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Article

A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences

1
Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, NL-6200 MD Maastricht, The Netherlands
2
Transparent MSK Research, NL-6221 BN Maastricht, The Netherlands
3
National Institute for Public Health and the Environment (RIVM), NL-3720 BA Bilthoven, The Netherlands
4
Biomax Informatics AG, 82152 Planegg, Germany
5
School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Nanomaterials 2020, 10(10), 2068; https://doi.org/10.3390/nano10102068
Received: 2 September 2020 / Revised: 28 September 2020 / Accepted: 14 October 2020 / Published: 20 October 2020
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine the FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated the feasibility of our method on three real use cases where researchers looked for six datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress, Gene Expression Omnibus, eNanoMapper, caNanoLab, NanoCommons and ChEMBL), a registry of repositories, and a searchable resource (Google Dataset Search) via application program interfaces (API) wherever possible. With our analysis, we found that the six datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and the presence of specific attributes in registries of repositories could increase FAIRness of datasets. View Full-Text
Keywords: FAIR guidelines; FAIR maturity indicators; life sciences; Jupyter Notebook FAIR guidelines; FAIR maturity indicators; life sciences; Jupyter Notebook
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MDPI and ACS Style

Ammar, A.; Bonaretti, S.; Winckers, L.; Quik, J.; Bakker, M.; Maier, D.; Lynch, I.; van Rijn, J.; Willighagen, E. A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences. Nanomaterials 2020, 10, 2068. https://doi.org/10.3390/nano10102068

AMA Style

Ammar A, Bonaretti S, Winckers L, Quik J, Bakker M, Maier D, Lynch I, van Rijn J, Willighagen E. A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences. Nanomaterials. 2020; 10(10):2068. https://doi.org/10.3390/nano10102068

Chicago/Turabian Style

Ammar, Ammar, Serena Bonaretti, Laurent Winckers, Joris Quik, Martine Bakker, Dieter Maier, Iseult Lynch, Jeaphianne van Rijn, and Egon Willighagen. 2020. "A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences" Nanomaterials 10, no. 10: 2068. https://doi.org/10.3390/nano10102068

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