Reproducible big data science: A case study in continuous FAIRness

Big biomedical data create exciting opportunities for discovery, but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code thr...

詳細記述

保存先:
書誌詳細
出版年:PLoS One
主要な著者: Madduri, Ravi, Chard, Kyle, D’Arcy, Mike, Jung, Segun C., Rodriguez, Alexis, Sulakhe, Dinanath, Deutsch, Eric, Funk, Cory, Heavner, Ben, Richards, Matthew, Shannon, Paul, Glusman, Gustavo, Price, Nathan, Kesselman, Carl, Foster, Ian
フォーマット: Artigo
言語:Inglês
出版事項: Public Library of Science 2019
主題:
Research Article
オンライン･アクセス:https://ncbi.nlm.nih.gov/pmc/articles/PMC6459504/
https://ncbi.nlm.nih.gov/pubmed/30973881
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0213013
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