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data_sheets_schema.yaml
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---
id: https://w3id.org/bridge2ai/data-sheets-schema
name: data-sheets-schema
title: data-sheets-schema
description: |-
A LinkML schema for Datasheets for Datasets.
license: MIT
see_also:
- https://bridge2ai.github.io/data-sheets-schema
prefixes:
biolink: https://w3id.org/biolink/vocab/
csvw: http://www.w3.org/ns/csvw#
data_sheets_schema: https://w3id.org/bridge2ai/data-sheets-schema/
datasets: https://w3id.org/linkml/report
dcat: http://www.w3.org/ns/dcat#
example: https://example.org/
formats: http://www.w3.org/ns/formats/
frictionless: https://specs.frictionlessdata.io/
linkml: https://w3id.org/linkml/
mediatypes: https://www.iana.org/assignments/media-types/
pav: http://purl.org/pav/
schema: http://schema.org/
sh: https://w3id.org/shacl/
skos: http://www.w3.org/2004/02/skos/core#
void: http://rdfs.org/ns/void#
B2AI_TOPIC: https://w3id.org/bridge2ai/b2ai-standards-registry/
B2AI_STANDARD: https://w3id.org/bridge2ai/b2ai-standards-registry/
B2AI_SUBSTRATE: https://w3id.org/bridge2ai/b2ai-standards-registry/
default_prefix: data_sheets_schema
default_range: string
imports:
- linkml:types
- standards_schema
- standards_organization_schema
## TYPES ##
## SUBSETS ##
subsets:
Motivation:
description: >-
The questions in this section are primarily intended to encourage dataset
creators to clearly articulate their reasons for creating the dataset and
to promote transparency about funding interests. The latter may be
particularly relevant for datasets created for research purposes.
Composition:
description: >-
The questions in this section are intended to provide dataset consumers
with the information they need to make informed decisions about using the
dataset for their chosen tasks. Some of the questions are designed to
elicit information about compliance with the EU’s General Data Protection
Regulation (GDPR) or comparable regulations in other jurisdictions.
Collection:
description: >-
The questions in this section are designed to elicit information that may
help researchers and practitioners to create alternative datasets with
similar characteristics.
Preprocessing-Cleaning-Labeling:
description: >-
The questions in this section are intended to provide dataset consumers
with the information they need to determine whether the “raw” data has
been processed in ways that are compatible with their chosen tasks.
Uses:
description: >-
The questions in this section are intended to encourage dataset creators
to reflect on the tasks for which the dataset should and should not be
used.
Distribution:
description: >-
The questions in this section pertain to dataset distribution.
Maintenance:
description: >-
The questions in this section are intended to encourage dataset creators
to plan for dataset maintenance and communicate this plan to dataset
consumers.
## CLASSES ##
classes:
# NamedThing is imported from Bridge2AI standards schema
# Adapted from linkml Datasets schema - see
# https://github.com/linkml/linkml-model/blob/main/linkml_model/model/schema/datasets.yaml
Information:
description: Grouping for datasets and data files
close_mappings:
- schema:CreativeWork
slots:
- compression
- conforms_to
- conforms_to_class
- conforms_to_schema
- created_by
- created_on
- description
- doi
- download_url
- id
- issued
- keywords
- language
- last_updated_on
- license
- modified_by
- page
- publisher
- status
- title
- version
- was_derived_from
# From linkml Datasets schema - see
# https://github.com/linkml/linkml-model/blob/main/linkml_model/model/schema/datasets.yaml
FormatDialect:
description: Additional format information for a file
attributes:
comment_prefix:
delimiter:
double_quote:
header:
quote_char:
slots:
- id
Person:
description: An individual human being.
is_a: NamedThing
attributes:
affiliation:
description: >-
The organization(s) to which the person belongs.
range: Organization
multivalued: true
email:
description: >-
The email address of the person.
range: string
DatasetProperty:
# Individual properties have their own attributes
description: >-
Represents a single property of a dataset, or a set of related properties.
is_a: NamedThing
attributes:
used_software:
description: >-
What software was used as part of this dataset property?
range: Software
multivalued: true
DatasetCollection:
aliases:
- file collection
- dataset collection
- data resource collection
tree_root: true
description: >-
A collection of related datasets, likely containing multiple files
of multiple potential purposes and properties.
exact_mappings:
- dcat:Dataset
close_mappings:
- dcat:Catalog
is_a: Information
attributes:
resources:
range: Dataset
multivalued: true
# TODO: consider how to distinguish between metadata only vs
# instances where data file is present (and we can extract metadata)
Dataset:
aliases:
- data resource
- data file
- data package
class_uri: dcat:Distribution
exact_mappings:
- schema:DataDownload
see_also:
- https://specs.frictionlessdata.io/data-resource
description: >-
A single component of related observations and/or information that can be
read, manipulated, transformed, and otherwise interpreted.
is_a: Information
slots:
- bytes
- dialect
- encoding
- format
- hash
- md5
- media_type
- path
- sha256
attributes:
purposes:
range: Purpose
multivalued: true
tasks:
range: Task
multivalued: true
addressing_gaps:
range: AddressingGap
multivalued: true
creators:
range: Creator
multivalued: true
funders:
range: FundingMechanism
multivalued: true
subsets:
range: DataSubset
multivalued: true
slot_uri: dcat:distribution
exact_mappings:
- schema:distribution
instances:
range: Instance
multivalued: true
anomalies:
range: DataAnomaly
multivalued: true
external_resources:
range: ExternalResource
multivalued: true
confidential_elements:
range: Confidentiality
multivalued: true
content_warnings:
range: ContentWarning
multivalued: true
subpopulations:
range: Subpopulation
multivalued: true
sensitive_elements:
range: SensitiveElement
multivalued: true
acquisition_methods:
range: InstanceAcquisition
multivalued: true
collection_mechanisms:
range: CollectionMechanism
multivalued: true
sampling_strategies:
range: SamplingStrategy
multivalued: true
data_collectors:
range: DataCollector
multivalued: true
collection_timeframes:
range: CollectionTimeframe
multivalued: true
ethical_reviews:
range: EthicalReview
multivalued: true
data_protection_impacts:
range: DataProtectionImpact
multivalued: true
preprocessing_strategies:
range: PreprocessingStrategy
multivalued: true
cleaning_strategies:
range: CleaningStrategy
multivalued: true
labeling_strategies:
range: LabelingStrategy
multivalued: true
raw_sources:
range: RawData
multivalued: true
existing_uses:
range: ExistingUse
multivalued: true
use_repository:
range: UseRepository
multivalued: true
other_tasks:
range: OtherTask
multivalued: true
future_use_impacts:
range: FutureUseImpact
multivalued: true
discouraged_uses:
range: DiscouragedUse
multivalued: true
distribution_formats:
range: DistributionFormat
multivalued: true
distribution_dates:
range: DistributionDate
multivalued: true
license_and_use_terms:
range: LicenseAndUseTerms
ip_restrictions:
range: IPRestrictions
regulatory_restrictions:
range: ExportControlRegulatoryRestrictions
maintainers:
range: Maintainer
multivalued: true
errata:
range: Erratum
multivalued: true
updates:
range: UpdatePlan
retention_limit:
range: RetentionLimits
version_access:
range: VersionAccess
extension_mechanism:
range: ExtensionMechanism
is_deidentified:
range: Deidentification
is_tabular:
range: boolean
DataSubset:
description: >-
A subset of a dataset, likely containing multiple files
of multiple potential purposes and properties.
is_a: Dataset
attributes:
is_data_split:
description: >-
Is this subset a split of the larger dataset,
e.g., is it a set for model training, testing,
or validation?
range: boolean
is_subpopulation:
description: >-
Is this subset a subpopulation of the larger dataset,
e.g., is it a set of data for a specific demographic?
range: boolean
Software:
description: >-
A software program or library.
is_a: NamedThing
attributes:
version:
range: string
license:
range: string
url:
range: string
Purpose:
description: >-
For what purpose was the dataset created?
is_a: DatasetProperty
attributes:
response:
range: string
in_subset:
- Motivation
Task:
description: >-
Was there a specific task in mind for the dataset's application?
is_a: DatasetProperty
attributes:
response:
range: string
in_subset:
- Motivation
AddressingGap:
description: >-
Was there a specific gap that needed to be filled by creation of the
dataset?
is_a: DatasetProperty
attributes:
response:
range: string
in_subset:
- Motivation
Creator:
description: >-
Who created the dataset (e.g., which team, research group) and on behalf
of which entity (e.g., company, institution, organization)?
This may also be considered a team.
is_a: DatasetProperty
attributes:
principal_investigator:
range: Person
affiliation:
# Note that creators have affiliations which may differ
# from that of their members
# Organization is imported from Bridge2AI standards schema
range: Organization
in_subset:
- Motivation
FundingMechanism:
description: >-
Who funded the creation of the dataset? If there is an associated grant,
please provide the name of the grantor and the grant name and number.
is_a: DatasetProperty
attributes:
grantor:
range: Grantor
grant:
range: Grant
in_subset:
- Motivation
Grantor:
# Note this will have a name slot due to being a NamedThing
description: >-
What is the name and/or identifier of the organization providing
monetary support or other resources supporting creation of the dataset?
# Organization is imported from Bridge2AI standards schema
is_a: Organization
Grant:
# Note this will have a name slot due to being a NamedThing
description: >-
What is the name and/or identifier of the specific mechanism providing
monetary support or other resources supporting creation of the dataset?
is_a: NamedThing
attributes:
grant_number: # TODO: formalize this more - probably not a URI though
description: The alphanumeric identifier for the grant.
range: string
Instance:
# Note this does not model the instance itself, but rather the
# description of each instance type or class.
description: >-
What do the instances that comprise the dataset represent (e.g.,
documents, photos, people, countries)?
is_a: DatasetProperty
attributes:
data_topic:
description: >-
What general topic does the data represent?
This should be one of the Bridge2AI standards data topics,
e.g., B2AI_TOPIC:26
range: uriorcurie
values_from:
- B2AI_TOPIC
instance_type:
description: >-
Are there multiple types of instances (e.g., movies, users, and
ratings; people and interactions between them; nodes and edges)?
range: string
data_substrate:
description: >-
What data does each instance consist of? “Raw” data (e.g., unprocessed
text or images) or features? This should be one of the Bridge2AI
standards data substrates, e.g., B2AI_SUBSTRATE:6
range: uriorcurie
values_from:
- B2AI_SUBSTRATE
counts:
description: >-
How many instances are there in total (of each type, if appropriate)?
range: integer
label:
description: >-
Is there a label or target associated with each instance?
range: boolean
label_description:
description: >-
If there is a label or target associated with each instance,
what pattern or format does it follow?
range: string
sampling_strategies:
range: SamplingStrategy
multivalued: true
missing_information:
range: MissingInfo
multivalued: true
in_subset:
- Composition
SamplingStrategy:
description: >-
Does the dataset contain all possible instances or is it a sample (not
necessarily random) of instances from a larger set? If the dataset is a
sample, then what is the larger set? Is the sample representative of the
larger set (e.g., geographic coverage)? If so, please describe how this
representativeness was validated/verified. If it is not representative of
the larger set, please describe why not (e.g., to cover a more diverse
range of instances, because instances were withheld or unavailable).
is_a: DatasetProperty
attributes:
is_sample:
range: boolean
multivalued: true
is_random:
range: boolean
multivalued: true
source_data:
range: string
multivalued: true
is_representative:
range: boolean
multivalued: true
representative_verification:
range: string
multivalued: true
why_not_representative:
range: string
multivalued: true
strategies:
description: >-
If the dataset is a sample from a larger set, what was the sampling
strategy (e.g., deterministic, probabilistic with specific sampling
probabilities)?
range: string
multivalued: true
in_subset:
- Composition
- Collection
MissingInfo:
description: >-
Is any information missing from individual instances? If so, please
provide a description, explaining why this information is missing (e.g.,
because it was unavailable). This does not include intentionally removed
information, but might include, e.g., redacted text.
is_a: DatasetProperty
attributes:
missing:
range: string
multivalued: true
why_missing:
range: string
multivalued: true
in_subset:
- Composition
Relationships:
description: >-
Are relationships between individual instances made explicit (e.g., users’
movie ratings, social network links)? If so, please describe how these
relationships are made explicit.
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Composition
Splits:
description: >-
Are there recommended data splits (e.g., training, development/validation,
testing)? If so, please provide a description of these splits, explaining
the rationale behind them.
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Composition
DataAnomaly:
description: >-
Are there any errors, sources of noise, or redundancies in the dataset?
If so, please provide a description.
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Composition
ExternalResource:
description: >-
Is the dataset self-contained, or does it link to or otherwise rely on
external resources (e.g., websites, tweets, other datasets)? If it links
to or relies on external resources, a) are there guarantees that they
will exist, and remain constant, over time; b) are there official
archival versions of the complete dataset (i.e., including the external
resources as they existed at the time the dataset was created); c) are
there any restrictions (e.g., licenses, fees) associated with any of the
external resources that might apply to a dataset consumer? Please provide
descriptions of all external resources and any restrictions associated
with them, as well as links or other access points, as appropriate.
is_a: DatasetProperty
attributes:
external_resources:
range: string
multivalued: true
future_guarantees:
range: string
multivalued: true
archival:
range: boolean
multivalued: true
restrictions:
range: string
multivalued: true
in_subset:
- Composition
Confidentiality:
description: >-
Does the dataset contain data that might be considered confidential (e.g.,
data that is protected by legal privilege or by doctor patient
confidentiality, data that includes the content of individuals’
non-public communications)?
is_a: DatasetProperty
attributes:
confidential_elements_present:
range: boolean
description:
range: string
multivalued: true
in_subset:
- Composition
ContentWarning:
description: >-
Does the dataset contain data that, if viewed directly, might be
offensive, insulting, threatening, or might otherwise cause anxiety? If
so, please describe why.
is_a: DatasetProperty
attributes:
content_warnings_present:
range: boolean
warnings:
range: string
multivalued: true
in_subset:
- Composition
Subpopulation:
description: >-
Does the dataset identify any subpopulations (e.g., by age, gender)? If
so, please describe how these subpopulations are identified and provide a
description of their respective distributions within the dataset.
is_a: DatasetProperty
attributes:
subpopulation_elements_present:
range: boolean
identification:
range: string
multivalued: true
distribution:
range: string
multivalued: true
in_subset:
- Composition
# TODO: consider specific PHI subtypes
# In practice, if data is Safe Harbor de-identified, that's one thing,
# but it could also be anonymized, and the standard for that isn't as
# explicit. See https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977668/
Deidentification:
description: >-
Is it possible to identify individuals (i.e., one or more natural
persons), either directly or indirectly (i.e., in combination with other
data) from the dataset?
is_a: DatasetProperty
attributes:
identifiable_elements_present:
range: boolean
description:
range: string
multivalued: true
in_subset:
- Composition
SensitiveElement:
description: >-
Does the dataset contain data that might be considered sensitive in any
way (e.g., data that reveals race or ethnic origins, sexual orientations,
religious beliefs, political opinions or union memberships, or locations;
financial or health data; biometric or genetic data; forms of government
identification, such as social security numbers; criminal history)?
is_a: DatasetProperty
attributes:
sensitive_elements_present:
range: boolean
description:
range: string
multivalued: true
in_subset:
- Composition
InstanceAcquisition:
description: >-
How was the data associated with each instance acquired? Was the data
directly observable (e.g., raw text, movie ratings), reported by subjects
(e.g., survey responses), or indirectly inferred/derived from other data
(e.g., part-of-speech tags, model-based guesses for age or language)? If
the data was reported by subjects or indirectly inferred/derived from
other data, was the data validated/verified?
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
was_directly_observed:
description: >-
Was the data directly observable (e.g., raw text, movie ratings)?
range: boolean
was_reported_by_subjects:
description: >-
Was the data reported by subjects (e.g., survey responses)?
range: boolean
was_inferred_derived:
description: >-
Was the data indirectly inferred/derived from other data (e.g.,
part-of-speech tags, model-based guesses for age or language)?
range: boolean
was_validated_verified:
description: >-
Was the data validated/verified?
range: boolean
in_subset:
- Collection
CollectionMechanism:
description: >-
What mechanisms or procedures were used to collect the data (e.g.,
hardware apparatuses or sensors, manual human curation, software programs,
software APIs)? How were these mechanisms or procedures validated?
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Collection
DataCollector:
description: >-
Who was involved in the data collection process (e.g., students,
crowdworkers, contractors) and how were they compensated (e.g., how much
were crowdworkers paid)?
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Collection
CollectionTimeframe:
description: >-
Over what timeframe was the data collected? Does this timeframe match the
creation timeframe of the data associated with the instances (e.g.,
recent crawl of old news articles)? If not, please describe the timeframe
in which the data associated with the instances was created.
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Collection
EthicalReview:
description: >-
Were any ethical review processes conducted (e.g., by an institutional
review board)? If so, please provide a description of these review
processes, including the outcomes, as well as a link or other access
point to any supporting documentation.
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Collection
DirectCollection:
description: >-
Did you collect the data from the individuals in question directly, or
obtain it via third parties or other sources (e.g., websites)?
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Collection
CollectionNotification:
description: >-
Were the individuals in question notified about the data collection? If
so, please describe (or show with screenshots or other information) how
notice was provided, and provide a link or other access point to, or
otherwise reproduce, the exact language of the notification itself.
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Collection
CollectionConsent:
description: >-
Did the individuals in question consent to the collection and use of
their data? If so, please describe (or show with screenshots or other
information) how consent was requested and provided, and provide a link
or other access point to, or otherwise reproduce, the exact language to
which the individuals consented.
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Collection
ConsentRevocation:
description: >-
If consent was obtained, were the consenting individuals provided with a
mechanism to revoke their consent in the future or 8 for certain uses? If
so, please provide a description, as well as a link or other access point
to the mechanism (if appropriate).
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Collection
DataProtectionImpact:
description: >-
Has an analysis of the potential impact of the dataset and its use on
data subjects (e.g., a data protection impact analysis) been conducted?
If so, please provide a description of this analysis, including the
outcomes, as well as a link or other access point to any supporting
documentation.
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Collection
PreprocessingStrategy:
description: >-
Was any preprocessing of the data done (e.g.,
discretization or bucketing, tokenization, SIFT
feature extraction)?
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Preprocessing-Cleaning-Labeling
CleaningStrategy:
description: >-
Was any cleaning of the data done (e.g.,
removal of instances, processing of missing values)?
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Preprocessing-Cleaning-Labeling
LabelingStrategy:
description: >-
Was any preprocessing/cleaning/labeling of the data done (e.g.,
part-of-speech tagging)?
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Preprocessing-Cleaning-Labeling
RawData:
description: >-
Was the “raw” data saved in addition to the preprocessed/cleaned/labeled
data (e.g., to support unanticipated future uses)? If so, please provide
a link or other access point to the “raw” data.
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Preprocessing-Cleaning-Labeling
ExistingUse:
description: >-
Has the dataset been used for any tasks already?
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Uses
- Maintenance
UseRepository:
description: >-
Is there a repository that links to any or all papers or systems that use
the dataset? If so, please provide a link or other access point.
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Uses
OtherTask:
description: >-
What (other) tasks could the dataset be used for?
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Uses
FutureUseImpact:
description: >-
Is there anything about the composition of the dataset or the way it was
collected and preprocessed/cleaned/labeled that might impact future uses?
For example, is there anything that a dataset consumer might need to know
to avoid uses that could result in unfair treatment of individuals or
groups (e.g., stereotyping, quality of service issues) or other risks or
harms (e.g., legal risks, financial harms)? If so, please provide a
description. Is there anything a dataset consumer could do to mitigate
these risks or harms?
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Uses
DiscouragedUse:
description: >-
Are there tasks for which the dataset should not be used?
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Uses
ThirdPartySharing:
description: >-
Will the dataset be distributed to third parties outside of the entity
(e.g., company, institution, organization) on behalf of which the dataset
was created?
is_a: DatasetProperty
attributes:
description:
range: boolean
in_subset:
- Distribution
DistributionFormat:
description: >-
How will the dataset will be distributed (e.g., tarball on website, API,
GitHub)?
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Distribution
DistributionDate:
description: >-
When will the dataset be distributed?
is_a: DatasetProperty
attributes:
description:
range: string
multivalued: true
in_subset:
- Distribution
LicenseAndUseTerms:
description: >-
Will the dataset be distributed under a copyright or other intellectual
property (IP) license, and/or under applicable terms of use (ToU)? If so,