) - or await user.is_admin() - ): - raise HTTPException( - status_code=status.HTTP_403_FORBIDDEN, - detail=f"This account is not a member of Collab #{live_paper.collab_id}", - ) - # todo: in case collab id is changed, check if the user has edit permissions for the - # original collab as well - - if live_paper.id is None: - live_paper.id = lp_id - elif live_paper.id != lp_id: - raise HTTPException( - status_code=status.HTTP_409_CONFLICT, - detail=f"Inconsistent ids: {lp_id} != {live_paper.id}", - ) - - kg_client = get_kg_client_for_user_account(token) - - kg_objects = live_paper.to_kg_objects(kg_client) - logger.info("Created objects") - - if not kg_objects["paper"][-1].exists(kg_client): - # here we use only the user client to check existence, since we have - # to be able to write to it anyway - raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, - detail=f"Live paper with id {lp_id} not found.", - ) - assert UUID(kg_objects["paper"][-1].uuid) == lp_id - - if live_paper.collab_id in ("myspace", "livepapers"): - kg_space = live_paper.collab_id - else: - kg_space = f"collab-{live_paper.collab_id}" - - for category in ("people", "paper", "sections"): # the order is important - for obj in kg_objects[category]: - obj.save(kg_client, space=obj.space or kg_space, recursive=True) - logger.info("Saved objects") - - return None - #return LivePaper.from_kg_object(lp, kg_client) - -# test lp_id: 5249159f-898c-4b60-80ef-95ddc6414557 - - -@router.put("/livepapers/{lp_id}/access_code", status_code=status.HTTP_200_OK) -async def set_access_code( - lp_id: Union[UUID, Slug], - access_code: AccessCode, - token: HTTPAuthorizationCredentials = Depends(auth) -): - _check_service_status() - - raise HTTPException( - status_code=status.HTTP_501_NOT_IMPLEMENTED, - detail="Not yet migrated", - ) - user = User(token, allow_anonymous=False) - logger.info("Beginning set access code") - - kg_client = get_kg_client_for_user_account(token) - lp = _get_live_paper_by_id_or_alias(lp_id, kg_client, scope="in progress") - - if lp: - if not ( - await user.can_edit_collab(lp.collab_id) - or await user.is_admin() - ): - raise HTTPException( - status_code=status.HTTP_403_FORBIDDEN, - detail=f"This account is not a member of Collab #{lp.collab_id}", - ) - - lp.access_code = access_code.value - lp.save(kg_client) - logger.info("Added/updated access code") - else: - raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, - detail=f"Live Paper {lp_id} not found.", - ) - return None - - - -@router.delete("/livepapers/{lp_id}", status_code=status.HTTP_200_OK) -async def delete_live_paper( - lp_id: UUID, #todo: handle alias - token: HTTPAuthorizationCredentials = Depends(auth) -): - _check_service_status() - user = User(token, allow_anonymous=False) - if not ( - await user.is_admin() - ): - raise HTTPException( - status_code=status.HTTP_403_FORBIDDEN, - detail=f"Deleting live papers is restricted to administrators - please contact EBRAINS support", - ) - - kg_user_client = get_kg_client_for_user_account(token) - live_paper = ompub.LivePaper.from_uuid(str(lp_id), kg_user_client, scope="in progress") - # todo: handle live_paper is None with 404 error - - live_paper.delete(kg_user_client) - # retrieve all versions - for version in as_list(live_paper.versions): - version = version.resolve(kg_user_client, scope="in progress") - # for each version, retrieve all sections - sections = ompub.LivePaperSection.list(kg_user_client, scope="in progress", is_part_of=version) - version.delete(kg_user_client) - # for each section, retrieve all resource items and any associated service links - for section in as_list(sections): - resource_items = ompub.LivePaperResourceItem.list(kg_user_client, scope="in progress", is_part_of=section, size=1000) - section.delete(kg_user_client) - for item in as_list(resource_items): - service_link = omcore.ServiceLink.list(kg_user_client, scope="in progress", data_location=item) - item.delete(kg_user_client) - for sl in as_list(service_link): - sl.delete(kg_user_client) diff --git a/validation_service_api/validation_service/tests/fixtures.py b/validation_service_api/validation_service/tests/fixtures.py index ef7af667..f60267b6 100644 --- a/validation_service_api/validation_service/tests/fixtures.py +++ b/validation_service_api/validation_service/tests/fixtures.py @@ -143,53 +143,3 @@ def _build_sample_result(model_instance_id, test_instance_id): "normalized_score": 0.2468, "timestamp": now.isoformat() } - - -def _build_sample_live_paper(): - now = datetime.now(timezone.utc) - alias = f"testlivepaper-apiv3beta-{now.strftime('%Y%m%d-%H%M%S')}" - return { - "lp_tool_version": "0.1", - "alias": alias, - "authors": [ - {"firstname": "Frodo", "lastname": "Baggins"}, - {"firstname": "Tom", "lastname": "Bombadil"}, - ], - "version": "v1", - "modified_date": now.isoformat(), - "corresponding_author": [ - {"firstname": "Tom", "lastname": "Bombadil"} - ], - "created_author": [ - {"firstname": "Frodo", "lastname": "Baggins"} - ], - "approved_author": {"firstname": "Tom", "lastname": "Bombadil"}, - "year": now.date().isoformat(), - "live_paper_title": f"TestLivePaper API v3beta {now.isoformat()}", - "associated_paper_title": "Neuroanatomy of Old Man Willow", - "journal": "eLife", - "url": f"https://example.com/{alias}", - "citation": f"Baggins F. and Bombadil T. ({now.year}) Neuroanatomy of Old Man Willow. eLife 999: e9999999", - "associated_paper_doi": "https://doi.org/10.1000/xyz123", - "abstract": "This is the abstract of the associated article", - "license": "Creative Commons Attribution 4.0 International", - "resources_description": "This is like the abstract of the live paper", - "collab_id": "myspace", - "resources": [ - { - "order": 0, - "type": "section_custom", - "title": f"A custom resource section, part of {alias}", - "icon": "pageview", - "description": "This is the section description", - "data": [ - { - "url": f"https://example.com/{alias}/datafile.txt", - "label": f"A resource, part of {alias}", - "view_url": f"https://example.com/viewer?project={alias}&file=datafile.txt", - "type": "URL" - } - ] - } - ] - } diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/0433eafb-4044-4f58-9d20-7dad2c69d39a.json b/validation_service_api/validation_service/tests/test_data/livepapers/0433eafb-4044-4f58-9d20-7dad2c69d39a.json deleted file mode 100644 index 45f7eb8c..00000000 --- a/validation_service_api/validation_service/tests/test_data/livepapers/0433eafb-4044-4f58-9d20-7dad2c69d39a.json +++ /dev/null @@ -1,392 +0,0 @@ -{ - "abstract": "The anatomy and physiology of synaptic connections in rodent hippocampal CA1 have been exhaustively characterized in recent decades. Yet, the resulting knowledge remains disparate and difficult to reconcile. Here, we present a data-driven approach to integrate the current state-of-the-art knowledge on the synaptic anatomy and physiology of rodent hippocampal CA1, including axo-dendritic innervation patterns, number of synapses per connection, quantal conductances, neurotransmitter release probability, and short-term plasticity into a single coherent resource. First, we undertook an extensive literature review of paired-recordings of hippocampal neurons and compiled experimental data on their synaptic anatomy and physiology. The data collected in this manner is sparse and inhomogeneous due to the diversity of experimental techniques used by different groups, which necessitates the need for an integrative framework to unify these data. To this end, we extended a previously developed workflow for the neocortex to constrain a unifying in silico reconstruction of the synaptic physiology of CA1 connections. Our work identifies gaps in the existing knowledge and provides a complementary resource towards a more complete quantification of synaptic anatomy and physiology in the rodent hippocampal CA1 region.", - "alias": "2020-ecker-et-al", - "approved_author": { - "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland", - "firstname": "Andr\u00e1s", - "lastname": "Ecker" - }, - "associated_paper_doi": "https://doi.org/10.1002/hipo.23220", - "associated_paper_title": "Data\u2010driven integration of hippocampal CA1 synaptic physiology in silico", - "associated_paper_volume": "30", - "associated_paper_issue": null, - "associated_paper_pagination": "1129-1145", - "authors": [ - { - "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland", - "firstname": "Andr\u00e1s", - "lastname": "Ecker" - }, - { - "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland", - "firstname": "Armando", - "lastname": "Romani" - }, - { - "affiliation": "Institute of Experimental Medicine, Budapest, Hungary; Faculty of Information Technology and Bionics, P\u00e1zm\u00e1ny P\u00e9ter Catholic University, Budapest, Hungary", - "firstname": "S\u00e1ra", - "lastname": "S\u00e1ray" - }, - { - "affiliation": "Institute of Experimental Medicine, Budapest, Hungary; Faculty of Information Technology and Bionics, P\u00e1zm\u00e1ny P\u00e9ter Catholic University, Budapest, Hungary", - "firstname": "Szabolcs", - "lastname": "K\u00e1li" - }, - { - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy", - "firstname": "Michele", - "lastname": "Migliore" - }, - { - "affiliation": "UCL School of Pharmacy, University College London, London, UK", - "firstname": "Joanne", - "lastname": "Falck" - }, - { - "affiliation": "UCL School of Pharmacy, University College London, London, UK; School of Life Sciences, University of Westminster, London, UK", - "firstname": "Sigrun", - "lastname": "Lange" - }, - { - "affiliation": "UCL School of Pharmacy, University College London, London, UK", - "firstname": "Audrey", - "lastname": "Mercer" - }, - { - "affiliation": "UCL School of Pharmacy, University College London, London, UK", - "firstname": "Alex M.", - "lastname": "Thomson" - }, - { - "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland", - "firstname": "Eilif B.", - "lastname": "Muller" - }, - { - "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland", - "firstname": "Michael W.", - "lastname": "Reimann" - }, - { - "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland", - "firstname": "Srikanth", - "lastname": "Ramaswamy" - } - ], - "citation": "András Ecker, Armando Romani, Sára Sáray, Szabolcs Káli, Michele Migliore, Joanne Falck, Sigrun Lange, Audrey Mercer, Alex M. Thomson, Eilif B. Muller, Michael W. Reimann & Srikanth Ramaswamy (2020). Data‐driven integration of hippocampal CA1 synaptic physiology in silico. Hippocampus, 30: 1129-1145.", - "collab_id": "livepapers", - "corresponding_author": [ - { - "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland", - "firstname": "Andr\u00e1s", - "lastname": "Ecker" - }, - { - "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland", - "firstname": "Srikanth", - "lastname": "Ramaswamy" - } - ], - "created_author": [ - { - "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland", - "firstname": "Andr\u00e1s", - "lastname": "Ecker" - } - ], - "doi": "https://doi.org/10.25493/E145-BQH", - "id": "0433eafb-4044-4f58-9d20-7dad2c69d39a", - "journal": "Hippocampus", - "license": "Creative Commons Attribution-NonCommercial 4.0 International", - "live_paper_title": "Data\u2010driven integration of hippocampal CA1 synaptic physiology in silico", - "lp_tool_version": "0.1", - "modified_date": "2021-08-10T10:52:03.325000+00:00", - 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"abstract": "Little is known about the properties and function of ion channels that affect synaptic terminalresting properties. One particular subthreshold-active ion channel, the Kv7 potassium channel, is highly localized to axons, but its role in regulating synaptic terminal intrinsic excitability and release is largely unexplored. Using electrophysiological recordings together with computational modeling, we found that the K V 7 current was active at rest in adult hippocampal mossy fiber synaptic terminals and enhanced their membrane conductance. The current also restrained action potential-induced Ca 2+ influx via N-and P/Q-type Ca 2+ channels in boutons. This was associated with a substantial reduction in the spike half-width and afterdepolarization following presynaptic spikes. Further, by constraining spike-induced Ca 2+ influx, the presynaptic K V 7 current decreased neurotransmission onto CA3 pyramidal neurons and short-term synaptic plasticity at the mossy fiber-CA3 synapse. This is a distinctive mechanism by which K V 7 channels influence hippocampal neuronal excitability and synaptic plasticity.", - "alias": "2019-martinello-et-al", - "approved_author": { - "affiliation": "UCL School of Pharmacy, University College London, London, UK", - "firstname": "Mala M.", - "lastname": "Shah" - }, - "associated_paper_doi": "https://doi.org/10.1038/s42003-019-0408-4", - "associated_paper_title": "The subthreshold-active KV7 current regulates neurotransmission by limiting spike-induced Ca2+ influx in hippocampal mossy fiber synaptic terminals", - "authors": [ - { - "affiliation": "UCL School of Pharmacy, University College London, London, UK", - "firstname": "Katiuscia", - "lastname": "Martinello" - }, - { - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy", - "firstname": "Elisabetta", - "lastname": "Giacalone" - }, - { - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy", - "firstname": "Michele", - "lastname": "Migliore" - }, - { - "affiliation": "Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK", - "firstname": "David A.", - "lastname": "Brown" - }, - { - "affiliation": "UCL School of Pharmacy, University College London, London, UK", - "firstname": "Mala M.", - "lastname": "Shah" - } - ], - "citation": "Katiuscia Martinello, Elisabetta Giacalone, Michele Migliore, David A. Brown & Mala M. Shah (2019). The subthreshold-active KV7 current regulates neurotransmission by limiting spike-induced Ca2+ influx in hippocampal mossy fiber synaptic terminals. Communications Biology, 2: 145.", - "collab_id": "livepapers", - "corresponding_author": [ - { - "affiliation": "UCL School of Pharmacy, University College London, London, UK", - "firstname": "Mala M.", - "lastname": "Shah" - } - ], - "created_author": [ - { - "affiliation": "UCL School of Pharmacy, University College London, London, UK", - "firstname": "Mala M.", - "lastname": "Shah" - } - ], - "doi": "https://doi.org/10.25493/SFPM-X28", - "id": "04ac2988-b717-469d-88ce-d54d02036eb1", - "journal": "Communications Biology", - "associated_paper_volume": "2", - "associated_paper_issue": null, - "associated_paper_pagination": "145", - "license": "Creative Commons Attribution-NonCommercial 4.0 International", - "live_paper_title": "The subthreshold-active KV7 current regulates neurotransmission by limiting spike-induced Ca2+ influx in hippocampal mossy fiber synaptic terminals", - "lp_tool_version": "0.1", - "modified_date": "2021-08-03T09:22:07.640000+00:00", - "resources": [ - { - "data": [], - "description": "

Download the experimental traces used to reproduce the plots shown in Fig. 3 \n of the main manuscript at the following link.\n

", - "icon": "timeline", - "order": 0, - "title": "Electrophysiological Traces", - "type": "section_custom" - }, - { - "data": [], - "description": "\n

\n
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\n A reduced self-consistent set of files needed to reproduce Fig. 4b,c\n and d of the paper is available on\n ModelDB\n
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\n The simulations will reproduce functional effects of Km removal in mossy\n fiber boutons.

\n The default settings will reproduce control condition in Fig. 4b,c (Single\n AP) and Fig. 4d (50Hz Train) of the paper, which refer to the last action\n potential and all action potentials shown in Fig. 3a and Fig. 3d\n respectively.
\n
\n HOW TO: Change settings by turning \"on\" the\n \"Reset/set parameters\" switch to run simulation with your own\n conductance configuration. Set gKM=0.0 or both conductances to\n 0.0 to reproduce respectively red and blue lines of the Fig. 4b,c,d.
\n Use the \"Single AP/50 Hz Train\" switch to simulate a single current\n injection or a 50Hz current pulses train, respectively.
\n Turn \"on\" the \"Persistent plot\" switch to compare plots of\n different simulations.\n

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", - "icon": "local_activity", - "order": 1, - "title": "ModelDB link and test simulations", - "type": "section_custom" - } - ], - "resources_description": "Data and models: data and models used in the paper are available at the links reported below.", - "url": "https://www.nature.com/articles/s42003-019-0408-4", - "version": "1", - "year": "2019-04-26" -} diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/0b52fda9-bfe5-4479-9fb0-7b6af8384c31.json b/validation_service_api/validation_service/tests/test_data/livepapers/0b52fda9-bfe5-4479-9fb0-7b6af8384c31.json deleted file mode 100644 index 8619d875..00000000 --- a/validation_service_api/validation_service/tests/test_data/livepapers/0b52fda9-bfe5-4479-9fb0-7b6af8384c31.json +++ /dev/null @@ -1,88 +0,0 @@ -{ - "abstract": "Detailed conductance-based nonlinear neuron models consisting of thousands of synapses are key for understanding of the computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of these models are computationally expensive, considerably curtailing their utility. Neuron_Reduce is a new analytical approach to reduce the morphological complexity and computational time of nonlinear neuron models. Synapses and active membrane channels are mapped to the reduced model preserving their transfer impedance to the soma; synapses with identical transfer impedance are merged into one NEURON process still retaining their individual activation times. Neuron_Reduce accelerates the simulations by 40-250 folds for a variety of cell types and realistic number (10,000-100,000) of synapses while closely replicating voltage dynamics and specific dendritic computations. The reduced neuron-models will enable realistic simulations of neural networks at unprecedented scale, including networks emerging from micro-connectomics efforts and biologically-inspired \"deep networks\". Neuron_Reduce is publicly available and is straightforward to implement.", - "alias": "2020-amsalem-et-al", - "approved_author": { - "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel", - "firstname": "Oren", - "lastname": "Amsalem" - }, - "associated_paper_doi": "https://doi.org/10.1038/s41467-019-13932-6", - "associated_paper_title": "An efficient analytical reduction of detailed nonlinear neuron models", - "authors": [ - { - "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel", - "firstname": "Oren", - "lastname": "Amsalem" - }, - { - "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel", - "firstname": "Guy", - "lastname": "Eyal" - }, - { - "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel", - "firstname": "Noa", - "lastname": "Rogozinski" - }, - { - "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland", - "firstname": "Michael", - "lastname": "Gevaert" - }, - { - "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland", - "firstname": "Pramod S.", - "lastname": "Kumbhar" - }, - { - "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland", - "firstname": "Felix", - "lastname": "Sch\u00fcrmann" - }, - { - "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel; Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel", - "firstname": "Idan", - "lastname": "Segev" - } - ], - "citation": "Oren Amsalem, Guy Eyal, Noa Rogozinski, Michael Gevaert, Pramod S. Kumbhar, Felix Schürmann & Idan Segev (2021). An efficient analytical reduction of detailed nonlinear neuron models. Nature Communications, 11: 288.", - "collab_id": "livepapers", - "corresponding_author": [ - { - "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel", - "firstname": "Oren", - "lastname": "Amsalem" - } - ], - "created_author": [ - { - "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel", - "firstname": "Oren", - "lastname": "Amsalem" - } - ], - "doi": "https://doi.org/10.25493/1D2M-CV0", - "id": "0b52fda9-bfe5-4479-9fb0-7b6af8384c31", - "journal": "Nature Communications", - "associated_paper_volume": "11", - "associated_paper_issue": null, - "associated_paper_pagination": "288", - "license": "Creative Commons Attribution-NonCommercial 4.0 International", - "live_paper_title": "An efficient analytical reduction of detailed nonlinear neuron models", - "lp_tool_version": "0.1", - "modified_date": "2021-08-10T08:41:53.320000+00:00", - "resources": [ - { - "data": [], - "description": "\n\n\n\n\n\n\n
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\n \n Click here to run simulation \n \n

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Neuron Reduce
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\n \n Click here to run simulation \n \n

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Detailed instruction and source code is available on GitHub: https://github.com/orena1/neuron_reduce
", - "icon": "pageview", - "order": 0, - "title": "Demonstration", - "type": "section_custom" - } - ], - "resources_description": null, - "url": "https://www.nature.com/articles/s41467-019-13932-6", - "version": "1", - "year": "2021-08-10" -} diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/0d6f04db-8886-4553-b950-a90fdf339b4a.json b/validation_service_api/validation_service/tests/test_data/livepapers/0d6f04db-8886-4553-b950-a90fdf339b4a.json deleted file mode 100644 index b1209fc9..00000000 --- a/validation_service_api/validation_service/tests/test_data/livepapers/0d6f04db-8886-4553-b950-a90fdf339b4a.json +++ /dev/null @@ -1,79 +0,0 @@ -{ - "abstract": "Bladder small DRG neurons, which are putative nociceptors pivotal to urinary bladder function, express more than a dozen different ionic membrane mechanisms: ion channels, pumps and exchangers. Small-conductance Ca 2+-activated K + (SK Ca) channels which were earlier thought to be gated solely by intracellular Ca 2+ concentration ([Ca] i) have recently been shown to exhibit inward rectification with respect to membrane potential. The effect of SK Ca inward rectification on the excitability of these neurons is unknown. Furthermore, studies on the role of K Ca channels in repetitive firing and their contributions to different types of afterhyperpolarization (AHP) in these neurons are lacking. In order to study these phenomena, we first constructed and validated a biophysically detailed single compartment model of bladder small DRG neuron soma constrained by physiological data. The model includes twenty-two major known membrane mechanisms along with intracellular Ca 2+ dynamics comprising Ca 2+ diffusion, cytoplasmic buffering, and endoplasmic reticulum (ER) and mitochondrial mechanisms. Using modelling studies, we show that inward rectification of SK Ca is an important parameter regulating neuronal repetitive firing and that its absence reduces action potential (AP) firing frequency. We also show that SK Ca is more potent in reducing AP spiking than the large-conductance K Ca channel (BK Ca) in these neurons. Moreover, BK Ca was found to contribute to the fast AHP (fAHP) and SK Ca to the medium-duration (mAHP) and slow AHP (sAHP). We also report that the slow inactivating A-type K + channel (slow K A) current in these neurons is composed of 2 components: an initial fast inactivating (time constant * 25-100 ms) and a slow inactivating (time constant * 200-800 ms) current. We discuss the implications of our findings, and how our detailed model can help further our understanding of the role of C-fibre afferents in the physiology of urinary bladder as well as in certain disorders.", - "alias": "2018-mandge-manchanda", - "approved_author": { - "affiliation": "Computational Neurophysiology Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India", - "firstname": "Darshan", - "lastname": "Mandge" - }, - "associated_paper_doi": "https://doi.org/10.1371/journal.pcbi.1006293", - "associated_paper_title": "A biophysically detailed computational model of urinary bladder small DRG neuron soma", - "associated_paper_issue": null, - "associated_paper_pagination": "e1006293", - "associated_paper_volume": "14", - "authors": [ - { - "affiliation": "Computational Neurophysiology Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India", - "firstname": "Darshan", - "lastname": "Mandge" - }, - { - "affiliation": "Computational Neurophysiology Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India", - "firstname": "Rohit", - "lastname": "Manchanda" - } - ], - "citation": "Darshan Mandge & Rohit Manchanda (2018). A biophysically detailed computational model of urinary bladder small DRG neuron soma. PLOS Computational Biology, 14: e1006293.", - "collab_id": "livepapers", - "corresponding_author": [ - { - "affiliation": "Computational Neurophysiology Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India", - "firstname": "Rohit", - "lastname": "Manchanda" - } - ], - "created_author": [ - { - "affiliation": "Computational Neurophysiology Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India", - "firstname": "Darshan", - "lastname": "Mandge" - } - ], - "doi": "https://doi.org/10.25493/GJBX-3K9", - "id": "0d6f04db-8886-4553-b950-a90fdf339b4a", - "journal": "PLOS Computational Biology", - "license": "Creative Commons Attribution-NonCommercial 4.0 International", - "live_paper_title": "A biophysically detailed computational model of urinary bladder small DRG neuron soma", - "lp_tool_version": "0.1", - "modified_date": "2021-07-30T11:37:37.783000+00:00", - "resources": [ - { - "data": [], - "description": "
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\n Fig. 1 Computational Model of Urinary Bladder Small DRG Neuron\n

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", - "icon": "info_outline", - "order": 0, - "title": "Model Structure", - "type": "section_custom" - }, - { - "data": [], - "description": "

\n The captions in Fig. 2, Fig. 3 and Fig. 4 are linked to a Python Jupyter Notebook \n available in the Collab dedicated to this Live Paper through the \n Human Brain Project Collaboratory platform.\n The Jupyter Notebook allows to generate the simulation figures reported in the \n paper using the respective simulation conditions reported in figure captions and text.\n
\n More details on how to use the notebook and the results it allows to reproduce are reported inline with the code.\n

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\n \n Fig. 2 AP Validation: [A] Single AP, [B] AP with a Long Duration Clamp\n \n

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\n \n Fig. 3 Simulated Subthreshold Potentials and an AP in the model\n \n

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\n \n Fig. 4 Comparing Simulated and Experimental Calcium Transients in the Cytoplasm (Red) and the Mitochondria (Blue).\n \n

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", - "icon": "info_outline", - "order": 1, - "title": "Model response to Current Clamps", - "type": "section_custom" - }, - { - "data": [], - "description": "
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The model used in the paper is available on \n Model DB\n

", - "icon": "local_activity", - "order": 2, - "title": "Model DB link", - "type": "section_custom" - } - ], - "resources_description": "This Live Paper introduces interactively urinary bladder small-diameter DRG neuron soma, including modelling response to current clamps for action potentials, subthreshold potentials as well as the cytoplasmic and mitochondrial calcium transients.", - "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006293", - "version": "1", - "year": "2018-07-18" -} \ No newline at end of file diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/15c1fb11-6239-4fef-b62c-e56bb065f100.json b/validation_service_api/validation_service/tests/test_data/livepapers/15c1fb11-6239-4fef-b62c-e56bb065f100.json deleted file mode 100644 index 3568814d..00000000 --- a/validation_service_api/validation_service/tests/test_data/livepapers/15c1fb11-6239-4fef-b62c-e56bb065f100.json +++ /dev/null @@ -1,200 +0,0 @@ -{ - "abstract": "We present detailed models of pyramidal cells from human neocortex, including models on their excitatory synapses, dendritic spines, dendritic NMDA- and somatic/axonal Na+ spikes that provided new insights into signal processing and computational capabilities of these principal cells. Six human layer 2 and layer 3 pyramidal cells (HL2/L3 PCs) were modeled, integrating detailed anatomical and physiological data from both fresh and postmortem tissues from human temporal cortex. The models predicted particularly large AMPA- and NMDA-conductances per synaptic contact (0.88 and 1.31 nS, respectively) and a steep dependence of the NMDA-conductance on voltage. These estimates were based on intracellular recordings from synaptically-connected HL2/L3 pairs, combined with extra-cellular current injections and use of synaptic blockers, and the assumption of five contacts per synaptic connection. A large dataset of high-resolution reconstructed HL2/L3 dendritic spines provided estimates for the EPSPs at the spine head (12.7 \u00b1 4.6 mV), spine base (9.7 \u00b1 5.0 mV), and soma (0.3 \u00b1 0.1 mV), and for the spine neck resistance (50\u201380 M\u03a9). Matching the shape and firing pattern of experimental somatic Na+-spikes provided estimates for the density of the somatic/axonal excitable membrane ion channels, predicting that 134 \u00b1 28 simultaneously activated HL2/L3-HL2/L3 synapses are required for generating (with 50% probability) a somatic Na+ spike. Dendritic NMDA spikes were triggered in the model when 20 \u00b1 10 excitatory spinous synapses were simultaneously activated on individual dendritic branches. The particularly large number of basal dendrites in HL2/L3 PCs and the distinctive cable elongation of their terminals imply that ~25 NMDA-spikes could be generated independently and simultaneously in these cells, as compared to ~14 in L2/3 PCs from the rat somatosensory cortex. These multi-sites non-linear signals, together with the large (~30,000) excitatory synapses/cell, equip human L2/L3 PCs with enhanced computational capabilities. Our study provides the most comprehensive model of any human neuron to-date demonstrating the biophysical and computational distinctiveness of human cortical neurons.", - "alias": "2018-eyal-et-al", - "approved_author": { - "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel; Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel", - "firstname": "Idan", - "lastname": "Segev" - }, - "associated_paper_doi": "https://doi.org/10.3389/fncel.2018.00181", - "associated_paper_title": "Human Cortical Pyramidal Neurons: From Spines to Spikes via Models", - "associated_paper_issue": null, - "associated_paper_pagination": "181", - "associated_paper_volume": "12", - "authors": [ - { - "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel", - "firstname": "Guy", - "lastname": "Eyal" - }, - { - "affiliation": "Department of Integrative Neurophysiology, Centre for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands; Department of Human Biology, Neuroscience Institute, University of Cape Town, Cape Town, South Africa", - "firstname": "Matthijs B.", - "lastname": "Verhoog" - }, - { - "affiliation": "Department of Integrative Neurophysiology, Centre for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands", - "firstname": "Guilherme", - "lastname": "Testa-Silva" - }, - { - "affiliation": "Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel", - "firstname": "Yair", - "lastname": "Deitcher" - }, - { - "affiliation": "Departamento de Neurobiolog\u00eda Funcional y de Sistemas, Instituto Cajal, Madrid, Spain; Laboratorio Cajal de Circuitos Corticales (CTB), Universidad Polit\u00e9cnica de Madrid, Madrid, Spain", - "firstname": "Ruth", - "lastname": "Benavides-Piccione" - }, - { - "affiliation": "Departamento de Neurobiolog\u00eda Funcional y de Sistemas, Instituto Cajal, Madrid, Spain; Laboratorio Cajal de Circuitos Corticales (CTB), Universidad Polit\u00e9cnica de Madrid, Madrid, Spain", - "firstname": "Javier", - "lastname": "DeFelipe" - }, - { - "affiliation": "Department of Integrative Neurophysiology, Centre for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands", - "firstname": "Christiaan P.J.", - "lastname": "De Kock" - }, - { - "affiliation": "Department of Integrative Neurophysiology, Centre for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands", - "firstname": "Huibert D.", - "lastname": "Mansvelder" - }, - { - "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel; Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel", - "firstname": "Idan", - "lastname": "Segev" - } - ], - "citation": "Guy Eyal, Matthijs B. Verhoog, Guilherme Testa-Silva, Yair Deitcher, Ruth Benavides-Piccione, Javier DeFelipe, Christiaan P.J. De Kock, Huibert D. Mansvelder & Idan Segev (2018). Human Cortical Pyramidal Neurons: From Spines to Spikes via Models. Frontiers in Cellular Neuroscience, 12: 181.", - "collab_id": "livepapers", - "corresponding_author": [ - { - "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel; Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel", - "firstname": "Idan", - "lastname": "Segev" - } - ], - "created_author": [ - { - "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel; Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel", - "firstname": "Idan", - "lastname": "Segev" - } - ], - "doi": "https://doi.org/10.25493/XFX6-3QD", - "id": "15c1fb11-6239-4fef-b62c-e56bb065f100", - "journal": "Frontiers in Cellular Neuroscience", - "license": "Creative Commons Attribution-NonCommercial 4.0 International", - "live_paper_title": "Human Cortical Pyramidal Neurons: From Spines to Spikes via Models", - "lp_tool_version": "0.1", - "modified_date": "2021-08-10T08:55:13.393000+00:00", - "resources": [ - { - "data": [], - "description": "
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", - "icon": "pageview", - "order": 0, - "title": "Demonstration", - "type": "section_custom" - }, - { - "data": [ - { - "identifier": "44514941-11b7-4d22-8a3e-f4f7ee7e122a", - "label": " 2013_03_06_cell03_789_H41_03", - "type": "URL", - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell0603_03_model_476/morphology/2013_03_06_cell03_789_H41_03.ASC", - "view_url": null - }, - { - "identifier": "1d555ef9-954b-403e-aa6d-f7eee1bda7b6", - "label": "2013_03_06_cell08_876_H41_05_Cell2", - "type": "URL", - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell0603_08_model_602/morphology/2013_03_06_cell08_876_H41_05_Cell2.ASC", - "view_url": null - }, - { - "identifier": "1f894a16-7b9d-405f-8090-ee2c2497d9f8", - "label": "2013_03_06_cell11_1125_H41_06", - "type": "URL", - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell0603_11_model_937/morphology/2013_03_06_cell11_1125_H41_06.ASC", - "view_url": null - }, - { - "identifier": "2b262857-7c00-46a9-ad2f-79152febbc10", - "label": "2013_03_13_cell03_1204_H42_02", - "type": "URL", - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell1303_03_model_448/morphology/2013_03_13_cell03_1204_H42_02.ASC", - "view_url": null - }, - { - "identifier": "77a4410c-8a52-41c3-a568-b7f2b6118b9f", - "label": "2013_03_13_cell05_675_H42_04", - "type": "URL", - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell1303_05_model_643/morphology/2013_03_13_cell05_675_H42_04.ASC", - "view_url": null - }, - { - "identifier": "ed69bf7d-89d5-4ddf-952b-ea85735fd87c", - "label": "2013_03_13_cell06_945_H42_05", - "type": "URL", - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell1303_06_model_263/morphology/2013_03_13_cell06_945_H42_05.ASC", - "view_url": null - } - ], - "description": "Morphology files (in .asc format) used in the paper for each etype (see Table S5 of the Supplementary Material):", - 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}, - { - "identifier": "cd2c314a-7910-401d-a0c1-f911e38ae402", - "label": "1503 cell 05", - "type": "URL", - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell1303_05_model_643.zip", - "view_url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2018-eyal-et-al/Model%20Catalog#model_id.df097922-57b8-4132-a78a-b9bfb5c35b00" - } - ], - "description": "The Model Catalog of the cells", - "icon": "local_activity", - "order": 2, - "title": "Cell model", - "type": "section_generic" - } - ], - "resources_description": "This Live Paper introduces interactively these 6 modeled human L2/3 neurons, including their morphology, experimental measurements and modelling response to current and synaptic inputs. The models are available to download and could also be simulated on the cloud with NEURON as a service. The user can manipulate current injections to the model and explore the voltage response at any dendritic/somatic and axonal loci.", - "url": "https://www.frontiersin.org/articles/10.3389/fncel.2018.00181/full", - "version": "1", - "year": "2018-06-29" -} \ No newline at end of file diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/1c1f53e4-55d1-45a3-a63a-a16c491a07f4.json b/validation_service_api/validation_service/tests/test_data/livepapers/1c1f53e4-55d1-45a3-a63a-a16c491a07f4.json deleted file mode 100644 index 80e7c877..00000000 --- a/validation_service_api/validation_service/tests/test_data/livepapers/1c1f53e4-55d1-45a3-a63a-a16c491a07f4.json +++ /dev/null @@ -1,135 +0,0 @@ -{ - "id": "1c1f53e4-55d1-45a3-a63a-a16c491a07f4", - "lp_tool_version": "0.1", - "alias": "2023-farisco-et-al", - "modified_date": "2023-09-26T15:00:55.441Z", - "authors": [ - { - "firstname": "Michele", - "lastname": "Farisco", - "affiliation": "Centre for Research Ethics and Bioethics, Uppsala University, Uppsala, Sweden; Biogem Molecular Biology and Genetics Research Institute, Ariano Irpino (AV), Italy" - }, - { - "firstname": "Kathinka", - "lastname": "Evers", - "affiliation": "Centre for Research Ethics and Bioethics, Uppsala University, Uppsala, Sweden; Biogem Molecular Biology and Genetics Research Institute, Ariano Irpino (AV), Italy" - }, - { - "firstname": "Jitka", - "lastname": "Annen", - "affiliation": "GIGA-Consciousness, Coma Science Group, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium; Joint International Research Unit on Consciousness, CERVO Brain Research Centre, U Laval, Canada; International Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China" - }, - { - "firstname": "Veronique", - "lastname": "Baldin", - "affiliation": "ALIS, French Association of Locked-in Syndrome, Boulogne-Billancourt, France" - }, - { - "firstname": "Alessandra", - "lastname": "Camassa", - "affiliation": "Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain" - }, - { - "firstname": "Benedetta", - "lastname": "Cecconi", - "affiliation": "GIGA-Consciousness, Coma Science Group, University of Liège, Belgium" - }, - { - "firstname": "Gustavo", - "lastname": "Deco", - "affiliation": "Catalan Institution for Research and Advanced Studies, Barcelona, Spain; Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain" - }, - { - "firstname": "Steven", - "lastname": "Laureys", - "affiliation": "GIGA-Consciousness, Coma Science Group, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium; Joint International Research Unit on Consciousness, CERVO Brain Research Centre, U Laval, Canada; International Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China" - }, - { - "firstname": "Rajanikant", - "lastname": "Panda", - "affiliation": "GIGA-Consciousness, Coma Science Group, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium" - }, - { - "firstname": "Arnau", - "lastname": "Manasanch", - "affiliation": "Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain" - }, - { - "firstname": "Maria Victoria", - "lastname": "Sánchez-Vives", - "affiliation": "Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Catalan Institution for Research and Advanced Studies, Barcelona, Spain" - }, - { - "firstname": "Gorka", - "lastname": "Zamora-López", - "affiliation": "Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain" - } - ], - "corresponding_author": [ - { - "firstname": "Michele", - "lastname": "Farisco", - "affiliation": "Centre for Research Ethics and Bioethics, Uppsala University, Uppsala, Sweden; Biogem Molecular Biology and Genetics Research Institute, Ariano Irpino (AV), Italy" - } - ], - "created_author": [ - { - "firstname": "Arnau", - "lastname": "Manasanch", - "affiliation": "Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain" - } - ], - "approved_author": { - "firstname": "Arnau", - "lastname": "Manasanch", - "affiliation": "Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain" - }, - "year": "2023-09-23", - "associated_paper_title": "Advancing the science of consciousness: from ethics to clinical care", - "live_paper_title": "Advancing the science of consciousness: from ethics to clinical care", - "doi": null, - "journal": null, - "url": "https://psyarxiv.com/sutrc/", - "citation": "Michele Farisco, Kathinka Evers, Jitka Annen, Veronique Baldin, Alessandra Camassa, Benedetta Cecconi, Gustavo Deco, Steven Laureys, Rajanikant Panda, Arnau Manasanch, Maria Victoria Sánchez-Vives & Gorka Zamora-López (2023). Advancing the science of consciousness: from ethics to clinical care. , .", - "associated_paper_doi": "https://doi.org/10.31234/osf.io/sutrc", - "abstract": "Significant advances in the scientific investigation of the neurobiology of consciousness have been slow to be translated into clinical settings, limited by factors of conceptual (e.g., what is consciousness?), methodological (e.g., how to identify reliable indicators of consciousness?), and technical (e.g., how to improve sensitivity and specificity of the technological identification of consciousness?) nature. In the present paper we aim at reducing the gap between research, clinical practice, patients’ and their caregivers’ needs regarding disorders of consciousness. By implementing a multidisciplinary and multidimensional approach, the paper focuses on disorders of consciousness: it starts from the review of some of the most promising measures of consciousness from brain activity (i.e., spectral measures, measures of functional connectivity, complexity-based measures). Next the paper introduces brain responses to illusions as a new indicator of consciousness (i.e., a feature that facilitates the attribution of consciousness), and illustrates the clinical operationalization of the indicators of consciousness through the case of virtual reality. Finally, the paper analyzes a set of urgent ethical issues and describes a model for assessing and dealing with those issues, concluding by elaborating key recommendations for improving the clinical treatment of patients with disorders of consciousness through a better translation of research into clinics.", - "license": "GNU General Public License v3.0 or later", - "collab_id": "livepapers", - "resources_description": "The resources from this paper are linked below. You will find the associated dataset and model as well as a Live Figure that is executable in EBRAINS (with an EBRAINS account).", - "resources": [ - { - "order": 0, - "type": "section_generic", - "title": "Data, Model and Live Figure", - "icon": "", - "description": "This section includes the data, model and Live Figure presented in the paper.", - "data": [ - { - "url": "https://data.kg.ebrains.eu/zip?container=https://data-proxy.ebrains.eu/api/v1/buckets/live-papers?prefix=2023-farisco-et-al/", - "label": "Live Figure", - "view_url": "https://lab.ch.ebrains.eu/hub/user-redirect/lab/tree/shared/Live%20Paper%20Ethics%20and%20Clinical%20Care/Live%20Figure/live_figure_clinical_cases.ipynb", - "type": "URL", - "identifier": "f8f1ab49-e9fc-4c30-8add-d7ad85c6fe3c" - }, - { - "url": "https://search.kg.ebrains.eu/instances/Dataset/9a05f491-3ef7-47be-93b2-0a6d8cd43ae0", - "label": "Results for complexity measures and a read-out of the state of cortical circuits after injury", - "view_url": null, - "type": "URL", - "identifier": "fbe0f0b9-43d0-4f52-ae12-e19cc1a7392a" - }, - { - "url": "https://wiki.ebrains.eu/bin/view/Collabs/showcase-3-tvb-brain-states-modelling", - "label": "TVB Model", - "view_url": null, - "type": "URL", - "identifier": "b29a150d-7ff4-4771-ad0c-522b5fa7bbbc" - } - ] - } - ], - "version": "v1", - "associated_paper_volume": null, - "associated_paper_issue": null, - "associated_paper_pagination": null -} \ No newline at end of file diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/203d1466-8792-4b05-b546-09ee178387c3.json b/validation_service_api/validation_service/tests/test_data/livepapers/203d1466-8792-4b05-b546-09ee178387c3.json deleted file mode 100644 index 44ae6d7e..00000000 --- a/validation_service_api/validation_service/tests/test_data/livepapers/203d1466-8792-4b05-b546-09ee178387c3.json +++ /dev/null @@ -1,151 +0,0 @@ -{ - "abstract": "Most animal species operate according to a 24-h period set by the suprachiasmatic nucleus (SCN) of the hypothalamus. The rhythmic activity of the SCNmodulates hippocampal-dependent memory, but the molecular and cellular mechanisms that account for this effect remain largely unknown. Here, we identify cell-typespecific structural and functional changes that occur with circadian rhythmicity in neurons and astrocytes in hippocampal area CA1. Pyramidal neurons change the surface expression of NMDA receptors. Astrocytes change their proximity to synapses. Together, these phenomena alter glutamate clearance, receptor activation, and integration of temporally clustered excitatory synaptic inputs, ultimately shaping hippocampaldependent learning in vivo. We identify corticosterone as a key contributor to changes in synaptic strength. These findings highlight important mechanisms through which neurons and astrocytes modify the molecular composition and structure of the synaptic environment, contribute to the local storage of information in the hippocampus, and alter the temporal dynamics of cognitive processing.", - "alias": "2020-mccauley-et-al", - "approved_author": { - "affiliation": "Department of Biology, SUNY Albany, NY, USA", - "firstname": "Annalisa", - "lastname": "Scimemi" - }, - "associated_paper_doi": "https://doi.org/10.1016/j.celrep.2020.108255", - "associated_paper_title": "Circadian Modulation of Neurons and Astrocytes Controls Synaptic Plasticity in Hippocampal Area CA1", - "authors": [ - { - "affiliation": "Department of Biology, SUNY Albany, NY, USA", - "firstname": "John P.", - "lastname": "McCauley" - }, - { - "affiliation": "Department of Biology, SUNY Albany, NY, USA", - "firstname": "Maurice A.", - "lastname": "Petroccione" - }, - { - "affiliation": "Department of Biology, SUNY Albany, NY, USA; Department of Physics, SUNY Albany, NY, USA", - "firstname": "Lianna Y.", - "lastname": "D'brant" - }, - { - "affiliation": "Department of Biology, SUNY Albany, NY, USA", - "firstname": "Gabrielle C.", - "lastname": "Todd" - }, - { - "affiliation": "Department of Biology, SUNY Albany, NY, USA", - "firstname": "Nurat", - "lastname": "Affinnih" - }, - { - "affiliation": "Department of Biology, SUNY Albany, NY, USA", - "firstname": "Justin J.", - "lastname": "Wisnoski" - }, - { - "affiliation": "Department of Biology, SUNY Albany, NY, USA", - "firstname": "Shergil", - "lastname": "Zahid" - }, - { - "affiliation": "Department of Biology, SUNY Albany, NY, USA; Bethlehem Central High School, Delmar, NY, USA", - "firstname": "Swasti", - "lastname": "Shree" - }, - { - "affiliation": "Federal University of S\u00e3o Paulo, Department of Biochemistry, S\u00e3o Paulo, Brazil; National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA", - "firstname": "Alioscka A.", - "lastname": "Sousa" - }, - { - "affiliation": "Department of Psychology, SUNY Albany, NY, USA", - "firstname": "Rose M.", - "lastname": "De Guzman" - }, - { - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy", - "firstname": "Rosanna", - "lastname": "Migliore" - }, - { - "affiliation": "Department of Biophysics, Lomonosov Moscow State University, Russia; Department of Molecular Neurobiology, Institute of Bioorganic Chemistry, Moscow, Russia", - "firstname": "Alexey", - "lastname": "Brazhe" - }, - { - "affiliation": "National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA", - "firstname": "Richard D.", - "lastname": "Leapman" - }, - { - "affiliation": "Department of Physics, SUNY Albany, NY, USA", - "firstname": "Alexander", - "lastname": "Khmaladze" - }, - { - "affiliation": "Department of Molecular Neurobiology, Institute of Bioorganic Chemistry, Moscow, Russia; Sechenov First Moscow State Medical University, Moscow, Russia", - "firstname": "Alexey", - "lastname": "Semyanov" - }, - { - "affiliation": "Department of Psychology, SUNY Albany, NY, USA", - "firstname": "Damian G.", - "lastname": "Zuloaga" - }, - { - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy", - "firstname": "Michele", - "lastname": "Migliore" - }, - { - "affiliation": "Department of Biology, SUNY Albany, NY, USA", - "firstname": "Annalisa", - "lastname": "Scimemi" - } - ], - "citation": "John P. McCauley, Maurice A. Petroccione, Lianna Y. D'brant, Gabrielle C. Todd, Nurat Affinnih, Justin J. Wisnoski, Shergil Zahid, Swasti Shree, Alioscka A. Sousa, Rose M. De Guzman, Rosanna Migliore, Alexey Brazhe, Richard D. Leapman, Alexander Khmaladze, Alexey Semyanov, Damian G. Zuloaga, Michele Migliore & Annalisa Scimemi (2020). Circadian Modulation of Neurons and Astrocytes Controls Synaptic Plasticity in Hippocampal Area CA1. Cell Reports, 33: 108255.", - "collab_id": "livepapers", - "corresponding_author": [ - { - "affiliation": "Department of Biology, SUNY Albany, NY, USA", - "firstname": "Annalisa", - "lastname": "Scimemi" - } - ], - "created_author": [ - { - "affiliation": "Department of Biology, SUNY Albany, NY, USA", - "firstname": "Annalisa", - "lastname": "Scimemi" - } - ], - "doi": "https://doi.org/10.25493/5MAD-5WQ", - "id": "203d1466-8792-4b05-b546-09ee178387c3", - "journal": "Cell Reports", - "associated_paper_volume": "33", - "associated_paper_issue": null, - "associated_paper_pagination": "108255", - "license": "Creative Commons Attribution-NonCommercial 4.0 International", - "live_paper_title": "Circadian Modulation of Neurons and Astrocytes Controls Synaptic Plasticity in Hippocampal Area CA1", - "lp_tool_version": "0.1", - "modified_date": "2021-08-05T09:07:05.443000+00:00", - "resources": [ - { - "data": [], - "description": "

Download the experimental data of the main manuscript at the following Open Science Framework link.

", - "icon": "timeline", - "order": 0, - "title": "Experimental Data", - "type": "section_custom" - }, - { - "data": [], - "description": "\n

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\n A reduced self-consistent set of files needed to reproduce Fig. 6c of\n the paper is available on\n ModelDB\n
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\n After selecting the options corresponding to the modulation of the NMDA\n receptor weights and the AMPA recovery time, simulation will reproduce the\n temporal summation of composite glutamatergic EPSPs at the stimulation\n frequency of 10 Hz (Fig. 6c of the paper).

\n The simulations were run to mimic glutamatergic EPSPs during the L phase\n (&#964rec=10 and wNMDA=0.009, the default\n settings) and in conditions that mimic the reduced NMDA receptor expression\n in the D phase (&#964rec=10 and wNMDA=0.00243,\n green line), the reduced AMPA EPSP summation during the D phase due to\n retraction of astrocytic processes (&#964rec=20 and\n wNMDA=0.009, yellow line), or both effects at the same time\n (&#964rec=20 and wNMDA=0.00243, brown line).
\n
\n HOWTO: Change settings by turning \"on\" the\n \"Reset/set parameters\" switch to run simulation with your own NMDA\n and AMPA receptor configuration.
\n Turn \"on\" the \"Persistent plot\" switch to compare plots of\n different simulations.\n

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", - "icon": "note_add", - "order": 1, - "title": "ModelDB link and test simulations", - "type": "section_custom" - } - ], - "resources_description": "Data and models: data and models used in the paper are available at the links reported below.", - "url": "https://www.cell.com/cell-reports/fulltext/S2211-1247(20)31244-4", - "version": "1", - "year": "2020-10-01" -} diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/3dce6a30-b879-4393-9c38-2f5906f949ad.json b/validation_service_api/validation_service/tests/test_data/livepapers/3dce6a30-b879-4393-9c38-2f5906f949ad.json deleted file mode 100644 index 57032c47..00000000 --- a/validation_service_api/validation_service/tests/test_data/livepapers/3dce6a30-b879-4393-9c38-2f5906f949ad.json +++ /dev/null @@ -1,101 +0,0 @@ -{ - "abstract": "Age-dependent accumulation of amyloid-beta provoking increasing brain amyloidopathy, triggers abnormal patterns of neuron activity and circuit synchronization in Alzheimer\u2019s disease (AD) as observed in human AD patients and AD mouse models. Recent studies on AD mouse models, mimicking this age-dependent amyloidopathy, identified alterations in CA1 neuron excitability. However, these models generally also overexpress mutated amyloid precursor protein (APP) and presenilin 1 (PS1) and there is a lack of a clear correlation of neuronal excitability alterations with progressive amyloidopathy. The active development of computational models of AD points out the need of collecting such experimental data to build a reliable disease model exhibiting AD-like disease progression. We therefore used the feature extraction tool of the Human Brain Project (HBP) Brain Simulation Platform to systematically analyze the excitability profile of CA1 pyramidal neuron in the APPPS1 mouse model. We identified specific features of neuron excitability that best correlate either with over-expression of mutated APP and PS1 or increasing ABeta-amyloidopathy. Notably, we report strong alterations in membrane time constant and action potential width and weak alterations in firing behavior. Also, using a CA1 pyramidal neuron model, we evidence amyloidopathy-dependent alterations in Ih. Finally, cluster analysis of these recordings showed that we could reliably assign a trace to its correct group, opening the door to a more refined, less variable analysis of AD-affected neurons. This inter-disciplinary analysis, bringing together experimentalists and modelers, helps to further unravel the neuronal mechanisms most affected by AD and to build a biologically-plausible computational model of the AD brain.", - "alias": "2021-vitale-et-al", - "approved_author": { - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy", - "firstname": "Paola", - "lastname": "Vitale" - }, - "associated_paper_doi": "https://doi.org/10.3389/fnagi.2021.668948", - "associated_paper_title": "Analysis of Age-Dependent Alterations in Excitability Properties of CA1 Pyramidal Neurons in an APPPS1 Model of Alzheimer's Disease", - "associated_paper_issue": null, - "associated_paper_pagination": "668948", - "associated_paper_volume": "13", - "authors": [ - { - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy", - "firstname": "Paola", - "lastname": "Vitale" - }, - { - "affiliation": "Institute of Molecular and Cellular Pharmacology, CNRS, UMR7275, Universit\u00e9 C\u00f4te d\u2019Azur, Valbonne, France", - "firstname": "Ana Rita", - "lastname": "Salgueiro-Pereira" - }, - { - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy", - "firstname": "Carmen A.", - "lastname": "Lupascu" - }, - { - "affiliation": "Biomedical Center, Ludwig-Maximilians-Universität München, Germany", - "firstname": "Michael", - "lastname": "Willem" - }, - { - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy", - "firstname": "Rosanna", - "lastname": "Migliore" - }, - { - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy", - "firstname": "Michele", - "lastname": "Migliore" - }, - { - "affiliation": "Institute of Molecular and Cellular Pharmacology, CNRS, UMR7275, Universit\u00e9 C\u00f4te d\u2019Azur, Valbonne, France", - "firstname": "H\u00e9l\u00e8ne", - "lastname": "Marie" - } - ], - "citation": "Paola Vitale, Ana Rita Salgueiro-Pereira, Carmen A. Lupascu, Michael Willem, Rosanna Migliore, Michele Migliore & Hélène Marie (2021). Analysis of Age-Dependent Alterations in Excitability Properties of CA1 Pyramidal Neurons in an APPPS1 Model of Alzheimer's Disease. Frontiers in Aging Neuroscience, 13: 668948.", - "collab_id": "livepapers", - "corresponding_author": [ - { - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy", - "firstname": "Michele", - "lastname": "Migliore" - }, - { - "affiliation": "Institute of Molecular and Cellular Pharmacology, CNRS, UMR7275, Universit\u00e9 C\u00f4te d\u2019Azur, Valbonne, France", - "firstname": "H\u00e9l\u00e8ne", - "lastname": "Marie" - } - ], - "created_author": [ - { - "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy", - "firstname": "Paola", - "lastname": "Vitale" - } - ], - "doi": "https://doi.org/10.25493/D4PT-QNB", - "id": "3dce6a30-b879-4393-9c38-2f5906f949ad", - "journal": "Frontiers in Aging Neuroscience", - "license": "Creative Commons Attribution-NonCommercial 4.0 International", - "live_paper_title": "Analysis of Age-Dependent Alterations in Excitability Properties of CA1 Pyramidal Neurons in an APPPS1 Model of Alzheimer\u2019s Disease", - "lp_tool_version": "0.1", - "modified_date": "2021-08-02T10:49:02.857000+00:00", - "resources": [ - { - "data": [], - "description": "

Download the experimental data of the main manuscript at the following link.

", - "icon": "timeline", - "order": 0, - "title": "Experimental Data", - "type": "section_custom" - }, - { - "data": [], - "description": "

A reduced self-consistent set of files needed to reproduce Fig. 3d of the paper is available on\n ModelDB.\n

", - "icon": "note_add", - "order": 1, - "title": "ModelDB link", - "type": "section_custom" - } - ], - "resources_description": "Data and models: data and models used in the paper are available at the links reported below.", - "url": "https://www.frontiersin.org/articles/10.3389/fnagi.2021.668948/full", - "version": "1", - "year": "2021-06-23" -} diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/42a90bce-d52c-4e55-b6a8-3ec5c14a828f.json b/validation_service_api/validation_service/tests/test_data/livepapers/42a90bce-d52c-4e55-b6a8-3ec5c14a828f.json deleted file mode 100644 index 7ddc88c4..00000000 --- a/validation_service_api/validation_service/tests/test_data/livepapers/42a90bce-d52c-4e55-b6a8-3ec5c14a828f.json +++ /dev/null @@ -1,219 +0,0 @@ -{ - "abstract": "The basal ganglia are involved in the motivational and habitual control of motor and cognitive behaviors. Striatum, the largest basal ganglia input stage, integrates cortical and thalamic inputs in functionally segregated cortico-basal ganglia-thalamic loops, and in addition the basal ganglia output nuclei control targets in the brainstem. Striatal function depends on the balance between the direct pathway medium spiny neurons (D1-MSNs) that express D1 dopamine receptors and the indirect pathway MSNs that express D2 dopamine receptors. The striatal microstructure is also divided into striosomes and matrix compartments, based on the differential expression of several proteins. Dopaminergic afferents from the midbrain and local cholinergic interneurons play crucial roles for basal ganglia function, and striatal signaling via the striosomes in turn regulates the midbrain dopaminergic system directly and via the lateral habenula. Consequently, abnormal functions of the basal ganglia neuromodulatory system underlie many neurological and psychiatric disorders. Neuromodulation acts on multiple structural levels, ranging from the subcellular level to behavior, both in health and disease. For example, neuromodulation affects membrane excitability and controls synaptic plasticity and thus learning in the basal ganglia. However, it is not clear on what time scales these different effects are implemented. Phosphorylation of ion channels and the resulting membrane effects are typically studied over minutes while it has been shown that neuromodulation can affect behavior within a few hundred milliseconds. So how do these seemingly contradictory effects fit together? Here we first briefly review neuromodulation of the basal ganglia, with a focus on dopamine. We furthermore use biophysically detailed multi-compartmental models to integrate experimental data regarding dopaminergic effects on individual membrane conductances with the aim to explain the resulting cellular level dopaminergic effects. In particular we predict dopaminergic effects on Kv4.2 in D1-MSNs. Finally, we also explore dynamical aspects of the onset of neuromodulation effects in multi-scale computational models combining biochemical signaling cascades and multi-compartmental neuron models.", - "alias": "2018-lindroos-et-al", - "approved_author": { - "affiliation": "Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden; Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden", - "firstname": "Jeanette", - "lastname": "Hellgren Kotaleski" - }, - "associated_paper_doi": "https://doi.org/10.3389/fncir.2018.00003", - "associated_paper_title": "Basal Ganglia Neuromodulation Over Multiple Temporal and Structural Scales\u2014Simulations of Direct Pathway MSNs Investigate the Fast Onset of Dopaminergic Effects and Predict the Role of Kv4.2", - "authors": [ - { - "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden", - "firstname": "Robert", - "lastname": "Lindroos" - }, - { - "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden", - "firstname": "Matthijs C.", - "lastname": "Dorst" - }, - { - "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden", - "firstname": "Kai", - "lastname": "Du" - }, - { - "affiliation": "Bernstein Center Freiburg, University of Freiburg, Freiburg im Breisgau, Germany", - "firstname": "Marko", - "lastname": "Filipovi\u0107" - }, - { - "affiliation": "Blue Brain Project, École Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland", - "firstname": "Daniel", - "lastname": "Keller" - }, - { - "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden", - "firstname": "Maya", - "lastname": "Ketzef" - }, - { - "affiliation": "Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden; Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden", - "firstname": "Alexander K.", - "lastname": "Kozlov" - }, - { - "affiliation": "Bernstein Center Freiburg, University of Freiburg, Freiburg im Breisgau, Germany; Department Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden", - "firstname": "Arvind", - "lastname": "Kumar" - }, - { - "affiliation": "Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden", - "firstname": "Mikael", - "lastname": "Lindahl" - }, - { - "affiliation": "Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland", - "firstname": "Anu G.", - "lastname": "Nair" - }, - { - "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden", - "firstname": "Juan", - "lastname": "P\u00e9rez-Fern\u00e1ndez" - }, - { - "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden", - "firstname": "Sten", - "lastname": "Grillner" - }, - { - "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden", - "firstname": "Gilad", - "lastname": "Silberberg" - }, - { - "affiliation": "Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden; Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden", - "firstname": "Jeanette", - "lastname": "Hellgren Kotaleski" - } - ], - "citation": "Robert Lindroos, Matthijs C. Dorst, Kai Du, Marko Filipović, Daniel Keller, Maya Ketzef, Alexander K. Kozlov, Arvind Kumar, Mikael Lindahl, Anu G. Nair, Juan Pérez-Fernández, Sten Grillner, Gilad Silberberg & Jeanette Hellgren Kotaleski (2018). Basal Ganglia Neuromodulation Over Multiple Temporal and Structural Scales—Simulations of Direct Pathway MSNs Investigate the Fast Onset of Dopaminergic Effects and Predict the Role of Kv4.2. Frontiers in Neural Circuits, 12: 3.", - "collab_id": "livepapers", - "corresponding_author": [ - { - "affiliation": "Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden; Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden", - "firstname": "Jeanette", - "lastname": "Hellgren Kotaleski" - } - ], - "created_author": [ - { - "affiliation": "Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden; Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden", - "firstname": "Jeanette", - "lastname": "Hellgren Kotaleski" - } - ], - "doi": "https://doi.org/10.25493/Q0D0-E6X", - "id": "42a90bce-d52c-4e55-b6a8-3ec5c14a828f", - "associated_paper_volume": "12", - "associated_paper_issue": null, - "associated_paper_pagination": "3", - "journal": "Frontiers in Neural Circuits", - "license": "Creative Commons Attribution-NonCommercial 4.0 International", - "live_paper_title": "Basal Ganglia Neuromodulation Over Multiple Temporal and Structural Scales\u2014Simulations of Direct Pathway MSNs Investigate the Fast Onset of Dopaminergic Effects and Predict the Role of Kv4.2", - "lp_tool_version": "0.1", - "modified_date": "2021-08-10T09:15:03.606000+00:00", - "resources": [ - { - "data": [ - { - "identifier": "8a320c75-1160-48a0-bc8c-33fa63232d6a", - "label": " WT-P270-20-14ak_1.03_SGA2-m12", - "type": "URL", - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_lindroos_et_al/models/MSND1/morphology/WT-P270-20-14ak_1.03_SGA2-m12.swc", - "view_url": null - } - ], - "description": "Morphology file (in .swc format) used in the paper:", - "icon": "settings_input_antenna", - "order": 0, - "title": "Morphologies", - "type": "section_morphology" - }, - { - "data": [ - { - "identifier": "f6ce72aa-3b69-4d85-af6d-e7c534c3e5a6", - "label": "bAP", - "type": "URL", - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_lindroos_et_al/expdata/bAP/bAP-DayEtAl2006-D1.csv", - "view_url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_lindroos_et_al/expdata/bAP/bAP-DayEtAl2006-D1.png" - }, - { - "identifier": "ec514f6c-d9f7-466e-9b6c-8d3bf42b8335", - "label": "f-I (cell 1)", - "type": "URL", - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_lindroos_et_al/expdata/FI/Planert2013-D1-FI-trace1.csv", - "view_url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_lindroos_et_al/expdata/FI/Planert2013-D1-FI-trace1.png" - }, - { - "identifier": "2c92a774-c0ac-4da8-ac4a-7b9f09bee8f7", - "label": "f-I (cell 2)", - "type": "URL", - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_lindroos_et_al/expdata/FI/Planert2013-D1-FI-trace2.csv", - "view_url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_lindroos_et_al/expdata/FI/Planert2013-D1-FI-trace2.png" - }, - { - "identifier": "0056ca1e-f105-4bde-a0fc-ca9abb7bf6d6", - "label": "f-I (cell 3)", - "type": "URL", - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_lindroos_et_al/expdata/FI/Planert2013-D1-FI-trace3.csv", - "view_url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_lindroos_et_al/expdata/FI/Planert2013-D1-FI-trace3.png" - }, - { - "identifier": "daecca5c-adb8-46ba-9e1a-d7232cc40c39", - "label": "f-I (cell 4)", - "type": "URL", - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_lindroos_et_al/expdata/FI/Planert2013-D1-FI-trace4.csv", - "view_url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_lindroos_et_al/expdata/FI/Planert2013-D1-FI-trace4.png" - }, - { - "identifier": "7ca376d6-7542-4eaf-9301-1e2d1e7b1124", - "label": "f-I (cell 5)", - "type": "URL", - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_lindroos_et_al/expdata/FI/Planert2013-D1-FI-trace5.csv", - 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}, - { - "identifier": "d89c075b-633e-413b-a539-1f9000dd0f8e", - "label": "f-I (cell 8)", - "type": "URL", - "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_lindroos_et_al/expdata/FI/Planert2013-D1-FI-trace8.csv", - "view_url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_lindroos_et_al/expdata/FI/Planert2013-D1-FI-trace8.png" - } - ], - "description": "Experimental data used to constrain the single-cell MSN model, digitized manually, from Planert et al. 2013 (frequency-current curves, f-I) and Day et al. 2008 (backpropagating action potential induced Ca transient as function of somatic distance normalized to proximal value, bAP).\n
\n
\n[1] Planert H, Berger TK Silberberg G (2013) Membrane properties of striatal direct and indirect pathway neurons in mouse and rat slices and their modulation by dopamine. PLoS One. 2013;8(3):e57054.\n\n[2] Day M, Wokosin D, Plotkin JL, Tian X, Surmeier DJ (2008) Differential excitability and modulation of striatal medium spiny neuron dendrites. J Neurosci. 2008 Nov 5;28(45):11603-14.", - "icon": "timeline", - "order": 1, - "title": "Experimental data", - "type": "section_generic" - }, - { - "data": [], - "description": "

\n Simulations of the single-cell medium spiny neuron model from the article using the BSP Neuron As A Service (NaaS) tool. Recommended current clamp parameters: amp 0.33 nA, tstop 1000 ms (default) or 3000 ms, dur 800 ms (default) or 2800 ms. Models implement dopaminergic modulation of the excitability at fixed dopamine levels, low (DA LO), medium (DA 0.5) and full effect (DA 1.0), as well as transient response to the dynamic dopamine application (DA(T)).\n

\n
\n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n \n
\n \n \n \n \n \n
\n
", - "icon": "local_play", - "order": 2, - "title": "Test simulations", - "type": "section_custom" - }, - { - "data": [], - "description": "

\n The complete source code of the model together with instructions on how to\n reproduce Figures 2, 3, 4 and 6 is available on \n ModelDB \n (accession number 237653). Rendered figures and experimental data can be accessed at the companion depository on \n GitHub.com.\n

", - "icon": "note_add", - "order": 3, - "title": "Source Code", - "type": "section_custom" - } - ], - "resources_description": "Data and models: data and models used in the paper are available at the links reported below, grouped into the following categories:", - "url": "https://doi.org/10.3389/fncir.2018.00003", - "version": "1", - "year": "2018-02-06" -} \ No newline at end of file diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/44f1d119-6233-44c4-9584-b2352a4c254e.json b/validation_service_api/validation_service/tests/test_data/livepapers/44f1d119-6233-44c4-9584-b2352a4c254e.json deleted file mode 100644 index 710f928c..00000000 --- a/validation_service_api/validation_service/tests/test_data/livepapers/44f1d119-6233-44c4-9584-b2352a4c254e.json +++ /dev/null @@ -1,91 +0,0 @@ -{ - "abstract": "Reconstructing neuronal microcircuits through computational models is fundamental to simulate local neuronal dynamics. Here a scaffold model of the cerebellum has been developed in order to flexibly place neurons in space, connect them synaptically, and endow neurons and synapses with biologically-grounded mechanisms. The scaffold model can keep neuronal morphology separated from network connectivity, which can in turn be obtained from convergence/divergence ratios and axonal/dendritic field 3D geometries. We first tested the scaffold on the cerebellar microcircuit, which presents a challenging 3D organization, at the same time providing appropriate datasets to validate emerging network behaviors. The scaffold was designed to integrate the cerebellar cortex with deep cerebellar nuclei (DCN), including different neuronal types: Golgi cells, granule cells, Purkinje cells, stellate cells, basket cells, and DCN principal cells. Mossy fiber inputs were conveyed through the glomeruli. An anisotropic volume (0.077 mm3) of mouse cerebellum was reconstructed, in which point-neuron models were tuned toward the specific discharge properties of neurons and were connected by exponentially decaying excitatory and inhibitory synapses. Simulations using both pyNEST and pyNEURON showed the emergence of organized spatio-temporal patterns of neuronal activity similar to those revealed experimentally in response to background noise and burst stimulation of mossy fiber bundles. Different configurations of granular and molecular layer connectivity consistently modified neuronal activation patterns, revealing the importance of structural constraints for cerebellar network functioning. The scaffold provided thus an effective workflow accounting for the complex architecture of the cerebellar network. In principle, the scaffold can incorporate cellular mechanisms at multiple levels of detail and be tuned to test different structural and functional hypotheses. A future implementation using detailed 3D multi-compartment neuron models and dynamic synapses will be needed to investigate the impact of single neuron properties on network computation.", - "alias": "2019-casali-et-al", - "approved_author": { - "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy", - "firstname": "Egidio", - "lastname": "D'Angelo" - }, - "associated_paper_doi": "https://doi.org/10.3389/fninf.2019.00037", - "associated_paper_title": "Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network", - "authors": [ - { - "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy", - "firstname": "Stefano", - "lastname": "Casali" - }, - { - "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy", - "firstname": "Elisa", - "lastname": "Marenzi" - }, - { - "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy", - "firstname": "Chaitanya", - "lastname": "Medini" - }, - { - "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy", - "firstname": "Claudia", - "lastname": "Casellato" - }, - { - "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy", - "firstname": "Egidio", - "lastname": "D'Angelo" - } - ], - "citation": "Stefano Casali, Elisa Marenzi, Chaitanya Medini, Claudia Casellato & Egidio D'Angelo (2019). Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network. Frontiers in Neuroinformatics, 13: 37.", - "collab_id": "livepapers", - "corresponding_author": [ - { - "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy", - "firstname": "Egidio", - "lastname": "D'Angelo" - }, - { - "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy", - "firstname": "Claudia", - "lastname": "Casellato" - } - ], - "created_author": [ - { - "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy", - "firstname": "Egidio", - "lastname": "D'Angelo" - } - ], - "doi": "https://doi.org/10.25493/68XT-RB1", - "id": "44f1d119-6233-44c4-9584-b2352a4c254e", - "journal": "Frontiers in Neuroinformatics", - "associated_paper_volume": "13", - "associated_paper_issue": null, - "associated_paper_pagination": "37", - "license": "Creative Commons Attribution-NonCommercial 4.0 International", - "live_paper_title": "Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network", - "lp_tool_version": "0.1", - "modified_date": "2021-08-02T10:42:14.298000+00:00", - "resources": [ - { - "data": [], - "description": "

The complete source code, used for the reconstruction and simulations described in paper, and compliant with the SONATA format, can be accessed and downloaded at the following github link.\n
\n
\n Please refer to the README file of the github repository for more details.\n

", - "icon": "note_add", - "order": 0, - "title": "Source Code", - "type": "section_custom" - }, - { - "data": [], - "description": "

\n The reconstruction and the functional simulations of the cerebellar microcircuit procedures, presented in the paper, are available among the \n \n Brain Simulation Platform Online Use Cases, as python Jupyter Notebooks.\n
\n
\n For circuit building: select the \"Cells Placement\" or the \"Connectome\" panel in the \"Circuit Building\" item of the Online Use Cases. Successively, click on the \"Rat Cerebellum Volume\" panel and follow the instruction to run the Jupyter Notebook.
\n
\n For functional simulation: select the \"Cerebellum\" panel in the \"Brain Area Circuit In Silico Experiments\" item of the Online Use Cases. Successively, click on the \"Functional Simulations with Point Neurons\" panel and follow the instructions to run the Jupyter Notebook.\n
\n
\n Please refer to the python code and the inline comments, which you will find in the Jupyter Notebooks, for details on how to reproduce Fig. 2 and Fig. 3 of the paper.\n \n

", - "icon": "local_activity", - "order": 1, - "title": "Test Simulations", - "type": "section_custom" - } - ], - "resources_description": "Data and models: all data and models used in the paper are available at the links reported below, grouped into the following categories:", - "url": "https://www.frontiersin.org/articles/10.3389/fninf.2019.00037/full", - "version": "1", - "year": "2019-05-15" -} \ No newline at end of file diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/5ec5bd1f-6e72-4c17-8c45-6a206c2c0c72.json b/validation_service_api/validation_service/tests/test_data/livepapers/5ec5bd1f-6e72-4c17-8c45-6a206c2c0c72.json deleted file mode 100644 index e8faaf9d..00000000 --- a/validation_service_api/validation_service/tests/test_data/livepapers/5ec5bd1f-6e72-4c17-8c45-6a206c2c0c72.json +++ /dev/null @@ -1,790 +0,0 @@ -{ - "lp_tool_version": "0.1", - "id": "5ec5bd1f-6e72-4c17-8c45-6a206c2c0c72", - "alias": "2022-appukuttan-davison", - "modified_date": "2022-10-06T14:54:33.241000+00:00", - "version": "1", - "authors": [ - { - "firstname": "Shailesh", - "lastname": "Appukuttan", - "affiliation": "Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Universit\u00e9 Paris-Saclay, Saclay, France" - }, - { - "firstname": "Andrew P.", - "lastname": "Davison", - "affiliation": "Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Universit\u00e9 Paris-Saclay, Saclay, France" - } - ], - "corresponding_author": [ - { - "firstname": "Shailesh", - "lastname": "Appukuttan", - "affiliation": "Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Universit\u00e9 Paris-Saclay, Saclay, France" - } - ], - "created_author": [ - { - "firstname": "Shailesh", - "lastname": "Appukuttan", - "affiliation": "Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Universit\u00e9 Paris-Saclay, Saclay, France" - } - ], - "approved_author": { - "firstname": "Shailesh", - "lastname": "Appukuttan", - "affiliation": "Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Universit\u00e9 Paris-Saclay, Saclay, France" - }, - "year": "2022-01-01", - "live_paper_title": "Reproducing and quantitatively validating a biologically-constrained point-neuron model of CA1 pyramidal cells", - "associated_paper_title": "Reproducing and quantitatively validating a biologically-constrained point-neuron model of CA1 pyramidal cells", - "journal": "Frontiers in Integrative Neuroscience", - "url": "https://doi.org/10.3389/fnint.2022.1041423", - "citation": "Shailesh Appukuttan & Andrew P. Davison (2022). Reproducing and quantitatively validating a biologically-constrained point-neuron model of CA1 pyramidal cells. Frontiers in Integrative Neuroscience, 16: 1041423.", - "doi": null, - "associated_paper_doi": "https://doi.org/10.3389/fnint.2022.1041423", - "associated_paper_volume": "16", - "associated_paper_issue": null, - "associated_paper_pagination": "1041423", - "abstract": "We have attempted to reproduce a biologically-constrained point-neuron model of CA1 pyramidal cells. The original models, developed for Brian simulator, captured the frequency-current profiles of both strongly and weakly adapting cells. As part of the present study, we reproduced the model for different simulators, namely Brian2 and NEURON. The reproductions were attempted independent of the original Brian implementation, relying solely on the published article.\n\nThe different implementations were quantitatively validated, to evaluate how well they mirror the original model. Additional tests were developed and packaged into a test suite, that helped further characterize and compare various aspects of these models, beyond the scope of the original study. Overall, we were able to reproduce the core features of the model, but observed certain unaccountable discrepancies.\n\nWe demonstrate an approach for undertaking these evaluations, using the SciUnit framework, that allows for such quantitative validations of scientific models, to verify their accurate replication and/or reproductions. All resources employed and developed in our study have been publicly shared via the EBRAINS Live Papers platform.", - "license": "Creative Commons Attribution-NonCommercial 4.0 International", - "resources_description": "All resources that were created and/or employed in our study, have been publicly shared here through this Live Paper. In addition, all the models and tests discussed in this study have been registered on the EBRAINS Model Validation Framework, thereby providing access to additional relevant metadata. Every validation result, along with data files and figures produced as part of the evaluation, are linked to the models and the tests, and publicly available online. These measures greatly help in satisfying the Findable (F), Accessible (A), and Reusable (R) aspects of the FAIR principles for sharing scientific data. Our GitHub repository also provides access to the files associated with this study.", - "collab_id": "livepapers", - "resources": [ - { - "order": 0, - "type": "section_custom", - "title": "Original Study", - "icon": "insert_drive_file", - "description": "

Title:

\n

\n 

\n  Simple, biologically-constrained CA1 pyramidal cell models using an intact, whole hippocampus context\n
\n\n

\n

 

\n

Link to article:

\n

\n 

\n  https://doi.org/10.12688/f1000research.3894.2
\n

\n

 

\n

Citation:

\n

\n 

\n    Ferguson KA, Huh CYL, Amilhon B et al. Simple, biologically-constrained CA1 pyramidal cell models using an intact, whole hippocampus context [version 2; peer review: 2 approved]. F1000Research 2015, 3:104
\n

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\n A test suite, named eFELunit, was developed in Python as a\n distributable package, containing several validation tests in addition to\n those described in the original study. This allows for a more comprehensive\n evaluation of the various aspects of the different implementations of the\n model.\n

\n
\n
\n \"SciUnit\n
\n

\nFigure shows the SciUnit based workflow of running validation tests. The capability acts as an interface between\nthe models and tests, and defines the functionalities demanded from the models. The solid arrows\nindicate the functions (in red) that are to be implemented by our models; these functions represent\nmore granular functionalities. The function (in blue) combines these more basic functions, to provide\na more comprehensive workflow, and can be directly utilized (shown by dashed arrows) by the tests to\nevaluate the model.

\n
\n

You can pip install the package as follows:

\n
\n  pip install eFELunit\n
\n

(note: use version >= 2.0.3)

\n

Example usage:

\n

\n  data = [{\"i_inj\": \"50 pA\", \"value\": \"60.99 mV\"}, {...}] # specify reference data\n  from eFELunit.tests import eFELfeatureTest\n  test = eFELfeatureTest(data, feature=\"AP1_amp\", force_run=True)\n  from Brian2.generic_Pyr_model_Brian2 import CA1_Pyr_Brian2_Template\n  model = CA1_Pyr_Brian2_Template(type='strong')\n  test.judge(model, deep_error=True)
\n
Currently available features:
\n
\n  'spikecount', \n  'time_to_first_spike', \n  'time_to_second_spike', \n  'time_to_last_spike', \n  'AP1_amp', \n  'AP2_amp', \n  'APlast_amp', \n  'AP1_peak',\n  'AP2_peak',\n  'AP1_width', \n  'AP2_width', \n  'APlast_width', \n  'iv_curve'
",
-      "data": []
-    },
-    {
-      "order": 6,
-      "type": "section_generic",
-      "title": "Jupyter Notebooks",
-      "icon": "format_list_bulleted",
-      "description": "These Jupyter Notebook will allow you to run sample simulations for the Brian2 and NEURON models from our study. They are implemented to be interactive: you can specify the model variant and the feature to be evaluated.\n

\nThe notebooks are stored on the EBRAINS Collaboratory. If you have an EBRAINS account, you can clone the notebooks, edit and run them in your own workspace. If you do not yet have an EBRAINS account, you can request the same for free. To do so, please visit: https://ebrains.eu/register",
-      "data": [
-        {
-          "url": "https://drive.ebrains.eu/f/866f3b248bd54af9ade1/?dl=1",
-          "label": "Brian2 Example",
-          "view_url": "https://lab.ch.ebrains.eu/hub/user-redirect/lab/tree/shared/Live%20Paper%3A%202022_Appukuttan_Davison/Brian2_eFELunit_example.ipynb",
-          "type": "URL",
-          "identifier": "4c2fb19b-a27d-40b3-8e5a-a6042d7d2149"
-        },
-        {
-          "url": "https://drive.ebrains.eu/f/877ad732f9724052bf30/?dl=1",
-          "label": "NEURON Example",
-          "view_url": "https://lab.ch.ebrains.eu/hub/user-redirect/lab/tree/shared/Live%20Paper%3A%202022_Appukuttan_Davison/Neuron_eFELunit_example.ipynb",
-          "type": "URL",
-          "identifier": "cc8bece4-0446-482b-8f90-b53ebeca7682"
-        }
-      ]
-    },
-    {
-      "order": 7,
-      "type": "section_custom",
-      "title": "Creating Python3 Virtual Environment",
-      "icon": "settings_brightness",
-      "description": "
\n  We shall make use of the virtualenvwrapper helper package for managing virtual environments.\n
\n
You can pip install this package as follows:
\n
\n  pip install virtualenvwrapper\n
\n
(for more detailed installation instructions, click here)
\n
 
\n
Python3 virtual environment:
\n
\n  Create a virtual environment named py3venv:\n  
\n  mkvirtualenv --python=\"/usr/bin/python3\" py3venv
\n  Run using python command.\n  
\n  Python 3.8.10 (default, Jun 22 2022, 20:18:18)\n  [GCC 9.4.0] on linux
\n
\n
\n  Next, we install all the required packages:\n  
\n  pip install scipy
\n  
\n  Successfully installed numpy-1.23.2 scipy-1.9.1
\n\n  
\n  pip install brian2
\n  
\n  Successfully installed MarkupSafe-2.1.1 brian2-2.5.1 cython-0.29.32 jinja2-3.1.2 mpmath-1.2.1 pyparsing-3.0.9 sympy-1.11.1
\n\n  
\n  pip install neuron
\n  
\n  Successfully installed neuron-8.2.1
\n\n  
\n  pip install sciunit
\n  
\n  Successfully installed asttokens-2.0.8 attrs-22.1.0 backcall-0.2.0 beautifulsoup4-4.11.1 bleach-5.0.1 bs4-0.0.1 cerberus-1.3.4 cycler-0.11.0 debugpy-1.6.3 decorator-5.1.1 deepdiff-5.8.1 defusedxml-0.7.1 entrypoints-0.4 executing-1.0.0 fastjsonschema-2.16.1 fonttools-4.37.1 gitdb-4.0.9 gitpython-3.1.27 importlib-metadata-3.10.1 importlib-resources-5.9.0 ipykernel-6.15.2 ipython-8.5.0 jedi-0.18.1 jsonpickle-2.2.0 jsonschema-4.15.0 jupyter-client-7.3.5 jupyter-core-4.11.1 jupyterlab-pygments-0.2.2 kiwisolver-1.4.4 lxml-4.9.1 matplotlib-3.5.3 matplotlib-inline-0.1.6 mistune-2.0.4 nbclient-0.6.7 nbconvert-7.0.0 nbformat-5.4.0 nest-asyncio-1.5.5 ordered-set-4.1.0 packaging-21.3 pandas-1.4.4 pandocfilters-1.5.0 parso-0.8.3 pexpect-4.8.0 pickleshare-0.7.5 pillow-9.2.0 pkgutil-resolve-name-1.3.10 prompt-toolkit-3.0.31 psutil-5.9.2 ptyprocess-0.7.0 pure-eval-0.2.2 pygments-2.13.0 pyrsistent-0.18.1 python-dateutil-2.8.2 pytz-2022.2.1 pyzmq-23.2.1 quantities-0.13.0 sciunit-0.2.7 six-1.16.0 smmap-5.0.0 soupsieve-2.3.2.post1 stack-data-0.5.0 tinycss2-1.1.1 tornado-6.2 traitlets-5.3.0 wcwidth-0.2.5 webencodings-0.5.1 zipp-3.8.1
\n\n  
\n  pip install efel
\n  
\n  Successfully installed efel-4.1.1
\n\n  
\n  pip install pathos
\n  
\n  Successfully installed dill-0.3.5.1 multiprocess-0.70.13 pathos-0.2.9 pox-0.3.1 ppft-1.7.6.5
\n\n  
\n  pip install eFELunit
\n  
\n  Successfully installed eFELunit-2.0.3 neo-0.11.0
\n
\n
\n  Click here to download the output of pip freeze for this virtual environment.\n
\n

\n
Example simulation:
\n
\n  (from within Brian2 directory of GitHub repo)\n  
Run using python command.\n  
\n  from generic_Pyr_model_Brian2 import CA1_Pyr_Brian2_Template\n  pyr = CA1_Pyr_Brian2_Template(type='strong')\n  print(pyr.name)\n  pyr.run_sample_sim()
\n
",
-      "data": []
-    },
-    {
-      "order": 8,
-      "type": "section_custom",
-      "title": "Creating Python2 Virtual Environment",
-      "icon": "settings_brightness",
-      "description": "
\n  We shall make use of the virtualenvwrapper helper package for managing virtual environments.\n
\n
You can pip install this package as follows:
\n
\n  pip install virtualenvwrapper\n
\n
(for more detailed installation instructions, click here)
\n
 
\n
Python2 virtual environment:
\n
\n  Create a virtual environment named py2venv:\n  
\n  mkvirtualenv --python=\"/usr/bin/python2\" py2venv
\n  Run using python2 command.\n  
\n  Python 2.7.18 (default, Jul  1 2022, 12:27:04) \n  [GCC 9.4.0] on linux2
\n
\n
\n  Next, we install all the required packages:\n  
\n  pip install scipy
\n  
\n  Successfully installed numpy-1.16.6 scipy-1.2.3
\n\n  
\n  pip install brian
\n  
\n  Successfully installed brian-1.4.4
\n\n  
\n  pip install sympy
\n  
\n  Successfully installed mpmath-1.2.1 sympy-1.5.1
\n\n  
\n  pip install pathlib
\n  
\n  Successfully installed pathlib-1.0.1
\n\n  
\n  pip install sciunit==0.2.2
\n  
\n  Successfully installed MarkupSafe-1.1.1 attrs-21.4.0 backports-abc-0.5 backports.functools-lru-cache-1.6.4 backports.shutil-get-terminal-size-1.0.0 backports.tempfile-1.0 backports.weakref-1.0.post1 beautifulsoup4-4.9.3 bleach-3.3.1 bs4-0.0.1 cerberus-1.3.4 configparser-4.0.2 contextlib2-0.6.0.post1 cycler-0.10.0 cypy-0.2.0 decorator-4.4.2 defusedxml-0.7.1 entrypoints-0.3 enum34-1.1.10 functools32-3.2.3.post2 futures-3.3.0 gitdb2-2.0.6 gitpython-2.1.15 importlib-metadata-2.1.3 ipykernel-4.10.1 ipython-5.10.0 ipython-genutils-0.2.0 jinja2-2.11.3 jsonschema-3.2.0 jupyter-client-5.3.5 jupyter-core-4.6.3 kiwisolver-1.1.0 lxml-4.9.1 matplotlib-2.2.5 mistune-0.8.4 nbconvert-5.6.1 nbformat-4.4.0 packaging-20.9 pandas-0.24.2 pandocfilters-1.5.0 pathlib2-2.3.7.post1 pexpect-4.8.0 pickleshare-0.7.5 prompt-toolkit-1.0.18 ptyprocess-0.7.0 pygments-2.5.2 pyparsing-2.4.7 pyrsistent-0.16.1 python-dateutil-2.8.2 pytz-2022.2.1 pyzmq-19.0.2 quantities-0.12.1 scandir-1.10.0 sciunit-0.2.2.1 simplegeneric-0.8.1 singledispatch-3.7.0 six-1.16.0 smmap-3.0.5 smmap2-3.0.1 soupsieve-1.9.6 subprocess32-3.5.4 testpath-0.4.4 tornado-5.1.1 traitlets-4.3.3 typing-3.10.0.0 wcwidth-0.2.5 webencodings-0.5.1 zipp-1.2.0
\n\n  
\n  pip install efel==3.2.4
\n  
\n  Successfully installed efel-3.2.4
\n\n  
\n  pip install pathos
\n  
\n  Successfully installed dill-0.3.5.1 multiprocess-0.70.13 pathos-0.2.9 pox-0.3.1 ppft-1.7.6.5
\n\n  
\n  pip install eFELunit
\n  
\n  Successfully installed eFELunit-2.0.1 neo-0.8.0
\n
\n
\n  Click here to download the output of pip freeze for this virtual environment.\n
\n

\n
Example simulation:
\n
\n  (from within Brian1 directory of GitHub repo)\n  
Run using python2 command.\n  
\n  from generic_Pyr_model_Brian1 import CA1_Pyr_Brian1_Template\n  pyr = CA1_Pyr_Brian1_Template(type='strong')\n  print(pyr.name)\n  pyr.run_sample_sim()
\n
",
-      "data": []
-    }
-  ]
-}
\ No newline at end of file
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/67806cc2-84e0-4bb3-ae52-8cc3e5abf738.json b/validation_service_api/validation_service/tests/test_data/livepapers/67806cc2-84e0-4bb3-ae52-8cc3e5abf738.json
deleted file mode 100644
index db288a52..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/67806cc2-84e0-4bb3-ae52-8cc3e5abf738.json
+++ /dev/null
@@ -1,137 +0,0 @@
-{
-    "abstract": "The basal ganglia play an important role in decision-making and selection of action primarily based on input from cortex, thalamus and the dopamine system. Their main input structure, striatum, is central to this process. It consists of two types of projection neurons, together representing 95% of the neurons, and 5% of interneurons, among which are the cholinergic, fastspiking and low threshold spiking subtypes. The membrane-properties, soma-dendritic shape and intrastriatal- and extrastriatal synaptic interactions of these neurons are quite well described in the mouse, and therefore they can be simulated in sufficient detail to capture their intrinsic properties, as well as the connectivity. We focus on simulation at the striatal cellular/microcircuit level, in which the molecular/subcellular and systems levels meet. We present for the first time a nearly full-scale model of the mouse striatum using available data on synaptic connectivity, cellular morphology and electrophysiological properties to create a microcircuit mimicking the real network. A striatal volume is populated with reconstructed neuronal morphologies with appropriate cell densities, and then we connect neurons together based on appositions between neurites as possible synapses and constrain them further with available connectivity data. Moreover, we simulate a subset of the striatum involving 10 000 neurons, with input from cortex, thalamus and the dopamine system, as a proof of principle. Simulation at this biological scale should serve as an invaluable tool to understand the mode of operation of this complex structure. This platform will be updated with new data and expanded to simulate the entire striatum.",
-    "alias": "2020-hjorth-et-al",
-    "approved_author": {
-        "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden",
-        "firstname": "Sten",
-        "lastname": "Grillner"
-    },
-    "associated_paper_doi": "https://doi.org/10.1073/pnas.2000671117",
-    "associated_paper_title": "The microcircuits of striatum in silico",
-    "associated_paper_volume": "117",
-    "associated_paper_issue": null,
-    "associated_paper_pagination": "9554-9565",
-    "authors": [
-        {
-            "affiliation": "Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden",
-            "firstname": "J. J. Johannes",
-            "lastname": "Hjorth"
-        },
-        {
-            "affiliation": "Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden; Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden",
-            "firstname": "Alexander K.",
-            "lastname": "Kozlov"
-        },
-        {
-            "affiliation": "Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden",
-            "firstname": "Ilaria",
-            "lastname": "Carannante"
-        },
-        {
-            "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden",
-            "firstname": "Johanna",
-            "lastname": "Frost Nyl\u00e9n"
-        },
-        {
-            "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden",
-            "firstname": "Robert",
-            "lastname": "Lindroos"
-        },
-        {
-            "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden",
-            "firstname": "Yvonne",
-            "lastname": "Johansson"
-        },
-        {
-            "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden",
-            "firstname": "Anna",
-            "lastname": "Tokarska"
-        },
-        {
-            "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden",
-            "firstname": "Matthijs C.",
-            "lastname": "Dorst"
-        },
-        {
-            "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden",
-            "firstname": "Shreyas M.",
-            "lastname": "Suryanarayana"
-        },
-        {
-            "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden",
-            "firstname": "Gilad",
-            "lastname": "Silberberg"
-        },
-        {
-            "affiliation": "Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden; Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden",
-            "firstname": "Jeanette",
-            "lastname": "Hellgren Kotaleski"
-        },
-        {
-            "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden",
-            "firstname": "Sten",
-            "lastname": "Grillner"
-        }
-    ],
-    "citation": "J. J. Johannes Hjorth, Alexander K. Kozlov, Ilaria Carannante, Johanna Frost Nylén, Robert Lindroos, Yvonne Johansson, Anna Tokarska, Matthijs C. Dorst, Shreyas M. Suryanarayana, Gilad Silberberg, Jeanette Hellgren Kotaleski & Sten Grillner (2021). The microcircuits of striatum in silico. Proceedings of the National Academy of Sciences, 117: 9554-9565.",
-    "collab_id": "livepapers",
-    "corresponding_author": [
-        {
-            "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden",
-            "firstname": "Sten",
-            "lastname": "Grillner"
-        }
-    ],
-    "created_author": [
-        {
-            "affiliation": "Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden",
-            "firstname": "Sten",
-            "lastname": "Grillner"
-        }
-    ],
-    "doi": "https://doi.org/10.25493/RR3S-54",
-    "id": "67806cc2-84e0-4bb3-ae52-8cc3e5abf738",
-    "journal": "Proceedings of the National Academy of Sciences",
-    "license": "Creative Commons Attribution-NonCommercial 4.0 International",
-    "live_paper_title": "The microcircuits of striatum in silico",
-    "lp_tool_version": "0.1",
-    "modified_date": "2021-08-10T08:34:35.334000+00:00",
-    "resources": [
-        {
-            "data": [],
-            "description": "
\n   Location of the striatum within the mouse brain, cell composition and neuron circuitry\n
\n
    \n   
  • \n      Dorsal striatum (transparent light blue) and reference volume (500 μm cube, red) are shown\n      using SBA Composer (Scalable Brain Atlas Composer, INCF):\n      \n      \n      
    \n      
    \n      \n          \n      \n   
    • \n   
  • \n      
    \n      Striatal microcircuit structure\n      
    \n         The C57BL/7J mouse striatum is around 21.5 mm3 (Allen Mouse Brain) with a total of 1.72 million neurons,\n         which corresponds to a density of 80,500 neurons/mm3. The cell populations are subdivided as follows assuming 95% of striatal projection neurons, in equal proportion for the direct pathway and indirect pathway (dSPN and iSPN, respectively), 1.3% fast-spiking interneurons (FS), 1.1% cholinergic interneurons (ChIN) and 0.8% low threshold-spiking interneurons (LTS). The striatal circuitry is generic for the entire structure.\n      
    \n      \n   
    • \n
",
-            "icon": "description",
-            "order": 0,
-            "title": "Striatum anatomy",
-            "type": "section_custom"
-        },
-        {
-            "data": [],
-            "description": "
\n   
\n      
\n         
\n            \n         
\n      
\n      
\n         
\n            
    \n               
  • \n                  
    str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508
    \n                  
    \n                     
    \n                        Model     \n                         \n                           \n                        \n                     
    \n                     
    Experimental data
    \n                     \n                        \n                        \n                        \n                        \n                        \n                        \n                     \n                  
    \n               
    • \n               
  • \n                  
    str-dspn-e150917_c10_D1-mWT-P270-20-v20190521
    \n                  
    \n                     
    \n                        Model     \n                         \n                           \n                        \n                     
    \n                     
    Experimental data
    \n                     \n                        \n                        \n                        \n                        \n                        \n                        \n                     \n                  
    \n               
    • \n               
  • \n                  
    str-dspn-e150917_c6_D1-m21-6-DE-v20190503
    \n                  
    \n                     
    \n                        Model     \n                         \n                           \n                        \n                     
    \n                     
    Experimental data
    \n                     \n                        \n                        \n                        \n                        \n                        \n                        \n                     \n                  
    \n               
    • \n               
  • \n                  
    str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521
    \n                  
    \n                     
    \n                        Model     \n                         \n                           \n                        \n                     
    \n                     
    Experimental data
    \n                     \n                        \n                        \n                        \n                        \n                        \n                        \n                     \n                  
    \n               
    • \n               
  • \n                  
    str-ispn-e150908_c4_D2-m51-5-DE-v20190611
    \n                  
    \n                     
    \n                        Model     \n                         \n                           \n                        \n                     
    \n                     
    Experimental data
    \n                     \n                        \n                        \n                        \n                        \n                        \n                        \n                     \n                  
    \n               
    • \n               
  • \n                  
    str-ispn-e150917_c11_D2-mWT-MSN1-v20190603
    \n                  
    \n                     
    \n                        Model     \n                         \n                           \n                        \n                     
    \n                     
    Experimental data
    \n                     \n                        \n                        \n                        \n                        \n                        \n                        \n                     \n                  
    \n               
    • \n               
  • \n                  
    str-ispn-e151123_c1_D2-mWT-P270-09-v20190527
    \n                  
    \n                     
    \n                        Model     \n                         \n                           \n                        \n                     
    \n                     
    Experimental data
    \n                     \n                        \n                        \n                        \n                        \n                        \n                        \n                     \n                  
    \n               
    • \n               
  • \n                  
    str-ispn-e160118_c10_D2-m46-3-DE-v20190529
    \n                  
    \n                     
    \n                        Model     \n                         \n                           \n                        \n                     
    \n                     
    Experimental data
    \n                     \n                        \n                        \n                        \n                        \n                        \n                        \n                     \n                  
    \n               
    • \n            
\n            
\n         
\n      
\n      
\n         
\n            
    \n               
  • \n                  
    str-fs-e160628_FS2-mMTC180800A-IDB-v20190226
    \n                  
    \n                     
    \n                        Model     \n                         \n                           \n                        \n                     
    \n                     
    Experimental data
    \n                     \n                        \n                        \n                        \n                        \n                        \n                        \n                        \n                        \n                     \n                  
    \n               
    • \n               
  • \n                  
    str-fs-e161024_FS16-mDR-rat-Mar-13-08-1-536-R-v20190225
    \n                  
    \n                     
    \n                        Model     \n                         \n                           \n                        \n                     
    \n                     
    Experimental data
    \n                     \n                        \n                        \n                        \n                        \n                        \n                        \n                        \n                        \n                     \n                  
    \n               
    • \n               
  • \n                  
    str-fs-e161205_FS1-mMTC180800A-IDB-v20190312
    \n                  
    \n                     
    \n                        Model     \n                         \n                           \n                        \n                     
    \n                     
    Experimental data
    \n                     \n                        \n                        \n                        \n                        \n                        \n                        \n                        \n                        \n                     \n                  
    \n               
    • \n               
  • \n                  
    str-fs-e180418_FS5-mMTC251001A-IDB-v20190301
    \n                  
    \n                     
    \n                        Model     \n                         \n                           \n                        \n                     
    \n                     
    Experimental data
    \n                     \n                        \n                        \n                        \n                        \n                        \n                        \n                     \n                  
    \n               
    • \n            
\n            
\n         
\n      
\n      
\n         
\n            
    \n               
  • \n                  
    str-lts-Experiment-9862_20181211
    \n                  
    \n                     
    \n                        Model parameters     \n                           \n                        \n                     
    \n                     
    Experimental data
    \n                     \n                     20181211_7_8_slice_L4_long.pxp (478 MB) \n                     \n                       
      \n                     
    Selected traces
    \n                     \n                        \n                        \n                        \n                        \n                        \n                        \n                     \n                  
    \n               
    • \n            
\n            
\n         
\n      
\n      
\n         
\n            
    \n               
  • \n                  
    str-chin-sbj4-170614_cell6-v20190816
    \n                  
    \n                     
    \n                        Model parameters     \n                           \n                        \n                     
    \n                     
    Experimental data
    \n                     \n                     sbj4-170614_cell6_original__md_20170614_cell_5_6_ChIN.pxp (81 MB) \n                     \n                       
      \n                     
    Selected traces
    \n                     \n                        \n                        \n                        \n                        \n                        \n                     \n                  
    \n               
    • \n            
\n            
\n         
\n      
\n   
\n
",
-            "icon": "settings_input_antenna",
-            "order": 1,
-            "title": "Single-cell models",
-            "type": "section_custom"
-        },
-        {
-            "data": [],
-            "description": "
\n   Making striatal microcircuit using Snudda software (see Source code below) is illustrated in a Jupyter notebook with step-by-step instructions for simulation and analysis of the outputs. For demonstration purpose, a tiny piece of the dorsal striatum containing only 100 neurons is simulated.\n
\n
\n   Jupyter notebook StriatumScaffoldExample-tiny on GitHub.com.\n
",
-            "icon": "timeline",
-            "order": 2,
-            "title": "Simulation and analysis",
-            "type": "section_custom"
-        },
-        {
-            "data": [],
-            "description": "
\n   The complete source code of the model building software Snudda is available on GitHub.com.\n
",
-            "icon": "code",
-            "order": 3,
-            "title": "Source code",
-            "type": "section_custom"
-        }
-    ],
-    "resources_description": "Data and models used in the paper are available at the links reported below.",
-    "url": "https://www.pnas.org/content/early/2020/04/21/2000671117",
-    "version": "1",
-    "year": "2021-08-10"
-}
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/84b9eb9f-996c-4b03-b81b-fb7871424b62.json b/validation_service_api/validation_service/tests/test_data/livepapers/84b9eb9f-996c-4b03-b81b-fb7871424b62.json
deleted file mode 100644
index bbe9bce9..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/84b9eb9f-996c-4b03-b81b-fb7871424b62.json
+++ /dev/null
@@ -1,196 +0,0 @@
-{
-    "abstract": "The cerebellar granule cells (GrCs) are classically described as a homogeneous neuronal population discharging regularly without adaptation. We show that GrCs in fact generate diverse response patterns to current injection and synaptic activation, ranging from adaptation to acceleration of firing. Adaptation was predicted by parameter optimization in detailed computational models based on available knowledge on GrC ionic channels. The models also predicted that acceleration required additional mechanisms. We found that yet unrecognized TRPM4 currents specifically accounted for firing acceleration and that adapting GrCs outperformed accelerating GrCs in transmitting high-frequency mossy fiber (MF) bursts over a background discharge. This implied that GrC subtypes identified by their electroresponsiveness corresponded to specific neurotransmitter release probability values. Simulations showed that fine-tuning of pre-and post-synaptic parameters generated effective MF-GrC transmission channels, which could enrich the processing of input spike patterns and enhance spatio-temporal recoding at the cerebellar input stage.",
-    "alias": "2020-masoli-et-al-a",
-    "approved_author": {
-        "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy",
-        "firstname": "Egidio",
-        "lastname": "D'Angelo"
-    },
-    "associated_paper_doi": "https://doi.org/10.1038/s42003-020-0953-x",
-    "associated_paper_title": "Parameter tuning differentiates granule cell subtypes enriching transmission properties at the cerebellum input stage",
-    "associated_paper_volume": "3",
-    "associated_paper_issue": null,
-    "associated_paper_pagination": "222",
-    "authors": [
-        {
-            "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy",
-            "firstname": "Stefano",
-            "lastname": "Masoli"
-        },
-        {
-            "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy",
-            "firstname": "Marialuisa",
-            "lastname": "Tognolina"
-        },
-        {
-            "affiliation": "Department of Molecular Medicine, University of Pavia, Italy",
-            "firstname": "Umberto",
-            "lastname": "Laforenza"
-        },
-        {
-            "affiliation": "Department of Biology and Biotechnology, University of Pavia, Italy",
-            "firstname": "Francesco",
-            "lastname": "Moccia"
-        },
-        {
-            "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy",
-            "firstname": "Egidio",
-            "lastname": "D'Angelo"
-        }
-    ],
-    "citation": "Stefano Masoli, Marialuisa Tognolina, Umberto Laforenza, Francesco Moccia & Egidio D'Angelo (2021). Parameter tuning differentiates granule cell subtypes enriching transmission properties at the cerebellum input stage. Communications Biology, 3: 222.",
-    "collab_id": "livepapers",
-    "corresponding_author": [
-        {
-            "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy",
-            "firstname": "Egidio",
-            "lastname": "D'Angelo"
-        }
-    ],
-    "created_author": [
-        {
-            "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy",
-            "firstname": "Egidio",
-            "lastname": "D'Angelo"
-        }
-    ],
-    "doi": "https://doi.org/10.25493/1JK4-44S",
-    "id": "84b9eb9f-996c-4b03-b81b-fb7871424b62",
-    "journal": "Communications Biology",
-    "license": "Creative Commons Attribution-NonCommercial 4.0 International",
-    "live_paper_title": "Parameter tuning differentiates granule cell subtypes enriching transmission properties at the cerebellum input stage",
-    "lp_tool_version": "0.1",
-    "modified_date": "2021-08-10T07:34:06.304000+00:00",
-    "resources": [
-        {
-            "data": [
-                {
-                    "identifier": "fe82c04d-4374-4a7f-87e2-611c6c70cc73",
-                    "label": "Granule cell dend-soma morphology",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2020_masoli_et_al_a/resources/cerebellum_circuits/BlueNaaS_models/live_paper_grc/GrC2018.asc",
-                    "view_url": null
-                }
-            ],
-            "description": "The morphology for the dendrites and soma is in .asc format. The axon was custom generated using a modified BluePyOpt class.",
-            "icon": "settings_input_antenna",
-            "order": 0,
-            "title": "Morphologies",
-            "type": "section_morphology"
-        },
-        {
-            "data": [
-                {
-                    "identifier": "5b7ed4fa-de90-42be-be7d-36af5ca8c67f",
-                    "label": "101018_0011 IV -70#-#Mild adapting",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2020_masoli_et_al_a/resources/cerebellum_circuits/BlueNaaS_models/live_paper_grc/trace/101018_0011%20IV%20-70.abf",
-                    "view_url": null
-                },
-                {
-                    "identifier": "2f6ae59c-3340-435d-ba02-d702138231ae",
-                    "label": "101018_0012 CC step#-#Mild adapting",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2020_masoli_et_al_a/resources/cerebellum_circuits/BlueNaaS_models/live_paper_grc/trace/101018_0012%20CC%20step.abf",
-                    "view_url": null
-                },
-                {
-                    "identifier": "4ffbf687-4d2a-4950-8591-3e189c47bf44",
-                    "label": "111018_0000 IV -70#-#Accelerating",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2020_masoli_et_al_a/resources/cerebellum_circuits/BlueNaaS_models/live_paper_grc/trace/111018_0000%20IV%20-70.abf",
-                    "view_url": null
-                },
-                {
-                    "identifier": "237e1c44-fc53-4db5-8d86-7e9f66641742",
-                    "label": "111018_0001 CC step#-#Accelerating",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2020_masoli_et_al_a/resources/cerebellum_circuits/BlueNaaS_models/live_paper_grc/trace/111018_0001%20CC%20step.abf",
-                    "view_url": null
-                },
-                {
-                    "identifier": "2bbb51d8-d936-4d9e-8d79-5b150a8f5915",
-                    "label": "18gen16_0004 IV -70#-#Strong adapting",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2020_masoli_et_al_a/resources/cerebellum_circuits/BlueNaaS_models/live_paper_grc/trace/18gen16_0004%20IV%20-70.abf",
-                    "view_url": null
-                },
-                {
-                    "identifier": "bf7c0f9f-7571-4322-9da6-9f83d98d537f",
-                    "label": "18gen16_0005 CC step#-#Strong adapting",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2020_masoli_et_al_a/resources/cerebellum_circuits/BlueNaaS_models/live_paper_grc/trace/18gen16_0005%20CC%20step.abf",
-                    "view_url": null
-                },
-                {
-                    "identifier": "a9c8c288-2535-48ef-9220-aca83687e8a1",
-                    "label": "200918_0000 IV -70#-#Regular",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2020_masoli_et_al_a/resources/cerebellum_circuits/BlueNaaS_models/live_paper_grc/trace/200918_0000%20IV%20-70.abf",
-                    "view_url": null
-                },
-                {
-                    "identifier": "54de67e7-95b0-4879-9a99-631fe45248b9",
-                    "label": "200918_0001 CC step#-#Regular",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2020_masoli_et_al_a/resources/cerebellum_circuits/BlueNaaS_models/live_paper_grc/trace/200918_0001%20CC%20step.abf",
-                    "view_url": null
-                }
-            ],
-            "description": null,
-            "icon": "timeline",
-            "order": 1,
-            "title": "Electrophysiological Traces",
-            "type": "section_traces"
-        },
-        {
-            "data": [],
-            "description": "

Use the BSP Neuron As A Service (NaaS) tool to do in silico experiments with the single cell models shown in Fig. 6A of the paper:\n   
\n   > Start by clicking on any button below to run a simulation.\n   
\n   > After entering the NaaS page, click on the \"Simulation\" tab, adjust the current strength with the appropriate value, and run the simulation by clicking the \"Start simulation\" button.\n   
\n   > To correctly run a simulation, the temperature need to be set at 32\u00b0, the vinit at -70mV and, to match the paper experiments, the positive current injections are: 10, 16 and 22pA. \n
\n
\n   \n      \n         \n            \n            \n            \n            \n         \n      \n   
\n               \n            \n               \n            \n               \n            \n               \n               \n               \n            
\n
",
-            "icon": "note_add",
-            "order": 2,
-            "title": "Cerebellar granule cells simulation with BlueNaaS",
-            "type": "section_custom"
-        },
-        {
-            "data": [
-                {
-                    "identifier": "eb9f4408-dc73-4fbd-bc4e-0dd9a5933545",
-                    "label": "Accelerating",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch:443/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/cerebellum_circuits/BlueNaaS_models/live_paper_grc/SM_GrC2019_accellerating.zip",
-                    "view_url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2020-masoli-et-al-a/Model%20Catalog#model_id.dae51b52-2229-446c-bb48-8176e2e66e7d"
-                },
-                {
-                    "identifier": "a3e2b1ed-dfe8-4ea2-95df-0a934365048d",
-                    "label": "Mild adapting",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch:443/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/cerebellum_circuits/BlueNaaS_models/live_paper_grc/SM_GrC2019_mild_adapting.zip",
-                    "view_url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2020-masoli-et-al-a/Model%20Catalog#model_id.70b01e7d-7cec-48f1-8934-76eb6edbb53f"
-                },
-                {
-                    "identifier": "40c12c7b-7799-4f7d-9e7a-a83e772c6159",
-                    "label": "Regular firing",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch:443/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/cerebellum_circuits/BlueNaaS_models/live_paper_grc/SM_GrC2019_non_adapting_low_ahp.zip",
-                    "view_url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2020-masoli-et-al-a/Model%20Catalog#model_id.12564edc-2199-421b-85f2-aa954250b35a"
-                },
-                {
-                    "identifier": "1b12c6e7-f490-4b6c-a1a4-d03a8840fb5f",
-                    "label": "Strong adapting",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch:443/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/cerebellum_circuits/BlueNaaS_models/live_paper_grc/SM_GrC2019_adapting.zip",
-                    "view_url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2020-masoli-et-al-a/Model%20Catalog#model_id.a222f243-2bf9-491b-93a1-8810505df71e"
-                }
-            ],
-            "description": "The optimization results for each morphology are individually available in the Model Catalog at the following links:",
-            "icon": "local_activity",
-            "order": 3,
-            "title": "Optimizations",
-            "type": "section_generic"
-        }
-    ],
-    "resources_description": "Data and models: Experimental data and models used in the paper are available at the links reported below, grouped into the following categories:",
-    "url": "https://www.nature.com/articles/s42003-020-0953-x",
-    "version": "1",
-    "year": "2021-08-10"
-}
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/88f46758-bb66-4a0f-a1b2-e914d93d2978.json b/validation_service_api/validation_service/tests/test_data/livepapers/88f46758-bb66-4a0f-a1b2-e914d93d2978.json
deleted file mode 100644
index 76d84c1c..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/88f46758-bb66-4a0f-a1b2-e914d93d2978.json
+++ /dev/null
@@ -1,88 +0,0 @@
-{
-    "abstract": "The brain\u2019s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model\u2019s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.",
-    "alias": "2021-giacopelli-et-al",
-    "approved_author": {
-        "affiliation": "Department of Mathematics and Informatics, University of Palermo, Italy; Institute of Biophysics, National Research Council, Palermo, Italy",
-        "firstname": "Domenico",
-        "lastname": "Tegolo"
-    },
-    "associated_paper_doi": "https://doi.org/10.1038/s41598-021-83759-z",
-    "associated_paper_title": "On the structural connectivity of large-scale models of brain networks at cellular level",
-    "authors": [
-        {
-            "affiliation": "Department of Mathematics and Informatics, University of Palermo, Italy; Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Giuseppe",
-            "lastname": "Giacopelli"
-        },
-        {
-            "affiliation": "Department of Mathematics and Informatics, University of Palermo, Italy; Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Domenico",
-            "lastname": "Tegolo"
-        },
-        {
-            "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Emiliano",
-            "lastname": "Spera"
-        },
-        {
-            "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Michele",
-            "lastname": "Migliore"
-        }
-    ],
-    "citation": "Giuseppe Giacopelli, Domenico Tegolo, Emiliano Spera & Michele Migliore (2021). On the structural connectivity of large-scale models of brain networks at cellular level. Scientific Reports, 11: 4345.",
-    "collab_id": "livepapers",
-    "corresponding_author": [
-        {
-            "affiliation": "Department of Mathematics and Informatics, University of Palermo, Italy; Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Domenico",
-            "lastname": "Tegolo"
-        }
-    ],
-    "created_author": [
-        {
-            "affiliation": "Department of Mathematics and Informatics, University of Palermo, Italy; Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Domenico",
-            "lastname": "Tegolo"
-        }
-    ],
-    "doi": "https://doi.org/10.25493/DAM6-FX7",
-    "id": "88f46758-bb66-4a0f-a1b2-e914d93d2978",
-    "journal": "Scientific Reports",
-    "associated_paper_volume": "11",
-    "associated_paper_issue": null,
-    "associated_paper_pagination": "4345",
-    "license": "Creative Commons Attribution-NonCommercial 4.0 International",
-    "live_paper_title": "On the structural connectivity of large-scale models of brain networks at cellular level",
-    "lp_tool_version": "0.1",
-    "modified_date": "2021-08-02T10:46:36.708000+00:00",
-    "resources": [
-        {
-            "data": [],
-            "description": "
\n   The connectivity matrices of the Markram's model can be found\n   \n   here.\n   \n   \n
",
-            "icon": "settings_input_antenna",
-            "order": 0,
-            "title": "Data",
-            "type": "section_custom"
-        },
-        {
-            "data": [],
-            "description": "
The code to reproduce qualitatively the raster plots in figures 3A, 3B and 3C is available on \n   ModelDB.\n
",
-            "icon": "note_add",
-            "order": 1,
-            "title": "Custom SectionModelDB link and test simulation",
-            "type": "section_custom"
-        },
-        {
-            "data": [],
-            "description": "
\n   A Web Application to run the code is available here\n   \n   CINECA.\n   \n   \n
",
-            "icon": "local_activity",
-            "order": 2,
-            "title": "Web Application",
-            "type": "section_custom"
-        }    ],
-    "resources_description": "Models and Web App: all the models used in the paper and the Web Application are available at the links reported below:",
-    "url": "https://www.nature.com/articles/s41598-021-83759-z.pdf",
-    "version": "1",
-    "year": "2021-02-23"
-}
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/93a5c03a-6995-47bc-af9f-4f0d85950d1d.json b/validation_service_api/validation_service/tests/test_data/livepapers/93a5c03a-6995-47bc-af9f-4f0d85950d1d.json
deleted file mode 100644
index 0b33306a..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/93a5c03a-6995-47bc-af9f-4f0d85950d1d.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
-    "abstract": "GABAergic transmission regulates neuronal excitability, dendritic integration of synaptic signals and oscillatory activity, thought to be involved in high cognitive functions. By anchoring synaptic receptors just opposite to release sites, the scaffold protein gephyrin plays a key role in these tasks. In addition, by regulating GABA A receptor trafficking, gephyrin contributes to maintain, at the network level, an appropriate balance between Excitation (E) and Inhibition (I), crucial for information processing. An E/I imbalance leads to neuropsychiatric disorders such as epilepsy, schizophrenia and autism. In this article, we exploit a previously published computational method to fit spontaneous synaptic events, using a simplified model of the subcellular pathways involving gephyrin at inhibitory synapses. The model was used to analyze experimental data recorded under different conditions, with the main goal to gain insights on the possible consequences of gephyrin block on IPSCs. The same approach can be useful, in general, to analyze experiments designed to block a single protein. The results suggested possible ways to correlate the changes observed in the amplitude and time course of individual events recorded after different experimental protocols with the changes that may occur in the main subcellular pathways involved in gephyrin-dependent transsynaptic signaling.",
-    "alias": "2020-lupascu-et-al",
-    "approved_author": {
-        "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-        "firstname": "Carmen A.",
-        "lastname": "Lupascu"
-    },
-    "associated_paper_doi": "https://doi.org/10.3389/fncel.2020.00173",
-    "associated_paper_title": "Computational Modeling of Inhibitory Transsynaptic Signaling in Hippocampal and Cortical Neurons Expressing Intrabodies Against Gephyrin",
-    "authors": [
-        {
-            "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Carmen A.",
-            "lastname": "Lupascu"
-        },
-        {
-            "affiliation": "European Brain Research Institute, Rome, Italy",
-            "firstname": "Annunziato",
-            "lastname": "Morabito"
-        },
-        {
-            "affiliation": "European Brain Research Institute, Rome, Italy",
-            "firstname": "Federica",
-            "lastname": "Ruggeri"
-        },
-        {
-            "affiliation": "European Brain Research Institute, Rome, Italy",
-            "firstname": "Chiara",
-            "lastname": "Parisi"
-        },
-        {
-            "affiliation": "European Brain Research Institute, Rome, Italy",
-            "firstname": "Domenico",
-            "lastname": "Pimpinella"
-        },
-        {
-            "affiliation": "European Brain Research Institute, Rome, Italy",
-            "firstname": "Rocco",
-            "lastname": "Pizzarelli"
-        },
-        {
-            "affiliation": "European Brain Research Institute, Rome, Italy",
-            "firstname": "Giovanni",
-            "lastname": "Meli"
-        },
-        {
-            "affiliation": "European Brain Research Institute, Rome, Italy",
-            "firstname": "Silvia",
-            "lastname": "Marinelli"
-        },
-        {
-            "affiliation": "European Brain Research Institute, Rome, Italy; Aix-Marseille University; Sapienza University of Rome",
-            "firstname": "Enrico",
-            "lastname": "Cherubini"
-        },
-        {
-            "affiliation": "Scuola Normale Superiore di Pisa; European Brain Research Institute, Rome, Italy",
-            "firstname": "Antonino",
-            "lastname": "Cattaneo"
-        },
-        {
-            "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Michele",
-            "lastname": "Migliore"
-        }
-    ],
-    "associated_paper_issue": null,
-    "associated_paper_pagination": "173",
-    "associated_paper_volume": "14",
-    "citation": "Carmen A. Lupascu, Annunziato Morabito, Federica Ruggeri, Chiara Parisi, Domenico Pimpinella, Rocco Pizzarelli, Giovanni Meli, Silvia Marinelli, Enrico Cherubini, Antonino Cattaneo & Michele Migliore (2020). Computational Modeling of Inhibitory Transsynaptic Signaling in Hippocampal and Cortical Neurons Expressing Intrabodies Against Gephyrin. Frontiers in Cellular Neuroscience, 14: 173.",
-    "collab_id": "livepapers",
-    "corresponding_author": [
-        {
-            "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Carmen A.",
-            "lastname": "Lupascu"
-        }
-    ],
-    "created_author": [
-        {
-            "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Carmen A.",
-            "lastname": "Lupascu"
-        }
-    ],
-    "doi": "https://doi.org/10.25493/SGAZ-Y51",
-    "id": "93a5c03a-6995-47bc-af9f-4f0d85950d1d",
-    "journal": "Frontiers in Cellular Neuroscience",
-    "license": "Creative Commons Attribution-NonCommercial 4.0 International",
-    "live_paper_title": "Computational Modeling of Inhibitory Transsynaptic Signaling in Hippocampal and Cortical Neurons Expressing Intrabodies Against Gephyrin",
-    "lp_tool_version": "0.1",
-    "modified_date": "2021-08-06T12:03:24.113000+00:00",
-    "resources": [
-        {
-            "data": [],
-            "description": "\n\n
\n   
\n      The electrophysiological traces are individually available in the\n      \n      Knowledge Graph.\n      \n      \n   
\n   
\n      
\n         \n      
\n   
\n   
\n      
\n         \n      
\n   
\n   
\n      
\n         \n      
\n   
\n   
\n      
\n         \n      
\n   
\n
",
-            "icon": "timeline",
-            "order": 0,
-            "title": "Electrophysiological Traces",
-            "type": "section_custom"
-        },
-        {
-            "data": [],
-            "description": "\n\n
\n
A reduced self-consistent set of files needed to reproduce the fittings in the paper is available on\n   ModelDB. \n   The kinetic model of synaptic transmission used in the paper is schematically illustrated below (Figure 1 of the paper):\n
\n
\n
\n   \"The\n   
We modeled the action of the variables GEPH(gephyrin clusters), NLG2 (Neuroligin/Neurexin clusters), N (Neurotransmitter molecules), and Ry (Postsynaptic receptors) through the following equations:\n      $$\\frac{dN}{dt} = \\beta \\cdot \\alpha_{f} \\cdot g(t) \\cdot NLG2 - \\alpha_{b} \\cdot N$$\n      $$\\frac{dNLG2}{dt} = \\frac{GEPH}{1+\\frac{GEPH}{2 \\cdot NLG2}}-\\phi \\cdot NLG2$$\n      $$\\frac{dR_{y}}{dt} = h \\cdot GEPH - h_{1} \\cdot R_{y}$$\n      after a synaptic activation, g(t) generates a number of neurotransmitter molecules, N, at a rate \\(β\\).\n      The synaptic current was calculated as:\n      $$I_{GABAA} = c_{1} \\cdot N \\cdot R_{y} \\cdot (v-e_{rev})$$ where \\(c_{1}\\) is a constant, v the membrane potential and \\(e_{rev}\\) the reversal potential.\n      The set of differential equation can be solved analytically. The current \\(I_{GABAA}\\) can be described as:\n      $$I_{GABAA} = I_{FACT} \\cdot \\frac{[(1-\\alpha_{b}\\tau_{d})-(1-\\alpha_{b}\\tau_{r})] \\cdot e^{-\\alpha_{b}\\cdot t}+(1-\\alpha_{b}\\tau_{r}) \\cdot e^{\\frac{-t}{\\tau_d}}-(1-\\alpha_{b}\\tau_{d}) \\cdot e^{\\frac{-t}{\\tau_{r}}}}{(1-\\alpha_{b}\\tau_{d})(1-\\alpha_{b}\\tau_{r})} \\cdot (v-e_{GABAA})$$\n      where $$I_{FACT} = c_{1} \\cdot \\frac{h}{h_{1}} \\cdot [\\frac{(2-\\phi)\\cdot GEPH^{2}}{2\\cdot\\phi}] \\cdot \\beta \\cdot \\alpha_{f} \\cdot w$$\n   
\n
\n
\n   
\n   To test the effects of each pathway on the overall current use the tool below to plot the trace resulting for a specific set of parameters.                                                                                                                        \n   The default settings are mean values of the optimized parameters under control condition from Table 3 of the paper.
\n   
\n   HOWTO:  Change settings by turning \"on\" the \"Reset/set parameters\" switch to plot the current with your own set of parameters. \n   Turn \"on\" the \"Persistent plot\" switch to compare plots of different traces.\n
\n
\n   
\n      
\n         
\n            \n               \n                  \n                     \n                     \n                     \n                     \n                     \n                     \n                  \n                  \n                     \n                     \n                     \n                     \n                  \n                  \n                     \n                     \n                     \n                     \n                  \n                  \n                     \n                     \n                     \n                     \n                  \n                  \n                     \n                     \n                     \n                     \n                  \n                  \n                     \n                     \n                     \n                     \n                  \n                  \n                     \n                     \n                  \n                  \n                     \n                  \n                  \n                     \n                  \n                  \n                  \n               \n            
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\n                     		\\(GEPH\\)\n                        
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\n                     		\\(v\\)\n                        
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\n         
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\n
",
-            "icon": "note_add",
-            "order": 1,
-            "title": "ModelDB link and test model",
-            "type": "section_custom"
-        },
-        {
-            "data": [],
-            "description": "
\n   Fitting single events with the model and data used in this paper can be done in this  collab   in the Human Brain Project Collaboratory. Step-by-step instructions for simulation and analysis of the outputs can be found in the  Guidebook of the Brain Simulation Platform.\n
",
-            "icon": "timeline",
-            "order": 2,
-            "title": "Simulation and analysis",
-            "type": "section_custom"
-        }
-    ],
-    "resources_description": null,
-    "url": "https://www.frontiersin.org/articles/10.3389/fncel.2020.00173/full",
-    "version": "1",
-    "year": "2020-06-16"
-}
\ No newline at end of file
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/9c00022b-82be-435e-b23f-bf4ee4cacc28.json b/validation_service_api/validation_service/tests/test_data/livepapers/9c00022b-82be-435e-b23f-bf4ee4cacc28.json
deleted file mode 100644
index 1954c7e8..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/9c00022b-82be-435e-b23f-bf4ee4cacc28.json
+++ /dev/null
@@ -1,210 +0,0 @@
-{
-    "abstract": "The advanced cognitive capabilities of the human brain are often attributed to our recently evolved neocortex. However, it is not known whether the basic building blocks of the human neocortex, the pyramidal neurons, possess unique biophysical properties that might impact on cortical computations. Here we show that layer 2/3 pyramidal neurons from human temporal cortex (HL2/3 PCs) have a specific membrane capacitance (Cm) of ~0.5 \u00b5F/cm2, half of the commonly accepted 'universal' value (~1 \u00b5F/cm2) for biological membranes. This finding was predicted by fitting in vitro voltage transients to theoretical transients then validated by direct measurement of Cm in nucleated patch experiments. Models of 3D reconstructed HL2/3 PCs demonstrated that such low Cm value significantly enhances both synaptic charge-transfer from dendrites to soma and spike propagation along the axon. This is the first demonstration that human cortical neurons have distinctive membrane properties, suggesting important implications for signal processing in human neocortex.",
-    "alias": "2016-eyal-et-al",
-    "approved_author": {
-        "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel; Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel",
-        "firstname": "Idan",
-        "lastname": "Segev"
-    },
-    "associated_paper_doi": "https://doi.org/10.7554/elife.16553.001",
-    "associated_paper_title": "Unique membrane properties and enhanced signal processing in human neocortical neurons",
-    "associated_paper_issue": null,
-    "associated_paper_volume": "5",
-    "associated_paper_pagination": "e16553",
-    "authors": [
-        {
-            "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel",
-            "firstname": "Guy",
-            "lastname": "Eyal"
-        },
-        {
-            "affiliation": "Department of Integrative Neurophysiology, Centre for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands; Department of Human Biology, Neuroscience Institute, University of Cape Town, Cape Town, South Africa",
-            "firstname": "Matthijs B.",
-            "lastname": "Verhoog"
-        },
-        {
-            "affiliation": "Department of Integrative Neurophysiology, Centre for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands",
-            "firstname": "Guilherme",
-            "lastname": "Testa-Silva"
-        },
-        {
-            "affiliation": "Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel",
-            "firstname": "Yair",
-            "lastname": "Deitcher"
-        },
-        {
-            "affiliation": "Department of Integrative Neurophysiology, Centre for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands",
-            "firstname": "Johannes C.",
-            "lastname": "Lodder"
-        },
-        {
-            "affiliation": "Departamento de Neurobiolog\u00eda Funcional y de Sistemas, Instituto Cajal, Madrid, Spain; Laboratorio Cajal de Circuitos Corticales (CTB), Universidad Polit\u00e9cnica de Madrid, Madrid, Spain",
-            "firstname": "Ruth",
-            "lastname": "Benavides-Piccione"
-        },
-        {
-            "affiliation": "Escuela T\u00e9cnica Superior de Ingenieros Inform\u00e1ticos, Universidad Polit\u00e9cnica de Madrid, Madrid, Spain",
-            "firstname": "Juan",
-            "lastname": "Morales"
-        },
-        {
-            "affiliation": "Departamento de Neurobiolog\u00eda Funcional y de Sistemas, Instituto Cajal, Madrid, Spain; Laboratorio Cajal de Circuitos Corticales (CTB), Universidad Polit\u00e9cnica de Madrid, Madrid, Spain",
-            "firstname": "Javier",
-            "lastname": "DeFelipe"
-        },
-        {
-            "affiliation": "Department of Integrative Neurophysiology, Centre for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands",
-            "firstname": "Christiaan P.J.",
-            "lastname": "De Kock"
-        },
-        {
-            "affiliation": "Department of Integrative Neurophysiology, Centre for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands",
-            "firstname": "Huibert D.",
-            "lastname": "Mansvelder"
-        },
-        {
-            "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel; Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel",
-            "firstname": "Idan",
-            "lastname": "Segev"
-        }
-    ],
-    "citation": "Guy Eyal, Matthijs B. Verhoog, Guilherme Testa-Silva, Yair Deitcher, Johannes C. Lodder, Ruth Benavides-Piccione, Juan Morales, Javier DeFelipe, Christiaan P.J. De Kock, Huibert D. Mansvelder & Idan Segev (2016). Unique membrane properties and enhanced signal processing in human neocortical neurons. eLife, 5: e16553.",
-    "collab_id": "livepapers",
-    "corresponding_author": [
-        {
-            "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel; Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel",
-            "firstname": "Idan",
-            "lastname": "Segev"
-        }
-    ],
-    "created_author": [
-        {
-            "affiliation": "Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel; Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel",
-            "firstname": "Idan",
-            "lastname": "Segev"
-        }
-    ],
-    "doi": "https://doi.org/10.25493/WBJ6-RJG",
-    "id": "9c00022b-82be-435e-b23f-bf4ee4cacc28",
-    "journal": "eLife",
-    "license": "Creative Commons Attribution-NonCommercial 4.0 International",
-    "live_paper_title": "Unique membrane properties and enhanced signal processing in human neocortical neurons",
-    "lp_tool_version": "0.1",
-    "modified_date": "2021-08-10T08:59:29.024000+00:00",
-    "resources": [
-        {
-            "data": [],
-            "description": "
\n   
\n      \n      \n      \n   
\n   
\n      \t\t\t\n   
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\n   
\n      \t\t\t\n   
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\n
\n
\n   
\n   
\n   
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\n
\n   \n      
Model response to clustered vs non clustered input
\n   \n
\n
\n   
\n      \n      \n      \n   
\n   
\n      \n      \n      \t\t\n   
\n
",
-            "icon": "pageview",
-            "order": 0,
-            "title": "Demonstration",
-            "type": "section_custom"
-        },
-        {
-            "data": [
-                {
-                    "identifier": "4f041c4a-a386-40b9-9457-4221857fbf3a",
-                    "label": " 2013_03_06_cell03_789_H41_03",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell0603_03_model_476/morphology/2013_03_06_cell03_789_H41_03.ASC",
-                    "view_url": null
-                },
-                {
-                    "identifier": "5323157b-2bc7-44e6-91e5-5c108c1ba94b",
-                    "label": "2013_03_06_cell08_876_H41_05_Cell2",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell0603_08_model_602/morphology/2013_03_06_cell08_876_H41_05_Cell2.ASC",
-                    "view_url": null
-                },
-                {
-                    "identifier": "e59c3299-97f9-49db-b4c6-895dd5c6c445",
-                    "label": "2013_03_06_cell11_1125_H41_06",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell0603_11_model_937/morphology/2013_03_06_cell11_1125_H41_06.ASC",
-                    "view_url": null
-                },
-                {
-                    "identifier": "023b413a-36c6-486e-a264-52d8e2e5054c",
-                    "label": "2013_03_13_cell03_1204_H42_02",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell1303_03_model_448/morphology/2013_03_13_cell03_1204_H42_02.ASC",
-                    "view_url": null
-                },
-                {
-                    "identifier": "1abdf5d0-e254-4902-9fc7-668b2432bca2",
-                    "label": "2013_03_13_cell05_675_H42_04",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell1303_05_model_643/morphology/2013_03_13_cell05_675_H42_04.ASC",
-                    "view_url": null
-                },
-                {
-                    "identifier": "02899f44-4ca2-46fb-86c5-40e62650eab8",
-                    "label": "2013_03_13_cell06_945_H42_05",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell1303_06_model_263/morphology/2013_03_13_cell06_945_H42_05.ASC",
-                    "view_url": null
-                }
-            ],
-            "description": "Morphology files (in .asc format) used in the paper for each etype (see Table S5 of the Supplementary Material):",
-            "icon": "settings_input_antenna",
-            "order": 1,
-            "title": "Morphologies",
-            "type": "section_morphology"
-        },
-        {
-            "data": [
-                {
-                    "identifier": "20d078bb-d2af-417a-a2e8-34782db8dec3",
-                    "label": "0603 cell 03",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell0603_03_model_476.zip",
-                    "view_url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2018-eyal-et-al/Model%20Catalog#model_id.456c1fac-39dd-4451-a768-9a4637d59b86"
-                },
-                {
-                    "identifier": "deeb9e9e-b089-49e3-ab42-2c725a528ff3",
-                    "label": "0603 cell 08",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell0603_08_model_602.zip",
-                    "view_url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2018-eyal-et-al/Model%20Catalog#model_id.5abf10fe-c2fc-4d77-b957-22b7868b4444"
-                },
-                {
-                    "identifier": "97ac602c-002a-4cc0-8362-6578e9433d62",
-                    "label": "0603 cell 11",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell0603_11_model_937.zip",
-                    "view_url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2018-eyal-et-al/Model%20Catalog#model_id.469e4b78-8b3b-459d-88b6-c9ad10ee11ea"
-                },
-                {
-                    "identifier": "fd133552-f922-4da6-9c5b-8f166b4759b2",
-                    "label": "1303 cell 03",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell1303_03_model_448.zip",
-                    "view_url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2018-eyal-et-al/Model%20Catalog#model_id.80ff1b87-be8a-4fea-8e14-f8fa5108ddfb"
-                },
-                {
-                    "identifier": "4db710ae-0dc4-4364-a0c4-cc9811b88954",
-                    "label": "1303 cell 06",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell1303_06_model_263.zip",
-                    "view_url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2018-eyal-et-al/Model%20Catalog#model_id.09120b3e-d4b3-494a-ab32-c6c472e5c95b"
-                },
-                {
-                    "identifier": "e8c6c1e6-a871-444c-a064-0ef099945cf5",
-                    "label": "1503 cell 05",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2018_eyal_et_al/cell1303_05_model_643.zip",
-                    "view_url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2018-eyal-et-al/Model%20Catalog#model_id.df097922-57b8-4132-a78a-b9bfb5c35b00"
-                }
-            ],
-            "description": "The Model Catalog of the cells",
-            "icon": "local_activity",
-            "order": 2,
-            "title": "Cell model",
-            "type": "section_generic"
-        }
-    ],
-    "resources_description": "This Live Paper introduces interactively these 6 modeled human L2/3 neurons, including their morphology, experimental measurements and modelling response to current and synaptic inputs. The models are available to download and could also be simulated on the cloud with NEURON as a service. The user can manipulate current injections to the model and explore the voltage response at any dendritic/somatic and axonal loci.",
-    "url": "https://elifesciences.org/articles/16553",
-    "version": "1",
-    "year": "2016-10-06"
-}
\ No newline at end of file
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/9d00321b-f927-4153-8c98-8f59e24bd5c6.json b/validation_service_api/validation_service/tests/test_data/livepapers/9d00321b-f927-4153-8c98-8f59e24bd5c6.json
deleted file mode 100644
index a0b0e949..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/9d00321b-f927-4153-8c98-8f59e24bd5c6.json
+++ /dev/null
@@ -1,81 +0,0 @@
-{
-    "abstract": "Sensorimotor signals are integrated and processed by the cerebellar circuit to predict accurate control of actions. In order to investigate how single neuron dynamics and geometrical modular connectivity affect cerebellar processing, we have built an olivocerebellar Spiking Neural Network (SNN) based on a novel simplification algorithm for single point models (Extended Generalized Leaky Integrate and Fire, EGLIF) capturing essential non-linear neuronal dynamics (e.g., pacemaking, bursting, adaptation, oscillation and resonance). EGLIF models specifically tuned for each neuron type were embedded into an olivocerebellar scaffold reproducing realistic spatial organization and physiological convergence and divergence ratios of connections. In order to emulate the circuit involved in an eye blink response to two associated stimuli, we modeled two adjacent olivocerebellar microcomplexes with a common mossy fiber input but different climbing fiber inputs (either on or off). EGLIF-SNN model simulations revealed the emergence of fundamental response properties in Purkinje cells (burstpause) and deep nuclei cells (pause-burst) similar to those reported in vivo. The expression of these properties depended on the specific activation of climbing fibers in the microcomplexes and did not emerge with scaffold models using simplified point neurons. This result supports the importance of embedding SNNs with realistic neuronal dynamics and appropriate connectivity and anticipates the scale-up of EGLIF-SNN and the embedding of plasticity rules required to investigate cerebellar functioning at multiple scales.",
-    "alias": "2019-geminiani-et-al",
-    "approved_author": {
-        "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; NEARLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy",
-        "firstname": "Alice",
-        "lastname": "Geminiani"
-    },
-    "associated_paper_doi": "https://doi.org/10.3389/fncom.2019.00068",
-    "associated_paper_title": "Response Dynamics in an Olivocerebellar Spiking Neural Network With Non-linear Neuron Properties",
-    "associated_paper_issue": null,
-    "associated_paper_pagination": "68",
-    "associated_paper_volume": "13",
-    "authors": [
-        {
-            "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; NEARLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy",
-            "firstname": "Alice",
-            "lastname": "Geminiani"
-        },
-        {
-            "affiliation": "NEARLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy",
-            "firstname": "Alessandra",
-            "lastname": "Pedrocchi"
-        },
-        {
-            "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy",
-            "firstname": "Egidio",
-            "lastname": "D'Angelo"
-        },
-        {
-            "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy",
-            "firstname": "Claudia",
-            "lastname": "Casellato"
-        }
-    ],
-    "citation": "Alice Geminiani, Alessandra Pedrocchi, Egidio D'Angelo & Claudia Casellato (2019). Response Dynamics in an Olivocerebellar Spiking Neural Network With Non-linear Neuron Properties. Frontiers in Computational Neuroscience, 13: 68.",
-    "collab_id": "livepapers",
-    "corresponding_author": [
-        {
-            "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; NEARLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy",
-            "firstname": "Alice",
-            "lastname": "Geminiani"
-        }
-    ],
-    "created_author": [
-        {
-            "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; NEARLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy",
-            "firstname": "Alice",
-            "lastname": "Geminiani"
-        }
-    ],
-    "doi": "https://doi.org/10.25493/3XVH-RS7",
-    "id": "9d00321b-f927-4153-8c98-8f59e24bd5c6",
-    "journal": "Frontiers in Computational Neuroscience",
-    "license": "Creative Commons Attribution-NonCommercial 4.0 International",
-    "live_paper_title": "Response Dynamics in an Olivocerebellar Spiking Neural Network With Non-linear Neuron Properties",
-    "lp_tool_version": "0.1",
-    "modified_date": "2021-09-28T08:30:20.186000+00:00",
-    "resources": [
-        {
-            "data": [],
-            "description": "
The source code, used for the simulations described in paper, can be accessed and downloaded at the following github link.\n   
\n   
\n   Please refer to the README file of the github repository for more details.\n
",
-            "icon": "note_add",
-            "order": 0,
-            "title": "Source Code",
-            "type": "section_custom"
-        },
-        {
-            "data": [],
-            "description": "
\n   The simulations of the olivocerebellar microcircuit presented in the paper are available as a python Jupyter Notebook on EBRAINS, in the following collaboratory:  \n   \n   Use Case: olivocerebellar scaffold with E-GLIF neurons.\n   
\n   
\n\n   Please refer to the python code and the inline comments in the notebook, for details on how to reproduce raster plots and spike histograms reported in Figs. 2 to 7 of the paper, for glomeruli, Granule cells, Golgi cells, Purkinje cells, Molecular Layer Interneurons and Deep Cerebellar Nuclei neurons.\n   \n
",
-            "icon": "local_activity",
-            "order": 1,
-            "title": "Test Simulations",
-            "type": "section_custom"
-        }
-    ],
-    "resources_description": "Data and models: all data and models used in the paper are available at the links reported below, grouped into the following categories:",
-    "url": "https://www.frontiersin.org/articles/10.3389/fncom.2019.00068/full",
-    "version": "1",
-    "year": "2019-10-01"
-}
\ No newline at end of file
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/9ef99ad2-233a-49d1-9499-6c1b6dd641f6.json b/validation_service_api/validation_service/tests/test_data/livepapers/9ef99ad2-233a-49d1-9499-6c1b6dd641f6.json
deleted file mode 100644
index 8d2a14a3..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/9ef99ad2-233a-49d1-9499-6c1b6dd641f6.json
+++ /dev/null
@@ -1,81 +0,0 @@
-{
-    "abstract": "A number of potentially important mechanisms have been identified as key players to generate epileptiform activity, such as genetic mutations, activity-dependent alteration of synaptic functions, and functional network reorganization at the macroscopic level. Here we study how network connectivity at cellular level can affect the onset of epileptiform activity, using computational model networks with different wiring properties. The model suggests that networks connected as in real brain circuits are more resistant to generate seizure-like activity. The results suggest new experimentally testable predictions on the cellular network connectivity in epileptic individuals, and highlight the importance of using the appropriate network connectivity to investigate epileptiform activity with computational models.",
-    "alias": "2021b-giacopelli-et-al",
-    "approved_author": {
-        "affiliation": "Department of Mathematics and Informatics, University of Palermo, Italy; Institute of Biophysics, National Research Council, Palermo, Italy",
-        "firstname": "Giuseppe",
-        "lastname": "Giacopelli"
-    },
-    "associated_paper_doi": "https://doi.org/10.1038/s41598-021-00283-w",
-    "associated_paper_title": "The role of network connectivity on epileptiform activity",
-    "associated_paper_volume": "11",
-    "associated_paper_issue": null,
-    "associated_paper_pagination": "20792",
-    "authors": [
-        {
-            "affiliation": "Department of Mathematics and Informatics, University of Palermo, Italy; Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Giuseppe",
-            "lastname": "Giacopelli"
-        },
-        {
-            "affiliation": "Department of Mathematics and Informatics, University of Palermo, Italy; Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Domenico",
-            "lastname": "Tegolo"
-        },
-        {
-            "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Michele",
-            "lastname": "Migliore"
-        }
-    ],
-    "citation": "Giuseppe Giacopelli, Domenico Tegolo & Michele Migliore (2021). The role of network connectivity on epileptiform activity. Scientific Reports, 11: 20792.",
-    "collab_id": "livepapers",
-    "corresponding_author": [
-        {
-            "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Michele",
-            "lastname": "Migliore"
-        }
-    ],
-    "created_author": [
-        {
-            "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Luca L.",
-            "lastname": "Bologna"
-        },
-        {
-            "affiliation": "Department of Mathematics and Informatics, University of Palermo, Italy; Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Giuseppe",
-            "lastname": "Giacopelli"
-        }
-    ],
-    "doi": "https://doi.org/10.25493/MZV9-ECW",
-    "id": "9ef99ad2-233a-49d1-9499-6c1b6dd641f6",
-    "journal": "Scientific Reports",
-    "license": "Creative Commons Attribution 4.0 International",
-    "live_paper_title": "The role of network connectivity on epileptiform activity",
-    "lp_tool_version": "0.1",
-    "modified_date": "2021-10-21T09:23:56.678000+00:00",
-    "resources": [
-        {
-            "data": [],
-            "description": "
A reduced self-consistent set of files able to reproduce Figure 4 of the paper is available on ModelDB.
",
-            "icon": "local_activity",
-            "order": 0,
-            "title": "Model",
-            "type": "section_custom"
-        },
-        {
-            "data": [],
-            "description": "
The Web Application will be available on CINECA.
",
-            "icon": "language",
-            "order": 1,
-            "title": "Web Application",
-            "type": "section_custom"
-        }
-    ],
-    "resources_description": "Models and Web App: the models used in the paper and the Web Application are available at the links reported below:",
-    "url": "https://www.nature.com/articles/s41598-021-00283-w",
-    "version": "1",
-    "year": "2021-10-12"
-}
\ No newline at end of file
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/b11ca08c-3fad-4020-85ca-adb2fd58541b.json b/validation_service_api/validation_service/tests/test_data/livepapers/b11ca08c-3fad-4020-85ca-adb2fd58541b.json
deleted file mode 100644
index ff352782..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/b11ca08c-3fad-4020-85ca-adb2fd58541b.json
+++ /dev/null
@@ -1,219 +0,0 @@
-{
-    "lp_tool_version": "0.1",
-    "id": "b11ca08c-3fad-4020-85ca-adb2fd58541b",
-    "alias": "2023-vanderlande-et-al",
-    "version": "v1",
-    "modified_date": "2023-09-26T07:33:39.011000+00:00",
-    "authors": [
-        {
-            "firstname": "Glenn J.M.",
-            "lastname": "van der Lande",
-            "affiliation": "GIGA-Consciousness, Coma Science Group, University of Li\u00e8ge, Belgium; Centre du Cerveau, University Hospital of Li\u00e8ge, Belgium"
-        },
-        {
-            "firstname": "Arnau",
-            "lastname": "Manasanch",
-            "affiliation": "Institut d'Investigacions Biom\u00e8diques August Pi i Sunyer (IDIBAPS), Barcelona, Spain"
-        },
-        {
-            "firstname": "Diana",
-            "lastname": "Casas-Torremocha",
-            "affiliation": "Institut d'Investigacions Biom\u00e8diques August Pi i Sunyer (IDIBAPS), Barcelona, Spain"
-        },
-        {
-            "firstname": "Leonardo",
-            "lastname": "Dalla Porta",
-            "affiliation": "Institut d'Investigacions Biom\u00e8diques August Pi i Sunyer (IDIBAPS), Barcelona, Spain"
-        },
-        {
-            "firstname": "Olivia",
-            "lastname": "Gosseries",
-            "affiliation": "GIGA-Consciousness, Coma Science Group, University of Li\u00e8ge, Belgium; Centre du Cerveau, University Hospital of Li\u00e8ge, Belgium"
-        },
-        {
-            "firstname": "Naji",
-            "lastname": "Alnagger",
-            "affiliation": "GIGA-Consciousness, Coma Science Group, University of Li\u00e8ge, Belgium; Centre du Cerveau, University Hospital of Li\u00e8ge, Belgium"
-        },
-        {
-            "firstname": "Alice",
-            "lastname": "Barra",
-            "affiliation": "GIGA-Consciousness, Coma Science Group, University of Li\u00e8ge, Belgium; Centre du Cerveau, University Hospital of Li\u00e8ge, Belgium"
-        },
-        {
-            "firstname": "Jorge F.",
-            "lastname": "Mej\u00edas",
-            "affiliation": "Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, the Netherlands"
-        },
-        {
-            "firstname": "Rajanikant",
-            "lastname": "Panda",
-            "affiliation": "GIGA-Consciousness, Coma Science Group, University of Li\u00e8ge, Belgium; Centre du Cerveau, University Hospital of Li\u00e8ge, Belgium"
-        },
-        {
-            "firstname": "Fabio",
-            "lastname": "Riefolo",
-            "affiliation": "Biomedical Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology, Spain; Institute Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Spain; Teamit Institute, Barcelona, Spain"
-        },
-        {
-            "firstname": "Aurore",
-            "lastname": "Thibaut",
-            "affiliation": "GIGA-Consciousness, Coma Science Group, University of Li\u00e8ge, Belgium; Centre du Cerveau, University Hospital of Li\u00e8ge, Belgium"
-        },
-        {
-            "firstname": "Vincent",
-            "lastname": "Bonhomme",
-            "affiliation": "Department of Anesthesia and Intensive Care Medicine, Li\u00e8ge University Hospital, Belgium; Anesthesia and Perioperative Neuroscience Laboratory, GIGA-Consciousness Thematic Unit, University of Li\u00e8ge, Belgium"
-        },
-        {
-            "firstname": "Bertrand",
-            "lastname": "Thirion",
-            "affiliation": "Inria Saclay - \u00cele-de-France Research Centre"
-        },
-        {
-            "firstname": "Francisco",
-            "lastname": "Clasca",
-            "affiliation": "Department of Anatomy and Neuroscience, School of Medicine, Universidad Aut\u00f3noma de Madrid, Spain"
-        },
-        {
-            "firstname": "Pau",
-            "lastname": "Gorostiza",
-            "affiliation": "Biomedical Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology, Spain; Institute Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Spain; Catalan Institution for Research and Advanced Studies, Barcelona, Spain"
-        },
-        {
-            "firstname": "Maria Victoria",
-            "lastname": "S\u00e1nchez-Vives",
-            "affiliation": "Institut d'Investigacions Biom\u00e8diques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Catalan Institution for Research and Advanced Studies, Barcelona, Spain"
-        },
-        {
-            "firstname": "Gustavo",
-            "lastname": "Deco",
-            "affiliation": "Catalan Institution for Research and Advanced Studies, Barcelona, Spain; Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain"
-        },
-        {
-            "firstname": "Steven",
-            "lastname": "Laureys",
-            "affiliation": "GIGA-Consciousness, Coma Science Group, University of Li\u00e8ge, Belgium; Centre du Cerveau, University Hospital of Li\u00e8ge, Belgium; Joint International Research Unit on Consciousness, CERVO Brain Research Centre, U Laval, Canada; International Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China"
-        },
-        {
-            "firstname": "Gorka",
-            "lastname": "Zamora-L\u00f3pez",
-            "affiliation": "Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain"
-        },
-        {
-            "firstname": "Jitka",
-            "lastname": "Annen",
-            "affiliation": "GIGA-Consciousness, Coma Science Group, University of Li\u00e8ge, Belgium; Centre du Cerveau, University Hospital of Li\u00e8ge, Belgium; Joint International Research Unit on Consciousness, CERVO Brain Research Centre, U Laval, Canada; International Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China"
-        }
-    ],
-    "corresponding_author": [
-        {
-            "firstname": "Glenn J.M.",
-            "lastname": "van der Lande",
-            "affiliation": "GIGA-Consciousness, Coma Science Group, University of Li\u00e8ge, Belgium; Centre du Cerveau, University Hospital of Li\u00e8ge, Belgium"
-        }
-    ],
-    "created_author": [
-        {
-            "firstname": "Arnau",
-            "lastname": "Manasanch",
-            "affiliation": "Institut d'Investigacions Biom\u00e8diques August Pi i Sunyer (IDIBAPS), Barcelona, Spain"
-        }
-    ],
-    "approved_author": {
-        "firstname": "Arnau",
-        "lastname": "Manasanch",
-        "affiliation": "Institut d'Investigacions Biom\u00e8diques August Pi i Sunyer (IDIBAPS), Barcelona, Spain"
-    },
-    "year": "2023-01-01",
-    "live_paper_title": "Live Paper: Identification and neuromodulation of brain states to promote recovery of consciousness",
-    "associated_paper_title": "Identification and neuromodulation of brain states to promote recovery of consciousness",
-    "journal": "Zenodo",
-    "url": "https://zenodo.org/record/8377867",
-    "citation": "Glenn J.M. van der Lande, Arnau Manasanch, Diana Casas-Torremocha, Leonardo Dalla Porta, Olivia Gosseries, Naji Alnagger, Alice Barra, Jorge F. Mej\u00edas, Rajanikant Panda, Fabio Riefolo, Aurore Thibaut, Vincent Bonhomme, Bertrand Thirion, Francisco Clasca, Pau Gorostiza, Maria Victoria S\u00e1nchez-Vives, Gustavo Deco, Steven Laureys, Gorka Zamora-L\u00f3pez & Jitka Annen (2023). Identification and neuromodulation of brain states to promote recovery of consciousness. Zenodo, : .",
-    "doi": null,
-    "associated_paper_doi": "https://doi.org/10.5281/zenodo.8377866",
-    "associated_paper_volume": "",
-    "associated_paper_issue": null,
-    "associated_paper_pagination": null,
-    "abstract": "Experimental and clinical studies of consciousness identify brain states (i.e., transient, relevant features of the brain associated with the state of consciousness) in a non-systematic manner and largely independent from the research into the induction of state changes. In this narrative review with a focus on patients with a disorder of consciousness (DoC), we synthesize advances on the identification of brain states associated with consciousness in animal models and physiological (sleep), pharmacological (anesthesia) and pathological (DoC) states of altered consciousness in human. We show that in reduced consciousness the frequencies in which the brain operates are slowed down and that the pattern of functional communication in the brain is sparser, less efficient, and less complex. The results also\nhighlight damaged resting state networks, in particular the default mode network, decreased connectivity in long-range connections and in the thalamocortical loops. Next, we show that therapeutic approaches to treat DoC, through pharmacology (e.g., amantadine, zolpidem), and (non-)invasive brain stimulation (e.g., transcranial current stimulation, deep brain stimulation) have shown some effectiveness to promote consciousness recovery. It seems that these deteriorated features of conscious\nbrain states may improve in response to these neuromodulation approaches, yet, targeting often remains non-specific and does not always lead to (behavioral) improvements. Furthermore, in silico model-based approaches allow the development of personalized assessment of the effect of treatment on brain-wide dynamics. Although still in infancy, the fields of brain state identification and neuromodulation of brain states in relation to consciousness are showing fascinating developments that, when united, might propel the development of new and better targeted techniques for DoC. For example, brain states could be identified in a predictive setting, and the theoretical and empirical testing (i.e., in animals, under anesthesia and patients with a DoC) of neuromodulation techniques to promote consciousness could be investigated. This review further helps to identify where challenges and opportunities lay for the maturation of brain state research in the context of states of consciousness. Finally, it aids in recognizing possibilities and obstacles for the clinical translation of these diagnostic techniques and neuromodulation treatment options across both the multi-modal and multi-species approaches outlined throughout the review. This paper presents interactive figures, supported by the Live Paper initiative of the Human Brain Project, enabling the interaction with data and figures illustrating the concepts in the paper through EBRAINS (go to https://wiki.ebrains.eu/bin/view/Collabs/live-paper-states-altered-consciousness and get started with an EBRAINS account).",
-    "license": "GNU General Public License v3.0 or later",
-    "resources_description": "The resources from this paper are linked below. You will find the associated datasets and models as well as a Live Figure that is executable in EBRAINS (with an EBRAINS account)",
-    "collab_id": "livepapers",
-    "resources": [
-        {
-            "order": 0,
-            "type": "section_generic",
-            "title": "Datasets",
-            "icon": "",
-            "description": null,
-            "data": [
-                {
-                    "url": "https://search.kg.ebrains.eu/instances/68a61eab-7ba9-47cf-be78-b9addd64bb2f",
-                    "label": "FDG-PET/CT data of healthy volunteers and patients with disorders of consciousness",
-                    "view_url": "https://search.kg.ebrains.eu/instances/68a61eab-7ba9-47cf-be78-b9addd64bb2f",
-                    "type": "URL",
-                    "identifier": "ce5af091-fa30-5cb2-b7ec-f9657e922963"
-                },
-                {
-                    "url": "https://search.kg.ebrains.eu/instances/8ddf749f-fb1d-4d16-acc3-fbde91b90e24",
-                    "label": "Individual Brain Charting",
-                    "view_url": "https://search.kg.ebrains.eu/instances/8ddf749f-fb1d-4d16-acc3-fbde91b90e24",
-                    "type": "URL",
-                    "identifier": "75aaa80b-b3e0-50fa-8a7d-63ac7357eff1"
-                },
-                {
-                    "url": "https://search.kg.ebrains.eu/instances/b5998ae0-7237-4626-8ca6-e9fe2e8389c9",
-                    "label": "Optimisation of photostimulation targeting muscarinic receptors",
-                    "view_url": "https://search.kg.ebrains.eu/instances/b5998ae0-7237-4626-8ca6-e9fe2e8389c9",
-                    "type": "URL",
-                    "identifier": "4eab276d-df7a-5a22-949a-611b1aeee608"
-                },
-                {
-                    "url": "https://search.kg.ebrains.eu/instances/ab2d4db0-4c97-442c-82f9-a3dade301e9f",
-                    "label": "TMS-EEG perturbation in patients with disorders of consciousness (v1)",
-                    "view_url": "https://search.kg.ebrains.eu/instances/ab2d4db0-4c97-442c-82f9-a3dade301e9f",
-                    "type": "URL",
-                    "identifier": "1eff8adb-6c1d-545b-b775-0902fd3afea1"
-                }
-            ]
-        },
-        {
-            "order": 1,
-            "type": "section_generic",
-            "title": "Live Figures",
-            "icon": "format_list_bulleted",
-            "description": "The following Live Figures can be executed in the links below in the JupyterLab instance hosted in EBRAINS. For that, you should have an EBRAINS account.",
-            "data": [
-                {
-                    "url": "https://data.kg.ebrains.eu/zip?container=https://data-proxy.ebrains.eu/api/v1/buckets/live-papers?prefix=2023_vanderlande_et_al/LiveFigure_tDCS/",
-                    "label": "Changes in TMS-triggered slow wave activity in DoC after tDCS treatment",
-                    "view_url": "https://lab.ch.ebrains.eu/hub/user-redirect/lab/tree/shared/Live%20Paper%3A%20Identification%20and%20neuromodulation%20of%20brain%20states%20to%20promote%20recovery%20of%20consciousness/LiveFigure_tDCS/tDCS.ipynb",
-                    "type": "URL",
-                    "identifier": "c9fe5287-2738-455b-9c44-633796966b54"
-                },
-                {
-                    "url": "https://data.kg.ebrains.eu/zip?container=https://data-proxy.ebrains.eu/api/v1/buckets/live-papers?prefix=2023_vanderlande_et_al/LiveFigure_Photopharmacology/",
-                    "label": "Control of brain state transitions with a photoswitchable muscarinic agonist",
-                    "view_url": "https://lab.ch.ebrains.eu/hub/user-redirect/lab/tree/shared/Live%20Paper%3A%20Identification%20and%20neuromodulation%20of%20brain%20states%20to%20promote%20recovery%20of%20consciousness/live_figure_3_photopharma/live_figure_3_photopharma.ipynb",
-                    "type": "URL",
-                    "identifier": "02dff6d3-4c23-4df9-97f1-2cf35997103d"
-                },
-                {
-                    "url": "https://data.kg.ebrains.eu/zip?container=https://data-proxy.ebrains.eu/api/v1/buckets/live-papers?prefix=2023_vanderlande_et_al/LiveFigure_ArousalAwareness/",
-                    "label": "Disorders of Consciousness and Examples of Brain States",
-                    "view_url": "https://lab.ch.ebrains.eu/hub/user-redirect/lab/tree/shared/Live%20Paper%3A%20Identification%20and%20neuromodulation%20of%20brain%20states%20to%20promote%20recovery%20of%20consciousness/LiveFigure_ArousalAwareness/ArousalAwareness.ipynb",
-                    "type": "URL",
-                    "identifier": "cace458e-b32b-4946-ae27-4b87905cbafc"
-                },
-                {
-                    "url": "https://data.kg.ebrains.eu/zip?container=https://data-proxy.ebrains.eu/api/v1/buckets/live-papers?prefix=2023_vanderlande_et_al/LiveFigure_Dynamics/",
-                    "label": "Functional connectivity patterns for dynamic resting-state fMRI analyses in Disorders of Consciousness",
-                    "view_url": "https://lab.ch.ebrains.eu/hub/user-redirect/lab/tree/shared/Live%20Paper%3A%20Identification%20and%20neuromodulation%20of%20brain%20states%20to%20promote%20recovery%20of%20consciousness/LiveFigure_Dynamics/Dynamics.ipynb",
-                    "type": "URL",
-                    "identifier": "44aeab66-d2dd-467d-8b5d-46c505a1698e"
-                }
-            ]
-        }
-    ]
-}
\ No newline at end of file
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/b3816c12-2d3a-430e-a6d4-139f0a132de7.json b/validation_service_api/validation_service/tests/test_data/livepapers/b3816c12-2d3a-430e-a6d4-139f0a132de7.json
deleted file mode 100644
index 36304efd..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/b3816c12-2d3a-430e-a6d4-139f0a132de7.json
+++ /dev/null
@@ -1,136 +0,0 @@
-{
-    "abstract": "In the last decade, optical imaging methods have significantly improved our understanding of the information processing principles in the brain. Although many promising tools have been designed, sensors of membrane potential are lagging behind the rest. Semiconductor nanoparticles are an attractive alternative to classical voltage indicators, such as voltage-sensitive dyes and proteins. Such nanoparticles exhibit high sensitivity to external electric fields via the quantum-confined Stark effect. Here we report the development of lipid-coated semiconductor voltage-sensitive nanorods (vsNRs) that self-insert into the neuronal membrane. We describe a workflow to detect and process the photoluminescent signal of vsNRs after wide-field time-lapse recordings. We also present data indicating that vsNRs are feasible for sensing membrane potential in neurons at a single-particle level. This shows the potential of vsNRs for detection of neuronal activity with unprecedentedly high spatial and temporal resolution.",
-    "alias": "2019-ludwig-et-al",
-    "approved_author": {
-        "affiliation": "L'Ecole Normale Sup\u00e9rieure, Institute of Biology (IBENS), Paris Sciences et Lettres (PSL), CNRS UMR 8197, Inserm 1024, Paris, France",
-        "firstname": "Antoine",
-        "lastname": "Triller"
-    },
-    "associated_paper_doi": "https://doi.org/10.1101/838342",
-    "associated_paper_title": "Feasibility analysis of semiconductor voltage nanosensors for neuronal membrane potential sensing",
-    "associated_paper_volume": null,
-    "associated_paper_issue": null,
-    "associated_paper_pagination": "838342",
-    "authors": [
-        {
-            "affiliation": "L'Ecole Normale Sup\u00e9rieure, Institute of Biology (IBENS), Paris Sciences et Lettres (PSL), CNRS UMR 8197, Inserm 1024, Paris, France; Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland",
-            "firstname": "Anastasia",
-            "lastname": "Ludwig"
-        },
-        {
-            "affiliation": "L'Ecole Normale Sup\u00e9rieure, Institute of Biology (IBENS), Paris Sciences et Lettres (PSL), CNRS UMR 8197, Inserm 1024, Paris, France",
-            "firstname": "Pablo",
-            "lastname": "Serna"
-        },
-        {
-            "affiliation": "Department of Physics, Institute for Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan, Israel",
-            "firstname": "Lion",
-            "lastname": "Morgenstein"
-        },
-        {
-            "affiliation": "Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel",
-            "firstname": "Gaoling",
-            "lastname": "Yang"
-        },
-        {
-            "affiliation": "Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel",
-            "firstname": "Omri",
-            "lastname": "Bar-Elli"
-        },
-        {
-            "affiliation": "Departments of Chemistry, Molecular & Cell Biology, and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA",
-            "firstname": "Gloria",
-            "lastname": "Ortiz"
-        },
-        {
-            "affiliation": "Departments of Chemistry, Molecular & Cell Biology, and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA",
-            "firstname": "Evan",
-            "lastname": "Miller"
-        },
-        {
-            "affiliation": "Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel",
-            "firstname": "Dan",
-            "lastname": "Oron"
-        },
-        {
-            "affiliation": "Department of Physics, Institute for Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan, Israel",
-            "firstname": "Asaf",
-            "lastname": "Grupi"
-        },
-        {
-            "affiliation": "Department of Chemistry and Biochemistry, Department of Physiology, and California NanoSystems Institute, University of California Los Angeles, Los Angeles, CA, USA; Department of Physics, Institute for Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan, Israel",
-            "firstname": "Shimon",
-            "lastname": "Weiss"
-        },
-        {
-            "affiliation": "L'Ecole Normale Sup\u00e9rieure, Institute of Biology (IBENS), Paris Sciences et Lettres (PSL), CNRS UMR 8197, Inserm 1024, Paris, France",
-            "firstname": "Antoine",
-            "lastname": "Triller"
-        }
-    ],
-    "citation": "Anastasia Ludwig, Pablo Serna, Lion Morgenstein, Gaoling Yang, Omri Bar-Elli, Gloria Ortiz, Evan Miller, Dan Oron, Asaf Grupi, Shimon Weiss & Antoine Triller (2019). Feasibility analysis of semiconductor voltage nanosensors for neuronal membrane potential sensing. bioRxiv, 838342.",
-    "collab_id": "livepapers",
-    "corresponding_author": [
-        {
-            "affiliation": "L'Ecole Normale Sup\u00e9rieure, Institute of Biology (IBENS), Paris Sciences et Lettres (PSL), CNRS UMR 8197, Inserm 1024, Paris, France; Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland",
-            "firstname": "Anastasia",
-            "lastname": "Ludwig"
-        },
-        {
-            "affiliation": "L'Ecole Normale Sup\u00e9rieure, Institute of Biology (IBENS), Paris Sciences et Lettres (PSL), CNRS UMR 8197, Inserm 1024, Paris, France",
-            "firstname": "Antoine",
-            "lastname": "Triller"
-        }
-    ],
-    "created_author": [
-        {
-            "affiliation": "L'Ecole Normale Sup\u00e9rieure, Institute of Biology (IBENS), Paris Sciences et Lettres (PSL), CNRS UMR 8197, Inserm 1024, Paris, France",
-            "firstname": "Antoine",
-            "lastname": "Triller"
-        }
-    ],
-    "doi": "https://doi.org/10.25493/2AAE-GAX",
-    "id": "b3816c12-2d3a-430e-a6d4-139f0a132de7",
-    "journal": "bioRxiv",
-    "license": "Creative Commons Attribution 4.0 International",
-    "live_paper_title": "Feasibility analysis of semiconductor voltage nanosensors for neuronal membrane potential sensing",
-    "lp_tool_version": "0.1",
-    "modified_date": "2021-08-02T10:45:09.330000+00:00",
-    "resources": [
-        {
-            "data": [
-                {
-                    "identifier": "2add1101-98d8-45dd-8e53-b204c738eb47",
-                    "label": "Analysis signal of vsNRs",
-                    "type": "URL",
-                    "url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2019-ludwig-et-al/Jupyter%20Notebook",
-                    "view_url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2019-ludwig-et-al/Jupyter%20Notebook"
-                },
-                {
-                    "identifier": "e2616e8b-22bd-491c-ac76-cc40058bb95e",
-                    "label": "EBRAINS Collab",
-                    "type": "URL",
-                    "url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2019-ludwig-et-al/",
-                    "view_url": "https://wiki.ebrains.eu/bin/view/Collabs/live-paper-2019-ludwig-et-al/"
-                }
-            ],
-            "description": "The following jupyter notebooks guide the user through the analysis protocols outlined in the manuscript.",
-            "icon": "note_add",
-            "order": 0,
-            "title": "Use Cases",
-            "type": "section_generic"
-        },
-        {
-            "data": [],
-            "description": "
The source code is available on github at the following url.\n   Please refer to the README file for more details.\n
",
-            "icon": "note_add",
-            "order": 1,
-            "title": "Source Code",
-            "type": "section_custom"
-        }
-    ],
-    "resources_description": "Here you can find the resources referred to in the manuscript.",
-    "url": "https://www.biorxiv.org/content/10.1101/838342v1",
-    "version": "1",
-    "year": "2019-05-26"
-}
\ No newline at end of file
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/b6917332-e092-4bf3-bf31-3f0d212ff861.json b/validation_service_api/validation_service/tests/test_data/livepapers/b6917332-e092-4bf3-bf31-3f0d212ff861.json
deleted file mode 100644
index ff288a42..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/b6917332-e092-4bf3-bf31-3f0d212ff861.json
+++ /dev/null
@@ -1,145 +0,0 @@
-{
-    "abstract": "Long-term potentiation and depression of synaptic activity in response to stimuli is a key factor in reinforcement learning. Strengthening of the corticostriatal synapses depends on the second messenger cAMP, whose synthesis is catalysed by the enzyme adenylyl cyclase 5 (AC5), which is itself regulated by the stimulatory G\u03b1olf and inhibitory G\u03b1i proteins. AC isoforms have been suggested to act as coincidence detectors, promoting cellular responses only when convergent regulatory signals occur close in time. However, the mechanism for this is currently unclear, and seems to lie in their diverse regulation patterns. Despite attempts to isolate the ternary complex, it is not known if G\u03b1olf and G\u03b1i can bind to AC5 simultaneously, nor what activity the complex would have. Using protein structure-based molecular dynamics simulations, we show that this complex is stable and inactive. These simulations, along with Brownian dynamics simulations to estimate protein association rates constants, constrain a kinetic model that shows that the presence of this ternary inactive complex is crucial for AC5\u2019s ability to detect coincident signals, producing a synergistic increase in cAMP. These results reveal some of the prerequisites for corticostriatal synaptic plasticity, and explain recent experimental data on cAMP concentrations following receptor activation. Moreover, they provide insights into the regulatory mechanisms that control signal processing by different AC isoforms.",
-    "alias": "2019-bruce-et-al",
-    "approved_author": {
-        "affiliation": "Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany",
-        "firstname": "Neil J.",
-        "lastname": "Bruce"
-    },
-    "associated_paper_doi": "https://doi.org/10.1371/journal.pcbi.1007382",
-    "associated_paper_title": "Regulation of adenylyl cyclase 5 in striatal neurons confers the ability to detect coincident neuromodulatory signals",
-    "associated_paper_volume": "15",
-    "associated_paper_issue": null,
-    "associated_paper_pagination": "e1007382",
-    "authors": [
-        {
-            "affiliation": "Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany",
-            "firstname": "Neil J.",
-            "lastname": "Bruce"
-        },
-        {
-            "affiliation": "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland",
-            "firstname": "Daniele",
-            "lastname": "Narzi"
-        },
-        {
-            "affiliation": "Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden",
-            "firstname": "Daniel",
-            "lastname": "Trpevski"
-        },
-        {
-            "affiliation": "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland; Department of Computer Science, Stanford University, Stanford, CA, USA",
-            "firstname": "Siri C.",
-            "lastname": "van Keulen"
-        },
-        {
-            "affiliation": "Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland",
-            "firstname": "Anu G.",
-            "lastname": "Nair"
-        },
-        {
-            "affiliation": "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland",
-            "firstname": "Ursula",
-            "lastname": "R\u00f6thlisberger"
-        },
-        {
-            "affiliation": "Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany; Zentrum für Molekulare Biologie der Universität Heidelberg, DKFZ-ZMBH Alliance, Heidelberg University, Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany; Department of Physics, Heidelberg University, Heidelberg, Germany",
-            "firstname": "Rebecca C.",
-            "lastname": "Wade"
-        },
-        {
-            "affiliation": "Institute for Advanced Simulation - Computational Biomedicine, Forschungszentrum Jülich, Jülich, Germany; Department of Physics, RWTH Aachen University, Aachen, Germany; Department of Neurobiology, RWTH Aachen University, Aachen, Germany; Institute for Advanced Simulation - Computational Biomedicine, Forschungszentrum Jülich, Jülich, Germany; Institute for Neuroscience and Medicine (INM-11), Forschungszentrum Jülich, Jülich, Germany",
-            "firstname": "Paolo",
-            "lastname": "Carloni"
-        },
-        {
-            "affiliation": "Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden; Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden",
-            "firstname": "Jeanette",
-            "lastname": "Hellgren Kotaleski"
-        }
-    ],
-    "citation": "Neil J. Bruce, Daniele Narzi, Daniel Trpevski, Siri C. van Keulen, Anu G. Nair, Ursula Röthlisberger, Rebecca C. Wade, Paolo Carloni & Jeanette Hellgren Kotaleski (2019). Regulation of adenylyl cyclase 5 in striatal neurons confers the ability to detect coincident neuromodulatory signals. PLOS Computational Biology, 15: e1007382.",
-    "collab_id": "livepapers",
-    "corresponding_author": [
-        {
-            "affiliation": "Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden; Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden",
-            "firstname": "Jeanette",
-            "lastname": "Hellgren Kotaleski"
-        },
-        {
-            "affiliation": "Institute for Advanced Simulation - Computational Biomedicine, Forschungszentrum Jülich, Jülich, Germany; Department of Physics, RWTH Aachen University, Aachen, Germany; Department of Neurobiology, RWTH Aachen University, Aachen, Germany; Institute for Advanced Simulation - Computational Biomedicine, Forschungszentrum Jülich, Jülich, Germany; Institute for Neuroscience and Medicine (INM-11), Forschungszentrum Jülich, Jülich, Germany",
-            "firstname": "Paolo",
-            "lastname": "Carloni"
-        },
-        {
-            "affiliation": "Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany; Zentrum für Molekulare Biologie der Universität Heidelberg, DKFZ-ZMBH Alliance, Heidelberg University, Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany; Department of Physics, Heidelberg University, Heidelberg, Germany",
-            "firstname": "Rebecca C.",
-            "lastname": "Wade"
-        },
-        {
-            "affiliation": "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland",
-            "firstname": "Ursula",
-            "lastname": "R\u00f6thlisberger"
-        }
-    ],
-    "created_author": [
-        {
-            "affiliation": "Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany",
-            "firstname": "Neil J.",
-            "lastname": "Bruce"
-        }
-    ],
-    "doi": "https://doi.org/10.25493/NH0B-7QQ",
-    "id": "b6917332-e092-4bf3-bf31-3f0d212ff861",
-    "journal": "PLOS Computational Biology",
-    "license": "Creative Commons Attribution 4.0 International",
-    "live_paper_title": "Regulation of adenylyl cyclase 5 in striatal neurons confers the ability to detect coincident neuromodulatory signals",
-    "lp_tool_version": "0.1",
-    "modified_date": "2021-09-29T11:27:25.282000+00:00",
-    "resources": [
-        {
-            "data": [],
-            "description": "\n\n\n\n
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3D experimental structural data used as input data for this work
\n
\n   \n      \n         \n            \n            \n            \n            \n         \n         \n            \n            \n            \n            \n         \n      \n   
\n               Complex of Gs-alpha with the catalytic domains of mammalian adenylyl cyclase\n            \n               PDB: 1AZS\n            \n               \n               save_alt
Download\n               \n            		\n               \n               3d_rotation
View\n               \n            
\n               Complex of Gs-alpha with the catalytic domains of mammalian adenylyl cyclase: complex with adenosine 5'-(alpha thio)-triphosphate (RP), Mg, and Mn\n            \n               PDB: 1CJK\n            \n               \n               save_alt
Download\n               \n            		\n               \n               3d_rotation
View\n               \n            
\n
",
-            "icon": "input",
-            "order": 0,
-            "title": "Input structural data",
-            "type": "section_custom"
-        },
-        {
-            "data": [],
-            "description": "\n\n\n\n
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Select molecular dynamics trajectories of complexes including apo or holo AC5
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\n      \n         \n            \n               \n               \n               \n               \n            \n            \n               \n               \n               \n               \n            \n            \n               \n               \n               \n               \n            \n         \n      
\n                  AC5 - Gαolf binary complex\n               \n                  \n                  save_altPDB structure file\n                  \n               \n                  \n                  save_altXTC trajectory file\n                  \n               \n                  \n                  3d_rotationView\n                  \n               
\n                  AC5 - Gαi binary complex\n               \n                  \n                  save_altPDB structure file\n                  \n               \n                  \n                  save_altXTC trajectory file\n                  \n               \n                  \n                  3d_rotationView\n                  \n               
\n                  Gαolf - AC5 - Gαi ternary complex\n               \n                  \n                  save_altPDB structure file\n                  \n               \n                  \n                  save_altXTC trajectory file\n                  \n               \n                  \n                  3d_rotationView\n                  \n               
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\n                  AC5 - Gαolf binary complex\n               \n                  \n                  save_altPDB structure file\n                  \n               \n                  \n                  save_altXTC trajectory file\n                  \n               \n                  \n                  3d_rotationView\n                  \n               
\n                  AC5 - Gαi binary complex\n               \n                  \n                  save_altPDB structure file\n                  \n               \n                  \n                  save_altXTC trajectory file\n                  \n               \n                  \n                  3d_rotationView\n                  \n               
\n                  Gαolf - AC5 - Gαi ternary complex\n               \n                  \n                  save_altPDB structure file\n                  \n               \n                  \n                  save_altXTC trajectory file\n                  \n               \n                  \n                  3d_rotationView\n                  \n               
\n   
\n
\n
Or download a zip file containing all molecular dynamics trajectories
\n\narchive Download\n",
-            "icon": "timeline",
-            "order": 1,
-            "title": "Molecular dynamics simulation trajectories",
-            "type": "section_custom"
-        },
-        {
-            "data": [],
-            "description": "\n\n\n\n\n
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Select electrostatic grid files calculated using snapshots from molecular dynamics trajectories of complexes including apo or holo AC5
\n
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Select a pair of interacting species
\n      \n   
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Select a pair of interacting species
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Or download a zip file containing all snapshots and grid files
\n\narchive Download\n",
-            "icon": "local_activity",
-            "order": 2,
-            "title": "MD snapshots and electrostatic grid files used in Brownian dynamics simulations",
-            "type": "section_custom"
-        },
-        {
-            "data": [],
-            "description": "
\n   The following forward rate constants (nM-1s-1, standard errors in \n   parentheses) were calculated using Brownian dynamics (BD) simulations. For each snapshot \n   the rate constant was estimated from four simulations of 50 000 BD trajectories. The \n   headings of each column show the two reactive species and the rate constant name used \n   the mathematical modelling.\n
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\n      \n         \n            \n               \n               \n               \n               \n               \n            \n         \n         \n            \n               \n               \n               \n               \n               \n            \n            \n               \n               \n               \n               \n               \n            \n            \n               \n               \n               \n               \n               \n            \n            \n               \n               \n               \n               \n               \n            \n            \n               \n               \n               \n               \n               \n            \n            \n               \n               \n               \n               \n               \n            \n            \n               \n               \n               \n               \n               \n            \n            \n               \n               \n               \n               \n               \n            \n         \n      
SnapshotAC5 - Gαolf  (kf1)AC5 - Gαi  (kf2)AC5⋅Gαolf - Gαi  (kf3)AC5⋅Gαi - Gαolf  (kf4)
10.003 (0.002)0.015 (0.002)0.009 (0.003)0.037 (0.004)
20.011 (0.003)0.018 (0.006)0.004 (0.001)0.007 (0.003)
30.038 (0.003)0.036 (0.006)0.011 (0.005)0.007 (0.003)
40.022 (0.006)0.008 (0.003)0.005 (0.002)0.0087 (0.0007)
50.016 (0.003)0.030 (0.004)0.06 (0.01)
60.013 (0.003)
70.027 (0.005)
Mean0.018 (0.007)0.02 (0.01)0.012 (0.008)0.02 (0.01)
\n   
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SnapshotAC5 - Gαolf  (kf1)AC5 - Gαi  (kf2)AC5⋅Gαolf - Gαi  (kf3)AC5⋅Gαi - Gαolf  (kf4)
10.013 (0.004)0.005 (0.002)0.004 (0.001)0.035 (0.004)
20.045 (0.007)0.028 (0.004)0.017 (0.004)0.016 (0.003)
30.019 (0.002)0.10 (0.01)0.032 (0.004)0.013 (0.002)
40.061 (0.006)0.016 (0.002)0.016 (0.003)
Mean0.026 (0.009)0.05 (0.01)0.017 (0.006)0.020 (0.006)
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The kinetic model and related scripts to produce some of the\n   figures shown in the published paper are available from \n   GitHub.\n
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diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/bee280cc-8184-4380-a2cb-a74b131de611.json b/validation_service_api/validation_service/tests/test_data/livepapers/bee280cc-8184-4380-a2cb-a74b131de611.json
deleted file mode 100644
index 51e0cac9..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/bee280cc-8184-4380-a2cb-a74b131de611.json
+++ /dev/null
@@ -1,545 +0,0 @@
-{
-  "abstract": "Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context. In addition, there is currently no simple way of quantitatively comparing different models regarding how closely they match specific experimental observations. This limits the evaluation, re-use and further development of the existing models. Further, the development of new models could also be significantly facilitated by the ability to rapidly test the behavior of model candidates against the relevant collection of experimental data. We address these problems for the representative case of the CA1 pyramidal cell of the rat hippocampus by developing an open-source Python test suite, which makes it possible to automatically and systematically test multiple properties of models by making quantitative comparisons between the models and electrophysiological data. The tests cover various aspects of somatic behavior, and signal propagation and integration in apical dendrites. To demonstrate the utility of our approach, we applied our tests to compare the behavior of several different rat hippocampal CA1 pyramidal cell models from the ModelDB database against electrophysiological data available in the literature, and evaluated how well these models match experimental observations in different domains. We also show how we employed the test suite to aid the development of models within the European Human Brain Project (HBP), and describe the integration of the tests into the validation framework developed in the HBP, with the aim of facilitating more reproducible and transparent model building in the neuroscience community.",
-  "alias": "2021-saray-et-al",
-  "approved_author": {
-    "affiliation": "Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Universit\u00e9 Paris-Saclay, Saclay, France",
-    "firstname": "Shailesh",
-    "lastname": "Appukuttan"
-  },
-  "associated_paper_doi": "https://doi.org/10.1371/journal.pcbi.1008114",
-  "associated_paper_title": "HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data",
-  "authors": [
-    {
-      "affiliation": "Institute of Experimental Medicine, Budapest, Hungary; Faculty of Information Technology and Bionics, P\u00e1zm\u00e1ny P\u00e9ter Catholic University, Budapest, Hungary",
-      "firstname": "S\u00e1ra",
-      "lastname": "S\u00e1ray"
-    },
-    {
-      "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland",
-      "firstname": "Christian A.",
-      "lastname": "R\u00f6ssert"
-    },
-    {
-      "affiliation": "Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Universit\u00e9 Paris-Saclay, Saclay, France",
-      "firstname": "Shailesh",
-      "lastname": "Appukuttan"
-    },
-    {
-      "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-      "firstname": "Rosanna",
-      "lastname": "Migliore"
-    },
-    {
-      "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-      "firstname": "Paola",
-      "lastname": "Vitale"
-    },
-    {
-      "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-      "firstname": "Carmen A.",
-      "lastname": "Lupascu"
-    },
-    {
-      "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-      "firstname": "Luca L.",
-      "lastname": "Bologna"
-    },
-    {
-      "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland",
-      "firstname": "Werner A.H.",
-      "lastname": "Van Geit"
-    },
-    {
-      "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland",
-      "firstname": "Armando",
-      "lastname": "Romani"
-    },
-    {
-      "affiliation": "Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Universit\u00e9 Paris-Saclay, Saclay, France",
-      "firstname": "Andrew P.",
-      "lastname": "Davison"
-    },
-    {
-      "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland; Department of Neurosciences, Faculty of Medicine, University of Montreal, Montreal, Canada; CHU Sainte-Justine Research Center, Montreal, Canada; Quebec Artificial Intelligence Institute (Mila), Montreal, Canada",
-      "firstname": "Eilif B.",
-      "lastname": "Muller"
-    },
-    {
-      "affiliation": "Institute of Experimental Medicine, Budapest, Hungary; Faculty of Information Technology and Bionics, P\u00e1zm\u00e1ny P\u00e9ter Catholic University, Budapest, Hungary",
-      "firstname": "Tam\u00e1s F.",
-      "lastname": "Freund"
-    },
-    {
-      "affiliation": "Institute of Experimental Medicine, Budapest, Hungary; Faculty of Information Technology and Bionics, P\u00e1zm\u00e1ny P\u00e9ter Catholic University, Budapest, Hungary",
-      "firstname": "Szabolcs",
-      "lastname": "K\u00e1li"
-    }
-  ],
-  "citation": "Sára Sáray, Christian A. Rössert, Shailesh Appukuttan, Rosanna Migliore, Paola Vitale, Carmen A. Lupascu, Luca L. Bologna, Werner A.H. Van Geit, Armando Romani, Andrew P. Davison, Eilif B. Muller, Tamás F. Freund & Szabolcs Káli (2021). HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data. PLOS Computational Biology, 17: e1008114.",
-  "collab_id": "livepapers",
-  "corresponding_author": [
-    {
-      "affiliation": "Institute of Experimental Medicine, Budapest, Hungary; Faculty of Information Technology and Bionics, P\u00e1zm\u00e1ny P\u00e9ter Catholic University, Budapest, Hungary",
-      "firstname": "Szabolcs",
-      "lastname": "K\u00e1li"
-    }
-  ],
-  "created_author": [
-    {
-      "affiliation": "Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Universit\u00e9 Paris-Saclay, Saclay, France",
-      "firstname": "Shailesh",
-      "lastname": "Appukuttan"
-    }
-  ],
-  "doi": "https://doi.org/10.25493/YSY5-YR5",
-  "id": "bee280cc-8184-4380-a2cb-a74b131de611",
-  "associated_paper_volume": "17",
-  "associated_paper_issue": null,
-  "associated_paper_pagination": "e1008114",
-  "journal": "PLOS Computational Biology",
-  "license": "Creative Commons Attribution-NonCommercial 4.0 International",
-  "live_paper_title": "HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data",
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-  "modified_date": "2021-10-13T16:35:38.436000+00:00",
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-      "description": "The following Jupyter notebooks help demonstrate how the tests available in HippoUnit can be used to validate computational models.",
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-      "title": "Jupyter Notebooks",
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-  "resources_description": "All info related to models, tests used in the paper are available at the links provided below, grouped into the following categories:",
-  "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008114",
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diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/c1573aeb-d139-42a2-a7fc-fd68319e428e.json b/validation_service_api/validation_service/tests/test_data/livepapers/c1573aeb-d139-42a2-a7fc-fd68319e428e.json
deleted file mode 100644
index 6519ccd8..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/c1573aeb-d139-42a2-a7fc-fd68319e428e.json
+++ /dev/null
@@ -1,1312 +0,0 @@
-{
-  "abstract": "The peak conductance of many ion channel types measured in any given animal is highly variable across neurons, both within and between neuronal populations. The current view is that this occurs  because a neuron needs to adapt its intrinsic electrophysiological properties either to maintain the same operative range in the presence of abnormal inputs or to compensate for the effects of  pathological conditions. Limited experimental and modeling evidence suggests this might be  implemented via the correlation and/or degeneracy in the function of multiple types of conductances. To study this mechanism in hippocampal CA1 neurons and interneurons, we systematically generated a set of morphologically and biophysically accurate models. We then analyzed the ensembles of peak conductance obtained for each model neuron. The results suggest that the set of conductances expressed in the various neuron types may be divided into two groups: one group is responsible for the major characteristics of the firing behavior in each population and the other more involved with  degeneracy. These models provide experimentally testable predictions on the combination and relative proportion of the different conductance types that should be present in hippocampal CA1  pyramidal cells and interneurons.",
-  "alias": "2018-migliore-et-al",
-  "approved_author": {
-    "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-    "firstname": "Luca L.",
-    "lastname": "Bologna"
-  },
-  "associated_paper_doi": "https://doi.org/10.1371/journal.pcbi.1006423",
-  "associated_paper_title": "The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow",
-  "authors": [
-    {
-      "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-      "firstname": "Rosanna",
-      "lastname": "Migliore"
-    },
-    {
-      "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-      "firstname": "Carmen A.",
-      "lastname": "Lupascu"
-    },
-    {
-      "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-      "firstname": "Luca L.",
-      "lastname": "Bologna"
-    },
-    {
-      "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland",
-      "firstname": "Armando",
-      "lastname": "Romani"
-    },
-    {
-      "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland",
-      "firstname": "Jean-Denis",
-      "lastname": "Courcol"
-    },
-    {
-      "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland",
-      "firstname": "Stefano",
-      "lastname": "Antonel"
-    },
-    {
-      "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland",
-      "firstname": "Werner A.H.",
-      "lastname": "Van Geit"
-    },
-    {
-      "affiliation": "UCL School of Pharmacy, University College London, London, UK",
-      "firstname": "Alex M.",
-      "lastname": "Thomson"
-    },
-    {
-      "affiliation": "UCL School of Pharmacy, University College London, London, UK",
-      "firstname": "Audrey",
-      "lastname": "Mercer"
-    },
-    {
-      "affiliation": "UCL School of Pharmacy, University College London, London, UK; School of Life Sciences, University of Westminster, London, UK",
-      "firstname": "Sigrun",
-      "lastname": "Lange"
-    },
-    {
-      "affiliation": "UCL School of Pharmacy, University College London, London, UK",
-      "firstname": "Joanne",
-      "lastname": "Falck"
-    },
-    {
-      "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland",
-      "firstname": "Christian A.",
-      "lastname": "R\u00f6ssert"
-    },
-    {
-      "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland",
-      "firstname": "Ying",
-      "lastname": "Shi"
-    },
-    {
-      "affiliation": "Laboratory of Neural Microcircuitry (LNMC), Brain Mind Institute, EPFL, Lausanne, Switzerland",
-      "firstname": "Olivier",
-      "lastname": "Hagens"
-    },
-    {
-      "affiliation": "Laboratory of Neural Microcircuitry (LNMC), Brain Mind Institute, EPFL, Lausanne, Switzerland",
-      "firstname": "Maurizio",
-      "lastname": "Pezzoli"
-    },
-    {
-      "affiliation": "Institute of Experimental Medicine, Budapest, Hungary; Faculty of Information Technology and Bionics, P\u00e1zm\u00e1ny P\u00e9ter Catholic University, Budapest, Hungary",
-      "firstname": "Tamás F.",
-      "lastname": "Freund"
-    },
-    {
-      "affiliation": "Institute of Experimental Medicine, Budapest, Hungary; Faculty of Information Technology and Bionics, P\u00e1zm\u00e1ny P\u00e9ter Catholic University, Budapest, Hungary",
-      "firstname": "Szabolcs",
-      "lastname": "Kali"
-    },
-    {
-      "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland",
-      "firstname": "Eilif B.",
-      "lastname": "Muller"
-    },
-    {
-      "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland",
-      "firstname": "Felix",
-      "lastname": "Sch\u00fcrmann"
-    },
-    {
-      "affiliation": "Blue Brain Project, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Geneva, Switzerland",
-      "firstname": "Henry",
-      "lastname": "Markram"
-    },
-    {
-      "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-      "firstname": "Michele",
-      "lastname": "Migliore"
-    }
-  ],
-  "citation": "Rosanna Migliore, Carmen A. Lupascu, Luca L. Bologna, Armando Romani, Jean-Denis Courcol, Stefano Antonel, Werner A.H. Van Geit, Alex M. Thomson, Audrey Mercer, Sigrun Lange, Joanne Falck, Christian A. Rössert, Ying Shi, Olivier Hagens, Maurizio Pezzoli, Tamás F. Freund, Szabolcs Kali, Eilif B. Muller, Felix Schürmann, Henry Markram & Michele Migliore (2018). The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow. PLOS Computational Biology, 14: e1006423.",
-  "collab_id": "livepapers",
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-      "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-      "firstname": "Rosanna",
-      "lastname": "Migliore"
-    }
-  ],
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-      "firstname": "Luca L.",
-      "lastname": "Bologna"
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-  "associated_paper_pagination": "e1006423",
-  "associated_paper_volume": "14",
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-  "journal": "PLOS Computational Biology",
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-  "live_paper_title": "The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow",
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A reduced self-consistent set of files needed to reproduce Fig.4A of the paper is available on\n    ModelDB.\n    
\n    
Use the BSP Neuron As A Service (NaaS) tool to do in silico experiments with the single cell models shown in Fig.4A:\n    
\n    > Start by clicking on any button below to run a simulation.\n    
\n    > After entering the NaaS page, click on the \"Simulation\" tab, adjust the current strength with the appropriate value, and run the simulation by clicking the \"Start simulation\" button.\n
\n
\n    \n        \n        \n            \n            \n            \n            \n        \n        \n            \n            \n            \n            \n        \n        \n    
\n                \n            \n                \n            \n                \n            \n                \n                \n                \n            
\n                \n                \n                \n            \n                \n            \n                \n            \n                \n            
\n
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-      ],
-      "description": "The optimization results for each morphology are individually available in the Model Catalog at the following links:",
-      "icon": "local_play",
-      "order": 3,
-      "title": "Optimizations",
-      "type": "section_models"
-    }
-  ],
-  "resources_description": "Data and models: all data and models used in the paper are available at the links reported below, grouped into the following categories:    ",
-  "url": "https://doi.org/10.1371/journal.pcbi.1006423",
-  "version": "1",
-  "year": "2018-01-01"
-}
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/cb7e5f66-0984-4f0c-acad-6281be4bb5c9.json b/validation_service_api/validation_service/tests/test_data/livepapers/cb7e5f66-0984-4f0c-acad-6281be4bb5c9.json
deleted file mode 100644
index 49bc9f7b..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/cb7e5f66-0984-4f0c-acad-6281be4bb5c9.json
+++ /dev/null
@@ -1,81 +0,0 @@
-{
-    "abstract": "Across the mammalian nervous system, neurotrophins control synaptic plasticity, neuromodulation, and neuronal growth. The neurotrophin Brain-Derived Neurotrophic Factor (BDNF) is known to promote structural and functional synaptic plasticity in the hippocampus, the cerebral cortex, and many other brain areas. In recent years, a wealth of data has been accumulated revealing the paramount importance of BDNF for neuronal function. BDNF signaling gives rise to multiple complex signaling pathways that mediate neuronal survival and differentiation during development, and formation of new memories. These different roles of BDNF for neuronal function have essential consequences if BDNF signaling in the brain is reduced. Thus, BDNF knockout mice or mice that are deficient in BDNF receptor signaling via TrkB and p75 receptors show deficits in neuronal development, synaptic plasticity, and memory formation. Accordingly, BDNF signaling dysfunctions are associated with many neurological and neurodegenerative conditions including Alzheimer's and Huntington's disease. However, despite the widespread implications of BDNF-dependent signaling in synaptic plasticity in healthy and pathological conditions, the interplay of the involved different biochemical pathways at the synaptic level remained mostly unknown. In this paper, we investigated the role of BDNF/TrkB signaling in spike-timing dependent plasticity (STDP) in rodent hippocampus CA1 pyramidal cells, by implementing the first subcellular model of BDNF regulated, spike timing-dependent long-term potentiation (t-LTP). The model is based on previously published experimental findings on STDP and accounts for the observed magnitude, time course, stimulation pattern and BDNF-dependence of t-LTP. It allows interpreting the main experimental findings concerning specific biomolecular processes, and it can be expanded to take into account more detailed biochemical reactions. The results point out a few predictions on how to enhance LTP induction in such a way to rescue or improve cognitive functions under pathological conditions.",
-    "alias": "2019-solinas-et-al",
-    "approved_author": {
-        "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-        "firstname": "Michele",
-        "lastname": "Migliore"
-    },
-    "associated_paper_doi": "https://doi.org/10.1371/journal.pcbi.1006975",
-    "associated_paper_title": "A kinetic model for Brain-Derived Neurotrophic Factor mediated spike timing-dependent LTP",
-    "associated_paper_issue": null,
-    "associated_paper_pagination": "e1006975",
-    "associated_paper_volume": "15",
-    "authors": [
-        {
-            "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy; Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland",
-            "firstname": "Sergio M.G.",
-            "lastname": "Solinas"
-        },
-        {
-            "affiliation": "Institute of Physiology, Otto-von-Guericke-University, Medical Faculty, Magdeburg, Germany; Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany",
-            "firstname": "Elke",
-            "lastname": "Edelmann"
-        },
-        {
-            "affiliation": "Institute of Physiology, Otto-von-Guericke-University, Medical Faculty, Magdeburg, Germany; Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany",
-            "firstname": "Volkmar",
-            "lastname": "Le\u00dfmann"
-        },
-        {
-            "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Michele",
-            "lastname": "Migliore"
-        }
-    ],
-    "citation": "Sergio M.G. Solinas, Elke Edelmann, Volkmar Leßmann & Michele Migliore (2019). A kinetic model for Brain-Derived Neurotrophic Factor mediated spike timing-dependent LTP. PLOS Computational Biology, 15: e1006975.",
-    "collab_id": "livepapers",
-    "corresponding_author": [
-        {
-            "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Michele",
-            "lastname": "Migliore"
-        }
-    ],
-    "created_author": [
-        {
-            "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Michele",
-            "lastname": "Migliore"
-        }
-    ],
-    "doi": "https://doi.org/10.25493/JA04-HTW",
-    "id": "cb7e5f66-0984-4f0c-acad-6281be4bb5c9",
-    "journal": "PLOS Computational Biology",
-    "license": "Creative Commons Attribution-NonCommercial 4.0 International",
-    "live_paper_title": "A kinetic model for Brain-Derived Neurotrophic Factor mediated spike timing-dependent LTP",
-    "lp_tool_version": "0.1",
-    "modified_date": "2021-08-03T09:34:24.942000+00:00",
-    "resources": [
-        {
-            "data": [],
-            "description": "
\n   All electrophysiological experimental data were taken from\n   \n   Edelmann et al. 2015, Neuron.\n   \n
",
-            "icon": "settings_input_antenna",
-            "order": 0,
-            "title": "Data",
-            "type": "section_custom"
-        },
-        {
-            "data": [],
-            "description": "\n
\n
\n  
\n    A reduced self-consistent set of files needed to reproduce Fig.3A,3B\n    and Fig.4A,4B of the paper is available on\n    ModelDB\n    
\n    
\n    The model shows how the back-propagating action potentials in the oblique\n    dendrites of CA1 neurons collide at the spines with a locally elicited EPSP\n    to trigger the release of a retrograde messenger or Brain-Derived\n    Neurotrophic Factor inducing the late expression of spike timing-dependent\n    plasticity.
\n    
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Set defaults
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\n            \n              Run\n            \n          
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\n          Please try again in a few seconds\n        
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",
-            "icon": "note_add",
-            "order": 1,
-            "title": "ModelDB link and test simulations",
-            "type": "section_custom"
-        }
-    ],
-    "resources_description": "Model and data: the model and the data used in the paper are available at the links reported in the sections below",
-    "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006975",
-    "version": "1",
-    "year": "2019-04-24"
-}
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/cbafb007-ff16-4171-b3eb-d97de7069c76.json b/validation_service_api/validation_service/tests/test_data/livepapers/cbafb007-ff16-4171-b3eb-d97de7069c76.json
deleted file mode 100644
index c8ef2f4e..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/cbafb007-ff16-4171-b3eb-d97de7069c76.json
+++ /dev/null
@@ -1,128 +0,0 @@
-{
-    "abstract": "The Golgi cells are the main inhibitory interneurons of the cerebellar granular layer. Although recent works have highlighted the complexity of their dendritic organization and synaptic inputs, the mechanisms through which these neurons integrate complex input patterns remained unknown. Here we have used 8 detailed morphological reconstructions to develop multicompartmental models of Golgi cells, in which Na, Ca, and K channels were distributed along dendrites, soma, axonal initial segment and axon. The models faithfully reproduced a rich pattern of electrophysiological and pharmacological properties and predicted the operating mechanisms of these neurons. Basal dendrites turned out to be more tightly electrically coupled to the axon initial segment than apical dendrites. During synaptic transmission, parallel fibers caused slow Ca-dependent depolarizations in apical dendrites that boosted the axon initial segment encoder and Na-spike backpropagation into basal dendrites, while inhibitory synapses effectively shunted backpropagating currents. This oriented dendritic processing set up a coincidence detector controlling voltage-dependent NMDA receptor unblock in basal dendrites, which, by regulating local calcium influx, may provide the basis for spike-timing dependent plasticity anticipated by theory.",
-    "alias": "2020-masoli-et-al-b",
-    "approved_author": {
-        "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy",
-        "firstname": "Egidio",
-        "lastname": "D'Angelo"
-    },
-    "associated_paper_doi": "https://doi.org/10.1371/journal.pcbi.1007937",
-    "associated_paper_title": "Cerebellar Golgi cell models predict dendritic processing and mechanisms of synaptic plasticity",
-    "associated_paper_volume": "16",
-    "associated_paper_issue": null,
-    "associated_paper_pagination": "e1007937",
-    "authors": [
-        {
-            "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy",
-            "firstname": "Stefano",
-            "lastname": "Masoli"
-        },
-        {
-            "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy",
-            "firstname": "Alessandra",
-            "lastname": "Ottaviani"
-        },
-        {
-            "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy",
-            "firstname": "Egidio",
-            "lastname": "D'Angelo"
-        }
-    ],
-    "citation": "Stefano Masoli, Alessandra Ottaviani & Egidio D'Angelo (2020). Cerebellar Golgi cell models predict dendritic processing and mechanisms of synaptic plasticity. PLOS Computational Biology, 16: e1007937.",
-    "collab_id": "livepapers",
-    "corresponding_author": [
-        {
-            "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy",
-            "firstname": "Egidio",
-            "lastname": "D'Angelo"
-        }
-    ],
-    "created_author": [
-        {
-            "affiliation": "Department of Brain and Behavioral Sciences, University of Pavia, Italy; Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy",
-            "firstname": "Egidio",
-            "lastname": "D'Angelo"
-        }
-    ],
-    "doi": "https://doi.org/10.25493/FA9E-CM0",
-    "id": "cbafb007-ff16-4171-b3eb-d97de7069c76",
-    "journal": "PLOS Computational Biology",
-    "license": "Creative Commons Attribution-NonCommercial 4.0 International",
-    "live_paper_title": "Cerebellar Golgi cell models predict dendritic processing and mechanisms of synaptic plasticity",
-    "lp_tool_version": "0.1",
-    "modified_date": "2021-08-10T07:35:45.351000+00:00",
-    "resources": [
-        {
-            "data": [
-                {
-                    "identifier": "8ba7dda4-cb3d-4c5e-8e97-78026c5dcb6c",
-                    "label": "Characterization of spatial distribution of activity in cerebellar cortical slices",
-                    "type": "URL",
-                    "url": "https://kg.ebrains.eu/search/?facet_type[0]=Dataset&q=%22golgi%20cells%22#Dataset/08cb7b89-9fd1-4374-a845-667d079daddf",
-                    "view_url": "https://kg.ebrains.eu/search/?facet_type[0]=Dataset&q=%22golgi%20cells%22#Dataset/08cb7b89-9fd1-4374-a845-667d079daddf"
-                },
-                {
-                    "identifier": "0e59cccb-7ccc-4311-86f8-2407682e0f7a",
-                    "label": "Investigation of spatial distribution of excitation in cerebellar cortical slices",
-                    "type": "URL",
-                    "url": "https://kg.ebrains.eu/search/?facet_type[0]=Dataset&q=%22golgi%20cells%22#Dataset/071117f4-3d05-400b-8e2a-696adb93346f",
-                    "view_url": "https://kg.ebrains.eu/search/?facet_type[0]=Dataset&q=%22golgi%20cells%22#Dataset/071117f4-3d05-400b-8e2a-696adb93346f"
-                },
-                {
-                    "identifier": "0ed8c37b-8e64-4854-85ed-907f99a7c900",
-                    "label": "Recordings of cerebellar neuronal firing induced by currents steps",
-                    "type": "URL",
-                    "url": "https://kg.ebrains.eu/search/?facet_type[0]=Dataset&q=%22golgi%20cells%22#Dataset/5e245cf67a42a14a56bc43913f7bf28a",
-                    "view_url": "https://kg.ebrains.eu/search/?facet_type[0]=Dataset&q=%22golgi%20cells%22#Dataset/5e245cf67a42a14a56bc43913f7bf28a"
-                },
-                {
-                    "identifier": "5b604601-aa75-4e80-afca-376d19efa08f",
-                    "label": "Recordings of spontaneous firing of cerebellar interneurons (Golgi cells)",
-                    "type": "URL",
-                    "url": "https://kg.ebrains.eu/search/?facet_type[0]=Dataset&q=%22golgi%20cells%22#Dataset/4197a2f5d71432349fb37ff3ad429580",
-                    "view_url": "https://kg.ebrains.eu/search/?facet_type[0]=Dataset&q=%22golgi%20cells%22#Dataset/4197a2f5d71432349fb37ff3ad429580"
-                },
-                {
-                    "identifier": "1aefb236-13a0-4b48-abef-45795a97bc7c",
-                    "label": "Whole cell patch-clamp recordings of cerebellar Golgi cells",
-                    "type": "URL",
-                    "url": "https://kg.ebrains.eu/search/?facet_type[0]=Dataset&q=%22golgi%20cells%22#Dataset/17196b79-04db-4ea4-bb69-d20aab6f1d62",
-                    "view_url": "https://kg.ebrains.eu/search/?facet_type[0]=Dataset&q=%22golgi%20cells%22#Dataset/17196b79-04db-4ea4-bb69-d20aab6f1d62"
-                }
-            ],
-            "description": "Electrophysiological data available in the HBP Knowledge Graph and are listed below:",
-            "icon": "settings_input_antenna",
-            "order": 0,
-            "title": "Electrophysiological data",
-            "type": "section_generic"
-        },
-        {
-            "data": [
-                {
-                    "identifier": "a7c79241-49d5-4356-98a7-9a9c21a8eecc",
-                    "label": "Golgi cell morphology 1",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/cerebellum_circuits/BlueNaaS_models/live_paper_GoC/pair-140514-C2-1_split_1.asc",
-                    "view_url": null
-                }
-            ],
-            "description": "The morphology for the dendrites and the soma is in .asc format. Modified from neuromorpho.org",
-            "icon": "settings_input_antenna",
-            "order": 1,
-            "title": "Morphologies",
-            "type": "section_morphology"
-        },
-        {
-            "data": [],
-            "description": "

Use the BSP Neuron As A Service (NaaS) tool to do in silico experiments with the single cell models shown in Fig. 1C,1D,3A,3B of the paper:\n   
\n   > Start by clicking on any button below to run a simulation.\n   
\n   > After entering the NaaS page, click on the \"Simulation\" tab, adjust the current strength with the appropriate value, and run the simulation by clicking the \"Start simulation\" button.\n   
\n   > To correctly run a simulation, the temperature need to be set at 32\u00b0, the vinit at -70mV and, to match the paper experiments, and fixed time step at 0.025.  Valid positive current injections are from 0.1 to 1nA. \n
\n
\n   \n      \n         \n            \n         \n      \n   
\n               \n            
\n
",
-            "icon": "note_add",
-            "order": 2,
-            "title": "Cerebellar Golgi cells simulation with BlueNaaS",
-            "type": "section_custom"
-        }
-    ],
-    "resources_description": null,
-    "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007937",
-    "version": "1",
-    "year": "2020-12-30"
-}
\ No newline at end of file
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/cf895d83-49b8-4c72-b1ac-8b974bbe4eb5.json b/validation_service_api/validation_service/tests/test_data/livepapers/cf895d83-49b8-4c72-b1ac-8b974bbe4eb5.json
deleted file mode 100644
index 73e0d0b7..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/cf895d83-49b8-4c72-b1ac-8b974bbe4eb5.json
+++ /dev/null
@@ -1,118 +0,0 @@
-{
-    "abstract": "Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, \u03c4, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the \u03c4RAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce \u03c4. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating \u03c4 and to decipher the molecular features leading to longer \u03c4 values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of \u03c4 for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of \u03c4. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the \u03c4RAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization.",
-    "alias": "2019-kokh-et-al",
-    "approved_author": {
-        "affiliation": "Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany",
-        "firstname": "Daria B.",
-        "lastname": "Kokh"
-    },
-    "associated_paper_doi": "https://doi.org/10.3389/fmolb.2019.00036",
-    "associated_paper_title": "Machine Learning Analysis of \u03c4RAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times",
-    "authors": [
-        {
-            "affiliation": "Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany",
-            "firstname": "Daria B.",
-            "lastname": "Kokh"
-        },
-        {
-            "affiliation": "Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany; Department of Biosciences, Heidelberg University, Germany",
-            "firstname": "Tom",
-            "lastname": "Kaufmann"
-        },
-        {
-            "affiliation": "Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany; Department of Biosciences, Heidelberg University, Germany",
-            "firstname": "Bastian",
-            "lastname": "Kister"
-        },
-        {
-            "affiliation": "Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany; Zentrum f\u00fcr Molekulare Biologie der Universit\u00e4t Heidelberg, DKFZ-ZMBH Alliance, Heidelberg University, Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany; Department of Physics, Heidelberg University, Heidelberg, Germany",
-            "firstname": "Rebecca C.",
-            "lastname": "Wade"
-        }
-    ],
-    "citation": "Daria B. Kokh, Tom Kaufmann, Bastian Kister & Rebecca C. Wade (2019). Machine Learning Analysis of τRAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times. Frontiers in Molecular Biosciences, 6: 36.",
-    "collab_id": "livepapers",
-    "corresponding_author": [
-        {
-            "affiliation": "Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany; Zentrum f\u00fcr Molekulare Biologie der Universit\u00e4t Heidelberg, DKFZ-ZMBH Alliance, Heidelberg University, Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany; Department of Physics, Heidelberg University, Heidelberg, Germany",
-            "firstname": "Rebecca C.",
-            "lastname": "Wade"
-        },
-        {
-            "affiliation": "Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany",
-            "firstname": "Daria B.",
-            "lastname": "Kokh"
-        }
-    ],
-    "created_author": [
-        {
-            "affiliation": "Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany",
-            "firstname": "Daria B.",
-            "lastname": "Kokh"
-        }
-    ],
-    "doi": "https://doi.org/10.25493/HC1Y-ZQ8",
-    "id": "cf895d83-49b8-4c72-b1ac-8b974bbe4eb5",
-    "associated_paper_volume": "6",
-    "associated_paper_issue": null,
-    "associated_paper_pagination": "36",
-    "journal": "Frontiers in Molecular Biosciences",
-    "license": "Creative Commons Attribution-NonCommercial 4.0 International",
-    "live_paper_title": "Machine Learning Analysis of \u03c4RAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times",
-    "lp_tool_version": "0.1",
-    "modified_date": "2021-09-29T11:48:13.484000+00:00",
-    "resources": [
-        {
-            "data": [],
-            "description": "\n\n\n\n
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Representative 3D experimental structural data:
\n
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\n               Complex of HSP90 with a resorcinol-type compound bound to loop-type conformation (shown in Fig.1A)\n            \n               PDB
5J2X\n            		\n               \n               archive
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View\n               \n            
\n               Complex of HSP90 with a recorcinol-type compound bound to the helix-type conformation of the binding site (shown in Fig.1B)\n            \n               PDB
5J9X\n            		\n               \n               archive
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\n            Complex of HSP90 with a tricyclic compound bound to helix-type conformation of the binding site (shown in Fig.1D)\n         \n            PDB
2YKI\n         		\n            \n            archive
Download\n            \n         		\n            \n            3d_rotation
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\n

\n
1D/2D structure data of molecular compound used in the study:
\n
\n   \n      \n         \n            \n            \n         \n         \n         \n         \n      \n   
\n               1D structures (SMILES format):\n            \n               \n               archive
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\n            2D structures (sdf format):\n         \n            \n            archive
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\n
",
-            "icon": "settings_input_antenna",
-            "order": 0,
-            "title": "Structural data",
-            "type": "section_custom"
-        },
-        {
-            "data": [
-                {
-                    "identifier": "933a6249-955f-401e-b722-aa8a7a65d683",
-                    "label": " Experimental Kinetics data",
-                    "type": "URL",
-                    "url": "https://object.cscs.ch/v1/AUTH_c0a333ecf7c045809321ce9d9ecdfdea/EBRAINS_live_papers/2019_kokh_et_al/data/exp-kinetics.xlsx",
-                    "view_url": null
-                }
-            ],
-            "description": "Kinetic rates of protein-ligand binding:\n

\nReferences:
\n\n[1]   Amaral, M., Kokh, D. B., Bomke, J., Wegener, A., Buchstaller, H. P., Eggenweiler, H. M., et al. (2017). Protein conformational flexibility modulates kinetics and thermodynamics of drug binding. Nat. Commun. 8, 2276.\n
\n[2]   Kokh, D., Amaral, M., Bomke, J., Graedler, U., Musil, D., Buchstaller, H., et al. (2018). Estimation of Drug-Target Residence Times by \u03c4- Random Acceleration Molecular Dynamics Simulations. J. Chem. Theory Comput 14, 3859\u20133869.\n
\n[3]   Schuetz, D. A., Richter, L., Grandits, M., Graedler, U., Buchstaller, H., Eggenweiler, H., et al. (2018b). Ligand Desolvation steers on-rate and impacts Drug Residence Time of Heat shock protein 90 ( Hsp90 ) Inhibitors. J. Med. Chem. 90, 4397\u20134411.",
-            "icon": "timeline",
-            "order": 1,
-            "title": "Experimental data",
-            "type": "section_generic"
-        },
-        {
-            "data": [],
-            "description": "\n\n\n\n
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    Exploring clustering of dissociation trajectories by the overall protein-ligand contact similarity
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    (Fig. 5)    \n                         		\n                        \n                        \n                        \n                        
    \n                        Claster composition\n                         		\n                        Helix-binding compounds (PDB 5J9X)\n                     \n                        \n                        3d_rotation
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    (Fig. 6)    \n                         		\n                        \n                        \n                        \n                        
    \n                        Claster composition\n                         		\n                        Indazole-type compounds  (PDB 5LNZ)\n                     \n                        \n                        3d_rotation
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",
-            "icon": "repeat",
-            "order": 2,
-            "title": "Models",
-            "type": "section_custom"
-        },
-        {
-            "data": [
-                {
-                    "identifier": "4e7ab210-55f3-4ba0-a820-005b3bf95881",
-                    "label": " Zenodo archive",
-                    "type": "URL",
-                    "url": "https://zenodo.org/record/3240554#.XWaCim5uKbg",
-                    "view_url": "https://zenodo.org/record/3240554#.XWaCim5uKbg"
-                }
-            ],
-            "description": "Complete archive incuding source codes and data:",
-            "icon": "note_add",
-            "order": 3,
-            "title": "Source code and data archive",
-            "type": "section_generic"
-        }
-    ],
-    "resources_description": "Here you can find the data used and generated in this work, along with the visualization of the main results produced.",
-    "url": "https://doi.org/10.3389/fmolb.2019.00036",
-    "version": "1",
-    "year": "2019-05-24"
-}
\ No newline at end of file
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/d36d1804-5b02-4e9b-bfbd-04f0de59686b.json b/validation_service_api/validation_service/tests/test_data/livepapers/d36d1804-5b02-4e9b-bfbd-04f0de59686b.json
deleted file mode 100644
index 6c3d46c2..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/d36d1804-5b02-4e9b-bfbd-04f0de59686b.json
+++ /dev/null
@@ -1,84 +0,0 @@
-{
-    "abstract": "Brain connectivity at the single neuron level can provide fundamental insights into how information is integrated and propagated within and between brain regions. However, it is almost impossible to adequately study this problem experimentally and, despite intense efforts in the field, no mathematical description has been obtained so far. Here, we present a mathematical framework based on a graph-theoretical approach that, starting from experimental data obtained from a few small subsets of neurons, can quantitatively explain and predict the corresponding full network properties. This model also changes the paradigm with which large-scale model networks can be built, from using probabilistic/empiric connections or limited data, to a process that can algorithmically generate neuronal networks connected as in the real system.",
-    "alias": "2020-giacopelli-et-al",
-    "approved_author": {
-        "affiliation": "Department of Mathematics and Informatics, University of Palermo, Italy; Institute of Biophysics, National Research Council, Palermo, Italy",
-        "firstname": "Domenico",
-        "lastname": "Tegolo"
-    },
-    "associated_paper_doi": "https://doi.org/10.1016/j.amc.2020.125150",
-    "associated_paper_title": "Graph-theoretical derivation of brain structural connectivity",
-    "authors": [
-        {
-            "affiliation": "Department of Mathematics and Informatics, University of Palermo, Italy; Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Giuseppe",
-            "lastname": "Giacopelli"
-        },
-        {
-            "affiliation": "Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Michele",
-            "lastname": "Migliore"
-        },
-        {
-            "affiliation": "Department of Mathematics and Informatics, University of Palermo, Italy; Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Domenico",
-            "lastname": "Tegolo"
-        }
-    ],
-    "citation": "Giuseppe Giacopelli, Michele Migliore & Domenico Tegolo (2020). Graph-theoretical derivation of brain structural connectivity. Applied Mathematics and Computation, 377: 125150.",
-    "collab_id": "livepapers",
-    "corresponding_author": [
-        {
-            "affiliation": "Department of Mathematics and Informatics, University of Palermo, Italy; Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Domenico",
-            "lastname": "Tegolo"
-        }
-    ],
-    "created_author": [
-        {
-            "affiliation": "Department of Mathematics and Informatics, University of Palermo, Italy; Institute of Biophysics, National Research Council, Palermo, Italy",
-            "firstname": "Domenico",
-            "lastname": "Tegolo"
-        }
-    ],
-    "doi": "https://doi.org/10.25493/RHR6-3F4",
-    "id": "d36d1804-5b02-4e9b-bfbd-04f0de59686b",
-    "associated_paper_volume": "377",
-    "associated_paper_issue": null,
-    "associated_paper_pagination": "125150",
-    "journal": "Applied Mathematics and Computation",
-    "license": "Creative Commons Attribution-NonCommercial 4.0 International",
-    "live_paper_title": "Graph-theoretical derivation of brain structural connectivity",
-    "lp_tool_version": "0.1",
-    "modified_date": "2021-08-03T12:59:43.240000+00:00",
-    "resources": [
-        {
-            "data": [],
-            "description": "
\n   The connectivity matrices of the Markram's model can be found\n   \n   here.\n   \n   \n
",
-            "icon": "settings_input_antenna",
-            "order": 0,
-            "title": "Data",
-            "type": "section_custom"
-        },
-        {
-            "data": [],
-            "description": "
A reduced self-consistent set of files to reproduce the figures 3B and 5A of the paper is available on\n   ModelDB.",
-            "icon": "note_add",
-            "order": 1,
-            "title": "ModelDB link and test simulation",
-            "type": "section_custom"
-        },
-        {
-            "data": [],
-            "description": "
\n   A Web Application to run the code is available at this link*.\n   
\n   
\n   * in order to access the web application you need an account on the HBP Collaboratory Platform. If you do not have one, please request it by sending an email to: support AT humanbrainproject.eu\n   \n
",
-            "icon": "local_play",
-            "order": 2,
-            "title": "Web Application",
-            "type": "section_custom"
-        }
-    ],
-    "resources_description": "Models and Web App: all the models used in the paper and the Web Application are available at the links reported below:",
-    "url": "https://www.sciencedirect.com/science/article/pii/S0096300320301193",
-    "version": "1",
-    "year": "2020-03-03"
-}
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/f633acd5-cab7-4d33-a9bb-4d167524c861.json b/validation_service_api/validation_service/tests/test_data/livepapers/f633acd5-cab7-4d33-a9bb-4d167524c861.json
deleted file mode 100644
index 12fdb4d9..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/f633acd5-cab7-4d33-a9bb-4d167524c861.json
+++ /dev/null
@@ -1,278 +0,0 @@
-{
-  "lp_tool_version": "0.1",
-  "id": "f633acd5-cab7-4d33-a9bb-4d167524c861",
-  "alias": "2023-muckli-et-al",
-  "version": "v1",
-  "modified_date": "2023-09-25T11:11:24.538000+00:00",
-  "authors": [
-    {
-      "firstname": "Lars",
-      "lastname": "Muckli",
-      "affiliation": "Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, UK; Imaging Centre for Excellence, Queen Elizabeth University Hospital, University of Glasgow, UK"
-    },
-    {
-      "firstname": "Lucy S.",
-      "lastname": "Petro",
-      "affiliation": "Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, UK; Imaging Centre for Excellence, Queen Elizabeth University Hospital, University of Glasgow, UK"
-    },
-    {
-      "firstname": "Clement",
-      "lastname": "Abbatecola",
-      "affiliation": "Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, UK; Imaging Centre for Excellence, Queen Elizabeth University Hospital, University of Glasgow, UK"
-    },
-    {
-      "firstname": "Ahsan",
-      "lastname": "Adeel",
-      "affiliation": "CMI Lab, Department of Computing Science and Mathematics, University of Stirling, UK; Oxford Computational Neuroscience Lab, Nuffield Department of Surgical Sciences, University of Oxford, UK"
-    },
-    {
-      "firstname": "Johanna",
-      "lastname": "Bergmann",
-      "affiliation": "Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany"
-    },
-    {
-      "firstname": "Nicolas",
-      "lastname": "Deperrois",
-      "affiliation": "Department of Physiology, University of Bern, Switzerland; Kirchhoff-Institut f\u00fcr Physik, Heidelberg University, Germany"
-    },
-    {
-      "firstname": "Alain",
-      "lastname": "Destexhe",
-      "affiliation": "Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Universit\u00e9 Paris-Saclay, Saclay, France"
-    },
-    {
-      "firstname": "Nikolaus",
-      "lastname": "Kriegeskorte",
-      "affiliation": "Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA"
-    },
-    {
-      "firstname": "Christiaan N.",
-      "lastname": "Levelt",
-      "affiliation": "Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands; Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, the Netherlands"
-    },
-    {
-      "firstname": "Wolfgang",
-      "lastname": "Maass",
-      "affiliation": "Institute of Theoretical Computer Science, Graz University of Technology, Austria"
-    },
-    {
-      "firstname": "Andrew T.",
-      "lastname": "Morgan",
-      "affiliation": "Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, USA; Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, UK"
-    },
-    {
-      "firstname": "Paolo",
-      "lastname": "Papale",
-      "affiliation": "Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, the Netherlands"
-    },
-    {
-      "firstname": "Cyriel M. A.",
-      "lastname": "Pennartz",
-      "affiliation": "Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, the Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, the Netherlands"
-    },
-    {
-      "firstname": "Benjamin",
-      "lastname": "Peters",
-      "affiliation": "Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, UK"
-    },
-    {
-      "firstname": "Mihai A.",
-      "lastname": "Petrovici",
-      "affiliation": "Department of Physiology, University of Bern, Switzerland; Kirchhoff-Institut f\u00fcr Physik, Heidelberg University, Germany"
-    },
-    {
-      "firstname": "William A.",
-      "lastname": "Phillips",
-      "affiliation": "Department of Psychology, University of Stirling, UK"
-    },
-    {
-      "firstname": "Pieter R.",
-      "lastname": "Roelfsema",
-      "affiliation": "Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW), Amsterdam, the Netherlands; Department of Integrative Neurophysiology, Centre for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands; Department of Neurosurgery, Academic Medical Centre, Amsterdam, the Netherlands; Laboratory of Visual Brain Therapy, Sorbonne Universit\u00e9, INSERM, CNRS, Institut de la Vision, Paris, France"
-    },
-    {
-      "firstname": "Robert N.S.",
-      "lastname": "Sachdev",
-      "affiliation": "Institute of Biology, Humboldt Universit\u00e4t zu Berlin, Germany"
-    },
-    {
-      "firstname": "Koen",
-      "lastname": "Seignette",
-      "affiliation": "Molecular Visual Plasticity Group, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands"
-    },
-    {
-      "firstname": "Matthew W.",
-      "lastname": "Self",
-      "affiliation": "Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW), Amsterdam, the Netherlands"
-    },
-    {
-      "firstname": "Fraser W.",
-      "lastname": "Smith",
-      "affiliation": "School of Psychology, University of East Anglia, Norwich, UK"
-    },
-    {
-      "firstname": "Johan F.",
-      "lastname": "Storm",
-      "affiliation": "Brain Signalling Group, Department of Physiology, Institute of Basic Medicine, University of Oslo, Norway"
-    },
-    {
-      "firstname": "Michele",
-      "lastname": "Svanera",
-      "affiliation": "Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, UK; Imaging Centre for Excellence, Queen Elizabeth University Hospital, University of Glasgow, UK"
-    },
-    {
-      "firstname": "Wim",
-      "lastname": "Vanduffel",
-      "affiliation": "Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Belgium; Leuven Brain Institute, KU Leuven, Belgium; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, USA; Department of Radiology, Harvard Medical School, Boston, USA"
-    },
-    {
-      "firstname": "Walter",
-      "lastname": "Senn",
-      "affiliation": "Department of Physiology, University of Bern, Switzerland; Kirchhoff-Institut f\u00fcr Physik, Heidelberg University, Germany"
-    },
-    {
-      "firstname": "Matthew E.",
-      "lastname": "Larkum",
-      "affiliation": "Institute of Biology, Humboldt Universit\u00e4t zu Berlin, Germany; Neurocure Center for Excellence, Charit\u00e9 Universit\u00e4tsmedizin Berlin & Humboldt Universit\u00e4t, Germany"
-    }
-  ],
-  "corresponding_author": [
-    {
-      "firstname": "Lars",
-      "lastname": "Muckli",
-      "affiliation": "Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, UK; Imaging Centre for Excellence, Queen Elizabeth University Hospital, University of Glasgow, UK"
-    },
-    {
-      "firstname": "Lucy S.",
-      "lastname": "Petro",
-      "affiliation": "Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, UK; Imaging Centre for Excellence, Queen Elizabeth University Hospital, University of Glasgow, UK"
-    }
-  ],
-  "created_author": [
-    {
-      "firstname": "Michele",
-      "lastname": "Svanera",
-      "affiliation": "Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, UK; Imaging Centre for Excellence, Queen Elizabeth University Hospital, University of Glasgow, UK"
-    }
-  ],
-  "approved_author": {
-    "firstname": "Michele",
-    "lastname": "Svanera",
-    "affiliation": "Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, UK; Imaging Centre for Excellence, Queen Elizabeth University Hospital, University of Glasgow, UK"
-  },
-  "year": "2023-09-20",
-  "live_paper_title": "The cortical microcircuitry of predictions and context: a multi-scale perspective",
-  "associated_paper_title": "The cortical microcircuitry of predictions and context: a multi-scale perspective",
-  "journal": null,
-  "url": null,
-  "citation": "Lars Muckli, Lucy S. Petro, Clement Abbatecola, Ahsan Adeel, Johanna Bergmann, Nicolas Deperrois, Alain Destexhe, Nikolaus Kriegeskorte, Christiaan N. Levelt, Wolfgang Maass, Andrew T. Morgan, Paolo Papale, Cyriel M. A. Pennartz, Benjamin Peters, Mihai A. Petrovici, William A. Phillips, Pieter R. Roelfsema, Robert N.S. Sachdev, Koen Seignette, Matthew W. Self, Fraser W. Smith, Johan F. Storm, Michele Svanera, Wim Vanduffel, Walter Senn & Matthew E. Larkum (2023). The cortical microcircuitry of predictions and context: a multi-scale perspective. , .",
-  "doi": null,
-  "associated_paper_doi": "https://doi.org/10.5281/zenodo.8380093",
-  "associated_paper_volume": null,
-  "associated_paper_issue": null,
-  "associated_paper_pagination": null,
-  "abstract": "Conscious cognition depends on the ability of the neocortex to generate internal models of the outside world. During wakefulness, the neocortex maintains and updates knowledge of the world and uses this knowledge through top-down projections to make predictions, test hypotheses, and/or contextualise input from the senses. How are these information streams combined in cortical microcircuits? Is their computational function to test internal models on the basis of their predictions or to contextualise sensory signals, or both? In addition to their somatic integration zones, many pyramidal neurons have a site of top-down and other contextual  information integration near the top of the apical dendrite\u2019s trunk. This architecture enables top-down contextualisation of bottom-up information, amplifying or attenuating sensory responses depending on prior knowledge and current context. However, current deep neural network models of sensory processing lack such a mechanism, and cognitive theories do not reach the explanatory level of intracellular two-compartment integration. In this interactive \u2018live\u2019 paper, we envision how a continued synthesis of multi-scale, multi-species experimental data and theoretical and data-driven models will drive further insights into the biophysics, microcircuitry and dynamics of context-sensitive two-compartment neurons, and their role in predictive cognition.",
-  "license": "GNU General Public License v3.0 or later",
-  "resources_description": null,
-  "collab_id": "livepapers",
-  "resources": [
-    {
-      "order": 0,
-      "type": "section_custom",
-      "title": "Main Figure (with links)",
-      "icon": "panorama",
-      "description": "
\n  To access the Jupyter notebook, simply click on any of the four quadrants\n  below.\n
\n
\n  \n  \n  \n  \n
\n",
-      "data": []
-    },
-    {
-      "order": 1,
-      "type": "section_generic",
-      "title": "Live figure Collabs links",
-      "icon": "live_tv",
-      "description": "Multispecies partial visual occlusion tasks to parameterise contextual feedback responses in non-stimulated primary visual cortex.",
-      "data": [
-        {
-          "url": "https://data.kg.ebrains.eu/zip?container=https://data-proxy.ebrains.eu/api/v1/buckets/live-papers?prefix=2023-muckli-et-al/",
-          "label": "Human and DNN",
-          "view_url": "https://lab.ch.ebrains.eu/hub/user-redirect/lab/tree/shared/The%20cortical%20microcircuitry%20of%20predictions%20and%20context%20-%20A%20multi-scale%20perspective/notebooks/Human%2BDNN.ipynb",
-          "type": "URL",
-          "identifier": "7db8ede7-565e-4999-a8d3-dfc4ef014922"
-        },
-        {
-          "url": "https://data.kg.ebrains.eu/zip?container=https://data-proxy.ebrains.eu/api/v1/buckets/live-papers?prefix=2023-muckli-et-al/",
-          "label": "Mice",
-          "view_url": "https://lab.ch.ebrains.eu/hub/user-redirect/lab/tree/shared/The%20cortical%20microcircuitry%20of%20predictions%20and%20context%20-%20A%20multi-scale%20perspective/notebooks/Mice.ipynb",
-          "type": "URL",
-          "identifier": "434a4827-c192-4afa-a7bc-a6fb58bf7972"
-        },
-        {
-          "url": "https://data.kg.ebrains.eu/zip?container=https://data-proxy.ebrains.eu/api/v1/buckets/live-papers?prefix=2023-muckli-et-al/",
-          "label": "Monkey",
-          "view_url": "https://lab.ch.ebrains.eu/hub/user-redirect/lab/tree/shared/The%20cortical%20microcircuitry%20of%20predictions%20and%20context%20-%20A%20multi-scale%20perspective/notebooks/Monkey.ipynb",
-          "type": "URL",
-          "identifier": "f09d0036-3928-440d-870d-ffd848701629"
-        }
-      ]
-    },
-    {
-      "order": 2,
-      "type": "section_generic",
-      "title": "Data availability",
-      "icon": "folder_open",
-      "description": "The human, monkey and mouse occlusion data are available on EBRAINS to download, or as Jupyter notebooks that can be used to reproduce the experimental data.  Access to this resource requires free user registration at this link.",
-      "data": [
-        {
-          "url": "https://doi.org/10.25493/Z60A-BGY",
-          "label": "Human fMRI",
-          "view_url": null,
-          "type": "URL",
-          "identifier": "95305997-478f-4ec1-8882-d474a1f7debe"
-        },
-        {
-          "url": "https://doi.org/10.25493/NXRY-0W6",
-          "label": "Mice",
-          "view_url": null,
-          "type": "URL",
-          "identifier": "68ec7095-6b22-4980-b91b-d5d08a84fded"
-        },
-        {
-          "url": "https://doi.org/10.25493/KABE-GS0",
-          "label": "Monkey",
-          "view_url": null,
-          "type": "URL",
-          "identifier": "233d7d7c-83c8-4b23-8a2c-6ea7ccc0beff"
-        }
-      ]
-    },
-    {
-      "order": 3,
-      "type": "section_generic",
-      "title": "Model availability",
-      "icon": "videogame_asset",
-      "description": "List of models used in the live paper.",
-      "data": [
-        {
-          "url": "https://doi.org/10.25493/A1WA-5RG",
-          "label": "Adversarial dreaming model",
-          "view_url": null,
-          "type": "URL",
-          "identifier": "cd51b2de-c011-427e-91b4-c2ec9b26c0be"
-        },
-        {
-          "url": "https://doi.org/10.25493/5Z86-K1Z",
-          "label": "Deep Neural Network for Image Completion",
-          "view_url": null,
-          "type": "URL",
-          "identifier": "0339c303-8262-4c68-92ed-7bb73ca925d4"
-        },
-        {
-          "url": "https://search.kg.ebrains.eu/instances/eae6a13b-4006-49df-bdcc-ec27c2409e72",
-          "label": "Recurrent DNN with feedbacks",
-          "view_url": null,
-          "type": "URL",
-          "identifier": "e1a3ae56-3b12-4f42-9b96-b66f00f5a92e"
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file
diff --git a/validation_service_api/validation_service/tests/test_data/livepapers/summary.json b/validation_service_api/validation_service/tests/test_data/livepapers/summary.json
deleted file mode 100644
index 87d0d56d..00000000
--- a/validation_service_api/validation_service/tests/test_data/livepapers/summary.json
+++ /dev/null
@@ -1,350 +0,0 @@
-[
-    {
-        "id": "9c00022b-82be-435e-b23f-bf4ee4cacc28",
-        "detail_path": "/livepapers/9c00022b-82be-435e-b23f-bf4ee4cacc28",
-        "modified_date": "2021-08-10T08:59:29.024000+00:00",
-        "citation": "Guy Eyal, Matthijs B. Verhoog, Guilherme Testa-Silva, Yair Deitcher, Johannes C. Lodder, Ruth Benavides-Piccione, Juan Morales, Javier DeFelipe, Christiaan P.J. De Kock, Huibert D. Mansvelder & Idan Segev (2016). Unique membrane properties and enhanced signal processing in human neocortical neurons. eLife, 5: e16553.",
-        "live_paper_title": "Unique membrane properties and enhanced signal processing in human neocortical neurons",
-        "associated_paper_title": "Unique membrane properties and enhanced signal processing in human neocortical neurons",
-        "year": "2016-10-06",
-        "collab_id": "livepapers",
-        "doi": "https://doi.org/10.25493/WBJ6-RJG",
-        "alias": "2016-eyal-et-al"
-    },
-    {
-        "id": "c1573aeb-d139-42a2-a7fc-fd68319e428e",
-        "detail_path": "/livepapers/c1573aeb-d139-42a2-a7fc-fd68319e428e",
-        "modified_date": "2021-08-10T09:26:55.917000+00:00",
-        "citation": "Rosanna Migliore, Carmen A. Lupascu, Luca L. Bologna, Armando Romani, Jean-Denis Courcol, Stefano Antonel, Werner A.H. Van Geit, Alex M. Thomson, Audrey Mercer, Sigrun Lange, Joanne Falck, Christian A. R\u00f6ssert, Ying Shi, Olivier Hagens, Maurizio Pezzoli, Tam\u00e1s F. Freund, Szabolcs Kali, Eilif B. Muller, Felix Sch\u00fcrmann, Henry Markram & Michele Migliore (2018). The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow. PLOS Computational Biology, 14: e1006423.",
-        "live_paper_title": "The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow",
-        "associated_paper_title": "The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow",
-        "year": "2018-01-01",
-        "collab_id": "livepapers",
-        "doi": "https://doi.org/10.25493/EF9C-ZKU",
-        "alias": "2018-migliore-et-al"
-    },
-    {
-        "id": "42a90bce-d52c-4e55-b6a8-3ec5c14a828f",
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-        "associated_paper_title": "An Adaptive Generalized Leaky Integrate-and-Fire model for hippocampal CA1 pyramidal neurons and interneurons",
-        "year": "2023-01-01",
-        "collab_id": "livepapers",
-        "doi": null,
-        "alias": "an-adaptive-generalized-leaky-integrate-and-fire-model-for-hippocampal-ca1-pyramidal-neurons-and-interneurons"
-    },
-    {
-        "id": "b11ca08c-3fad-4020-85ca-adb2fd58541b",
-        "detail_path": "/livepapers/b11ca08c-3fad-4020-85ca-adb2fd58541b",
-        "modified_date": "2023-09-26T07:33:39.011000+00:00",
-        "citation": "Glenn J.M. van der Lande, Arnau Manasanch, Diana Casas-Torremocha, Leonardo Dalla Porta, Olivia Gosseries, Naji Alnagger, Alice Barra, Jorge F. Mej\u00edas, Rajanikant Panda, Fabio Riefolo, Aurore Thibaut, Vincent Bonhomme, Bertrand Thirion, Francisco Clasca, Pau Gorostiza, Maria Victoria S\u00e1nchez-Vives, Gustavo Deco, Steven Laureys, Gorka Zamora-L\u00f3pez & Jitka Annen (2023). Identification and neuromodulation of brain states to promote recovery of consciousness. Zenodo, : .",
-        "live_paper_title": "Live Paper: Identification and neuromodulation of brain states to promote recovery of consciousness",
-        "associated_paper_title": "Identification and neuromodulation of brain states to promote recovery of consciousness",
-        "year": "2023-01-01",
-        "collab_id": "livepapers",
-        "doi": null,
-        "alias": "2023-vanderlande-et-al"
-    },
-    {
-        "id": "f633acd5-cab7-4d33-a9bb-4d167524c861",
-        "detail_path": "/livepapers/f633acd5-cab7-4d33-a9bb-4d167524c861",
-        "modified_date": "2023-09-25T11:11:24.538000+00:00",
-        "citation": "Lars Muckli, Lucy S. Petro, Clement Abbatecola, Ahsan Adeel, Johanna Bergmann, Nicolas Deperrois, Alain Destexhe, Nikolaus Kriegeskorte, Christiaan N. Levelt, Wolfgang Maass, Andrew T. Morgan, Paolo Papale, Cyriel M. A. Pennartz, Benjamin Peters, Mihai A. Petrovici, William A. Phillips, Pieter R. Roelfsema, Robert N.S. Sachdev, Koen Seignette, Matthew W. Self, Fraser W. Smith, Johan F. Storm, Michele Svanera, Wim Vanduffel, Walter Senn & Matthew E. Larkum (2023). The cortical microcircuitry of predictions and context: a multi-scale perspective. , .",
-        "live_paper_title": "The cortical microcircuitry of predictions and context: a multi-scale perspective",
-        "associated_paper_title": "The cortical microcircuitry of predictions and context: a multi-scale perspective",
-        "year": "2023-09-20",
-        "collab_id": "livepapers",
-        "doi": null,
-        "alias": "2023-muckli-et-al"
-    },
-    {
-        "id": "1c1f53e4-55d1-45a3-a63a-a16c491a07f4",
-        "detail_path": "/livepapers/1c1f53e4-55d1-45a3-a63a-a16c491a07f4",
-        "modified_date": "2023-09-26T15:00:55.441000+00:00",
-        "citation": "Michele Farisco, Kathinka Evers, Jitka Annen, Veronique Baldin, Alessandra Camassa, Benedetta Cecconi, Gustavo Deco, Steven Laureys, Rajanikant Panda, Arnau Manasanch, Maria Victoria S\u00e1nchez-Vives & Gorka Zamora-L\u00f3pez (2023). Advancing the science of consciousness: from ethics to clinical care. , .",
-        "live_paper_title": "Advancing the science of consciousness: from ethics to clinical care",
-        "associated_paper_title": "Advancing the science of consciousness: from ethics to clinical care",
-        "year": "2023-09-23",
-        "collab_id": "livepapers",
-        "doi": null,
-        "alias": "2023-farisco-et-al"
-    }
-]
\ No newline at end of file
diff --git a/validation_service_api/validation_service/tests/test_data_models.py b/validation_service_api/validation_service/tests/test_data_models.py
index c820d8cb..720cbf30 100644
--- a/validation_service_api/validation_service/tests/test_data_models.py
+++ b/validation_service_api/validation_service/tests/test_data_models.py
@@ -3,12 +3,7 @@
 from pydantic import parse_obj_as
 
 sys.path.append(".")
-from validation_service.data_models import (
-    LivePaperDataItem,
-    LivePaperSection,
-    LivePaper,
-    LivePaperSummary,
-)
+
 import validation_service.examples
 
 EXAMPLES = validation_service.examples.EXAMPLES
@@ -42,76 +37,3 @@ def query(
         self, query, filter=None, space=None, from_index=0, size=100, scope="released"
     ):
         return MockKGResult()
-
-
-class TestLivePapers:
-    def test_conversion_to_kg_objects(self):
-        pydantic_obj = parse_obj_as(LivePaper, EXAMPLES["LivePaper"])
-        kg_client = MockKGClient()
-        kg_objects = pydantic_obj.to_kg_objects(kg_client)
-
-    # def test_conversion_from_kg_objects(self):
-
-    # def test_roundtrip(self):
-    #     pydantic_obj = parse_obj_as(LivePaper, EXAMPLES["LivePaper"])
-    #     kg_client = MockKGClient()
-    #     kg_objects = pydantic_obj.to_kg_objects(kg_client)
-
-    #     new_pydantic_obj = LivePaper.from_kg_object(
-    #         lpv=kg_objects["paper"][0],
-    #         lp=kg_objects["paper"][0],  # temporary, to be fixed
-    #         kg_client=kg_client)
-
-
-class TestLivePaperSummary:
-    def test_from_kg_query(self):
-        test_data = {
-            "@context": {"@vocab": "https://schema.hbp.eu/myQuery/"},
-            "alias": "2020-mccauley-et-al",
-            "versions": [
-                {
-                    "modified_date": "2021-08-05T09:07:05.443000+00:00",
-                    "lp_doi": "https://doi.org/10.25493/5MAD-5WQ",
-                    "related_publication": {
-                        "doi": "https://doi.org/10.1016/j.celrep.2020.108255",
-                        "citation_data": {
-                            "title": "Circadian Modulation of Neurons and Astrocytes Controls Synaptic Plasticity in Hippocampal Area CA1",
-                            "pagination": "108255",
-                            "is_part_of": {
-                                "volume_number": "33",
-                                "is_part_of": {"journal": "Cell Reports"},
-                            },
-                            "authors": [
-                                {"given_name": "John P.", "family_name": "McCauley"},
-                                {
-                                    "given_name": "Maurice A.",
-                                    "family_name": "Petroccione",
-                                },
-                                {"given_name": "Annalisa", "family_name": "Scimemi"},
-                            ],
-                            "publication_date": "2020-10-01T00:00:00",
-                        },
-                    },
-                    "name": "Circadian Modulation of Neurons and Astrocytes Controls Synaptic Plasticity in Hippocampal Area CA1",
-                }
-            ],
-            "name": "Circadian Modulation of Neurons and Astrocytes Controls Synaptic Plasticity in Hippocampal Area CA1",
-            "id": "https://kg.ebrains.eu/api/instances/203d1466-8792-4b05-b546-09ee178387c3",
-            "space": "livepapers",
-        }
-        pydantic_obj = LivePaperSummary.from_kg_query(
-            item=test_data, client=MockKGClient()
-        )
-        assert json.loads(pydantic_obj.json()) == {
-            "alias": "2020-mccauley-et-al",
-            "associated_paper_title": "Circadian Modulation of Neurons and Astrocytes Controls Synaptic Plasticity in Hippocampal Area CA1",
-            "citation": "John P. McCauley, Maurice A. Petroccione & Annalisa Scimemi (2020). Circadian Modulation of Neurons and Astrocytes Controls Synaptic Plasticity in Hippocampal Area CA1. Cell Reports, 33: 108255.",
-            "collab_id": "livepapers",
-            "detail_path": "/livepapers/203d1466-8792-4b05-b546-09ee178387c3",
-            "doi": "https://doi.org/10.25493/5MAD-5WQ",
-            "id": "203d1466-8792-4b05-b546-09ee178387c3",
-            "live_paper_title": "Circadian Modulation of Neurons and Astrocytes Controls "
-            "Synaptic Plasticity in Hippocampal Area CA1",
-            "modified_date": "2021-08-05T09:07:05.443000+00:00",
-            "year": "2020-10-01",
-        }
diff --git a/validation_service_api/validation_service/tests/test_livepapers.py b/validation_service_api/validation_service/tests/test_livepapers.py
deleted file mode 100644
index b7e67f68..00000000
--- a/validation_service_api/validation_service/tests/test_livepapers.py
+++ /dev/null
@@ -1,225 +0,0 @@
-from glob import glob
-import json
-import os
-from time import sleep
-from uuid import UUID
-import logging
-
-import pytest
-from .fixtures import client, token, AUTH_HEADER, _build_sample_live_paper
-
-
-def check_live_paper(output, input, mode="summmary"):
-    keys = [
-        "alias",
-        "associated_paper_title",
-        "collab_id",
-        "doi",
-        "id",
-        "live_paper_title",
-        "modified_date",
-        "year",
-    ]  # "citation" <-- to fix
-    if mode == "full":
-        keys += [
-            "abstract",
-            "associated_paper_doi",
-            "journal",
-            "license",
-            "name",
-            "resources_description",
-            "url",
-            "version",
-        ]
-    for key in keys:
-        if key in input:
-            assert output[key] == input[key]
-    if mode == "full":
-        # check authors
-        for key in ("authors", "corresponding_author", "created_author"):
-            assert len(output[key]) == len(input[key])
-            for person_out, person_in in zip(output[key], input[key]):
-                assert person_out["lastname"] == person_in["lastname"]
-                assert person_out["firstname"] == person_in["firstname"]
-                if "affiliation" in person_in:
-                    assert person_out["affiliation"] == person_in["affiliation"]
-        # check resources
-        assert len(output["resources"]) == len(input["resources"])
-        for section_out, section_in in zip(
-            sorted(output["resources"], key=lambda sec: sec["order"]),
-            sorted(input["resources"], key=lambda sec: sec["order"]),
-        ):
-            for key in ("description", "title", "type"):  # , "icon"): <-- todo
-                assert section_out[key] == section_in[key]
-            assert len(section_out["data"]) == len(section_in["data"])
-            for data_item_in, data_item_out in zip(
-                sorted(section_out["data"], key=lambda item: item["label"]),
-                sorted(section_in["data"], key=lambda item: item["label"]),
-            ):
-                for key in ("label", "type", "url", "view_url"):
-                    assert data_item_out[key] == data_item_in[key]
-
-
-def test_create_and_delete_live_paper(caplog):
-    caplog.set_level(logging.DEBUG)
-
-    payload = _build_sample_live_paper()
-    # create
-    response = client.post(f"/livepapers/", json=payload, headers=AUTH_HEADER)
-    assert response.status_code == 201
-    posted_lp = response.json()
-    check_live_paper(posted_lp, payload, mode="summary")
-
-    # check we can retrieve live paper
-    sleep(10)  # need to wait a short time to allow KG to become consistent
-    lp_uuid = posted_lp["id"]
-    response = client.get(f"/livepapers/{lp_uuid}", headers=AUTH_HEADER)
-    assert response.status_code == 200
-    retrieved_lp = response.json()
-    check_live_paper(retrieved_lp, payload, mode="full")
-
-    # delete again
-    response = client.delete(f"/livepapers/{lp_uuid}", headers=AUTH_HEADER)
-    assert response.status_code == 200
-
-    # todo: check lp no longer exists
-    response = client.get(f"/livepapers/{lp_uuid}", headers=AUTH_HEADER)
-    assert response.status_code == 404
-
-
-def test_update_live_paper(caplog):
-    # caplog.set_level(logging.INFO)
-    payload = _build_sample_live_paper()
-    # create
-    response = client.post(f"/livepapers/", json=payload, headers=AUTH_HEADER)
-    assert response.status_code == 201
-    posted_lp = response.json()
-    check_live_paper(posted_lp, payload, mode="summary")
-
-    # make changes
-    changes = {
-        "alias": payload["alias"] + "-changed",
-        "live_paper_title": payload["live_paper_title"] + " (changed)",
-        "corresponding_author": [{"firstname": "Frodo", "lastname": "Baggins"}],
-        "abstract": "The previous description was too short",
-    }
-    # update
-    updated_payload = payload.copy()
-    updated_payload.update(changes)
-    response = client.put(
-        f"/livepapers/{posted_lp['id']}", json=updated_payload, headers=AUTH_HEADER
-    )
-    assert response.status_code == 200
-
-    # get updated
-    response = client.get(f"/livepapers/{posted_lp['id']}", headers=AUTH_HEADER)
-    assert response.status_code == 200
-    updated_lp = response.json()
-    check_live_paper(updated_lp, updated_payload, mode="full")
-
-    # delete everything
-    response = client.delete(f"/livepapers/{posted_lp['id']}", headers=AUTH_HEADER)
-    assert response.status_code == 200
-
-
-def test_minimal_with_existing_author(caplog):
-    payload = {
-        "id": None,
-        "lp_tool_version": "0.2",
-        "standalone": True,
-        "modified_date": "2023-01-26T08:15:19.132000+00:00",
-        "authors": [
-            {"firstname": "Andrew", "lastname": "Davison", "affiliation": "NeuroPSI"}
-        ],
-        "corresponding_author": [
-            {"firstname": "Andrew", "lastname": "Davison", "affiliation": "NeuroPSI"}
-        ],
-        "created_author": [
-            {"firstname": "Andrew", "lastname": "Davison", "affiliation": "NeuroPSI"}
-        ],
-        "approved_author": {
-            "firstname": "Andrew",
-            "lastname": "Davison",
-            "affiliation": "NeuroPSI",
-        },
-        "year": None,
-        "associated_paper_title": None,  # todo: test with empty string "",
-        "live_paper_title": "Minimal example of a live paper for testing",
-        "doi": None,
-        "paper_published": True,
-        "journal": None,
-        "url": None,
-        "citation": None,
-        "associated_paper_doi": None,
-        "abstract": None,
-        "license": "Creative Commons Attribution 4.0 International",
-        "collab_id": "model-validation",
-        "resources_description": "In this article we demonstrate ...",
-        "resources": [],
-    }
-    response = client.post(f"/livepapers/", json=payload, headers=AUTH_HEADER)
-    assert response.status_code == 201
-    posted_lp = response.json()
-    payload["id"] = posted_lp["id"]
-    check_live_paper(posted_lp, payload, mode="summary")
-    # delete again
-    response = client.delete(f"/livepapers/{posted_lp['id']}", headers=AUTH_HEADER)
-
-
-def _get_published_papers():
-    skip = [
-
-    ]
-    file_paths = []
-    this_dir = os.path.dirname(__file__)
-    glob_pattern = os.path.join(this_dir, "test_data/livepapers/*.json")
-    for file_path in glob(glob_pattern):
-        file_label = os.path.basename(file_path).split(".")[0]
-        if file_label not in skip:
-            try:
-                UUID(file_label)
-            except ValueError:
-                pass
-            else:
-                file_paths.append(file_path)
-    return file_paths
-
-
-@pytest.mark.parametrize("file_path", _get_published_papers()) #[("3+5", 8), ("2+4", 6), ("6*9", 42)])
-def test_published_papers(file_path):
-    """For published live papers, check the API gives the expected results."""
-    file_label = os.path.basename(file_path).split(".")[0]
-    lp_uuid = file_label
-    with open(file_path) as fp:
-        expected_lp = json.load(fp)
-
-    response = client.get(f"/livepapers-published/{lp_uuid}", headers=AUTH_HEADER)
-    assert response.status_code == 200
-    retrieved_lp = response.json()
-
-    for key in ("modified_date",):
-        # certain fields should be ignored in the comparison
-        for d in (retrieved_lp, expected_lp):
-            d.pop(key)
-        for resource_key in ("icon",):
-            for d in (retrieved_lp, expected_lp):
-                for item in d["resources"]:
-                    item.pop(resource_key)
-                    # temporary special case
-                    if item["data"] and item["data"][0]["label"] == "control_3-4_months/191129004_S24.abf":
-                        item["data"] = []
-    assert retrieved_lp == expected_lp
-
-
-def test_published_paper_list():
-    """For published live papers, check the API gives the expected results."""
-    this_dir = os.path.dirname(__file__)
-    file_path = f"{this_dir}/test_data/livepapers/summary.json"
-    with open(file_path) as fp:
-        expected = json.load(fp)
-
-    response = client.get(f"/livepapers-published/", headers=AUTH_HEADER)
-    assert response.status_code == 200
-    retrieved = response.json()
-
-    assert retrieved == expected