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Experimental results from the Empirical Investigation of the Completeness of Datasets Documentation on ML/AI repositories

This is the raw data from the empirical investigation of the paper “Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation”. This work aim of this work is to investigate the state of dataset documentation practices, measuring the completeness of the documentation of several popular datasets in ML/AI repositories. We created a dataset documentation schema-the Documentation Test Sheet (DTS)-that identifies the information that should always be attached to a dataset (to ensure proper dataset choice and informed use), according to relevant studies in the literature. We verified 100 popular datasets from four different repositories with the DTS to investigate which information were present.

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    This is the raw data from the empirical investigation of the paper...

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    Paper DOI: https://doi.org/10.1007/978-3-031-49008-8_7 A copy of this item is...

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Additional Info
Field Value
Associate Project FAIR
Group Social Impact of AI and explainable ML
Involved Institutions Nexa Center for Internet & Society, Politecnico di Torino
Involved People Rondina, Marco, [email protected], orcid.org/0009-0008-8819-3623
Involved People Vetrò, Antonio, [email protected], orcid.org/0000-0003-2027-3308
Involved People De Martin, Juan Carlos, [email protected], orcid.org/0000-0002-7867-1926
SoBigData Node SoBigData EU
SoBigData Node SoBigData IT
State Complete
Thematic Cluster Other
system:type Experiment
Management Info
Field Value
Author Rondina Marco
Maintainer Rondina Marco
Version 1
Last Updated 23 November 2024, 16:06 (CET)
Created 23 November 2024, 16:06 (CET)