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

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Date

2020-06-01

Author

Sınacı, Ali Anıl
Nunez-Benjumea, Francisco J.
Gencturk, Mert
Jauer, Malte-Levin
Deserno, Thomas
Chronaki, Catherine
Cangioli, Giorgio
Cavero-Barca, Carlos
Rodriguez-Perez, Juan M.
Perez-Perez, Manuel M.
Erturkmen, Gokce B. Laleci
Hernandez-Perez, Tony
Mendez-Rodriguez, Eva
Parra-Calderon, Carlos L.

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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.

Subject Keywords

Health Informatics, Health Information Management, Advanced and Specialised Nursing

URI

https://hdl.handle.net/11511/51901

Journal

METHODS OF INFORMATION IN MEDICINE

DOI

https://doi.org/10.1055/s-0040-1713684

Collections

Department of Computer Engineering, Article

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Citation Formats

A. A. Sınacı et al., “From Raw Data to FAIR Data: The FAIRification Workflow for Health Research,” METHODS OF INFORMATION IN MEDICINE, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/51901.