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

Research output: Contribution to journalArticlepeer-review

Standard

A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences. / Ammar, Ammar; Bonaretti, Serena; Winckers, Laurent; Quik, Joris; Bakker, Martine; Maier, Dieter; Lynch, Iseult; Rijn, Jeaphianne van; Willighagen, Egon.

In: Nanomaterials, Vol. 10, No. 10, 2068, 20.10.2020, p. 1-14.

Research output: Contribution to journalArticlepeer-review

Harvard

Ammar, A, Bonaretti, S, Winckers, L, Quik, J, Bakker, M, Maier, D, Lynch, I, Rijn, JV & Willighagen, E 2020, 'A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences', Nanomaterials, vol. 10, no. 10, 2068, pp. 1-14. https://doi.org/10.3390/nano10102068

APA

Ammar, A., Bonaretti, S., Winckers, L., Quik, J., Bakker, M., Maier, D., Lynch, I., Rijn, J. V., & Willighagen, E. (2020). A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences. Nanomaterials, 10(10), 1-14. [2068]. https://doi.org/10.3390/nano10102068

Vancouver

Ammar A, Bonaretti S, Winckers L, Quik J, Bakker M, Maier D et al. A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences. Nanomaterials. 2020 Oct 20;10(10):1-14. 2068. https://doi.org/10.3390/nano10102068

Author

Ammar, Ammar ; Bonaretti, Serena ; Winckers, Laurent ; Quik, Joris ; Bakker, Martine ; Maier, Dieter ; Lynch, Iseult ; Rijn, Jeaphianne van ; Willighagen, Egon. / A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences. In: Nanomaterials. 2020 ; Vol. 10, No. 10. pp. 1-14.

Bibtex

@article{c9d961c2d49640ee8a0db9c7ef7998b8,
title = "A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences",
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.",
keywords = "FAIR guidelines, FAIR maturity indicators, Jupyter Notebook, Life sciences",
author = "Ammar Ammar and Serena Bonaretti and Laurent Winckers and Joris Quik and Martine Bakker and Dieter Maier and Iseult Lynch and Rijn, {Jeaphianne van} and Egon Willighagen",
year = "2020",
month = oct,
day = "20",
doi = "10.3390/nano10102068",
language = "English",
volume = "10",
pages = "1--14",
journal = "Nanomaterials",
issn = "2079-4991",
publisher = "MDPI",
number = "10",

}

RIS

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T1 - A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences

AU - Ammar, Ammar

AU - Bonaretti, Serena

AU - Winckers, Laurent

AU - Quik, Joris

AU - Bakker, Martine

AU - Maier, Dieter

AU - Lynch, Iseult

AU - Rijn, Jeaphianne van

AU - Willighagen, Egon

PY - 2020/10/20

Y1 - 2020/10/20

N2 - 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.

AB - 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.

KW - FAIR guidelines

KW - FAIR maturity indicators

KW - Jupyter Notebook

KW - Life sciences

UR - https://doi.org/10.3390/nano10102068

UR - http://www.scopus.com/inward/record.url?scp=85093684755&partnerID=8YFLogxK

U2 - 10.3390/nano10102068

DO - 10.3390/nano10102068

M3 - Article

C2 - 33092028

VL - 10

SP - 1

EP - 14

JO - Nanomaterials

JF - Nanomaterials

SN - 2079-4991

IS - 10

M1 - 2068

ER -