CONTINUUM Books
Open Research Data in Machine Learning
A handbook
Handbook · multiple authors
Open Research Data in Machine Learning
A handbook
Published 24 April 2026 · 186 pages · English
ISBN 978-0-00-100003-6 · DOI 10.xxxx/ctm.bk.2026.0003
Sample book. Title, authors and content are fictional demonstration material.
About this book
A step-by-step handbook for sharing datasets, code and models so that others can find, cite and reuse them. It covers licences, documentation, identifiers, repositories, privacy, and long-term preservation, with checklists that can be adopted by labs and venues.
Keywords open data · FAIR · documentation · preservation
Read or download
open-research-data-ml-handbook.pdf
open-research-data-ml-handbook.epub
open-research-data-ml-handbook.html
Contents
- 1Why share?pp. 1–16PDF
- 2Licences and permissionspp. 17–42PDF
- 3Documenting datasets and modelspp. 43–82PDF
- 4Persistent identifierspp. 83–108PDF
- 5Privacy and sensitive datapp. 109–140PDF
- 6Software and long-term preservationpp. 141–170PDF
- 7Checklistspp. 171–186PDF
About the authors
- Fatima Zahra El Idrissi · Meridian University, Morocco
Works on research data management, FAIR principles and dataset documentation.
- Ravi Menon · Open Systems Lab, India
Develops research software and long-term preservation workflows.
- Ingrid Novak · Lakeshore Centre for Machine Learning, Czechia
Studies reproducibility, research software and open-science practice in machine learning.
How to cite
Idrissi, F. Z. E., Menon, R., & Novak, I. (2026). Open Research Data in Machine Learning: A handbook (1st edition). CONTINUUM Books. doi:10.xxxx/ctm.bk.2026.0003