Ten simple rules for starting (and sustaining) an academic data science initiative-Reference-Cited by-同舟云学术

Ten simple rules for starting (and sustaining) an academic data science initiative

Published:2021-02-18 Issue:2 Volume:17 Page:e1008628
ISSN:1553-7358
Container-title:PLOS Computational Biology
language:en
Short-container-title:PLoS Comput Biol

Author:

Parker Micaela S.^ORCID,Burgess Arlyn E.,Bourne Philip E.^ORCID

Publisher

Public Library of Science (PLoS)

Subject

Computational Theory and Mathematics,Cellular and Molecular Neuroscience,Genetics,Molecular Biology,Ecology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics

Reference24 articles.

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2. Moore-Sloan Data Science Environments. Creating Institutional Change in Data Science. 2018. Available from: https://doi.org/10.6069/v5s4-8n41

3. Katz L. Evaluation of the Moore-Sloan Data Science Environments. Abt Associates; 2019 Feb. Final Report. Available from: https://doi.org/10.6069/X07Z-VJ63

4. Katz L. Academic Data Science Centers in the United States: A Study of 20 Universities. Abt Associates; 2018 Dec. Report. Available from: https://doi.org/10.6069/H63V-V078

5. The principles of tomorrow's university. Version: 1; Peer review: 2 approved;DS Katz;F1000Research,2018

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