diff --git a/CHANGES.rst b/CHANGES.rst
index f98acddcf..a666c8dbd 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -17,6 +17,10 @@ New features
 
 Changes
 -------
+* New dataset fetching methods have been added: :func:`fetch_videogame_sales`,
+  :func:`fetch_bike_sharing`, :func:`fetch_flight_delays`,
+  :func:`fetch_country_happiness`, and removed :func:`fetch_road_safety`.
+  :pr:`1218` by :user:`Vincent Maladiere <Vincent-Maladiere>`
 
 Bug fixes
 ---------
@@ -30,7 +34,7 @@ Release 0.4.1
 Changes
 -------
 * :class: `TableReport` has `write_html` method
-  :pr:`1190` by :user: `Mojdeh Rastgoo<mrastgoo>`.
+  :pr:`1190` by :user:`Mojdeh Rastgoo<mrastgoo>`.
 
 * A new parameter ``verbose`` has been added to the :class:`TableReport` to toggle on or off the
   printing of progress information when a report is being generated.
diff --git a/doc/reference/index.rst b/doc/reference/index.rst
index 8d7646848..06b2b1cec 100644
--- a/doc/reference/index.rst
+++ b/doc/reference/index.rst
@@ -138,17 +138,21 @@ Downloading a dataset
    :template: base.rst
    :nosignatures:
 
+   fetch_bike_sharing
+   fetch_country_happiness
+   fetch_credit_fraud
+   fetch_drug_directory
    fetch_employee_salaries
+   fetch_flight_delays
+   fetch_ken_embeddings
+   fetch_ken_table_aliases
+   fetch_ken_types
    fetch_medical_charge
    fetch_midwest_survey
+   fetch_movielens
    fetch_open_payments
-   fetch_road_safety
+   fetch_toxicity
    fetch_traffic_violations
-   fetch_drug_directory
-   fetch_world_bank_indicator
-   fetch_movielens
-   fetch_credit_fraud
-   fetch_ken_table_aliases
-   fetch_ken_types
-   fetch_ken_embeddings
+   fetch_videogame_sales
+   get_data_dir
    make_deduplication_data
diff --git a/examples/03_datetime_encoder.py b/examples/03_datetime_encoder.py
index 53ee5f456..8eb6d9267 100644
--- a/examples/03_datetime_encoder.py
+++ b/examples/03_datetime_encoder.py
@@ -51,10 +51,10 @@
 
 import pandas as pd
 
-data = pd.read_csv(
-    "https://raw.githubusercontent.com/skrub-data/datasets/master"
-    "/data/bike-sharing-dataset.csv"
-)
+from skrub import datasets
+
+data = datasets.fetch_bike_sharing().bike_sharing
+
 # Extract our input data (X) and the target column (y)
 y = data["cnt"]
 X = data[["date", "holiday", "temp", "hum", "windspeed", "weathersit"]]
diff --git a/examples/04_fuzzy_joining.py b/examples/04_fuzzy_joining.py
index ba0396bdf..744a0a1bf 100644
--- a/examples/04_fuzzy_joining.py
+++ b/examples/04_fuzzy_joining.py
@@ -32,16 +32,10 @@
 # --------------------------------
 #
 # We import the happiness score table first:
-import pandas as pd
+from skrub import datasets
 
-df = pd.read_csv(
-    (
-        "https://raw.githubusercontent.com/skrub-data/datasets/"
-        "master/data/Happiness_report_2022.csv"
-    ),
-    thousands=",",
-)
-df.drop(df.tail(1).index, inplace=True)
+happiness_data = datasets.fetch_country_happiness()
+df = happiness_data.happiness_report
 
 ###############################################################################
 # Let's look at the table:
@@ -66,23 +60,20 @@
 # complete our covariates (X table).
 #
 # Interesting tables can be found on `the World Bank open data platform
-# <https://data.worldbank.org/>`_, for which we have a downloading
-# function:
-from skrub.datasets import fetch_world_bank_indicator
-
-###############################################################################
+# <https://data.worldbank.org/>`_, which are also available in the dataset
 # We extract the table containing GDP per capita by country:
-gdp_per_capita = fetch_world_bank_indicator(indicator_id="NY.GDP.PCAP.CD").X
+
+gdp_per_capita = happiness_data.GDP_per_capita
 gdp_per_capita.head(3)
 
 ###############################################################################
 # Then another table, with life expectancy by country:
-life_exp = fetch_world_bank_indicator("SP.DYN.LE00.IN").X
+life_exp = happiness_data.life_expectancy
 life_exp.head(3)
 
 ###############################################################################
 # And a table with legal rights strength by country:
-legal_rights = fetch_world_bank_indicator("IC.LGL.CRED.XQ").X
+legal_rights = happiness_data.legal_rights_index
 legal_rights.head(3)
 
 ###############################################################################
diff --git a/examples/06_ken_embeddings.py b/examples/06_ken_embeddings.py
index 13a2509a2..6615f9737 100644
--- a/examples/06_ken_embeddings.py
+++ b/examples/06_ken_embeddings.py
@@ -44,18 +44,16 @@
 # Let's retrieve the dataset:
 import pandas as pd
 
-X = pd.read_csv(
-    "https://raw.githubusercontent.com/William2064888/vgsales.csv/main/vgsales.csv",
-    sep=";",
-    on_bad_lines="skip",
-)
-# Shuffle the data
-X = X.sample(frac=1, random_state=11, ignore_index=True)
+from skrub import datasets
+
+data = datasets.fetch_videogame_sales()
+
+X = data.X
 X.head(3)
 
 ###############################################################################
 # Our goal will be to predict the sales amount (y, our target column):
-y = X["Global_Sales"]
+y = data.y
 y
 
 
@@ -81,9 +79,9 @@
 # Before moving further, let's carry out some basic preprocessing:
 
 # Get a mask of the rows with missing values in "Publisher" and "Global_Sales"
-mask = X.isna()["Publisher"] | X.isna()["Global_Sales"]
+mask = X["Publisher"].isna() | y.isna()
 # And remove them
-X.dropna(subset=["Publisher", "Global_Sales"], inplace=True)
+X = X[~mask]
 y = y[~mask]
 
 ###############################################################################
@@ -202,14 +200,18 @@
 
 ###############################################################################
 # The |Pipeline| can now be readily applied to the dataframe for prediction:
-from sklearn.model_selection import cross_validate
+from sklearn.model_selection import KFold, cross_validate
 
 # We will save the results in a dictionary:
 all_r2_scores = dict()
 all_rmse_scores = dict()
 
+# The dataset is ordered by rank (most sales first so we need to shuffle before
+# splitting into cross-validation folds)
+cv = KFold(shuffle=True, random_state=0)
+
 cv_results = cross_validate(
-    pipeline, X_full, y, scoring=["r2", "neg_root_mean_squared_error"]
+    pipeline, X_full, y, scoring=["r2", "neg_root_mean_squared_error"], cv=cv
 )
 
 all_r2_scores["Base features"] = cv_results["test_r2"]
@@ -243,7 +245,7 @@
 ###############################################################################
 # Let's look at the results:
 cv_results = cross_validate(
-    pipeline2, X_full, y, scoring=["r2", "neg_root_mean_squared_error"]
+    pipeline2, X_full, y, scoring=["r2", "neg_root_mean_squared_error"], cv=cv
 )
 
 all_r2_scores["KEN features"] = cv_results["test_r2"]
@@ -287,7 +289,7 @@
 ###############################################################################
 # Let's look at the results:
 cv_results = cross_validate(
-    pipeline3, X_full, y, scoring=["r2", "neg_root_mean_squared_error"]
+    pipeline3, X_full, y, scoring=["r2", "neg_root_mean_squared_error"], cv=cv
 )
 
 all_r2_scores["Base + KEN features"] = cv_results["test_r2"]
diff --git a/examples/07_multiple_key_join.py b/examples/07_multiple_key_join.py
index 97b234175..773224499 100644
--- a/examples/07_multiple_key_join.py
+++ b/examples/07_multiple_key_join.py
@@ -45,10 +45,11 @@
 
 import pandas as pd
 
-from skrub.datasets import fetch_figshare
+from skrub.datasets import fetch_flight_delays
 
+dataset = fetch_flight_delays()
 seed = 1
-flights = fetch_figshare("41771418").X
+flights = dataset.flights
 
 # Sampling for faster computation.
 flights = flights.sample(5_000, random_state=seed, ignore_index=True)
@@ -75,7 +76,7 @@
 #     - The ``airports`` dataset, with information such as their name
 #       and location (longitude, latitude).
 
-airports = fetch_figshare("41710257").X
+airports = dataset.airports
 airports.head()
 
 ########################################################################
@@ -85,7 +86,7 @@
 #       Both tables are from the Global Historical Climatology Network.
 #       Here, we consider only weather measurements from 2008.
 
-weather = fetch_figshare("41771457").X
+weather = dataset.weather
 # Sampling for faster computation.
 weather = weather.sample(10_000, random_state=seed, ignore_index=True)
 weather.head()
@@ -94,7 +95,7 @@
 #     - The ``stations`` dataset. Provides location of all the weather
 #       measurement stations in the US.
 
-stations = fetch_figshare("41710524").X
+stations = dataset.stations
 stations.head()
 
 ###############################################################################
diff --git a/examples/09_interpolation_join.py b/examples/09_interpolation_join.py
index de20980b5..a69d12fd5 100644
--- a/examples/09_interpolation_join.py
+++ b/examples/09_interpolation_join.py
@@ -35,14 +35,18 @@
 # stations’ latitude and longitude. We subsample these large tables for the example to
 # run faster.
 
-from skrub.datasets import fetch_figshare
+import pandas as pd
 
-weather = fetch_figshare("41771457").X
+from skrub.datasets import fetch_flight_delays
+
+dataset = fetch_flight_delays()
+weather = dataset.weather
 weather = weather.sample(100_000, random_state=0, ignore_index=True)
-stations = fetch_figshare("41710524").X
+stations = dataset.stations
 weather = stations.merge(weather, on="ID")[
     ["LATITUDE", "LONGITUDE", "YEAR/MONTH/DAY", "TMAX", "PRCP", "SNOW"]
 ]
+weather["YEAR/MONTH/DAY"] = pd.to_datetime(weather["YEAR/MONTH/DAY"])
 
 ######################################################################
 # The ``'TMAX'`` is in tenths of degree Celsius -- a ``'TMAX'`` of 297 means the maximum
@@ -124,9 +128,11 @@
 # ``'Origin'`` which refers to the departure airport’s IATA code. We use only a subset
 # to speed up the example.
 
-flights = fetch_figshare("41771418").X[["Year_Month_DayofMonth", "Origin", "ArrDelay"]]
+flights = dataset.flights
+flights["Year_Month_DayofMonth"] = pd.to_datetime(flights["Year_Month_DayofMonth"])
+flights = flights[["Year_Month_DayofMonth", "Origin", "ArrDelay"]]
 flights = flights.sample(20_000, random_state=0, ignore_index=True)
-airports = fetch_figshare("41710257").X[["iata", "airport", "state", "lat", "long"]]
+airports = dataset.airports[["iata", "airport", "state", "lat", "long"]]
 flights = flights.merge(airports, left_on="Origin", right_on="iata")
 # printing the first row is more readable than the head() when we have many columns
 flights.iloc[0]
diff --git a/pixi.lock b/pixi.lock
index 11ee6aed8..0bea12472 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -13,21 +13,23 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.16.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py39hf88036b_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.16.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotlipy-0.7.0-py39h8cd3c5a_1007.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.8.30-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.12.14-hbcca054_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.2-h3394656_1.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.8-py39h9399b63_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/chardet-3.0.4-py39h079e4ff_1008.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.10-py39h9399b63_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cryptography-44.0.0-py39h7170ec2_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cyrus-sasl-2.1.27-h54b06d7_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.4-h5888daf_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
@@ -39,22 +41,19 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.22.5-he02047a_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/glib-2.82.2-h44428e9_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.82.2-h4833e2c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/glib-2.82.2-h07242d1_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.82.2-h4833e2c_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h59595ed_1003.conda
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.0.1-pyhd8ed1ab_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-10.2.0-h4bba637_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/idna-2.10-pyh9f0ad1d_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/imagesize-1.4.1-pyhd8ed1ab_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.5.0-pyha770c72_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.6.1-pyha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.2-pyhd8ed1ab_1.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda
@@ -65,14 +64,14 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-20_linux64_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcap-2.69-h0f662aa_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcap-2.71-h39aace5_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-20_linux64_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp19.1-19.1.4-default_hb5137d0_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libclang13-19.1.4-default_h9c6a7e4_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp19.1-19.1.7-default_hb5137d0_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libclang13-19.1.7-default_h9c6a7e4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h4637d8d_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.22-hb9d3cd8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.123-hb9d3cd8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20191231-he28a2e2_2.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.23-h4ddbbb0_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.124-hb9d3cd8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20240808-pl5321h7949ede_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda
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@@ -80,17 +79,14 @@ environments:
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   name: glog
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-  build: py311h0f6cedb_2
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+  build: py311h0f6cedb_3
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-  md5: d3454be6955e776ff9ccf19d19c96263
+  url: https://conda.anaconda.org/conda-forge/linux-64/gmpy2-2.1.5-py311h0f6cedb_3.conda
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   license_family: LGPL
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-  size: 203933
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 - kind: conda
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+  build: py311hb5d9ff4_3
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   subdir: osx-arm64
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-  md5: 4140e387d4190f195091169a57da2ded
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/gmpy2-2.1.5-py311hb5d9ff4_3.conda
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+  md5: 5ebaa44b9a984799aea445996f63f002
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-  md5: af9faf103fb57241246416dc70b466f7
+  url: https://conda.anaconda.org/conda-forge/linux-64/gmpy2-2.1.5-py312h7201bc8_3.conda
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 - kind: conda
   name: graphite2
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   license: MIT
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   license: MIT
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   noarch: python
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 - kind: conda
   name: httpcore
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   - certifi
   license: BSD-3-Clause
   license_family: BSD
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-- kind: conda
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+- kind: conda
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+  url: https://conda.anaconda.org/conda-forge/noarch/jupytext-1.16.6-pyh80e38bb_0.conda
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   - abseil-cpp =20240722.0
   license: Apache-2.0
   license_family: Apache
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-  size: 1310521
-  timestamp: 1727295454064
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   name: libabseil
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-  build: cxx17_hac325c4_1
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   license_family: Apache
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-  size: 1170354
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   name: libabseil
   version: '20240722.0'
-  build: cxx17_he0c23c2_1
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   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 1777570
-  timestamp: 1727296115119
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   name: libabseil
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+  url: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240722.0-cxx17_hbbce691_4.conda
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   - abseil-cpp =20240722.0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 1179072
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-15.0.2-h9b1cf7e_54_cpu.conda
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   - libcxx >=17
-  - libgoogle-cloud >=2.31.0,<2.32.0a0
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   - libre2-11 >=2024.7.2
-  - libutf8proc >=2.8.0,<3.0a0
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   - libzlib >=1.3.1,<2.0a0
-  - lz4-c >=1.9.3,<1.10.0a0
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   - orc >=2.0.3,<2.0.4.0a0
   - re2
   - snappy >=1.2.1,<1.3.0a0
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-  - libutf8proc <2.9
   constrains:
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-  - arrow-cpp <0.0a0
   - parquet-cpp <0.0a0
+  - arrow-cpp <0.0a0
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 5762965
-  timestamp: 1732236179706
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   version: 15.0.2
-  build: h80cb48f_47_cpu
-  build_number: 47
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/libarrow-15.0.2-h80cb48f_47_cpu.conda
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-  md5: f4d94ac1c6831df42ce89184031ad584
+  build: ha9ea875_54_cpu
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+  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-15.0.2-ha9ea875_54_cpu.conda
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   - libabseil * cxx17*
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   - libbrotlidec >=1.1.0,<1.2.0a0
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-  - libgoogle-cloud >=2.31.0,<2.32.0a0
-  - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
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+  - libgoogle-cloud-storage >=2.34.0,<2.35.0a0
   - libre2-11 >=2024.7.2
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-  - openssl >=3.4.0,<4.0a0
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-  - libutf8proc <2.9
   constrains:
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   - parquet-cpp <0.0a0
   - apache-arrow-proc =*=cpu
+  - arrow-cpp <0.0a0
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 4955935
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-15.0.2-hbde6d31_47_cpu.conda
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+  build: hbfda4fa_54_cpu
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+  url: https://conda.anaconda.org/conda-forge/osx-64/libarrow-15.0.2-hbfda4fa_54_cpu.conda
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-  - aws-crt-cpp >=0.29.5,<0.29.6.0a0
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+  - __osx >=10.13
+  - aws-crt-cpp >=0.29.9,<0.29.10.0a0
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   - libcxx >=17
-  - libgoogle-cloud >=2.31.0,<2.32.0a0
-  - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
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   - re2
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-  - libutf8proc <2.9
   constrains:
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-  - apache-arrow-proc =*=cpu
   - arrow-cpp <0.0a0
+  - apache-arrow-proc =*=cpu
+  - parquet-cpp <0.0a0
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 5143434
-  timestamp: 1732235890698
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-  build: he4bedbb_47_cpu
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-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-15.0.2-he4bedbb_47_cpu.conda
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-  md5: abf4287c7dd296bb165fdc3f8fd23f95
+  build: hdd9fb1f_54_cpu
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+  url: https://conda.anaconda.org/conda-forge/win-64/libarrow-15.0.2-hdd9fb1f_54_cpu.conda
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   license: Apache-2.0
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+  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-19.0.0-h00a82cf_6_cpu.conda
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   license: Apache-2.0
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-  - libgoogle-cloud >=2.31.0,<2.32.0a0
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+  - libgoogle-cloud >=2.34.0,<2.35.0a0
+  - libgoogle-cloud-storage >=2.34.0,<2.35.0a0
+  - libopentelemetry-cpp >=1.18.0,<1.19.0a0
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   - libre2-11 >=2024.7.2
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   - libzlib >=1.3.1,<2.0a0
-  - lz4-c >=1.9.3,<1.10.0a0
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   - orc >=2.0.3,<2.0.4.0a0
   - re2
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   - apache-arrow-proc =*=cpu
   license: Apache-2.0
   purls: []
-  size: 5475725
-  timestamp: 1732947802614
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-  - libgcc >=13
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+  - libarrow-dataset 19.0.0 hf07054f_6_cpu
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   license: Apache-2.0
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-  - libarrow 18.1.0 h654e1bb_2_cpu
-  - libarrow-acero 18.1.0 h605b82c_2_cpu
-  - libarrow-dataset 18.1.0 h605b82c_2_cpu
+  - libarrow 19.0.0 h3deb67b_6_cpu
+  - libarrow-acero 19.0.0 ha6338a2_6_cpu
+  - libarrow-dataset 19.0.0 ha6338a2_6_cpu
   - libcxx >=18
-  - libprotobuf >=5.28.2,<5.28.3.0a0
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-  build: 1_h86c2bf4_netlib
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 - kind: conda
   name: libblas
   version: 3.9.0
-  build: 25_linux64_openblas
-  build_number: 25
+  build: 26_linux64_openblas
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-  - libcblas 3.9.0 25_linux64_openblas
+  - libcblas 3.9.0 26_linux64_openblas
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+  - liblapacke 3.9.0 26_linux64_openblas
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-  - liblapacke 3.9.0 25_linux64_openblas
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 15677
-  timestamp: 1729642900350
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-  build: 25_osx64_openblas
-  build_number: 25
+  build: 26_osx64_openblas
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-  - liblapacke 3.9.0 25_osx64_openblas
+  - libcblas 3.9.0 26_osx64_openblas
+  - liblapack 3.9.0 26_osx64_openblas
   - blas * openblas
-  - libcblas 3.9.0 25_osx64_openblas
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   license: BSD-3-Clause
   license_family: BSD
   purls: []
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+  - liblapacke 3.9.0 26_osxarm64_openblas
+  - libcblas 3.9.0 26_osxarm64_openblas
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-  - liblapacke 3.9.0 25_osxarm64_openblas
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   license: BSD-3-Clause
   license_family: BSD
   purls: []
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+  - mkl 2024.2.2 h66d3029_15
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+  - liblapack 3.9.0 26_win64_mkl
   - blas * mkl
-  - libcblas 3.9.0 25_win64_mkl
-  - liblapack 3.9.0 25_win64_mkl
-  - liblapacke 3.9.0 25_win64_mkl
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   license: BSD-3-Clause
   license_family: BSD
   purls: []
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   timestamp: 1725268003553
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   name: libcap
-  version: '2.69'
-  build: h0f662aa_0
+  version: '2.71'
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   license_family: BSD
   purls: []
-  size: 100582
-  timestamp: 1684162447012
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 - kind: conda
   name: libcblas
   version: 3.9.0
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 - kind: conda
   name: libcblas
   version: 3.9.0
-  build: 25_linux64_openblas
-  build_number: 25
+  build: 26_linux64_openblas
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+  - liblapack 3.9.0 26_linux64_openblas
+  - liblapacke 3.9.0 26_linux64_openblas
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-  - liblapacke 3.9.0 25_linux64_openblas
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 15613
-  timestamp: 1729642905619
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   version: 3.9.0
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-  - liblapacke 3.9.0 25_osx64_openblas
+  - liblapack 3.9.0 26_osx64_openblas
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   license: BSD-3-Clause
   license_family: BSD
   purls: []
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   - blas * openblas
-  - liblapack 3.9.0 25_osxarm64_openblas
-  - liblapacke 3.9.0 25_osxarm64_openblas
   license: BSD-3-Clause
   license_family: BSD
   purls: []
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+  - liblapack 3.9.0 26_win64_mkl
   - blas * mkl
-  - liblapack 3.9.0 25_win64_mkl
-  - liblapacke 3.9.0 25_win64_mkl
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+  url: https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp19.1-19.1.7-default_hb5137d0_0.conda
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-  - libllvm19 >=19.1.4,<19.2.0a0
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   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
   purls: []
-  size: 20526176
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 - kind: conda
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   - libxkbcommon >=1.7.0,<2.0a0
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   - libzlib >=1.3.1,<2.0a0
   - mysql-libs >=9.0.1,<9.1.0a0
-  - openssl >=3.3.2,<4.0a0
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   - wayland >=1.23.1,<2.0a0
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   - xorg-libxcomposite >=0.4.6,<1.0a0
-  - xorg-libxcursor >=1.2.2,<2.0a0
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   - xorg-libxext >=1.3.6,<2.0a0
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+  - qt 6.8.1
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   name: re2
   version: 2024.07.02
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 - kind: conda
   name: readline
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   timestamp: 1679532707590
 - kind: conda
   name: referencing
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+  version: 0.36.1
   build: pyhd8ed1ab_0
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   noarch: python
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+  md5: dbb48421efd666ea133c6d5e67291766
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+  - python >=3.9
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   license_family: MIT
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+  - pkg:pypi/referencing?source=hash-mapping
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 - kind: conda
   name: regex
   version: 2024.11.6
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-  - pkg:pypi/regex?source=conda-forge-mapping
+  - pkg:pypi/regex?source=hash-mapping
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+  - pkg:pypi/regex?source=hash-mapping
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+  - pkg:pypi/regex?source=hash-mapping
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+  - pkg:pypi/regex?source=hash-mapping
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-  - pkg:pypi/regex?source=conda-forge-mapping
+  - pkg:pypi/regex?source=hash-mapping
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   timestamp: 1730952378415
+- kind: conda
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+  build: pyhd8ed1ab_1
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   noarch: python
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 - kind: conda
   name: rfc3339-validator
   version: 0.1.4
-  build: pyhd8ed1ab_0
+  build: pyhd8ed1ab_1
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   subdir: noarch
   noarch: python
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+  - python >=3.9
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 - kind: conda
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+  - pkg:pypi/rfc3986-validator?source=hash-mapping
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+  md5: 16669f8098b2f4a8560727efb9e65afd
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   - libcblas >=3.9.0,<4.0a0
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-  - libgfortran
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-  - numpy <2.3
+  - numpy <2.5
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+  - numpy <2.5
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   - libcblas >=3.9.0,<4.0a0
-  - libgcc >=13
-  - libgfortran
-  - libgfortran5 >=13.3.0
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+  - libgfortran5 >=13.2.0
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-  - libstdcxx >=13
-  - numpy <2.4
+  - numpy <2.5
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+  version: 1.15.1
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-  - libgfortran 5.*
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   - numpy >=1.23.5
   - python >=3.13,<3.14.0a0
   - python_abi 3.13.* *_cp313
+  - ucrt >=10.0.20348.0
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   name: seaborn
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   license: BSD-3-Clause
   license_family: BSD
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 - kind: conda
   name: sentence-transformers
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+  build: pyhd8ed1ab_1
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   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/sentence-transformers-3.3.1-pyhd8ed1ab_0.conda
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+  url: https://conda.anaconda.org/conda-forge/noarch/sentence-transformers-3.3.1-pyhd8ed1ab_1.conda
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   - numpy
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-  - python >=3.8
+  - python >=3.9
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   - scikit-learn
   - scipy
@@ -30675,27 +31073,26 @@ packages:
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   license_family: APACHE
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   name: setuptools
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+  version: 75.8.0
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   license_family: GPL
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   name: six
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 - kind: pypi
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   version: 0.5.dev0
   path: .
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+  sha256: 9564127ec8917fc746b9de14215bc2d6435ecd278a1740c25596a3f48f35714e
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@@ -30771,6 +31168,7 @@ packages:
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   - jinja2>=3.1.2
   - matplotlib>=3.4.3
+  - requests>=2.25.0
   - ipykernel ; extra == 'dev'
   - ipython ; extra == 'dev'
   - jupyterlab ; extra == 'dev'
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   name: snappy
   version: 1.2.1
-  build: h23299a8_0
+  build: h500f7fa_1
+  build_number: 1
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/snappy-1.2.1-h23299a8_0.conda
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-  md5: 7635a408509e20dcfc7653ca305ad799
+  url: https://conda.anaconda.org/conda-forge/win-64/snappy-1.2.1-h500f7fa_1.conda
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+  md5: e32fb978aaea855ddce624eb8c8eb69a
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   - vc >=14.2,<15
@@ -30812,73 +31211,78 @@ packages:
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   license_family: BSD
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-  size: 59350
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+  size: 59757
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+  build: h8bd8927_1
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+  md5: 3b3e64af585eadfb52bb90b553db5edf
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-  - libstdcxx-ng >=12
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libstdcxx >=13
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   license_family: BSD
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+  build: h98b9ce2_1
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+  build: pyhd8ed1ab_1
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+  md5: bf7a226e58dfb8346c70df36065d86c9
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+  - pkg:pypi/snowballstemmer?source=hash-mapping
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+  - pkg:pypi/soupsieve?source=hash-mapping
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-  md5: c568e260463da2528ecfd7c5a0b41bbd
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+  - colorama >=0.4.5
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   - imagesize >=1.3
-  - importlib-metadata >=6.0
-  - jinja2 >=3.1
-  - packaging >=23.0
-  - pygments >=2.17
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+  - jinja2 >=3.0
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+  - pygments >=2.14
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+  - requests >=2.25.0
+  - snowballstemmer >=2.0
   - sphinxcontrib-applehelp
   - sphinxcontrib-devhelp
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@@ -30946,18 +31350,19 @@ packages:
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   license_family: BSD
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-  - cuda-cudart
+  - cuda-version >=11.2,<12
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   timestamp: 1725305688706
 - kind: conda
@@ -33769,7 +34162,7 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
+  - pkg:pypi/zstandard?source=hash-mapping
   size: 320624
   timestamp: 1725305934189
 - kind: conda
@@ -33792,7 +34185,7 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
+  - pkg:pypi/zstandard?source=hash-mapping
   size: 419552
   timestamp: 1725305670210
 - kind: conda
@@ -33816,7 +34209,7 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
+  - pkg:pypi/zstandard?source=hash-mapping
   size: 325703
   timestamp: 1725305947138
 - kind: conda
@@ -33839,7 +34232,7 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
+  - pkg:pypi/zstandard?source=hash-mapping
   size: 424424
   timestamp: 1725305749031
 - kind: conda
@@ -33861,7 +34254,7 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
+  - pkg:pypi/zstandard?source=hash-mapping
   size: 417943
   timestamp: 1725305677487
 - kind: conda
@@ -33884,101 +34277,9 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
+  - pkg:pypi/zstandard?source=hash-mapping
   size: 336496
   timestamp: 1725305912716
-- kind: conda
-  name: zstandard
-  version: 0.23.0
-  build: py39h08a7858_1
-  build_number: 1
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py39h08a7858_1.conda
-  sha256: 76a45ef349517eaab1492f17f9c807ccbf1971961c6e90d454fbedbed7e257ad
-  md5: cd9fa334e11886738f17254f52210bc3
-  depends:
-  - __glibc >=2.17,<3.0.a0
-  - cffi >=1.11
-  - libgcc >=13
-  - python >=3.9,<3.10.0a0
-  - python_abi 3.9.* *_cp39
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
-  size: 407017
-  timestamp: 1725305769438
-- kind: conda
-  name: zstandard
-  version: 0.23.0
-  build: py39h9bf74da_1
-  build_number: 1
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py39h9bf74da_1.conda
-  sha256: 1446c0495565d6d7b364e0b2021d0309d21a34cb7d6bd19eced1a483fabfb915
-  md5: 5f1f0f75ebd24882ccf44d145939b104
-  depends:
-  - cffi >=1.11
-  - python >=3.9,<3.10.0a0
-  - python_abi 3.9.* *_cp39
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
-  size: 311116
-  timestamp: 1725305993710
-- kind: conda
-  name: zstandard
-  version: 0.23.0
-  build: py39hc23f734_1
-  build_number: 1
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.23.0-py39hc23f734_1.conda
-  sha256: a854d10abb45924bd96f2fc94ec0693663b928a2c1a9e373b4437e2662ace38b
-  md5: 5da66224731aea611c4bf331e057f23d
-  depends:
-  - __osx >=10.13
-  - cffi >=1.11
-  - python >=3.9,<3.10.0a0
-  - python_abi 3.9.* *_cp39
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
-  size: 401328
-  timestamp: 1725305650930
-- kind: conda
-  name: zstandard
-  version: 0.23.0
-  build: py39hcf1bb16_1
-  build_number: 1
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py39hcf1bb16_1.conda
-  sha256: c4cb4a1bb5609c16f27a3f4355cddc77e6c0e9e3083340f413f9de9b8266e03f
-  md5: 8cbaf074d564f304ae7fd29ba39184be
-  depends:
-  - __osx >=11.0
-  - cffi >=1.11
-  - python >=3.9,<3.10.0a0
-  - python >=3.9,<3.10.0a0 *_cpython
-  - python_abi 3.9.* *_cp39
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
-  size: 320027
-  timestamp: 1725305723442
 - kind: conda
   name: zstd
   version: 1.5.6
diff --git a/pyproject.toml b/pyproject.toml
index 68d0f3693..f593f6c9f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,6 +32,7 @@ dependencies = [
     "scipy>=1.9.3",
     "jinja2>=3.1.2",
     "matplotlib>=3.4.3",
+    "requests>=2.25.0",
 ]
 
 [tool.setuptools.dynamic]
@@ -96,6 +97,7 @@ scikit-learn = ">=1.2.1"
 scipy = ">=1.9.3"
 jinja2 = ">=3.1.2"
 matplotlib = ">=3.4.3"
+requests = ">=2.25.0"
 
 [tool.pixi.feature.doc.dependencies]
 jupyterlite-sphinx = "*"
@@ -149,6 +151,7 @@ scikit-learn = "==1.2.1"
 scipy = "==1.9.3"
 jinja2 = "==3.1.2"
 matplotlib = "==3.4.3"
+requests = "==2.25.0"
 
 [tool.pixi.feature.min-optional-dependencies.dependencies]
 pyarrow = "==15.0.2"
diff --git a/skrub/datasets/__init__.py b/skrub/datasets/__init__.py
index d6e8d1f2c..414d38563 100644
--- a/skrub/datasets/__init__.py
+++ b/skrub/datasets/__init__.py
@@ -1,19 +1,17 @@
 from ._fetching import (
-    DatasetAll,
-    DatasetInfoOnly,
+    fetch_bike_sharing,
+    fetch_country_happiness,
     fetch_credit_fraud,
     fetch_drug_directory,
     fetch_employee_salaries,
-    fetch_figshare,
+    fetch_flight_delays,
     fetch_medical_charge,
     fetch_midwest_survey,
     fetch_movielens,
     fetch_open_payments,
-    fetch_road_safety,
     fetch_toxicity,
     fetch_traffic_violations,
-    fetch_world_bank_indicator,
-    get_data_dir,
+    fetch_videogame_sales,
 )
 from ._generating import make_deduplication_data
 from ._ken_embeddings import (
@@ -21,25 +19,25 @@
     fetch_ken_table_aliases,
     fetch_ken_types,
 )
+from ._utils import get_data_dir
 
 __all__ = [
-    "DatasetAll",
-    "DatasetInfoOnly",
+    "fetch_bike_sharing",
+    "fetch_country_happiness",
+    "fetch_credit_fraud",
     "fetch_drug_directory",
     "fetch_employee_salaries",
+    "fetch_flight_delays",
+    "fetch_ken_embeddings",
+    "fetch_ken_table_aliases",
+    "fetch_ken_types",
     "fetch_medical_charge",
     "fetch_midwest_survey",
+    "fetch_movielens",
     "fetch_open_payments",
-    "fetch_road_safety",
-    "fetch_traffic_violations",
-    "fetch_world_bank_indicator",
-    "fetch_figshare",
-    "fetch_credit_fraud",
     "fetch_toxicity",
-    "fetch_movielens",
+    "fetch_traffic_violations",
+    "fetch_videogame_sales",
     "get_data_dir",
     "make_deduplication_data",
-    "fetch_ken_embeddings",
-    "fetch_ken_table_aliases",
-    "fetch_ken_types",
 ]
diff --git a/skrub/datasets/_fetching.py b/skrub/datasets/_fetching.py
index fa60984a8..0e6a5724d 100644
--- a/skrub/datasets/_fetching.py
+++ b/skrub/datasets/_fetching.py
@@ -1,1193 +1,377 @@
 """
-Fetching functions to retrieve example datasets, using fetch_openml.
-
-Public API functions should return either a DatasetInfoOnly or a DatasetAll.
+Fetching functions to retrieve example datasets from GitHub and OSF.
 """
 
-# Future notes:
-# - Watch out for ``fetch_openml()`` API modifications:
-# as of january 2021, the function is marked as experimental.
-
-from __future__ import annotations
-
-import gzip
-import json
-import urllib.request
-import warnings
-from dataclasses import dataclass
-from itertools import chain
-from pathlib import Path
-from typing import Any, Literal, TextIO
-from urllib.error import URLError
-from zipfile import BadZipFile, ZipFile
-
-import pandas as pd
-from sklearn import __version__ as sklearn_version
-from sklearn.datasets import fetch_openml
-from sklearn.datasets._base import _sha256
-from sklearn.utils import Bunch
-from sklearn.utils.fixes import parse_version
-
-from skrub._utils import import_optional_dependency
-from skrub.datasets._utils import get_data_dir
-
-# Ignore lines too long, first docstring lines can't be cut
-# flake8: noqa: E501
-
-
-# Directory where the ``.gz`` files containing the
-# details on downloaded datasets are stored.
-# Note: the tree structure is created by ``fetch_openml()``.
-# As of october 2020, this function is annotated as
-# ``Experimental`` so the structure might change in future releases.
-# This path will be concatenated to the skrub data directory,
-# available via the function ``get_data_dir()``.
-DETAILS_DIRECTORY: str = "openml/openml.org/api/v1/json/data/"
-
-# Same as above; for the datasets features location.
-FEATURES_DIRECTORY: str = "openml/openml.org/api/v1/json/data/features/"
-
-# Same as above; for the datasets data location.
-DATA_DIRECTORY: str = "openml/openml.org/data/v1/download/"
-
-# The IDs of the datasets, from OpenML.
-# For each dataset, its URL is constructed as follows:
-openml_url: str = "https://www.openml.org/d/{ID}"
-ROAD_SAFETY_ID: int = 42803
-OPEN_PAYMENTS_ID: int = 42738
-MIDWEST_SURVEY_ID: int = 42805
-MEDICAL_CHARGE_ID: int = 42720
-EMPLOYEE_SALARIES_ID: int = 42125
-TRAFFIC_VIOLATIONS_ID: int = 42132
-DRUG_DIRECTORY_ID: int = 43044
-
-MOVIELENS_URL = "https://files.grouplens.org/datasets/movielens/{zip_directory}.zip"
-
-# A dictionary storing the sha256 hashes of the figshare files
-figshare_id_to_hash = {
-    39142985: "47d73381ef72b050002a8642194c6718a4954ec9e6c556f4c4ddc6ed84ceec92",
-    39149066: "e479cf9741a90c40401697e7fa54409e3b9cfa09f27502877382e64e86fbfcd0",
-    39149069: "7b0dcdb15d3aeecba6022c929665ee064f6fb4b8b94186a6e89b6fbc781b3775",
-    39149072: "4f58f15168bb8a6cc8b152bd48995bc7d1a4d4d89a9e22d87aa51ccf30118122",
-    39149075: "7037603362af1d4bf73551d50644c0957cb91d2b4892e75413f57f415962029a",
-    39254360: "531130c714ba6ee9902108d4010f42388aa9c0b3167d124cd57e2c632df3e05a",
-    39266300: "37b23b2c37a1f7ff906bc7951cbed4be15d8417dad0762092282f7b491cf8c21",
-    39266678: "4e041322985e078de8b08acfd44b93a5ce347c1e501e9d869651e753de747ba1",
-    40019230: "4d43fed75dba1e59a5587bf31c1addf2647a1f15ebea66e93177ccda41e18f2f",
-    40019788: "67ae86496c8a08c6cc352f573160a094f605e7e0da022eb91c603abb7edf3747",
-}
-
-
-@dataclass(unsafe_hash=True)
-class Details:
-    name: str
-    file_id: str
-    description: str
-
-
-@dataclass(unsafe_hash=True)
-class Features:
-    names: list[str]
-
-
-@dataclass(unsafe_hash=True)
-class DatasetAll:
-    """
-    Represents a dataset and its information.
-    With this state, the dataset is loaded in memory as a DataFrame (`X` and `y`).
-    Additional information such as `path` and `read_csv_kwargs` are provided
-    in case the dataframe has to be read from disk, as such:
-
-    .. code:: python
+from ._utils import load_dataset_files, load_simple_dataset
 
-        ds = fetch_employee_salaries(load_dataframe=False)
-        df = pd.read_csv(ds.path, **ds.read_csv_kwargs)
-    """
-
-    name: str
-    description: str
-    source: str
-    target: str
-    X: pd.DataFrame
-    y: pd.Series
-    path: Path
-    read_csv_kwargs: dict[str, Any]
-
-    def __eq__(self, other: DatasetAll) -> bool:
-        """
-        Implemented for the tests to work without bloating the code.
-        The main reason for which it's needed is that equality between
-        DataFrame (`X` and `y`) is often ambiguous and will raise an error.
-        """
-        return (
-            self.name == other.name
-            and self.description == other.description
-            and self.source == other.source
-            and self.target == other.target
-            and self.X.equals(other.X)
-            and self.y.equals(other.y)
-            and self.path == other.path
-            and self.read_csv_kwargs == other.read_csv_kwargs
-        )
-
-
-@dataclass(unsafe_hash=True)
-class DatasetInfoOnly:
-    """
-    Represents a dataset and its information.
-    With this state, the dataset is NOT loaded in memory, but can be read
-    with `path` and `read_csv_kwargs`, as such:
 
-    .. code:: python
+def fetch_employee_salaries(data_home=None):
+    """Fetches the employee salaries dataset (regression), available at \
+        https://github.com/skrub-data/skrub-data-files
 
-        ds = fetch_employee_salaries(load_dataframe=False)
-        df = pd.read_csv(ds.path, **ds.read_csv_kwargs)
-    """
-
-    name: str
-    description: str
-    source: str
-    target: str
-    path: Path
-    read_csv_kwargs: dict[str, Any]
-
-
-def _fetch_openml_dataset(
-    dataset_id: int,
-    data_directory: Path | None = None,
-) -> dict[str, Any]:
-    """
-    Gets a dataset from OpenML (https://www.openml.org).
+    Description of the dataset:
+        Annual salary information including gross pay and overtime pay for all
+        active, permanent employees of Montgomery County, MD paid in calendar
+        year 2016. This information will be published annually each year.
 
     Parameters
     ----------
-    dataset_id : int
-        The ID of the dataset to fetch.
-    data_directory : pathlib.Path, optional
-        The directory where the dataset is stored.
-        By default, a subdirectory "openml" in the skrub data directory.
+    data_home: str or path, default=None
+        The directory where to download and unzip the files.
 
     Returns
     -------
-    mapping of str to any
-        A dictionary containing:
-          - `description` : str
-              The description of the dataset,
-              as gathered from OpenML.
-          - `source` : str
-              The dataset's URL from OpenML.
-          - `path` : pathlib.Path
-              The local path leading to the dataset,
-              saved as a CSV file.
+    bunch : sklearn.utils.Bunch
+        A dictionary-like object with the following keys:
+
+        - employee_salaries : pd.DataFrame, the dataframe
+        - X : pd.DataFrame, features, i.e. the dataframe without the target labels
+        - y : pd.DataFrame, target labels
+        - metadata : a dictionary containing the name, description, source and target
     """
-    if data_directory is None:
-        data_directory = get_data_dir(name="openml")
-
-    # Make path absolute
-    data_directory = data_directory.resolve()
-    data_directory.mkdir(parents=True, exist_ok=True)
-
-    # Construct the path to the gzip file containing the details on a dataset.
-    details_gz_path = data_directory / DETAILS_DIRECTORY / f"{dataset_id}.gz"
-    features_gz_path = data_directory / FEATURES_DIRECTORY / f"{dataset_id}.gz"
-
-    if not details_gz_path.is_file() or not features_gz_path.is_file():
-        # If the details file or the features file don't exist,
-        # download the dataset.
-        _download_and_write_openml_dataset(
-            dataset_id=dataset_id, data_directory=data_directory
-        )
-    details = _get_details(details_gz_path)
-
-    # The file ID is required because the data file is named after this ID,
-    # and not after the dataset's.
-    file_id = details.file_id
-    csv_path = data_directory / f"{details.name}.csv"
-
-    data_gz_path = data_directory / DATA_DIRECTORY / f"{file_id}.gz"
-
-    if not data_gz_path.is_file():
-        # double-check.
-        # If the data file does not exist, download the dataset.
-        _download_and_write_openml_dataset(
-            dataset_id=dataset_id, data_directory=data_directory
-        )
-
-    if not csv_path.is_file():
-        # If the CSV file does not exist, use the dataset
-        # downloaded by ``fetch_openml()`` to construct it.
-        features = _get_features(features_gz_path)
-        _export_gz_data_to_csv(data_gz_path, csv_path, features)
-
-    url = openml_url.format(ID=dataset_id)
-
-    return {
-        "description": details.description,
-        "source": url,
-        "path": csv_path.resolve(),
-    }
-
-
-def _fetch_world_bank_data(
-    indicator_id: str,
-    data_directory: Path | None = None,
-) -> dict[str, Any]:
-    """Gets a dataset from World Bank open data platform (https://data.worldbank.org/).
+    return load_simple_dataset("employee_salaries", data_home)
 
-    Parameters
-    ----------
-    indicator_id : str
-        The ID of the indicator's dataset to fetch.
-    data_directory : pathlib.Path, optional
-        The directory where the dataset is stored.
-        By default, a subdirectory "world_bank" in the skrub data directory.
 
-    Returns
-    -------
-    mapping of str to any
-        A dictionary containing:
-          - `description` : str
-              The description of the dataset,
-              as gathered from World Bank data.
-          - `source` : str
-              The dataset's URL from the World Bank data platform.
-          - `path` : pathlib.Path
-              The local path leading to the dataset,
-              saved as a CSV file.
-    """
-    if data_directory is None:
-        data_directory = get_data_dir(name="world_bank")
-
-    csv_path = (data_directory / f"{indicator_id}.csv").resolve()
-    data_directory.mkdir(parents=True, exist_ok=True)
-    url = f"https://api.worldbank.org/v2/en/indicator/{indicator_id}?downloadformat=csv"
-    if csv_path.is_file():
-        df = pd.read_csv(csv_path, nrows=0)
-        indicator_name = df.columns[1]
-    else:
-        warnings.warn(
-            (
-                f"Could not find the dataset {indicator_id!r} locally. "
-                "Downloading it from the World Bank; this might take a while... "
-                "If it is interrupted, some files might be invalid/incomplete: "
-                "if on the following run, the fetching raises errors, you can try "
-                f"fixing this issue by deleting the directory {csv_path!s}."
-            ),
-            UserWarning,
-            stacklevel=2,
-        )
-        try:
-            filehandle, _ = urllib.request.urlretrieve(url)
-            zip_file_object = ZipFile(filehandle, "r")
-            for name in zip_file_object.namelist():
-                if "Metadata" not in name:
-                    true_file = name
-                    break
-            else:
-                raise FileNotFoundError(
-                    "Could not find any non-metadata file "
-                    f"for indicator {indicator_id!r}."
-                )
-            file = zip_file_object.open(true_file)
-        except BadZipFile as e:
-            raise FileNotFoundError(
-                "Couldn't find csv file, the indicator id "
-                f"{indicator_id!r} seems invalid."
-            ) from e
-        except URLError:
-            raise URLError("No internet connection or the website is down.")
-        # Read and modify the csv file
-        df = pd.read_csv(file, skiprows=3)  # FIXME: why three rows?
-        indicator_name = df.iloc[0, 2]
-        df[indicator_name] = df.stack().groupby(level=0).last()
-        df = df[df[indicator_name] != indicator_id]
-        df = df[["Country Name", indicator_name]]
-
-        df.to_csv(csv_path, index=False)
-    description = f"This table shows the {indicator_name!r} World Bank indicator."
-    return {
-        "dataset_name": indicator_name,
-        "description": description,
-        "source": url,
-        "path": csv_path,
-    }
-
-
-def _fetch_figshare(
-    figshare_id: str,
-    data_directory: Path | None = None,
-) -> dict[str, Any]:
-    """Fetch a dataset from figshare using the download ID number.
+def fetch_medical_charge(data_home=None):
+    """Fetches the medical charge dataset (regression), available at \
+        https://github.com/skrub-data/skrub-data-files
+
+    Description of the dataset:
+        The Inpatient Utilization and Payment Public Use File (Inpatient PUF)
+        provides information on inpatient discharges for Medicare
+        fee-for-service beneficiaries. The Inpatient PUF includes information
+        on utilization, payment (total payment and Medicare payment), and
+        hospital-specific charges for the more than 3,000 U.S. hospitals that
+        receive Medicare Inpatient Prospective Payment System (IPPS) payments.
+        The PUF is organized by hospital and Medicare Severity Diagnosis
+        Related Group (MS-DRG) and covers Fiscal Year (FY) 2011 through FY 2016.
 
     Parameters
     ----------
-    figshare_id : str
-        The ID of the dataset to fetch.
-    data_directory : pathlib.Path, optional
-        The directory where the dataset is stored.
-        By default, a subdirectory "figshare" in the skrub data directory.
+    data_home: str or path, default=None
+        The directory where to download and unzip the files.
 
     Returns
     -------
-    mapping of str to any
-        A dictionary containing:
-          - `description` : str
-              The description of the dataset.
-          - `source` : str
-              The dataset's URL.
-          - `path` : pathlib.Path
-              The local path leading to the dataset,
-              saved as a parquet file.
-
-    Notes
-    -----
-    The files are read and returned in parquet format, this function needs
-    pyarrow installed to run correctly.
-    """
-    if data_directory is None:
-        data_directory = get_data_dir(name="figshare")
-
-    parquet_path = (data_directory / f"figshare_{figshare_id}.parquet").resolve()
-    data_directory.mkdir(parents=True, exist_ok=True)
-    url = f"https://ndownloader.figshare.com/files/{figshare_id}"
-    description = f"This table shows the {figshare_id!r} figshare file."
-    file_paths = [
-        file
-        for file in data_directory.iterdir()
-        if file.name.startswith(f"figshare_{figshare_id}")
-    ]
-    if len(file_paths) > 0:
-        if len(file_paths) == 1:
-            parquet_paths = [str(file_paths[0].resolve())]
-        else:
-            parquet_paths = []
-            for path in file_paths:
-                parquet_path = str(path.resolve())
-                parquet_paths += [parquet_path]
-        return {
-            "dataset_name": figshare_id,
-            "description": description,
-            "source": url,
-            "path": parquet_paths,
-        }
-    else:
-        warnings.warn(
-            (
-                f"Could not find the dataset {figshare_id!r} locally. "
-                "Downloading it from figshare; this might take a while... "
-                "If it is interrupted, some files might be invalid/incomplete: "
-                "if on the following run, the fetching raises errors, you can try "
-                f"fixing this issue by deleting the directory {parquet_path!s}."
-            ),
-            UserWarning,
-            stacklevel=2,
-        )
-        import_optional_dependency(
-            "pyarrow", extra="pyarrow is required for parquet support."
-        )
-        from pyarrow.parquet import ParquetFile
-
-        try:
-            filehandle, _ = urllib.request.urlretrieve(url)
-
-            # checksum the file
-            checksum = _sha256(filehandle)
-            if figshare_id in figshare_id_to_hash:
-                expected_checksum = figshare_id_to_hash[figshare_id]
-                if checksum != expected_checksum:
-                    raise OSError(
-                        f"{filehandle!r} SHA256 checksum differs from "
-                        f"expected ({checksum}!={expected_checksum}) ; "
-                        "file is probably corrupted. Please try again. "
-                        "If the error persists, please open an issue on GitHub. "
-                    )
-
-            df = ParquetFile(filehandle)
-            record = df.iter_batches(
-                batch_size=1_000_000,
-            )
-            idx = []
-            for _, x in enumerate(
-                chain(range(0, df.metadata.num_rows, 1_000_000), [df.metadata.num_rows])
-            ):
-                idx += [x]
-            parquet_paths = []
-            for i in range(1, len(idx)):
-                parquet_path = (
-                    data_directory / f"figshare_{figshare_id}_{idx[i]}.parquet"
-                ).resolve()
-                batch = next(record).to_pandas()
-                batch.to_parquet(parquet_path, index=False)
-                parquet_paths += [parquet_path]
-            return {
-                "dataset_name": figshare_id,
-                "description": description,
-                "source": url,
-                "path": parquet_paths,
-            }
-        except URLError:
-            raise URLError("No internet connection or the website is down.")
-
-
-def _fetch_movielens(dataset_id: str, data_directory: Path | None = None) -> dict[str]:
-    """Downloads a subset of the Movielens dataset.
+    bunch : sklearn.utils.Bunch
+        A dictionary-like object with the following keys:
 
-    Parameters
-    ----------
-    data_directory : :obj:`~pathlib.Path`
-        The directory in which the data will be saved.
+        - medical_charge : pd.DataFrame, the dataframe
+        - X : pd.DataFrame, features, i.e. the dataframe without the target labels
+        - y : pd.DataFrame, target labels
+        - metadata : a dictionary containing the name, description, source and target
     """
-    if data_directory is None:
-        data_directory = get_data_dir()
-
-    options = ["movies", "ratings"]
-    if dataset_id not in options:
-        raise ValueError(f"dataset_id options are {options}, got '{dataset_id}'.")
-
-    zip_directory = Path("ml-latest-small")
-    file_path = data_directory / zip_directory / f"{dataset_id}.csv"
-    detail_path = data_directory / zip_directory / "README.txt"
-    if not file_path.is_file() or not detail_path.is_file():
-        # If the details file or the features file don't exist,
-        # download the dataset.
-        warnings.warn(
-            (
-                f"Could not find the dataset {dataset_id!r} locally. "
-                "Downloading it from MovieLens; this might take a while... "
-                "If it is interrupted, some files might be invalid/incomplete: "
-                "if on the following run, the fetching raises errors, you can try "
-                f"fixing this issue by deleting the directory {data_directory!s}."
-            ),
-            UserWarning,
-            stacklevel=2,
-        )
-        _download_and_write_movielens_dataset(
-            dataset_id,
-            data_directory,
-            zip_directory,
-        )
-
-    description = open(detail_path).read()
-
-    url = MOVIELENS_URL.format(zip_directory=zip_directory)
-
-    return {
-        "description": description,
-        "source": url,
-        "path": Path(data_directory) / zip_directory / f"{dataset_id}.csv",
-    }
-
-
-def _download_and_write_movielens_dataset(dataset_id, data_directory, zip_directory):
-    url = MOVIELENS_URL.format(zip_directory=zip_directory)
-    tmp_file = None
-    try:
-        tmp_file, _ = urllib.request.urlretrieve(url)
-        data_file = str((zip_directory / f"{dataset_id}.csv").as_posix())
-        readme_file = str((zip_directory / "README.txt").as_posix())
-        with ZipFile(tmp_file, "r") as zip_file:
-            zip_file.extractall(
-                data_directory,
-                members=[data_file, readme_file],
-            )
-    except Exception:
-        if tmp_file is not None and Path(tmp_file).exists():
-            Path(tmp_file).unlink()
-        raise
-
-
-def _download_and_write_openml_dataset(dataset_id: int, data_directory: Path) -> None:
-    """Downloads a dataset from OpenML, taking care of creating the directories.
+    return load_simple_dataset("medical_charge", data_home)
 
-    Parameters
-    ----------
-    dataset_id : int
-        The ID of the dataset to download.
-    data_directory : pathlib.Path
-        The directory in which the data will be saved.
-
-    Raises
-    ------
-    ValueError
-        If the ID is incorrect (does not exist on OpenML)
-    urllib.error.URLError
-        If there is no Internet connection.
-    """
-    # The ``fetch_openml()`` function returns a Scikit-Learn ``Bunch`` object,
-    # which behaves just like a ``namedtuple``.
-    # However, we do not want to save this data into memory:
-    # we will read it from the disk later.
-    kwargs = {}
-    if parse_version("1.2") <= parse_version(sklearn_version) < parse_version("1.2.2"):
-        # Avoid the warning, but don't use auto yet because of
-        # https://github.com/scikit-learn/scikit-learn/issues/25478
-        kwargs.update(
-            {
-                "parser": "liac-arff",
-            }
-        )
-    elif parse_version(sklearn_version) >= parse_version("1.2.2"):
-        kwargs.update(
-            {
-                "parser": "auto",
-            }
-        )
-    fetch_openml(
-        data_id=dataset_id,
-        data_home=str(data_directory),
-        as_frame=True,
-        **kwargs,
-    )
 
+def fetch_midwest_survey(data_home=None):
+    """Fetches the midwest survey dataset (classification), available at \
+        https://github.com/skrub-data/skrub-data-files
 
-def _read_json_from_gz(compressed_dir_path: Path) -> dict:
-    """Opens a gzip file, reads its content (JSON expected), and returns a dictionary.
+    Description of the dataset:
+        Survey to know if people self-identify as Midwesterners.
 
     Parameters
     ----------
-    compressed_dir_path : pathlib.Path
-        Path to the `.gz` file to read.
+    data_home: str or path, default=None
+        The directory where to download and unzip the files.
 
     Returns
     -------
-    dict
-        The information contained in the file,
-        parsed from plain-text JSON.
-    """
-    if not compressed_dir_path.is_file():
-        raise FileNotFoundError(f"Couldn't find file {compressed_dir_path!s}")
+    bunch : sklearn.utils.Bunch
+        A dictionary-like object with the following keys:
 
-    # Read content
-    with gzip.open(compressed_dir_path, mode="rt") as gz:
-        content = gz.read()
+        - midwest_survey : pd.DataFrame, the dataframe
+        - X : pd.DataFrame, features, i.e. the dataframe without the target labels
+        - y : pd.DataFrame, target labels
+        - metadata : a dictionary containing the name, description, source and target
+    """
+    return load_simple_dataset("midwest_survey", data_home)
 
-    details_json = json.JSONDecoder().decode(content)
-    return details_json
 
+def fetch_open_payments(data_home=None):
+    """Fetches the open payments dataset (classification), available at \
+        https://github.com/skrub-data/skrub-data-files
 
-def _get_details(compressed_dir_path: Path) -> Details:
-    """Gets useful details from the details file.
+    Description of the dataset:
+        Payments given by healthcare manufacturing companies to medical doctors
+        or hospitals.
 
     Parameters
     ----------
-    compressed_dir_path : pathlib.Path
-        The path to the `.gz` file containing the details.
+    data_home: str or path, default=None
+        The directory where to download and unzip the files.
 
     Returns
     -------
-    Details
-        A Details instance.
+    bunch : sklearn.utils.Bunch
+        A dictionary-like object with the following keys:
+
+        - open_payments : pd.DataFrame, the dataframe
+        - X : pd.DataFrame, features, i.e. the dataframe without the target labels
+        - y : pd.DataFrame, target labels
+        - metadata : a dictionary containing the name, description, source and target
     """
-    details = _read_json_from_gz(compressed_dir_path)["data_set_description"]
-    # We filter out the irrelevant information.
-    # If you want to modify this list (to add or remove items)
-    # you must also modify the ``Details`` object definition.
-    return Details(
-        name=details["name"],
-        file_id=details["file_id"],
-        description=details["description"],
-    )
+    return load_simple_dataset("open_payments", data_home)
 
 
-def _get_features(compressed_dir_path: Path) -> Features:
-    """Gets features that can be inserted in the CSV file.
+def fetch_traffic_violations(data_home=None):
+    """Fetches the traffic violations dataset (classification), available at \
+        https://github.com/skrub-data/skrub-data-files
 
-    The most important feature are the column names.
+    Description of the dataset:
+        This dataset contains traffic violation information from all electronic
+        traffic violations issued in the Montgomery County, MD. Any information
+        that can be used to uniquely identify the vehicle, the vehicle owner or
+        the officer issuing the violation will not be published.
 
     Parameters
     ----------
-    compressed_dir_path : pathlib.Path
-        Path to the gzip file containing the features.
+    data_home: str or path, default=None
+        The directory where to download and unzip the files.
 
     Returns
     -------
-    Features
-        A Features instance.
-    """
-    raw_features = _read_json_from_gz(compressed_dir_path)["data_features"]
-    # We filter out the irrelevant information.
-    # If you want to modify this list (to add or remove items)
-    # you must also modify the ``Features`` object definition.
-    return Features(names=[column["name"] for column in raw_features["feature"]])
-
-
-def _export_gz_data_to_csv(
-    compressed_dir_path: Path, destination_file: Path, features: Features
-) -> None:
-    """Reads a gzip file containing ARFF data, and writes it to a CSV file.
+    bunch : sklearn.utils.Bunch
+        A dictionary-like object with the following keys:
 
-    Parameters
-    ----------
-    compressed_dir_path : pathlib.Path
-        Path to the `.gz` file containing the ARFF data.
-    destination_file : pathlib.Path
-        A CSV file to write to.
-    features : Features
-        A Features instance containing the first CSV line (the column names).
+        - traffic_violations : pd.DataFrame, the dataframe
+        - X : pd.DataFrame, features, i.e. the dataframe without the target labels
+        - y : pd.DataFrame, target labels
+        - metadata : a dictionary containing the name, description, source and target
     """
-    atdata_found = False
-    with destination_file.open(mode="w", encoding="utf8") as csv:
-        with gzip.open(compressed_dir_path, mode="rt", encoding="utf8") as gz:
-            gz: TextIO  # Clarify for IDEs
-            csv.write(_features_to_csv_format(features))
-            csv.write("\n")
-            # We will look at each line of the file until we find
-            # "@data": only after this tag is the actual CSV data.
-            for line in gz.readlines():
-                if not atdata_found:
-                    if line.lower().startswith("@data"):
-                        atdata_found = True
-                else:
-                    csv.write(line)
-
-
-def _features_to_csv_format(features: Features) -> str:
-    return ",".join(features.names)
-
-
-def _fetch_dataset_as_dataclass(
-    source: Literal["openml", "world_bank", "figshare"],
-    dataset_name: str,
-    dataset_id: int | str,
-    target: str | None,
-    load_dataframe: bool,
-    data_directory: Path | str | None = None,
-    read_csv_kwargs: dict | None = None,
-) -> DatasetAll | DatasetInfoOnly:
-    """Fetches a dataset from a source, and returns it as a dataclass.
-
-    Takes a dataset identifier, a target column name (if applicable),
-    and some additional keyword arguments for read_csv.
-
-    If you don't need the dataset to be loaded in memory,
-    pass `load_dataframe=False`.
-
-    To save/load the dataset to/from a specific directory,
-    pass `data_directory`. If `None`, uses the default skrub
-    data directory.
-
-    If the dataset doesn't have a target (unsupervised learning or inapplicable),
-    explicitly specify `target=None`.
+    return load_simple_dataset("traffic_violations", data_home)
 
-    Returns
-    -------
-    DatasetAll
-        If `load_dataframe=True`
 
-    DatasetInfoOnly
-        If `load_dataframe=False`
-    """
-    if isinstance(data_directory, str):
-        data_directory = Path(data_directory)
-
-    if source == "openml":
-        info = _fetch_openml_dataset(dataset_id, data_directory)
-    elif source == "world_bank":
-        info = _fetch_world_bank_data(dataset_id, data_directory)
-    elif source == "figshare":
-        info = _fetch_figshare(dataset_id, data_directory)
-    elif source == "movielens":
-        info = _fetch_movielens(dataset_id, data_directory)
-    else:
-        raise ValueError(f"Unknown source {source!r}")
-
-    if read_csv_kwargs is None:
-        read_csv_kwargs = {}
-
-    if target is None:
-        target = []
-
-    if load_dataframe:
-        if source == "figshare":
-            df = pd.read_parquet(info["path"])
-        else:
-            df = pd.read_csv(info["path"], **read_csv_kwargs)
-        y = df[target]
-        X = df.drop(target, axis="columns")
-        dataset = DatasetAll(
-            name=dataset_name,
-            description=info["description"],
-            source=info["source"],
-            target=target,
-            X=X,
-            y=y,
-            path=info["path"],
-            read_csv_kwargs=read_csv_kwargs,
-        )
-    else:
-        dataset = DatasetInfoOnly(
-            name=dataset_name,
-            description=info["description"],
-            source=info["source"],
-            target=target,
-            path=info["path"],
-            read_csv_kwargs=read_csv_kwargs,
-        )
-
-    return dataset
-
-
-# Datasets fetchers section
-# Public API
-
-
-def fetch_employee_salaries(
-    *,
-    load_dataframe: bool = True,
-    drop_linked: bool = True,
-    drop_irrelevant: bool = True,
-    overload_job_titles: bool = True,
-    data_directory: Path | str | None = None,
-) -> DatasetAll | DatasetInfoOnly:
-    """Fetches the employee salaries dataset (regression), available at https://openml.org/d/42125
+def fetch_drug_directory(data_home=None):
+    """Fetches the drug directory dataset (classification), available at \
+        https://github.com/skrub-data/skrub-data-files
 
     Description of the dataset:
-        Annual salary information including gross pay and overtime pay for all
-        active, permanent employees of Montgomery County, MD paid in calendar
-        year 2016. This information will be published annually each year.
+        Product listing data submitted to the U.S. FDA for all unfinished,
+        unapproved drugs.
 
     Parameters
     ----------
-    drop_linked : bool, default=True
-        Drops columns "2016_gross_pay_received" and "2016_overtime_pay",
-        which are closely linked to "current_annual_salary", the target.
-
-    drop_irrelevant : bool, default=True
-        Drops column "full_name", which is usually irrelevant to the
-        statistical analysis.
-
-    overload_job_titles : bool, default=True
-        Uses the column `underfilled_job_title` to enrich the
-        `employee_position_title` column, as it contains more detailed
-        information about the job title.
-
-    data_directory: pathlib.Path or str, optional
-        The directory where the dataset is stored.
+    data_home: str or path, default=None
+        The directory where to download and unzip the files.
 
     Returns
     -------
-    DatasetAll
-        If `load_dataframe=True`
+    bunch : sklearn.utils.Bunch
+        A dictionary-like object with the following keys:
 
-    DatasetInfoOnly
-        If `load_dataframe=False`
+        - drug_directory : pd.DataFrame, the dataframe
+        - X : pd.DataFrame, features, i.e. the dataframe without the target labels
+        - y : pd.DataFrame, target labels
+        - metadata : a dictionary containing the name, description, source and target
     """
-    dataset = _fetch_dataset_as_dataclass(
-        source="openml",
-        dataset_name="Employee salaries",
-        dataset_id=EMPLOYEE_SALARIES_ID,
-        target="current_annual_salary",
-        read_csv_kwargs={
-            "quotechar": "'",
-            "escapechar": "\\",
-            "na_values": ["?"],
-        },
-        load_dataframe=load_dataframe,
-        data_directory=data_directory,
-    )
-    if load_dataframe:
-        if drop_linked:
-            dataset.X.drop(
-                ["2016_gross_pay_received", "2016_overtime_pay"], axis=1, inplace=True
-            )
-        if drop_irrelevant:
-            dataset.X.drop(["full_name"], axis=1, inplace=True)
-        if overload_job_titles:
-            dataset.X["employee_position_title"] = dataset.X[
-                "underfilled_job_title"
-            ].fillna(dataset.X["employee_position_title"])
-            dataset.X.drop(
-                labels=["underfilled_job_title"], axis="columns", inplace=True
-            )
-
-    return dataset
-
-
-def fetch_road_safety(
-    *,
-    load_dataframe: bool = True,
-    data_directory: Path | str | None = None,
-) -> DatasetAll | DatasetInfoOnly:
-    """Fetches the road safety dataset (classification), available at https://openml.org/d/42803
+    return load_simple_dataset("drug_directory", data_home)
 
-    Description of the dataset:
-        Data reported to the police about the circumstances of personal injury
-        road accidents in Great Britain from 1979, and the maker and model
-        information of vehicles involved in the respective accident.
-        This version includes data up to 2015.
 
-    Returns
-    -------
-    DatasetAll
-        If `load_dataframe=True`
+def fetch_credit_fraud(data_home=None):
+    """Fetch the credit fraud dataset (classification) available at \
+        https://github.com/skrub-data/skrub-data-files
 
-    DatasetInfoOnly
-        If `load_dataframe=False`
-    """
-    return _fetch_dataset_as_dataclass(
-        source="openml",
-        dataset_name="Road safety",
-        dataset_id=ROAD_SAFETY_ID,
-        target="Sex_of_Driver",
-        read_csv_kwargs={
-            "na_values": ["?"],
-        },
-        load_dataframe=load_dataframe,
-        data_directory=data_directory,
-    )
+    This is an imbalanced binary classification use-case. This dataset consists in
+    two tables:
 
+    - baskets, containing the binary fraud target label
+    - products
 
-def fetch_medical_charge(
-    *,
-    load_dataframe: bool = True,
-    data_directory: Path | str | None = None,
-) -> DatasetAll | DatasetInfoOnly:
-    """Fetches the medical charge dataset (regression), available at https://openml.org/d/42720
+    Baskets contain at least one product each, so aggregation then joining operations
+    are required to build a design matrix.
 
-    Description of the dataset:
-        The Inpatient Utilization and Payment Public Use File (Inpatient PUF)
-        provides information on inpatient discharges for Medicare
-        fee-for-service beneficiaries. The Inpatient PUF includes information
-        on utilization, payment (total payment and Medicare payment), and
-        hospital-specific charges for the more than 3,000 U.S. hospitals that
-        receive Medicare Inpatient Prospective Payment System (IPPS) payments.
-        The PUF is organized by hospital and Medicare Severity Diagnosis
-        Related Group (MS-DRG) and covers Fiscal Year (FY) 2011 through FY 2016.
+    Parameters
+    ----------
+    data_home: str or path, default=None
+        The directory where to download and unzip the files.
 
     Returns
     -------
-    DatasetAll
-        If `load_dataframe=True`
+    bunch : sklearn.utils.Bunch
+        A dictionary-like object with the following keys:
 
-    DatasetInfoOnly
-        If `load_dataframe=False`
+        - baskets : pd.DataFrame, table containing baskets ID and target
+        - product : pd.DataFrame, table containing features about products contained in
+          baskets
+        - metadata : a dictionary containing the name, description, source and target
     """
-    return _fetch_dataset_as_dataclass(
-        source="openml",
-        dataset_name="Medical charge",
-        dataset_id=MEDICAL_CHARGE_ID,
-        target="Average_Total_Payments",
-        read_csv_kwargs={
-            "quotechar": "'",
-            "escapechar": "\\",
-        },
-        load_dataframe=load_dataframe,
-        data_directory=data_directory,
-    )
-
+    return load_dataset_files("credit_fraud", data_home)
 
-def fetch_midwest_survey(
-    *,
-    load_dataframe: bool = True,
-    data_directory: Path | str | None = None,
-) -> DatasetAll | DatasetInfoOnly:
-    """Fetches the midwest survey dataset (classification), available at https://openml.org/d/42805
-
-    Description of the dataset:
-        Survey to know if people self-identify as Midwesterners.
 
-    Returns
-    -------
-    DatasetAll
-        If `load_dataframe=True`
-
-    DatasetInfoOnly
-        If `load_dataframe=False`
-    """
-    return _fetch_dataset_as_dataclass(
-        source="openml",
-        dataset_name="Midwest survey",
-        dataset_id=MIDWEST_SURVEY_ID,
-        target="Census_Region",
-        read_csv_kwargs={
-            "quotechar": "'",
-            "escapechar": "\\",
-        },
-        load_dataframe=load_dataframe,
-        data_directory=data_directory,
-    )
+def fetch_toxicity(data_home=None):
+    """Fetch the toxicity dataset (classification) available at \
+        https://github.com/skrub-data/skrub-data-files
 
+    This is a balanced binary classification use-case, where the single table
+    consists in only two columns:
+    - `text`: the text of the comment
+    - `is_toxic`: whether or not the comment is toxic
 
-def fetch_open_payments(
-    *,
-    load_dataframe: bool = True,
-    data_directory: Path | str | None = None,
-) -> DatasetAll | DatasetInfoOnly:
-    """Fetches the open payments dataset (classification), available at https://openml.org/d/42738
-
-    Description of the dataset:
-        Payments given by healthcare manufacturing companies to medical doctors
-        or hospitals.
+    Parameters
+    ----------
+    data_home: str or path, default=None
+        The directory where to download and unzip the files.
 
     Returns
     -------
-    DatasetAll
-        If `load_dataframe=True`
+    bunch : sklearn.utils.Bunch
+        A dictionary-like object with the following keys:
 
-    DatasetInfoOnly
-        If `load_dataframe=False`
+        - toxicity : pd.DataFrame, the dataframe
+        - X : pd.DataFrame, features, i.e. the dataframe without the target labels
+        - y : pd.DataFrame, target labels
+        - metadata : a dictionary containing the name, description, source and target
     """
-    return _fetch_dataset_as_dataclass(
-        source="openml",
-        dataset_name="Open payments",
-        dataset_id=OPEN_PAYMENTS_ID,
-        target="status",
-        read_csv_kwargs={
-            "quotechar": "'",
-            "escapechar": "\\",
-            "na_values": ["?"],
-        },
-        load_dataframe=load_dataframe,
-        data_directory=data_directory,
-    )
-
-
-def fetch_traffic_violations(
-    *,
-    load_dataframe: bool = True,
-    data_directory: Path | str | None = None,
-) -> DatasetAll | DatasetInfoOnly:
-    """Fetches the traffic violations dataset (classification), available at https://openml.org/d/42132
+    return load_simple_dataset("toxicity", data_home)
 
-    Description of the dataset:
-        This dataset contains traffic violation information from all electronic
-        traffic violations issued in the Montgomery County, MD. Any information
-        that can be used to uniquely identify the vehicle, the vehicle owner or
-        the officer issuing the violation will not be published.
 
-    Returns
-    -------
-    DatasetAll
-        If `load_dataframe=True`
+def fetch_videogame_sales(data_home=None):
+    """Fetch the videogame sales dataset (regression) available at \
+        https://github.com/skrub-data/skrub-data-files
 
-    DatasetInfoOnly
-        If `load_dataframe=False`
-    """
-    return _fetch_dataset_as_dataclass(
-        source="openml",
-        dataset_name="Traffic violations",
-        dataset_id=TRAFFIC_VIOLATIONS_ID,
-        target="violation_type",
-        read_csv_kwargs={
-            "quotechar": "'",
-            "escapechar": "\\",
-            "na_values": ["?"],
-        },
-        load_dataframe=load_dataframe,
-        data_directory=data_directory,
-    )
+    This is a regression use-case, where the single table contains information
+    about videogames such as the publisher and platform, and the goal is to
+    predict the number of sales worldwide.
 
+    .. warning::
 
-def fetch_drug_directory(
-    *,
-    load_dataframe: bool = True,
-    data_directory: Path | str | None = None,
-) -> DatasetAll | DatasetInfoOnly:
-    """Fetches the drug directory dataset (classification), available at https://openml.org/d/43044
+        The original dataset is ordered by decreasing number of sales. This
+        should be taken into account for cross-validation. Depending on the
+        desired setting, one might consider shuffling the rows or ordering by
+        publication year and splitting by year.
 
-    Description of the dataset:
-        Product listing data submitted to the U.S. FDA for all unfinished,
-        unapproved drugs.
+    Parameters
+    ----------
+    data_home: str or path, default=None
+        The directory where to download and unzip the files.
 
     Returns
     -------
-    DatasetAll
-        If `load_dataframe=True`
+    bunch : sklearn.utils.Bunch
+        A dictionary-like object with the following keys:
 
-    DatasetInfoOnly
-        If `load_dataframe=False`
+        - videogame_sales : pd.DataFrame, the full dataframe
+        - X : pd.DataFrame, features, i.e. the dataframe without the target labels
+        - y : pd.DataFrame, target labels
+        - metadata : a dictionary containing the name, source and target
     """
-    return _fetch_dataset_as_dataclass(
-        source="openml",
-        dataset_name="Drug directory",
-        dataset_id=DRUG_DIRECTORY_ID,
-        target="PRODUCTTYPENAME",
-        read_csv_kwargs={
-            "quotechar": "'",
-            "escapechar": "\\",
-        },
-        load_dataframe=load_dataframe,
-        data_directory=data_directory,
+
+    result = load_simple_dataset("videogame_sales", data_home)
+    result["X"] = result["X"].drop(
+        columns=["Rank", "NA_Sales", "EU_Sales", "JP_Sales", "Other_Sales"]
     )
+    return result
 
 
-def fetch_world_bank_indicator(
-    indicator_id: str,
-    *,
-    load_dataframe: bool = True,
-    data_directory: Path | str | None = None,
-) -> DatasetAll | DatasetInfoOnly:
-    """Fetches a dataset of an indicator from the World Bank open data platform.
+def fetch_bike_sharing(data_home=None):
+    """Fetch the bike sharing dataset (regression) available at \
+        https://github.com/skrub-data/skrub-data-files
 
-    Description of the dataset:
-        The dataset contains two columns: the indicator value and the
-        country names. A list of all available indicators can be found
-        at https://data.worldbank.org/indicator.
+    This is a regression use-case, where the goal is to predict demand for a
+    bike-sharing service. The features are the dates and holiday and weather
+    information.
+
+    Parameters
+    ----------
+    data_home: str or path, default=None
+        The directory where to download and unzip the files.
 
     Returns
     -------
-    DatasetAll
-        If `load_dataframe=True`
+    bunch : sklearn.utils.Bunch
+        A dictionary-like object with the following keys:
 
-    DatasetInfoOnly
-        If `load_dataframe=False`
+        - bike_sharing : pd.DataFrame, the full dataframe
+        - X : pd.DataFrame, features, i.e. the dataframe without the target labels
+        - y : pd.DataFrame, target labels
+        - metadata : a dictionary containing the name and target
     """
-    return _fetch_dataset_as_dataclass(
-        source="world_bank",
-        dataset_name=f"World Bank indicator {indicator_id!r}",
-        dataset_id=indicator_id,
-        target=None,
-        load_dataframe=load_dataframe,
-        data_directory=data_directory,
-    )
 
+    return load_simple_dataset("bike_sharing", data_home)
 
-def fetch_figshare(
-    figshare_id: str,
-    *,
-    target=None,
-    load_dataframe: bool = True,
-    data_directory: Path | str | None = None,
-) -> DatasetAll | DatasetInfoOnly:
-    """Fetches a table of from figshare.
 
-    Returns
-    -------
-    DatasetAll
-        If `load_dataframe=True`
-
-    DatasetInfoOnly
-        If `load_dataframe=False`
-    """
-    return _fetch_dataset_as_dataclass(
-        source="figshare",
-        dataset_name=f"figshare_{figshare_id!r}",
-        dataset_id=figshare_id,
-        target=target,
-        load_dataframe=load_dataframe,
-        data_directory=data_directory,
-    )
+def fetch_movielens(data_home=None):
+    """Fetch the movielens dataset (regression) available at \
+        https://github.com/skrub-data/skrub-data-files
 
-
-def fetch_movielens(
-    dataset_id: str = "ratings",
-    *,
-    load_dataframe: bool = True,
-    data_directory: Path | None = None,
-) -> DatasetAll | DatasetInfoOnly:
-    """Fetches a dataset from Movielens.
+    This is a regression use-case, where the goal is to predict movie ratings.
+    More details are provided in the output's ``metadata['description']``.
 
     Parameters
     ----------
-    dataset_id : str
-        Either 'ratings' or 'movies'
+    data_home: str or path, default=None
+        The directory where to download and unzip the files.
 
     Returns
     -------
-    :obj:`DatasetAll`
-        If `load_dataframe=True`
+    bunch : sklearn.utils.Bunch
+        A dictionary-like object with the following keys:
 
-    :obj:`DatasetInfoOnly`
-        If `load_dataframe=False`
+        - movies : pd.DataFrame, movie ID, title and genres
+        - ratings: pd.DataFrame, user ID, movie ID, rating
+        - metadata : a dictionary containing the name source and description
     """
-    return _fetch_dataset_as_dataclass(
-        source="movielens",
-        dataset_name="ml-latest-small",
-        dataset_id=dataset_id,
-        target=None,
-        load_dataframe=load_dataframe,
-        data_directory=data_directory,
-    )
 
+    return load_dataset_files("movielens", data_home)
 
-def fetch_credit_fraud(load_dataframe=True, data_directory=None):
-    """Fetch the credit fraud dataset from figshare.
 
-    This is an imbalanced binary classification use-case. This dataset consists in
-    two tables:
+def fetch_flight_delays(data_home=None):
+    """Fetch the flight delays dataset (regression) available at \
+        https://github.com/skrub-data/skrub-data-files
 
-    - baskets, containing the binary fraud target label
-    - products
-
-    Baskets contain at least one product each, so aggregation then joining operations
-    are required to build a design matrix.
-
-    More details on \
-        `Figshare <https://figshare.com/articles/dataset/bnp_fraud_parquet/26892673>`_
+    This is a regression use-case, where the goal is to predict flight delays.
 
     Parameters
     ----------
-    load_dataframe : bool, default=True
-        Whether or not to load the dataset in memory after download.
-
-    data_directory : str, default=None
-        The directory to which the dataset will be written during the download.
-        If None, the directory is set to ~/skrub_data.
+    data_home: str or path, default=None
+        The directory where to download and unzip the files.
 
     Returns
     -------
     bunch : sklearn.utils.Bunch
-        A dictionnary-like object, whose fields are:
-        - product : pd.DataFrame
-        - baskets : pd.DataFrame
-        - source_product : str
-        - source_baskets : str
-        - path_product : str
-        - path_baskets : str
+        A dictionary-like object with the following keys:
+
+        - flights: information about the flights, including departure and
+          arrival airports, and delay.
+        - airports: information about airports, such as city and coordinates.
+          The airport's ``iata`` can be matched to the flights' ``Origin`` and
+          ``Dest``.
+        - weather: weather data that could be used to help improve the delay
+          predictions. Note the weather data is not measured at the airports
+          directly but at weather stations, whose location and information is
+          provided in ``stations``.
+        - stations: information about the weather stations. ``weather`` and
+          ``stations`` can be joined on their ``ID`` columns. Weather stations
+          can only be matched to the nearest airport based on the latitude and
+          longitude.
+        - metadata : a dictionary containing the name  of the dataset.
     """
-    dataset_name_to_id = {
-        "products": "49176205",
-        "baskets": "49176202",
-    }
-    bunch = Bunch()
-    for dataset_name, figshare_id in dataset_name_to_id.items():
-        dataset = fetch_figshare(
-            figshare_id,
-            load_dataframe=load_dataframe,
-            data_directory=data_directory,
-        )
-        bunch[dataset_name] = dataset.X
-        bunch[f"source_{dataset_name}"] = dataset.source
-        bunch[f"path_{dataset_name}"] = dataset.path
-
-    return bunch
-
-
-def fetch_toxicity(load_dataframe=True, data_directory=None):
-    """Fetch the toxicity dataset from figshare.
+    return load_dataset_files("flight_delays", data_home)
 
-    This is a balanced binary classification use-case, where the single table
-    consists in only two columns:
-    - `text`: the text of the comment
-    - `is_toxic`: whether or not the comment is toxic
+
+def fetch_country_happiness(data_home=None):
+    """Fetch the happiness index dataset (regression) available at \
+        https://github.com/skrub-data/skrub-data-files
+
+    This is a regression use-case, where the goal is to predict the happiness
+    index. The dataset contains data from the `2022 World Happiness Report
+    <https://worldhappiness.report/>`_, and from `the World Bank open data
+    platform <https://data.worldbank.org/>`_.
 
     Parameters
     ----------
-    load_dataframe : bool, default=True
-        Whether or not to load the dataset in memory after download.
-
-    data_directory : str, default=None
-        The directory to which the dataset will be written during the download.
-        If None, the directory is set to ~/skrub_data.
+    data_home: str or path, default=None
+        The directory where to download and unzip the files.
 
     Returns
     -------
-    dataset : DatasetAll
-        A dataclass object whose fields are:
-        - name: str
-        - description: str
-        - source: str
-        - target: str
-        - X: pd.DataFrame
-        - y: pd.Series
-        - path: Path
-    """
+    bunch : sklearn.utils.Bunch
+        A dictionary-like object with the following keys:
 
-    figshare_id = "49823901"
-    dataset = fetch_figshare(
-        figshare_id,
-        load_dataframe=load_dataframe,
-        data_directory=data_directory,
-        target="is_toxic",
-    )
-    return dataset
+        - ``happiness_report``: dataframe, data from the world happiness report
+        - ``GDP_per_capita``, ``life_expectancy``, ``legal_rights_index``:
+          corresponding tables from the World Bank.
+    """
+    return load_dataset_files("country_happiness", data_home)
diff --git a/skrub/datasets/_ken_embeddings.py b/skrub/datasets/_ken_embeddings.py
index 174f8e3fb..8d8d902cc 100644
--- a/skrub/datasets/_ken_embeddings.py
+++ b/skrub/datasets/_ken_embeddings.py
@@ -1,15 +1,35 @@
 """
 Get the Wikipedia embeddings for feature augmentation.
 """
+# Required for ignoring lines too long in the docstrings
+# flake8: noqa: E501
+
+import urllib.request
+import warnings
+from dataclasses import dataclass
+from itertools import chain
+from pathlib import Path
+from urllib.error import URLError
 
 import pandas as pd
 from sklearn.decomposition import PCA
 
-from skrub.datasets import fetch_figshare
+from skrub._utils import import_optional_dependency
 
-# Required for ignoring lines too long in the docstrings
-# flake8: noqa: E501
+from ._utils import _sha256, get_data_dir
 
+figshare_id_to_hash = {
+    39142985: "47d73381ef72b050002a8642194c6718a4954ec9e6c556f4c4ddc6ed84ceec92",
+    39149066: "e479cf9741a90c40401697e7fa54409e3b9cfa09f27502877382e64e86fbfcd0",
+    39149069: "7b0dcdb15d3aeecba6022c929665ee064f6fb4b8b94186a6e89b6fbc781b3775",
+    39149072: "4f58f15168bb8a6cc8b152bd48995bc7d1a4d4d89a9e22d87aa51ccf30118122",
+    39149075: "7037603362af1d4bf73551d50644c0957cb91d2b4892e75413f57f415962029a",
+    39254360: "531130c714ba6ee9902108d4010f42388aa9c0b3167d124cd57e2c632df3e05a",
+    39266300: "37b23b2c37a1f7ff906bc7951cbed4be15d8417dad0762092282f7b491cf8c21",
+    39266678: "4e041322985e078de8b08acfd44b93a5ce347c1e501e9d869651e753de747ba1",
+    40019230: "4d43fed75dba1e59a5587bf31c1addf2647a1f15ebea66e93177ccda41e18f2f",
+    40019788: "67ae86496c8a08c6cc352f573160a094f605e7e0da022eb91c603abb7edf3747",
+}
 
 _correspondence_table_url = (
     "https://raw.githubusercontent.com/skrub-data/datasets"
@@ -17,6 +37,287 @@
 )
 
 
+@dataclass(unsafe_hash=True)
+class DatasetAll:
+    """
+    Represents a dataset and its information.
+    With this state, the dataset is loaded in memory as a DataFrame (`X` and `y`).
+    Additional information such as `path` and `read_csv_kwargs` are provided
+    in case the dataframe has to be read from disk, as such:
+
+    .. code:: python
+
+        ds = fetch_employee_salaries(load_dataframe=False)
+        df = pd.read_csv(ds.path, **ds.read_csv_kwargs)
+    """
+
+    name: str
+    description: str
+    source: str
+    target: str
+    X: pd.DataFrame
+    y: pd.Series
+    path: Path
+    read_csv_kwargs: dict[str]
+
+    def __eq__(self, other):
+        """
+        Implemented for the tests to work without bloating the code.
+        The main reason for which it's needed is that equality between
+        DataFrame (`X` and `y`) is often ambiguous and will raise an error.
+        """
+        return (
+            self.name == other.name
+            and self.description == other.description
+            and self.source == other.source
+            and self.target == other.target
+            and self.X.equals(other.X)
+            and self.y.equals(other.y)
+            and self.path == other.path
+            and self.read_csv_kwargs == other.read_csv_kwargs
+        )
+
+
+@dataclass(unsafe_hash=True)
+class DatasetInfoOnly:
+    """
+    Represents a dataset and its information.
+    With this state, the dataset is NOT loaded in memory, but can be read
+    with `path` and `read_csv_kwargs`, as such:
+
+    .. code:: python
+
+        ds = fetch_employee_salaries(load_dataframe=False)
+        df = pd.read_csv(ds.path, **ds.read_csv_kwargs)
+    """
+
+    name: str
+    description: str
+    source: str
+    target: str
+    path: Path
+    read_csv_kwargs: dict[str]
+
+
+def fetch_figshare(
+    figshare_id: str,
+    *,
+    target=None,
+    load_dataframe=True,
+    data_directory=None,
+):
+    """Fetches a table of from figshare.
+
+    Returns
+    -------
+    DatasetAll
+        If `load_dataframe=True`
+
+    DatasetInfoOnly
+        If `load_dataframe=False`
+    """
+    return _fetch_dataset_as_dataclass(
+        source="figshare",
+        dataset_name=f"figshare_{figshare_id!r}",
+        dataset_id=figshare_id,
+        target=target,
+        load_dataframe=load_dataframe,
+        data_directory=data_directory,
+    )
+
+
+def _fetch_dataset_as_dataclass(
+    source,
+    dataset_name,
+    dataset_id,
+    target,
+    load_dataframe,
+    data_directory=None,
+    read_csv_kwargs=None,
+):
+    """Fetches a dataset from a source, and returns it as a dataclass.
+
+    Takes a dataset identifier, a target column name (if applicable),
+    and some additional keyword arguments for read_csv.
+
+    If you don't need the dataset to be loaded in memory,
+    pass `load_dataframe=False`.
+
+    To save/load the dataset to/from a specific directory,
+    pass `data_directory`. If `None`, uses the default skrub
+    data directory.
+
+    If the dataset doesn't have a target (unsupervised learning or inapplicable),
+    explicitly specify `target=None`.
+
+    Returns
+    -------
+    DatasetAll
+        If `load_dataframe=True`
+
+    DatasetInfoOnly
+        If `load_dataframe=False`
+    """
+    if isinstance(data_directory, str):
+        data_directory = Path(data_directory)
+
+    if source == "figshare":
+        info = _fetch_figshare(dataset_id, data_directory)
+    else:
+        raise ValueError(f"Unknown source {source!r}")
+
+    if read_csv_kwargs is None:
+        read_csv_kwargs = {}
+
+    if target is None:
+        target = []
+
+    if load_dataframe:
+        if source == "figshare":
+            df = pd.read_parquet(info["path"])
+        else:
+            df = pd.read_csv(info["path"], **read_csv_kwargs)
+        y = df[target]
+        X = df.drop(target, axis="columns")
+        dataset = DatasetAll(
+            name=dataset_name,
+            description=info["description"],
+            source=info["source"],
+            target=target,
+            X=X,
+            y=y,
+            path=info["path"],
+            read_csv_kwargs=read_csv_kwargs,
+        )
+    else:
+        dataset = DatasetInfoOnly(
+            name=dataset_name,
+            description=info["description"],
+            source=info["source"],
+            target=target,
+            path=info["path"],
+            read_csv_kwargs=read_csv_kwargs,
+        )
+
+    return dataset
+
+
+def _fetch_figshare(
+    figshare_id,
+    data_directory,
+):
+    """Fetch a dataset from figshare using the download ID number.
+
+    Parameters
+    ----------
+    figshare_id : str
+        The ID of the dataset to fetch.
+    data_directory : pathlib.Path, optional
+        The directory where the dataset is stored.
+        By default, a subdirectory "figshare" in the skrub data directory.
+
+    Returns
+    -------
+    mapping of str to any
+        A dictionary containing:
+          - `description` : str
+              The description of the dataset.
+          - `source` : str
+              The dataset's URL.
+          - `path` : pathlib.Path
+              The local path leading to the dataset,
+              saved as a parquet file.
+
+    Notes
+    -----
+    The files are read and returned in parquet format, this function needs
+    pyarrow installed to run correctly.
+    """
+    if data_directory is None:
+        data_directory = get_data_dir(name="figshare")
+
+    parquet_path = (data_directory / f"figshare_{figshare_id}.parquet").resolve()
+    data_directory.mkdir(parents=True, exist_ok=True)
+    url = f"https://ndownloader.figshare.com/files/{figshare_id}"
+    description = f"This table shows the {figshare_id!r} figshare file."
+    file_paths = [
+        file
+        for file in data_directory.iterdir()
+        if file.name.startswith(f"figshare_{figshare_id}")
+    ]
+    if len(file_paths) > 0:
+        if len(file_paths) == 1:
+            parquet_paths = [str(file_paths[0].resolve())]
+        else:
+            parquet_paths = []
+            for path in file_paths:
+                parquet_path = str(path.resolve())
+                parquet_paths += [parquet_path]
+        return {
+            "dataset_name": figshare_id,
+            "description": description,
+            "source": url,
+            "path": parquet_paths,
+        }
+    else:
+        warnings.warn(
+            (
+                f"Could not find the dataset {figshare_id!r} locally. "
+                "Downloading it from figshare; this might take a while... "
+                "If it is interrupted, some files might be invalid/incomplete: "
+                "if on the following run, the fetching raises errors, you can try "
+                f"fixing this issue by deleting the directory {parquet_path!s}."
+            ),
+            UserWarning,
+            stacklevel=2,
+        )
+        import_optional_dependency(
+            "pyarrow", extra="pyarrow is required for parquet support."
+        )
+        from pyarrow.parquet import ParquetFile
+
+        try:
+            filehandle, _ = urllib.request.urlretrieve(url)
+
+            # checksum the file
+            checksum = _sha256(filehandle)
+            if figshare_id in figshare_id_to_hash:
+                expected_checksum = figshare_id_to_hash[figshare_id]
+                if checksum != expected_checksum:
+                    raise OSError(
+                        f"{filehandle!r} SHA256 checksum differs from "
+                        f"expected ({checksum}!={expected_checksum}) ; "
+                        "file is probably corrupted. Please try again. "
+                        "If the error persists, please open an issue on GitHub. "
+                    )
+
+            df = ParquetFile(filehandle)
+            record = df.iter_batches(
+                batch_size=1_000_000,
+            )
+            idx = []
+            for _, x in enumerate(
+                chain(range(0, df.metadata.num_rows, 1_000_000), [df.metadata.num_rows])
+            ):
+                idx += [x]
+            parquet_paths = []
+            for i in range(1, len(idx)):
+                parquet_path = (
+                    data_directory / f"figshare_{figshare_id}_{idx[i]}.parquet"
+                ).resolve()
+                batch = next(record).to_pandas()
+                batch.to_parquet(parquet_path, index=False)
+                parquet_paths += [parquet_path]
+            return {
+                "dataset_name": figshare_id,
+                "description": description,
+                "source": url,
+                "path": parquet_paths,
+            }
+        except URLError:
+            raise URLError("No internet connection or the website is down.")
+
+
 def fetch_ken_table_aliases():
     """Get the supported aliases of embedded KEN entities tables.
 
diff --git a/skrub/datasets/_utils.py b/skrub/datasets/_utils.py
index aba51f5e6..033a57896 100644
--- a/skrub/datasets/_utils.py
+++ b/skrub/datasets/_utils.py
@@ -1,5 +1,117 @@
+import hashlib
+import json
+import os
+import shutil
+import tempfile
+import time
+import warnings
 from pathlib import Path
 
+import pandas as pd
+import requests
+from sklearn.utils import Bunch
+
+DATASET_INFO = {
+    "bike_sharing": {
+        "urls": [
+            "https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/bike_sharing.zip",
+            "https://osf.io/download/3z4qc",
+        ],
+        "sha256": "33745414801712034cf1d8615d7f086bba411ea8e44bfffefc0c6f23cb8afb83",
+    },
+    "country_happiness": {
+        "urls": [
+            "https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/country_happiness.zip",
+            "https://osf.io/download/8a6wm",
+        ],
+        "sha256": "10b35da781a13a94dedcfeb43b291d16677b06a781e9b88a780f04ad173b422d",
+    },
+    "credit_fraud": {
+        "urls": [
+            "https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/credit_fraud.zip",
+            "https://osf.io/download/y8qg5",
+        ],
+        "sha256": "ec40d370a275d4bd2637d4c617120e91e2e7946d23c93b1a1ea7df824ee1e514",
+    },
+    "drug_directory": {
+        "urls": [
+            "https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/drug_directory.zip",
+            "https://osf.io/download/rtgk5",
+        ],
+        "sha256": "0c3885894baf02fc787109801ec2c34cc25cd4a31e0066a16941b74157474887",
+    },
+    "employee_salaries": {
+        "urls": [
+            "https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/employee_salaries.zip",
+            "https://osf.io/download/c592f",
+        ],
+        "sha256": "4b4919f38d921014cb1fd24ad302f44bccc55d1eeeeb8482902b09d9b43576cb",
+    },
+    "flight_delays": {
+        "urls": [
+            "https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/flight_delays.zip",
+            "https://osf.io/download/45xu3",
+        ],
+        "sha256": "f26ed72db5792dba3c6f0c32bdd83438e49b1e6e007a6e4e467f805207b2e4ab",
+    },
+    "medical_charge": {
+        "urls": [
+            "https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/medical_charge.zip",
+            "https://osf.io/download/g8cvw",
+        ],
+        "sha256": "4850651103b7c7580587aafaccc05ca7a31125767d4da662e87890346f984b93",
+    },
+    "midwest_survey": {
+        "urls": [
+            "https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/midwest_survey.zip",
+            "https://osf.io/download/aedqu",
+        ],
+        "sha256": "94d5005402e5e72c2d5ce62f4d3115742dd12190db85920159b2ed8f44df7fc2",
+    },
+    "movielens": {
+        "urls": [
+            "https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/movielens.zip",
+            "https://osf.io/download/z5yqv",
+        ],
+        "sha256": "d6b22c707f9a1605da5616ac1a601f4090467c1a02fa663195a42cf80f32fd57",
+    },
+    "open_payments": {
+        "urls": [
+            "https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/open_payments.zip",
+            "https://osf.io/download/a7p9w",
+        ],
+        "sha256": "ead65dcb8d45ec16ab30dd71025c3cfc5730128f85eeb19ce6f56670923f04ba",
+    },
+    "road_safety": {
+        "urls": [
+            "https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/road_safety.zip",
+            "https://osf.io/download/bae6d",
+        ],
+        "sha256": "035df2a644ba2be52022aa9ca5f41790a24cbd9c76434c3e5224c8c218cf6f87",
+    },
+    "toxicity": {
+        "urls": [
+            "https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/toxicity.zip",
+            "https://osf.io/download/zebm7",
+        ],
+        "sha256": "ee187c119925ea4cdb9abd7f0f3758159f042e71b172cafe5b784d79c7590ce3",
+    },
+    "traffic_violations": {
+        "urls": [
+            "https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/traffic_violations.zip",
+            "https://osf.io/download/av4cw",
+        ],
+        "sha256": "b52a145a34b1866b6deee7cbfd1c0b8d2af3bbd53fb5658b155f752ac7d85ce0",
+    },
+    "videogame_sales": {
+        "urls": [
+            "https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/videogame_sales.zip",
+            "https://osf.io/download/g2fw4",
+        ],
+        "sha256": "3e6d995af025b8a3a1dc64983aa9d53c3c6e72150644d42c58c8b86888c3dacd",
+    },
+}
+
 
 def get_data_home(data_home=None):
     """Returns the path of the skrub data directory.
@@ -54,3 +166,189 @@ def get_data_dir(name=None, data_home=None):
     if name is not None:
         data_dir = data_dir / name
     return data_dir
+
+
+def load_simple_dataset(dataset_name, data_home=None):
+    """Load a dataframe and its metadata based on its dataset_name.
+
+    The data will be downloaded if not found locally.
+    For e.g. the credit_fraud dataset, the filesystem will look like:
+
+    <data_home>/
+        fraud/
+            fraud.zip
+            fraud/
+                baskets.csv
+                products.csv
+                metadata.json
+
+    Parameters
+    ----------
+    dataset_name : str
+        The name of the dataset to load. The name must be a key of `DATASET_INFO`.
+
+    data_home : path, default=None
+        The directory where to download and unpack a zip file. If None, 'skrub_data'
+        is used.
+
+    Returns
+    -------
+    bunch : sklearn.utils.Bunch
+        A dictionary-like object with the following keys:
+
+        - <dataset_name> : pd.DataFrame, the dataframe
+        - X : pd.DataFrame, features, i.e. the dataframe without the target labels
+        - y : pd.DataFrame, target labels
+        - metadata : a dictionary containing the name, description, source and target
+          (description and source may be missing)
+    """
+    bunch = load_dataset_files(dataset_name, data_home)
+    bunch["X"] = bunch[dataset_name]
+    if (target := bunch.metadata.get("target", None)) is not None:
+        bunch["y"] = bunch["X"][target]
+        bunch["X"] = bunch["X"].drop(columns=target)
+    return bunch
+
+
+def load_dataset_files(dataset_name, data_home):
+    data_home = get_data_home(data_home)
+    dataset_dir = data_home / dataset_name
+    datafiles_dir = dataset_dir / dataset_name
+
+    archive_path = dataset_dir / f"{dataset_name}.zip"
+    metadata = DATASET_INFO[dataset_name]
+
+    if archive_path.exists():
+        expected_checksum = metadata["sha256"]
+        checksum = _sha256(archive_path)
+        if expected_checksum != checksum:
+            warnings.warn(
+                f"SHA256 checksum of existing local file {archive_path.name} "
+                f"({checksum}) differs from expected ({expected_checksum}): "
+                f"re-downloading from {metadata['urls']} ."
+            )
+            try:
+                shutil.rmtree(datafiles_dir)
+            except FileNotFoundError:
+                pass
+            archive_path.unlink()
+
+    if not datafiles_dir.exists():
+        _extract_archive(dataset_dir, archive_path)
+
+    bunch = Bunch()
+    for file_path in datafiles_dir.iterdir():
+        if file_path.suffix == ".csv":
+            bunch[file_path.stem] = pd.read_csv(file_path)
+        elif file_path.suffix == ".json":
+            bunch[file_path.stem] = json.loads(file_path.read_text(encoding="utf-8"))
+
+    return bunch
+
+
+def _extract_archive(dataset_dir, archive_path):
+    dataset_name = dataset_dir.name
+
+    if not archive_path.exists():
+        _download_archive(dataset_name, archive_path)
+
+    try:
+        temp_dir = tempfile.mkdtemp(dir=dataset_dir)
+        shutil.unpack_archive(archive_path, temp_dir, format="zip")
+        path_source = Path(temp_dir) / dataset_name
+        path_source.rename(dataset_dir / dataset_name)
+    except (Exception, KeyboardInterrupt):
+        try:
+            archive_path.unlink()
+        except Exception:
+            pass
+        raise
+    finally:
+        try:
+            shutil.rmtree(temp_dir)
+        except Exception:
+            pass
+
+
+def _download_archive(
+    dataset_name,
+    archive_path,
+    retry=3,
+    delay=1,
+    timeout=3,
+    chunk_size=4096,
+):
+    metadata = DATASET_INFO[dataset_name]
+    remote_checksum = metadata["sha256"]
+
+    for idx in range(1, retry + 1):
+        for target_url in metadata["urls"]:
+            print(
+                f"Downloading {dataset_name!r} from {target_url} (attempt"
+                f" {idx}/{retry})"
+            )
+            try:
+                _stream_download(
+                    archive_path,
+                    target_url,
+                    timeout,
+                    chunk_size,
+                    remote_checksum,
+                )
+                return
+            except Exception as e:
+                print(repr(e))
+
+        time.sleep(delay)
+        delay *= 3
+
+    else:
+        raise OSError(
+            f"Can't download the file {dataset_name!r} from urls {metadata['urls']}."
+        )
+
+
+def _stream_download(
+    archive_path,
+    target_url,
+    timeout,
+    chunk_size,
+    remote_checksum,
+):
+    dataset_dir = archive_path.parent
+    dataset_dir.mkdir(exist_ok=True)
+
+    # We don't use `NamedTemporaryFile` because if the download is successful we
+    # want to rename the temp file rather than removing it
+    temp_file, temp_file_path = tempfile.mkstemp(
+        prefix=archive_path.stem + ".part_", dir=dataset_dir
+    )
+    os.close(temp_file)
+
+    try:
+        temp_file_path = Path(temp_file_path)
+        with open(temp_file_path, "wb") as tf:
+            response = requests.get(target_url, timeout=timeout, stream=True)
+            for chunk in response.iter_content(chunk_size):
+                tf.write(chunk)
+
+        if _sha256(temp_file_path) != remote_checksum:
+            raise OSError(
+                f"File {archive_path.stem!r} checksum verification has failed."
+            )
+        temp_file_path.rename(archive_path)
+    finally:
+        try:
+            temp_file_path.unlink()
+        except Exception:
+            pass
+
+
+def _sha256(path):
+    """Calculate the sha256 hash of the file at path."""
+    sha256hash = hashlib.sha256()
+    chunk_size = 8192
+    with open(path, "rb") as f:
+        while buffer := f.read(chunk_size):
+            sha256hash.update(buffer)
+    return sha256hash.hexdigest()
diff --git a/skrub/datasets/tests/test_fetching.py b/skrub/datasets/tests/test_fetching.py
index 00d781928..bc2a9c2fb 100644
--- a/skrub/datasets/tests/test_fetching.py
+++ b/skrub/datasets/tests/test_fetching.py
@@ -1,292 +1,75 @@
-import re
 from tempfile import TemporaryDirectory
-from unittest import mock
-from urllib.error import URLError
 
 import pandas as pd
 import pytest
-from pandas.testing import assert_frame_equal
+import requests
+from pandas.testing import assert_frame_equal, assert_series_equal
 
-from skrub.datasets import _fetching
+from skrub.datasets import _fetching, _utils
 
 
-def _has_data_id(call, data_id):
-    # Unpacking copied from `mock._Call.__eq__`
-    if len(call) == 2:
-        args, kwargs = call
-    else:
-        name, args, kwargs = call
-    return kwargs["data_id"] == data_id
+def _get_table_names_from_bunch(bunch):
+    return [k for k in bunch if isinstance(bunch[k], pd.DataFrame)]
 
 
-@mock.patch(
-    "skrub.datasets._fetching.fetch_openml",
-    side_effect=_fetching.fetch_openml,
-)
-def test_openml_fetching(fetch_openml_mock):
-    """
-    Downloads a small dataset (midwest survey) and performs a bunch of tests
-    that asserts the fetching function works correctly.
-
-    We don't download all datasets as it would take way too long:
-    see https://github.com/skrub-data/skrub/issues/523
-    """
-    with TemporaryDirectory() as temp_dir:
-        # Download the dataset without loading it in memory.
-        dataset = _fetching.fetch_midwest_survey(
-            data_directory=temp_dir,
-            load_dataframe=False,
-        )
-        fetch_openml_mock.assert_called_once()
-        assert _has_data_id(fetch_openml_mock.call_args, _fetching.MIDWEST_SURVEY_ID)
-        fetch_openml_mock.reset_mock()
-        assert isinstance(dataset, _fetching.DatasetInfoOnly)
-        # Briefly load the dataframe in memory, to test that reading
-        # from disk works.
-        assert pd.read_csv(dataset.path, **dataset.read_csv_kwargs).shape == (2494, 29)
-        assert dataset.name == (
-            _fetching.fetch_midwest_survey.__name__[len("fetch_") :]
-            .replace("_", " ")
-            .capitalize()
-        )
-        assert dataset.source.startswith("https://www.openml.org/")
-        assert str(_fetching.MIDWEST_SURVEY_ID) in dataset.source
-        # Now, load it into memory, and expect `fetch_openml`
-        # to not be called because the dataset is already on disk.
-        dataset = _fetching.fetch_midwest_survey(data_directory=temp_dir)
-        fetch_openml_mock.assert_not_called()
-        fetch_openml_mock.reset_mock()
-        assert dataset.X.shape == (2494, 28)
-        assert dataset.y.shape == (2494,)
-
-
-def test_openml_datasets_exist():
-    """
-    Queries OpenML to see if the datasets are still available on the website.
-    """
-    openml = pytest.importorskip("openml")
-    openml.datasets.check_datasets_active(
-        dataset_ids=[
-            _fetching.ROAD_SAFETY_ID,
-            _fetching.OPEN_PAYMENTS_ID,
-            _fetching.MIDWEST_SURVEY_ID,
-            _fetching.MEDICAL_CHARGE_ID,
-            _fetching.EMPLOYEE_SALARIES_ID,
-            _fetching.TRAFFIC_VIOLATIONS_ID,
-            _fetching.DRUG_DIRECTORY_ID,
-        ],
-        raise_error_if_not_exist=True,
-    )
-
-
-@mock.patch("skrub.datasets._fetching.fetch_openml")
-def test_openml_datasets_calls(fetch_openml_mock):
-    """
-    Checks that calling the fetching functions actually calls
-    `sklearn.datasets.fetch_openml`.
-    Complementary to `test_openml_fetching`
-    """
-    with TemporaryDirectory() as temp_dir:
-        for fetching_function, identifier in [
-            (_fetching.fetch_road_safety, _fetching.ROAD_SAFETY_ID),
-            (_fetching.fetch_open_payments, _fetching.OPEN_PAYMENTS_ID),
-            (_fetching.fetch_midwest_survey, _fetching.MIDWEST_SURVEY_ID),
-            (_fetching.fetch_medical_charge, _fetching.MEDICAL_CHARGE_ID),
-            (_fetching.fetch_employee_salaries, _fetching.EMPLOYEE_SALARIES_ID),
-            (_fetching.fetch_traffic_violations, _fetching.TRAFFIC_VIOLATIONS_ID),
-            (_fetching.fetch_drug_directory, _fetching.DRUG_DIRECTORY_ID),
-        ]:
-            try:
-                fetching_function(data_directory=temp_dir)
-            except FileNotFoundError:
-                pass
-            fetch_openml_mock.assert_called_once()
-            assert _has_data_id(fetch_openml_mock.call_args, identifier)
-            fetch_openml_mock.reset_mock()
-
-
-def test_fetch_world_bank_indicator():
-    """
-    Tests the ``fetch_world_bank_indicator()``
-    function in a real environment.
-    """
-    test_dataset = {
-        "id": "NY.GDP.PCAP.CD",
-        "desc_start": "This table shows",
-        "url": "https://api.worldbank.org/v2/en/indicator/NY.GDP.PCAP.CD?downloadformat=csv",  # noqa
-        "dataset_columns_count": 2,
-    }
-
+@pytest.mark.parametrize("dataset_name", ["employee_salaries", "drug_directory"])
+def test_fetching(monkeypatch, dataset_name):
     with TemporaryDirectory() as temp_dir:
-        try:
-            # First, we want to purposefully test FileNotFoundError exceptions.
-            with pytest.raises(FileNotFoundError):
-                assert _fetching.fetch_world_bank_indicator(
-                    indicator_id=0, data_directory=temp_dir
-                )
-                assert _fetching.fetch_world_bank_indicator(
-                    indicator_id=2**32,
-                    data_directory=temp_dir,
-                )
+        fetch_func = getattr(_fetching, f"fetch_{dataset_name}")
+        bunch = fetch_func(data_home=temp_dir)
 
-            # Valid call
-            returned_info = _fetching.fetch_world_bank_indicator(
-                indicator_id=test_dataset["id"],
-                data_directory=temp_dir,
-            )
+        def _error_on_get(*args, **kwargs):
+            raise AssertionError("request called")
 
-        except (ConnectionError, URLError):
-            pytest.skip(
-                "Exception: Skipping this test because we encountered an "
-                "issue probably related to an Internet connection problem. "
-            )
-            return
+        # Raise an error if requests.get() is called
+        monkeypatch.setattr(requests, "get", _error_on_get)
 
-        assert returned_info.description.startswith(test_dataset["desc_start"])
-        assert returned_info.source == test_dataset["url"]
-        assert returned_info.path.is_file()
+        # calling the fetch function one more time
+        local_bunch = fetch_func(data_home=temp_dir)
 
-        dataset = pd.read_csv(returned_info.path)
+    for table_name in _get_table_names_from_bunch(bunch):
+        assert_frame_equal(bunch[table_name], local_bunch[table_name])
 
-        assert dataset.columns[0] == "Country Name"
-        assert dataset.shape[1] == test_dataset["dataset_columns_count"]
+    if hasattr(bunch, "y"):
+        if isinstance(bunch.y, pd.Series):
+            assert_series_equal(bunch.y, local_bunch.y)
+        else:
+            assert_frame_equal(bunch.y, local_bunch.y)
+    assert bunch["metadata"] == local_bunch["metadata"]
 
-        # Now that we have verified the file is on disk, we want to test
-        # whether calling the function again reads it from disk (it should)
-        # or queries the network again (it shouldn't).
-        with mock.patch("urllib.request.urlretrieve") as mock_urlretrieve:
-            # Same valid call as above
-            disk_loaded_info = _fetching.fetch_world_bank_indicator(
-                indicator_id=test_dataset["id"],
-                data_directory=temp_dir,
-            )
-            mock_urlretrieve.assert_not_called()
-            assert disk_loaded_info == returned_info
 
+def test_fetching_wrong_checksum(monkeypatch):
+    dataset_info = _utils.DATASET_INFO["employee_salaries"]
+    monkeypatch.setitem(dataset_info, "sha256", "bad_checksum")
+    with pytest.raises(OSError, match=".*Can't download"):
+        with TemporaryDirectory() as temp_dir:
+            _fetching.fetch_employee_salaries(data_home=temp_dir)
 
-@pytest.mark.xfail(run=False, reason="temporary SSL certificate issue")
-def test_fetch_movielens():
-    """
-    Tests the ``fetch_movielens()`` function in a real environment.
-    """
-    test_dataset = {
-        "id": "ratings",
-        "desc_start": "Summary\n=======\n\nThis dataset (ml-latest-small)",
-        "url": (
-            "https://files.grouplens.org/datasets/movielens/ml-latest-small.zip"
-        ),  # noqa
-        "dataset_columns_count": 4,
-    }
 
+def test_warning_redownload_checksum_has_changed(monkeypatch):
     with TemporaryDirectory() as temp_dir:
-        try:
-            # First, we want to purposefully test ValueError exceptions.
-            msg = "dataset_id options are ['movies', 'ratings'], got 'wrong_name'."
-            with pytest.raises(ValueError, match=re.escape(msg)):
-                assert _fetching.fetch_movielens(
-                    dataset_id="wrong_name", data_directory=temp_dir
-                )
-
-            # Valid call
-            returned_info = _fetching.fetch_movielens(
-                dataset_id="ratings",
-                data_directory=temp_dir,
-            )
-
-        except (ConnectionError, URLError):
-            pytest.skip(
-                "Exception: Skipping this test because we encountered an "
-                "issue probably related to an Internet connection problem. "
-            )
-            return
+        _ = _fetching.fetch_employee_salaries(data_home=temp_dir)
 
-        assert returned_info.description.startswith(test_dataset["desc_start"])
-        assert returned_info.source == test_dataset["url"]
-        assert returned_info.path.is_file()
+        # altering the checksum to trigger a new download
+        dataset_info = _utils.DATASET_INFO["employee_salaries"]
+        monkeypatch.setitem(dataset_info, "sha256", "bad_checksum")
 
-        dataset = pd.read_csv(returned_info.path)
-
-        assert dataset.columns[0] == "userId"
-        assert dataset.shape[1] == test_dataset["dataset_columns_count"]
-
-        # Now that we have verified the file is on disk, we want to test
-        # whether calling the function again reads it from disk (it should)
-        # or queries the network again (it shouldn't).
-        with mock.patch("urllib.request.urlretrieve") as mock_urlretrieve:
-            # Same valid call as above
-            disk_loaded_info = _fetching.fetch_movielens(
-                dataset_id=test_dataset["id"],
-                data_directory=temp_dir,
-            )
-            mock_urlretrieve.assert_not_called()
-            assert disk_loaded_info == returned_info
-
-
-def test_fetch_credit_fraud():
-    pytest.importorskip("pyarrow")
-    with TemporaryDirectory() as temp_dir:
-        try:
-            # Valid call
-            bunch = _fetching.fetch_credit_fraud(
-                data_directory=temp_dir,
-            )
-
-        except (ConnectionError, URLError):
-            pytest.skip(
-                "Exception: Skipping this test because we encountered an "
-                "issue probably related to an Internet connection problem. "
-            )
-            return
-
-        assert (
-            bunch.source_products == "https://ndownloader.figshare.com/files/49176205"
-        )
-        assert bunch.source_baskets == "https://ndownloader.figshare.com/files/49176202"
+        with pytest.warns(match=r".*re-downloading.*"):
+            # In the context of testing, altering the checksum will also raise an
+            # error after re-downloading, which we doesn't want.
+            try:
+                _ = _fetching.fetch_employee_salaries(data_home=temp_dir)
+            except OSError:
+                pass
 
-        assert_frame_equal(bunch.products, pd.read_parquet(bunch.path_products))
-        assert_frame_equal(bunch.baskets, pd.read_parquet(bunch.path_baskets))
 
-        # Now that we have verified the file is on disk, we want to test
-        # whether calling the function again reads it from disk (it should)
-        # or queries the network again (it shouldn't).
-        with mock.patch("urllib.request.urlretrieve") as mock_urlretrieve:
-            # Same valid call as above
-            disk_bunch = _fetching.fetch_credit_fraud(
-                data_directory=temp_dir,
-            )
-            mock_urlretrieve.assert_not_called()
-            assert_frame_equal(bunch.products, disk_bunch.products)
-            assert_frame_equal(bunch.baskets, disk_bunch.baskets)
+def test_cant_download(monkeypatch):
+    def _error_on_get(*args, **kwargs):
+        raise Exception("some error happened")
 
+    # Raise an error when requests.get() is called
+    monkeypatch.setattr(requests, "get", _error_on_get)
 
-def test_fetch_toxicity():
-    pytest.importorskip("pyarrow")
     with TemporaryDirectory() as temp_dir:
-        try:
-            # Valid call
-            bunch = _fetching.fetch_toxicity(
-                data_directory=temp_dir,
-            )
-
-        except (ConnectionError, URLError):
-            pytest.skip(
-                "Exception: Skipping this test because we encountered an "
-                "issue probably related to an Internet connection problem. "
-            )
-            return
-
-        assert bunch.source == "https://ndownloader.figshare.com/files/49823901"
-
-        df = pd.concat([bunch.X, bunch.y], axis=1)
-        assert_frame_equal(df, pd.read_parquet(bunch.path))
-
-        # Now that we have verified the file is on disk, we want to test
-        # whether calling the function again reads it from disk (which it should)
-        # or queries the network again (which it shouldn't).
-        with mock.patch("urllib.request.urlretrieve") as mock_urlretrieve:
-            # Same valid call as above
-            disk_bunch = _fetching.fetch_toxicity(
-                data_directory=temp_dir,
-            )
-            mock_urlretrieve.assert_not_called()
-            assert_frame_equal(bunch.X, disk_bunch.X)
+        with pytest.raises(OSError, match="Can't download"):
+            _ = _fetching.fetch_employee_salaries(data_home=temp_dir)