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

Ammar Ammar,Jeaphianne Van Rijn,Dieter Maier,Iseult Lynch,Egon Willighagen,Laurent Winckers,Serena Bonaretti,Martine Bakker,Joris Quik

doi:10.3390/nano10102068

Abstract

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.

Highlights

Data sharing and data reuse are two complementary aspects of modern research
Parkinsons_AE: What are the differentially expressed genes between normal subjects and subjects with Parkinson’s diseases in the brain frontal lobe? To answer this question, the researcher looked for a dataset in the search engine of ArrayExpress (AE), a repository for microarray gene expression data based at the European Bioinformatics Institute (EBI), United Kingdom [19];
NBIA_GEO: What is the effect of the WDR45 gene mutation in the brain? In this case, the researcher looked for a dataset in the search engine of Gene Expression Omnibus (GEO), a repository containing gene expression and other functional genomics data hosted at the National Center for Biotechnology Information (NCBI), United States [20]

Summary

Introduction

Data sharing and data reuse are two complementary aspects of modern research. Researchers share their data for a sense of community, to demonstrate integrity of acquired data, and to enhance the quality and reproducibility of research [1]. Researchers are eager to reuse available data to integrate information that answer interdisciplinary research questions and to optimize use of funding [4]. Vines et al demonstrated that the availability of existing datasets associated with published articles decreases 17% per year due to the lack of appropriate hardware to access old storage media or because data were lost [7]. Data sharing and reuse need appropriate infrastructure, standards, and policies [5]

Methods

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Discussion

Conclusion