From Raw Data to FAIR Data: The FAIRification Workflow for Health Research

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From Raw Data to FAIR Data: The FAIRification Workflow for Health Research

Author(s): Sinaci, A. Anil; Nuñez-Benjumea, Francisco J.; Gencturk, Mert; Jauer, Malte Levin; Deserno, Thomas; Chronaki, Catherine; Cangioli, Giorgio; Cavero-Barca, Carlos; Rodríguez-Pérez, Juan M.; Pérez-Pérez, Manuel M.; Laleci Erturkmen, Gokce B.; Hernández Pérez, Antonio; Méndez Rodríguez, Eva María; Parra-Calderon, Carlos L.
Publisher: Thieme
Issued date: 2020-06-01
Citation: Methods of information in medicine. 2020 Jun;59(S 01):e21-e32.
ISSN: 0026-1270
DOI: https://doi.org/10.1055/s-0040-1713684
xmlui.dri2xhtml.METS-1.0.item-contributor-funder: European Commission
Sponsor: This work was performed in the scope of FAIR4Healthproject. FAIR4Health has received funding from the European Union’s Horizon 2020 research and innovationprogramme under grant agreement number 824666.
Project: info:eu-repo/grantAgreement/EC/H2020/824666
Keywords: Interoperability , Data science , Data curation , Data anonymization , Metadata
URI: http://hdl.handle.net/10016/32661
Rights: © 2020 Georg Thieme Verlag KG
Atribución-NoComercial-CompartirIgual 3.0 España
© 2020 Georg Thieme Verlag KG

Abstract:

BackgroundFAIR (findability, accessibility, interoperability, and reusability) guidingprinciples seek the reuse of data and other digital research input, output, and objects(algorithms, tools, and workflows that led to that data) making themfindable, accessibl [+]

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