CC BY-NC-ND 4.0 · Methods Inf Med 2020; 59(S 01): e21-e32
DOI: 10.1055/s-0040-1713684
Original Article for a Focus Theme
Georg Thieme Verlag KG Stuttgart · New York

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

A. Anil Sinaci
1  SRDC Software Research Development and Consultancy Corporation, Ankara, Turkey
,
Francisco J. Núñez-Benjumea
2  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
,
Mert Gencturk
1  SRDC Software Research Development and Consultancy Corporation, Ankara, Turkey
,
Malte-Levin Jauer
3  Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
,
Thomas Deserno
3  Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
,
Catherine Chronaki
4  Health Level Seven International Foundation, Brussels, Belgium
,
Giorgio Cangioli
4  Health Level Seven International Foundation, Brussels, Belgium
,
Carlos Cavero-Barca
5  Atos, Group of Health, Atos Research and Innovation (ARI), Madrid, Spain
,
Juan M. Rodríguez-Pérez
5  Atos, Group of Health, Atos Research and Innovation (ARI), Madrid, Spain
,
Manuel M. Pérez-Pérez
5  Atos, Group of Health, Atos Research and Innovation (ARI), Madrid, Spain
,
Gokce B. Laleci Erturkmen
1  SRDC Software Research Development and Consultancy Corporation, Ankara, Turkey
,
Tony Hernández-Pérez
6  Department of Library and Information Sciences, Universidad Carlos III de Madrid, Madrid, Spain
,
Eva Méndez-Rodríguez
6  Department of Library and Information Sciences, Universidad Carlos III de Madrid, Madrid, Spain
,
Carlos L. Parra-Calderón
2  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
› Author Affiliations
Funding This work was performed in the scope of FAIR4Health project31. FAIR4Health has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666.
Further Information

Publication History

31 July 2019

06 May 2020

Publication Date:
03 July 2020 (online)

  

Abstract

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.