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Detailed Implementation of a Reproducible Machine Learning-Enabled Workflow Cover

Detailed Implementation of a Reproducible Machine Learning-Enabled Workflow

Data Science Journal
Volume 23 (2024): Issue 1
By: Kenneth E. Schackart III,  Heidi J. Imker and  Charles E. Cook  
Open Access
|Apr 2024

Authors

Kenneth E. Schackart III

schackartk1@gmail.com

Global Biodata Coalition, 12 quai Saint-Jean 67080, Strasbourg, FR; Department of Biosystems Engineering, The University of Arizona, Tucson, Arizona 85721

Heidi J. Imker

imker@illinois.edu

Global Biodata Coalition, 12 quai Saint-Jean 67080, Strasbourg, FR; University Library, University of Illinois at Urbana-Champaign, Urbana, Illinois 31821

Charles E. Cook

ccook@globalbiodata.org

Global Biodata Coalition, 12 quai Saint-Jean 67080, Strasbourg
DOI: https://doi.org/10.5334/dsj-2024-023 | Journal eISSN: 1683-1470
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Language: English
Submitted on: Dec 9, 2023
|
Accepted on: Apr 8, 2024
|
Published on: Apr 29, 2024
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
computational reproducibility,
open science,
research software,
FAIR data,
machine learning workflow,
biodata resource inventory

© 2024 Kenneth E. Schackart III, Heidi J. Imker, Charles E. Cook, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

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