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/core/openml-datasets/
https://datahub.io/core/openml-datasets/_r/-/FRESHNESS_CHECK.md
https://datahub.io/core/openml-datasets/_r/-/README.md
https://datahub.io/core/openml-datasets/_r/-/UPDATE_SCRIPT_MAINTENANCE_REPORT.md
https://datahub.io/core/openml-datasets/_r/-/data/Bioresponse/Bioresponse.arff
https://datahub.io/core/openml-datasets/_r/-/data/Bioresponse/Bioresponse.csv
https://datahub.io/core/openml-datasets/_r/-/data/Bioresponse/README.md
https://datahub.io/core/openml-datasets/_r/-/data/Bioresponse/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/Click_prediction_small/Click_prediction_small.arff
https://datahub.io/core/openml-datasets/_r/-/data/Click_prediction_small/Click_prediction_small.csv
https://datahub.io/core/openml-datasets/_r/-/data/Click_prediction_small/README.md
https://datahub.io/core/openml-datasets/_r/-/data/Click_prediction_small/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/IMDB.drama/IMDB.drama.arff
https://datahub.io/core/openml-datasets/_r/-/data/IMDB.drama/IMDB.drama.csv
https://datahub.io/core/openml-datasets/_r/-/data/IMDB.drama/README.md
https://datahub.io/core/openml-datasets/_r/-/data/IMDB.drama/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/MagicTelescope/MagicTelescope.arff
https://datahub.io/core/openml-datasets/_r/-/data/MagicTelescope/MagicTelescope.csv
https://datahub.io/core/openml-datasets/_r/-/data/MagicTelescope/README.md
https://datahub.io/core/openml-datasets/_r/-/data/MagicTelescope/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/Satellite/README.md
https://datahub.io/core/openml-datasets/_r/-/data/Satellite/Satellite.arff
https://datahub.io/core/openml-datasets/_r/-/data/Satellite/Satellite.csv
https://datahub.io/core/openml-datasets/_r/-/data/Satellite/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/SpeedDating/README.md
https://datahub.io/core/openml-datasets/_r/-/data/SpeedDating/SpeedDating.arff
https://datahub.io/core/openml-datasets/_r/-/data/SpeedDating/SpeedDating.csv
https://datahub.io/core/openml-datasets/_r/-/data/SpeedDating/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/abalone/README.md
https://datahub.io/core/openml-datasets/_r/-/data/abalone/abalone.arff
https://datahub.io/core/openml-datasets/_r/-/data/abalone/abalone.csv
https://datahub.io/core/openml-datasets/_r/-/data/abalone/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/adult/README.md
https://datahub.io/core/openml-datasets/_r/-/data/adult/adult.arff
https://datahub.io/core/openml-datasets/_r/-/data/adult/adult.csv
https://datahub.io/core/openml-datasets/_r/-/data/adult/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/airlines/README.md
https://datahub.io/core/openml-datasets/_r/-/data/airlines/airlines.arff
https://datahub.io/core/openml-datasets/_r/-/data/airlines/airlines.csv
https://datahub.io/core/openml-datasets/_r/-/data/airlines/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/amazon-commerce-reviews/README.md
https://datahub.io/core/openml-datasets/_r/-/data/amazon-commerce-reviews/amazon-commerce-reviews.arff
https://datahub.io/core/openml-datasets/_r/-/data/amazon-commerce-reviews/amazon-commerce-reviews.csv
https://datahub.io/core/openml-datasets/_r/-/data/amazon-commerce-reviews/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/anneal/README.md
https://datahub.io/core/openml-datasets/_r/-/data/anneal/anneal.arff
https://datahub.io/core/openml-datasets/_r/-/data/anneal/anneal.csv
https://datahub.io/core/openml-datasets/_r/-/data/anneal/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/arrhythmia/README.md
https://datahub.io/core/openml-datasets/_r/-/data/arrhythmia/arrhythmia.arff
https://datahub.io/core/openml-datasets/_r/-/data/arrhythmia/arrhythmia.csv
https://datahub.io/core/openml-datasets/_r/-/data/arrhythmia/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/autoUniv-au6-1000/README.md
https://datahub.io/core/openml-datasets/_r/-/data/autoUniv-au6-1000/autoUniv-au6-1000.arff
https://datahub.io/core/openml-datasets/_r/-/data/autoUniv-au6-1000/autoUniv-au6-1000.csv
https://datahub.io/core/openml-datasets/_r/-/data/autoUniv-au6-1000/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/autos/README.md
https://datahub.io/core/openml-datasets/_r/-/data/autos/autos.arff
https://datahub.io/core/openml-datasets/_r/-/data/autos/autos.csv
https://datahub.io/core/openml-datasets/_r/-/data/autos/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/bank-marketing/README.md
https://datahub.io/core/openml-datasets/_r/-/data/bank-marketing/bank-marketing.arff
https://datahub.io/core/openml-datasets/_r/-/data/bank-marketing/bank-marketing.csv
https://datahub.io/core/openml-datasets/_r/-/data/bank-marketing/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/banknote-authentication/README.md
https://datahub.io/core/openml-datasets/_r/-/data/banknote-authentication/banknote-authentication.arff
https://datahub.io/core/openml-datasets/_r/-/data/banknote-authentication/banknote-authentication.csv
https://datahub.io/core/openml-datasets/_r/-/data/banknote-authentication/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/blood-transfusion-service-center/README.md
https://datahub.io/core/openml-datasets/_r/-/data/blood-transfusion-service-center/blood-transfusion-service-center.arff
https://datahub.io/core/openml-datasets/_r/-/data/blood-transfusion-service-center/blood-transfusion-service-center.csv
https://datahub.io/core/openml-datasets/_r/-/data/blood-transfusion-service-center/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/breast-cancer/README.md
https://datahub.io/core/openml-datasets/_r/-/data/breast-cancer/breast-cancer.arff
https://datahub.io/core/openml-datasets/_r/-/data/breast-cancer/breast-cancer.csv
https://datahub.io/core/openml-datasets/_r/-/data/breast-cancer/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/breast-w/README.md
https://datahub.io/core/openml-datasets/_r/-/data/breast-w/breast-w.arff
https://datahub.io/core/openml-datasets/_r/-/data/breast-w/breast-w.csv
https://datahub.io/core/openml-datasets/_r/-/data/breast-w/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/cardiotocography/README.md
https://datahub.io/core/openml-datasets/_r/-/data/cardiotocography/cardiotocography.arff
https://datahub.io/core/openml-datasets/_r/-/data/cardiotocography/cardiotocography.csv
https://datahub.io/core/openml-datasets/_r/-/data/cardiotocography/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/climate-model-simulation-crashes/README.md
https://datahub.io/core/openml-datasets/_r/-/data/climate-model-simulation-crashes/climate-model-simulation-crashes.arff
https://datahub.io/core/openml-datasets/_r/-/data/climate-model-simulation-crashes/climate-model-simulation-crashes.csv
https://datahub.io/core/openml-datasets/_r/-/data/climate-model-simulation-crashes/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/cmc/README.md
https://datahub.io/core/openml-datasets/_r/-/data/cmc/cmc.arff
https://datahub.io/core/openml-datasets/_r/-/data/cmc/cmc.csv
https://datahub.io/core/openml-datasets/_r/-/data/cmc/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/credit-approval/README.md
https://datahub.io/core/openml-datasets/_r/-/data/credit-approval/credit-approval.arff
https://datahub.io/core/openml-datasets/_r/-/data/credit-approval/credit-approval.csv
https://datahub.io/core/openml-datasets/_r/-/data/credit-approval/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/credit-g/README.md
https://datahub.io/core/openml-datasets/_r/-/data/credit-g/credit-g.arff
https://datahub.io/core/openml-datasets/_r/-/data/credit-g/credit-g.csv
https://datahub.io/core/openml-datasets/_r/-/data/credit-g/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/diabetes/README.md
https://datahub.io/core/openml-datasets/_r/-/data/diabetes/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/diabetes/diabetes.arff
https://datahub.io/core/openml-datasets/_r/-/data/diabetes/diabetes.csv
https://datahub.io/core/openml-datasets/_r/-/data/eeg-eye-state/README.md
https://datahub.io/core/openml-datasets/_r/-/data/eeg-eye-state/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/eeg-eye-state/eeg-eye-state.arff
https://datahub.io/core/openml-datasets/_r/-/data/eeg-eye-state/eeg-eye-state.csv
https://datahub.io/core/openml-datasets/_r/-/data/electricity/README.md
https://datahub.io/core/openml-datasets/_r/-/data/electricity/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/electricity/electricity.arff
https://datahub.io/core/openml-datasets/_r/-/data/electricity/electricity.csv
https://datahub.io/core/openml-datasets/_r/-/data/fbis.wc/README.md
https://datahub.io/core/openml-datasets/_r/-/data/fbis.wc/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/fbis.wc/fbis.wc.arff
https://datahub.io/core/openml-datasets/_r/-/data/fbis.wc/fbis.wc.csv
https://datahub.io/core/openml-datasets/_r/-/data/first-order-theorem-proving/README.md
https://datahub.io/core/openml-datasets/_r/-/data/first-order-theorem-proving/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/first-order-theorem-proving/first-order-theorem-proving.arff
https://datahub.io/core/openml-datasets/_r/-/data/first-order-theorem-proving/first-order-theorem-proving.csv
https://datahub.io/core/openml-datasets/_r/-/data/gas-drift/README.md
https://datahub.io/core/openml-datasets/_r/-/data/gas-drift/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/gas-drift/gas-drift.arff
https://datahub.io/core/openml-datasets/_r/-/data/gas-drift/gas-drift.csv
https://datahub.io/core/openml-datasets/_r/-/data/gina_agnostic/README.md
https://datahub.io/core/openml-datasets/_r/-/data/gina_agnostic/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/gina_agnostic/gina_agnostic.arff
https://datahub.io/core/openml-datasets/_r/-/data/gina_agnostic/gina_agnostic.csv
https://datahub.io/core/openml-datasets/_r/-/data/gina_prior2/README.md
https://datahub.io/core/openml-datasets/_r/-/data/gina_prior2/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/gina_prior2/gina_prior2.arff
https://datahub.io/core/openml-datasets/_r/-/data/gina_prior2/gina_prior2.csv
https://datahub.io/core/openml-datasets/_r/-/data/glass/README.md
https://datahub.io/core/openml-datasets/_r/-/data/glass/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/glass/glass.arff
https://datahub.io/core/openml-datasets/_r/-/data/glass/glass.csv
https://datahub.io/core/openml-datasets/_r/-/data/haberman/README.md
https://datahub.io/core/openml-datasets/_r/-/data/haberman/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/haberman/haberman.arff
https://datahub.io/core/openml-datasets/_r/-/data/haberman/haberman.csv
https://datahub.io/core/openml-datasets/_r/-/data/heart-statlog/README.md
https://datahub.io/core/openml-datasets/_r/-/data/heart-statlog/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/heart-statlog/heart-statlog.arff
https://datahub.io/core/openml-datasets/_r/-/data/heart-statlog/heart-statlog.csv
https://datahub.io/core/openml-datasets/_r/-/data/hill-valley/README.md
https://datahub.io/core/openml-datasets/_r/-/data/hill-valley/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/hill-valley/hill-valley.arff
https://datahub.io/core/openml-datasets/_r/-/data/hill-valley/hill-valley.csv
https://datahub.io/core/openml-datasets/_r/-/data/ilpd/README.md
https://datahub.io/core/openml-datasets/_r/-/data/ilpd/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/ilpd/ilpd.arff
https://datahub.io/core/openml-datasets/_r/-/data/ilpd/ilpd.csv
https://datahub.io/core/openml-datasets/_r/-/data/ionosphere/README.md
https://datahub.io/core/openml-datasets/_r/-/data/ionosphere/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/ionosphere/ionosphere.arff
https://datahub.io/core/openml-datasets/_r/-/data/ionosphere/ionosphere.csv
https://datahub.io/core/openml-datasets/_r/-/data/iris/README.md
https://datahub.io/core/openml-datasets/_r/-/data/iris/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/iris/iris.arff
https://datahub.io/core/openml-datasets/_r/-/data/iris/iris.csv
https://datahub.io/core/openml-datasets/_r/-/data/isolet/README.md
https://datahub.io/core/openml-datasets/_r/-/data/isolet/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/isolet/isolet.arff
https://datahub.io/core/openml-datasets/_r/-/data/isolet/isolet.csv
https://datahub.io/core/openml-datasets/_r/-/data/jm1/README.md
https://datahub.io/core/openml-datasets/_r/-/data/jm1/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/jm1/jm1.arff
https://datahub.io/core/openml-datasets/_r/-/data/jm1/jm1.csv
https://datahub.io/core/openml-datasets/_r/-/data/kc1/README.md
https://datahub.io/core/openml-datasets/_r/-/data/kc1/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/kc1/kc1.arff
https://datahub.io/core/openml-datasets/_r/-/data/kc1/kc1.csv
https://datahub.io/core/openml-datasets/_r/-/data/kc2/README.md
https://datahub.io/core/openml-datasets/_r/-/data/kc2/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/kc2/kc2.arff
https://datahub.io/core/openml-datasets/_r/-/data/kc2/kc2.csv
https://datahub.io/core/openml-datasets/_r/-/data/kr-vs-kp/README.md
https://datahub.io/core/openml-datasets/_r/-/data/kr-vs-kp/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/kr-vs-kp/kr-vs-kp.arff
https://datahub.io/core/openml-datasets/_r/-/data/kr-vs-kp/kr-vs-kp.csv
https://datahub.io/core/openml-datasets/_r/-/data/kropt/README.md
https://datahub.io/core/openml-datasets/_r/-/data/kropt/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/kropt/kropt.arff
https://datahub.io/core/openml-datasets/_r/-/data/kropt/kropt.csv
https://datahub.io/core/openml-datasets/_r/-/data/letter/README.md
https://datahub.io/core/openml-datasets/_r/-/data/letter/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/letter/letter.arff
https://datahub.io/core/openml-datasets/_r/-/data/letter/letter.csv
https://datahub.io/core/openml-datasets/_r/-/data/liver-disorders/README.md
https://datahub.io/core/openml-datasets/_r/-/data/liver-disorders/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/liver-disorders/liver-disorders.arff
https://datahub.io/core/openml-datasets/_r/-/data/liver-disorders/liver-disorders.csv
https://datahub.io/core/openml-datasets/_r/-/data/lung-cancer/README.md
https://datahub.io/core/openml-datasets/_r/-/data/lung-cancer/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/lung-cancer/lung-cancer.arff
https://datahub.io/core/openml-datasets/_r/-/data/lung-cancer/lung-cancer.csv
https://datahub.io/core/openml-datasets/_r/-/data/lymph/README.md
https://datahub.io/core/openml-datasets/_r/-/data/lymph/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/lymph/lymph.arff
https://datahub.io/core/openml-datasets/_r/-/data/lymph/lymph.csv
https://datahub.io/core/openml-datasets/_r/-/data/madelon/README.md
https://datahub.io/core/openml-datasets/_r/-/data/madelon/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/madelon/madelon.arff
https://datahub.io/core/openml-datasets/_r/-/data/madelon/madelon.csv
https://datahub.io/core/openml-datasets/_r/-/data/mammography/README.md
https://datahub.io/core/openml-datasets/_r/-/data/mammography/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/mammography/mammography.arff
https://datahub.io/core/openml-datasets/_r/-/data/mammography/mammography.csv
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-factors/README.md
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-factors/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-factors/mfeat-factors.arff
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-factors/mfeat-factors.csv
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-karhunen/README.md
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-karhunen/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-karhunen/mfeat-karhunen.arff
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-karhunen/mfeat-karhunen.csv
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-morphological/README.md
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-morphological/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-morphological/mfeat-morphological.arff
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-morphological/mfeat-morphological.csv
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-pixel/README.md
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-pixel/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-pixel/mfeat-pixel.arff
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-pixel/mfeat-pixel.csv
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-zernike/README.md
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-zernike/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-zernike/mfeat-zernike.arff
https://datahub.io/core/openml-datasets/_r/-/data/mfeat-zernike/mfeat-zernike.csv
https://datahub.io/core/openml-datasets/_r/-/data/mozilla4/README.md
https://datahub.io/core/openml-datasets/_r/-/data/mozilla4/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/mozilla4/mozilla4.arff
https://datahub.io/core/openml-datasets/_r/-/data/mozilla4/mozilla4.csv
https://datahub.io/core/openml-datasets/_r/-/data/mushroom/README.md
https://datahub.io/core/openml-datasets/_r/-/data/mushroom/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/mushroom/mushroom.arff
https://datahub.io/core/openml-datasets/_r/-/data/mushroom/mushroom.csv
https://datahub.io/core/openml-datasets/_r/-/data/musk/README.md
https://datahub.io/core/openml-datasets/_r/-/data/musk/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/musk/musk.arff
https://datahub.io/core/openml-datasets/_r/-/data/musk/musk.csv
https://datahub.io/core/openml-datasets/_r/-/data/nursery/README.md
https://datahub.io/core/openml-datasets/_r/-/data/nursery/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/nursery/nursery.arff
https://datahub.io/core/openml-datasets/_r/-/data/nursery/nursery.csv
https://datahub.io/core/openml-datasets/_r/-/data/oil_spill/README.md
https://datahub.io/core/openml-datasets/_r/-/data/oil_spill/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/oil_spill/oil_spill.arff
https://datahub.io/core/openml-datasets/_r/-/data/oil_spill/oil_spill.csv
https://datahub.io/core/openml-datasets/_r/-/data/one-hundred-plants-shape/README.md
https://datahub.io/core/openml-datasets/_r/-/data/one-hundred-plants-shape/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/one-hundred-plants-shape/one-hundred-plants-shape.arff
https://datahub.io/core/openml-datasets/_r/-/data/one-hundred-plants-shape/one-hundred-plants-shape.csv
https://datahub.io/core/openml-datasets/_r/-/data/one-hundred-plants-texture/README.md
https://datahub.io/core/openml-datasets/_r/-/data/one-hundred-plants-texture/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/one-hundred-plants-texture/one-hundred-plants-texture.arff
https://datahub.io/core/openml-datasets/_r/-/data/one-hundred-plants-texture/one-hundred-plants-texture.csv
https://datahub.io/core/openml-datasets/_r/-/data/optdigits/README.md
https://datahub.io/core/openml-datasets/_r/-/data/optdigits/datapackage.json
https://datahub.io/core/openml-datasets/_r/-/data/optdigits/optdigits.arff
https://datahub.io/core/openml-datasets/_r/-/data/optdigits/optdigits.csv
https://datahub.io/core/openml-datasets/_r/-/data/page-blocks/README.md
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Key Files

Start with these files — they give you everything you need to understand and access the dataset.

https://datahub.io/core/openml-datasets/_r/-/data/Bioresponse/datapackage.json
README.mddocumentation
https://datahub.io/core/openml-datasets/_r/-/README.md
Typical Usage
  1. 1. Fetch data/Bioresponse/datapackage.json to inspect schema and resources
  2. 2. Download data resources listed in data/Bioresponse/datapackage.json
  3. 3. Read README.md for full context

Data Previews

speeddating

Unsupported data preview format `arff`

speeddating

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Schema

nametypeformat
has_nullnumberdefault
wavenumberdefault
genderstringdefault
agenumberdefault
age_onumberdefault
d_agenumberdefault
d_d_agestringdefault
racestringdefault
race_ostringdefault
sameracenumberdefault
importance_same_racenumberdefault
importance_same_religionnumberdefault
d_importance_same_racestringdefault
d_importance_same_religionstringdefault
fieldstringdefault
pref_o_attractivenumberdefault
pref_o_sincerenumberdefault
pref_o_intelligencenumberdefault
pref_o_funnynumberdefault
pref_o_ambitiousnumberdefault
pref_o_shared_interestsnumberdefault
d_pref_o_attractivestringdefault
d_pref_o_sincerestringdefault
d_pref_o_intelligencestringdefault
d_pref_o_funnystringdefault
d_pref_o_ambitiousstringdefault
d_pref_o_shared_interestsstringdefault
attractive_onumberdefault
sinsere_onumberdefault
intelligence_onumberdefault
funny_onumberdefault
ambitous_onumberdefault
shared_interests_onumberdefault
d_attractive_ostringdefault
d_sinsere_ostringdefault
d_intelligence_ostringdefault
d_funny_ostringdefault
d_ambitous_ostringdefault
d_shared_interests_ostringdefault
attractive_importantnumberdefault
sincere_importantnumberdefault
intellicence_importantnumberdefault
funny_importantnumberdefault
ambtition_importantnumberdefault
shared_interests_importantnumberdefault
d_attractive_importantstringdefault
d_sincere_importantstringdefault
d_intellicence_importantstringdefault
d_funny_importantstringdefault
d_ambtition_importantstringdefault
d_shared_interests_importantstringdefault
attractivenumberdefault
sincerenumberdefault
intelligencenumberdefault
funnynumberdefault
ambitionnumberdefault
d_attractivestringdefault
d_sincerestringdefault
d_intelligencestringdefault
d_funnystringdefault
d_ambitionstringdefault
attractive_partnernumberdefault
sincere_partnernumberdefault
intelligence_partnernumberdefault
funny_partnernumberdefault
ambition_partnernumberdefault
shared_interests_partnernumberdefault
d_attractive_partnerstringdefault
d_sincere_partnerstringdefault
d_intelligence_partnerstringdefault
d_funny_partnerstringdefault
d_ambition_partnerstringdefault
d_shared_interests_partnerstringdefault
sportsnumberdefault
tvsportsnumberdefault
exercisenumberdefault
diningnumberdefault
museumsnumberdefault
artnumberdefault
hikingnumberdefault
gamingnumberdefault
clubbingnumberdefault
readingnumberdefault
tvnumberdefault
theaternumberdefault
moviesnumberdefault
concertsnumberdefault
musicnumberdefault
shoppingnumberdefault
yoganumberdefault
d_sportsstringdefault
d_tvsportsstringdefault
d_exercisestringdefault
d_diningstringdefault
d_museumsstringdefault
d_artstringdefault
d_hikingstringdefault
d_gamingstringdefault
d_clubbingstringdefault
d_readingstringdefault
d_tvstringdefault
d_theaterstringdefault
d_moviesstringdefault
d_concertsstringdefault
d_musicstringdefault
d_shoppingstringdefault
d_yogastringdefault
interests_correlatenumberdefault
d_interests_correlatestringdefault
expected_happy_with_sd_peoplenumberdefault
expected_num_interested_in_menumberdefault
expected_num_matchesnumberdefault
d_expected_happy_with_sd_peoplestringdefault
d_expected_num_interested_in_mestringdefault
d_expected_num_matchesstringdefault
likenumberdefault
guess_prob_likednumberdefault
d_likestringdefault
d_guess_prob_likedstringdefault
metnumberdefault
decisionnumberdefault
decision_onumberdefault
matchnumberdefault

Data Files

FileDescriptionSizeLast modifiedDownload
speeddating
5 MB3 months ago
speeddating
speeddating
4.97 MB3 months ago
speeddating
FilesSizeFormatCreatedUpdatedLicenseSource
29.97 MBarff, csvabout 2 months agoOpen Data Commons Public Domain Dedication and License

The resources for this dataset can be found at https://www.openml.org/d/40536

Author: Ray Fisman and Sheena Iyengar
Source: Columbia Business School - 2004
Please cite: None

This data was gathered from participants in experimental speed dating events from 2002-2004. During the events, the attendees would have a four-minute "first date" with every other participant of the opposite sex. At the end of their four minutes, participants were asked if they would like to see their date again. They were also asked to rate their date on six attributes: Attractiveness, Sincerity, Intelligence, Fun, Ambition, and Shared Interests. The dataset also includes questionnaire data gathered from participants at different points in the process. These fields include: demographics, dating habits, self-perception across key attributes, beliefs on what others find valuable in a mate, and lifestyle information.

Attribute Information

 * gender: Gender of self  
 * age: Age of self  
 * age_o: Age of partner  
 * d_age: Difference in age  
 * race: Race of self  
 * race_o: Race of partner  
 * samerace: Whether the two persons have the same race or not.  
 * importance_same_race: How important is it that partner is of same race?  
 * importance_same_religion: How important is it that partner has same religion?  
 * field: Field of study  
 * pref_o_attractive: How important does partner rate attractiveness  
 * pref_o_sinsere: How important does partner rate sincerity  
 * pref_o_intelligence: How important does partner rate intelligence  
 * pref_o_funny: How important does partner rate being funny  
 * pref_o_ambitious: How important does partner rate ambition  
 * pref_o_shared_interests: How important does partner rate having shared interests  
 * attractive_o: Rating by partner (about me) at night of event on attractiveness  
 * sincere_o: Rating by partner (about me) at night of event on sincerity  
 * intelligence_o: Rating by partner (about me) at night of event on intelligence  
 * funny_o: Rating by partner (about me) at night of event on being funny  
 * ambitous_o: Rating by partner (about me) at night of event on being ambitious  
 * shared_interests_o: Rating by partner (about me) at night of event on shared interest  
 * attractive_important: What do you look for in a partner - attractiveness  
 * sincere_important: What do you look for in a partner - sincerity  
 * intellicence_important: What do you look for in a partner - intelligence  
 * funny_important: What do you look for in a partner - being funny  
 * ambtition_important: What do you look for in a partner - ambition  
 * shared_interests_important: What do you look for in a partner - shared interests  
 * attractive: Rate yourself - attractiveness  
 * sincere: Rate yourself - sincerity   
 * intelligence: Rate yourself - intelligence   
 * funny: Rate yourself - being funny   
 * ambition: Rate yourself - ambition  
 * attractive_partner: Rate your partner - attractiveness  
 * sincere_partner: Rate your partner - sincerity   
 * intelligence_partner: Rate your partner - intelligence   
 * funny_partner: Rate your partner - being funny   
 * ambition_partner: Rate your partner - ambition   
 * shared_interests_partner: Rate your partner - shared interests  
 * sports: Your own interests [1-10]  
 * tvsports  
 * exercise  
 * dining  
 * museums  
 * art  
 * hiking  
 * gaming  
 * clubbing  
 * reading  
 * tv  
 * theater  
 * movies  
 * concerts  
 * music  
 * shopping  
 * yoga  
 * interests_correlate: Correlation between participant’s and partner’s ratings of interests.  
 * expected_happy_with_sd_people: How happy do you expect to be with the people you meet during the speed-dating event?  
 * expected_num_interested_in_me: Out of the 20 people you will meet, how many do you expect will be interested in dating you?  
 * expected_num_matches: How many matches do you expect to get?  
 * like: Did you like your partner?  
 * guess_prob_liked: How likely do you think it is that your partner likes you?   
 * met: Have you met your partner before?  
 * decision: Decision at night of event.
 * decision_o: Decision of partner at night of event.  
 * match: Match (yes/no)

Relevant paper

Raymond Fisman; Sheena S. Iyengar; Emir Kamenica; Itamar Simonson.
Gender Differences in Mate Selection: Evidence From a Speed Dating Experiment.
The Quarterly Journal of Economics, Volume 121, Issue 2, 1 May 2006, Pages 673–697,
https://doi.org/10.1162/qjec.2006.121.2.673