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

Autor:	Sinaci AA; SRDC Software Research Development and Consultancy Corporation, Ankara, Turkey., Núñez-Benjumea FJ; Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville/Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, Spain., Gencturk M; SRDC Software Research Development and Consultancy Corporation, Ankara, Turkey., Jauer ML; Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany., Deserno T; Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany., Chronaki C; Health Level Seven International Foundation, Brussels, Belgium., Cangioli G; Health Level Seven International Foundation, Brussels, Belgium., Cavero-Barca C; Atos, Group of Health, Atos Research and Innovation (ARI), Madrid, Spain., Rodríguez-Pérez JM; Atos, Group of Health, Atos Research and Innovation (ARI), Madrid, Spain., Pérez-Pérez MM; Atos, Group of Health, Atos Research and Innovation (ARI), Madrid, Spain., Laleci Erturkmen GB; SRDC Software Research Development and Consultancy Corporation, Ankara, Turkey., Hernández-Pérez T; Department of Library and Information Sciences, Universidad Carlos III de Madrid, Madrid, Spain., Méndez-Rodríguez E; Department of Library and Information Sciences, Universidad Carlos III de Madrid, Madrid, Spain., Parra-Calderón CL; Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville/Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, Spain.
Jazyk:	angličtina
Zdroj:	Methods of information in medicine [Methods Inf Med] 2020 Jun; Vol. 59 (S 01), pp. e21-e32. Date of Electronic Publication: 2020 Jul 03.
DOI:	10.1055/s-0040-1713684
Abstrakt:	Background: FAIR (findability, accessibility, interoperability, and reusability) guiding principles seek the reuse of data and other digital research input, output, and objects (algorithms, tools, and workflows that led to that data) making them findable, accessible, interoperable, and reusable. GO FAIR - a bottom-up, stakeholder driven and self-governed initiative - defined a seven-step FAIRification process focusing on data, but also indicating the required work for metadata. This FAIRification process aims at addressing the translation of raw datasets into FAIR datasets in a general way, without considering specific requirements and challenges that may arise when dealing with some particular types of data. Objectives: This scientific contribution addresses the architecture design of an open technological solution built upon the FAIRification process proposed by "GO FAIR" which addresses the identified gaps that such process has when dealing with health datasets. Methods: A common FAIRification workflow was developed by applying restrictions on existing steps and introducing new steps for specific requirements of health data. These requirements have been elicited after analyzing the FAIRification workflow from different perspectives: technical barriers, ethical implications, and legal framework. This analysis identified gaps when applying the FAIRification process proposed by GO FAIR to health research data management in terms of data curation, validation, deidentification, versioning, and indexing. Results: A technological architecture based on the use of Health Level Seven International (HL7) FHIR (fast health care interoperability resources) resources is proposed to support the revised FAIRification workflow. Discussion: Research funding agencies all over the world increasingly demand the application of the FAIR guiding principles to health research output. Existing tools do not fully address the identified needs for health data management. Therefore, researchers may benefit in the coming years from a common framework that supports the proposed FAIRification workflow applied to health datasets. Conclusion: Routine health care datasets or data resulting from health research can be FAIRified, shared and reused within the health research community following the proposed FAIRification workflow and implementing technical architecture. Competing Interests: A.A.S. reports grants from EU H2020 Program, during the conduct of the study. (Georg Thieme Verlag KG Stuttgart · New York.)
Databáze:	MEDLINE
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