Enhancing Reuse of Data and Biological Material in Medical Research: From FAIR to FAIR-Health

Authors	HOLUB Petr KOHLMAYER Florian PRASSER Fabian MAYRHOFER Michaela Theresia SCHLUNDER Irene MARTIN Gillian M. CASATI Sara KOUMAKIS Lefteris WUTTE Andrea KOZERA Lukas STRAPAGIEL Dominik ANTON Gabriele ZANETTI Gianluigi SEZERMAN Osman Ugur MENDY Maimuna VALÍK Dalibor LAVITRANO Marialuisa DAGHER Georges ZATLOUKAL Kurt VAN OMMEN GertJan B. LITTON Jan-Eric
Year of publication	2018
Type	Article in Periodical
Magazine / Source	Biopreservation and Biobanking
Citation
Web	https://www.liebertpub.com/doi/abs/10.1089/bio.2017.0110
Doi	http://dx.doi.org/10.1089/bio.2017.0110
Keywords	FAIR (Findable, Accessible, Interoperable, and Reusable) principles; provenance information management; privacy protection; open science; quality; incentives
Description	The known challenge of underutilization of data and biological material from biorepositories as potential resources for medical research has been the focus of discussion for over a decade. Recently developed guidelines for improved data availability and reusability-entitled FAIR Principles (Findability, Accessibility, Interoperability, and Reusability)-are likely to address only parts of the problem. In this article, we argue that biological material and data should be viewed as a unified resource. This approach would facilitate access to complete provenance information, which is a prerequisite for reproducibility and meaningful integration of the data. A unified view also allows for optimization of long-term storage strategies, as demonstrated in the case of biobanks. We propose an extension of the FAIR Principles to include the following additional components: (1) quality aspects related to research reproducibility and meaningful reuse of the data, (2) incentives to stimulate effective enrichment of data sets and biological material collections and its reuse on all levels, and (3) privacy-respecting approaches for working with the human material and data. These FAIR-Health principles should then be applied to both the biological material and data. We also propose the development of common guidelines for cloud architectures, due to the unprecedented growth of volume and breadth of medical data generation, as well as the associated need to process the data efficiently.