Library Digital Collections
Library Digital Collections

Data from: Toward Enhanced Reusability: A Comparative Analysis of Metadata for Machine Learning Objects and Their Characteristics in Generalist and Specialist Repositories

Component

Supplemental Table A - Characteristics of the UC San Diego Library Digital Collections

File Size
  • 14.9 KB
File Format
  • XLSX
Description

Table based on the Generalist Repository Comparison Chart produced by Stall et al. 2020 (https://doi.org/10.5281/zenodo.3946720).

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Supplemental Table B - Metadata Crosswalk

File Size
  • 35.1 KB
File Format
  • XLSX
Description

Equivalent properties across the eight repositories mapped onto a common property name.

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Supplemental Table C - Number of machine learning objects published by year and repository, 2000-2021 (Figure 1 data)

File Size
  • 15.1 KB
File Format
  • XLSX
Description

Data used to generate Figure 1, machine learning objects by year published and repository, 2000-2021.

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Supplemental Table D - File Format Categories

File Size
  • 25.1 KB
File Format
  • XLSX
Description

File extensions extracted from file names in metadata records grouped into format categories representing broader groups.

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Supplemental Table E - Percentage of objects containing file formats (Figure 2 data)

File Size
  • 17.2 KB
File Format
  • XLSX
Description

Data used to generate Figure 2, percentage of objects containing file format category in repositories.

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Supplemental Table F - License Normalization

File Size
  • 16.5 KB
File Format
  • XLSX
Description

Licenses categorized broadly into one of fourteen types according to rights reserved, from least restrictive (“No rights reserved,” “Attribution,” “Attribution-NoDerivs,” etc.) to most restrictive (“All rights reserved"), as described in the SPDX License List (https://spdx.org/licenses/), published by the Linux Foundation.

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Code for Metadata Extracts and Data Analysis (GitHub Repository)

File Size
  • 898 MB
File Format
  • ZIP Format
Description

Code used to extract metadata via API or web scraping from repositories. [Note that APIs and UIs change regularly - consult the website documentation before reusing this code.] Jupyter Notebooks and R scripts to process raw JSON metadata extracts and calculate summary statistics. Includes code to create Supplemental Tables C, D, and E, as well as code to create Figure 1, Figure 2, and Table 3 in Labou et al. 2024.

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Repository Metadata Extracts

File Size
  • 710 MB
File Format
  • ZIP Format
Description

Full metadata extracts from seven repositories, in JSON format, and the UC San Diego Library Digital Collections extract in .xlsx format.

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Resource Types for Figshare and Zenodo Objects

File Size
  • 15.9 KB
File Format
  • XLSX
Description

Full list of resource types for Figshare and Zenodo objects. Labou et al. 2024 limited analyses to the subset of Figshare objects tagged as “dataset”, “software”, or “model”, and to the subset of Zenodo objects classified as “Dataset” or “Software”.

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Collection
Cite This Work

Labou, Stephanie; Pennington, Abigail; Yoo, Ho Jung S.; Baluja, Michael (2024). Data from: Toward Enhanced Reusability: A Comparative Analysis of Metadata for Machine Learning Objects and Their Characteristics in Generalist and Specialist Repositories. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0JS9QMH

Description

This dataset contains data reported in the paper, Labou et al. 2024, which aims to understand how researchers are currently documenting ML research outputs for sharing, and the extent to which repository metadata fields enable reuse of ML objects. Contents of the dataset include: Supplemental Tables referenced in the paper, a snapshot of the code used to query or web scrape data repositories for ML objects, metadata extracts from the repositories, and a snapshot of the code used to analyze the extracts.

Creation Date
  • 2021 to 2023
Date Issued
  • 2024
Authors
Programmer
Technical Details

Code used to access all APIs was developed using Python 3.10.0, with tested compatibility back to Python 3.9.2. This implementation relies heavily on packages such as "requests" for the public APIs, and "selenium" and "beautifulsoup4" for web scraping when necessary. See requirements.txt files in Code for Metadata Extracts and Data Analysis for full details.

Funding

Librarians Association of the University of California (LAUC) 2020-2021; Research Data Curation Program, UC San Diego Library.

Topics

Formats

View formats within this collection

Language
  • English
Identifier

Identifier: Abigail Pennington: https://orcid.org/0000-0002-9364-1995

Identifier: Ho Jung S. Yoo: https://orcid.org/0000-0001-9677-0947

Identifier: Stephanie Labou: https://orcid.org/0000-0001-5633-5983

Related Resources
License

Creative Commons Attribution 4.0 International Public License

Rights Holder
  • UC Regents
Copyright

Under copyright (US)

Use: This work is available from the UC San Diego Library. This digital copy of the work is intended to support research, teaching, and private study.

Constraint(s) on Use: This work is protected by the U.S. Copyright Law (Title 17, U.S.C.). Use of this work beyond that allowed by "fair use" or any license applied to this work requires written permission of the copyright holder(s). Responsibility for obtaining permissions and any use and distribution of this work rests exclusively with the user and not the UC San Diego Library. Inquiries can be made to the UC San Diego Library program having custody of the work.

Digital Object Made Available By

Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp)

Last Modified

2024-06-28