FAIRshake: Toolkit to Evaluate the FAIRness of Research Digital Resources

Clarke, Daniel J B; Wang, Lily; Jones, Alex L.; Wojciechowicz, Megan L.; Torre, Denis; Jagodnik, Kathleen M.; Jenkins, Sherry L.; McQuilton, Peter; Flamholz, Zachary; Silverstein, Moshe C.; Schilder, Brian M; Robasky, Kimberly; Castillo, Claris; Idaszak, Ray; Ahalt, Stanley C.; Williams, Jason; Schürer, Stephan C.; Cooper, Daniel J.; Azevedo, Ricardo de Miranda; Klenk, Juergen; Haendel, Melissa; Nedzel, Jared L.; Avillach, Paul; Shimoyama, Mary; Harris, Rayna M.; Gamble, Meredith; Poten, Rudy; Charbonneau, Amanda; Larkin, Jennie E.; Brown, C. Titus; Bonazzi, Vivien; Dumontier, Michel; Sansone, Susanna Assunta; Ma’ayan, Avi

doi:10.1016/j.cels.2019.09.011

Cited by 41 publications

(40 citation statements)

References 11 publications

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“…Some funders such as the EU and NIH are developing policies around FAIR data which may include a more formal assessment of FAIRness. Such tools include FAIRmetrics 1 , FAIR Maturity Indicators (Wilkinson et al 2019)), FAIRshake (Clarke et al 2019) and the FORCE11/Research Data Alliance evaluation criteria (McQuilton et al 2020).…”

Section: Discussionmentioning

confidence: 99%

“…As documented by the Neuroscience Information Framework (NIF; neuinfo.org), on-line data repositories are diverse, each with their own custom user interfaces and few standards as to how they should be designed and the functions they should support [5]. With data repositories increasing in importance, groups have been developing recommendations on a basic set of functions that these repositories should support (e.g., [6][7][8][9][10][11]. Many of these focus on FAIR, e.g., FAIRshake [6] but they are by no means the only criteria.…”

Section: Introductionmentioning

confidence: 99%

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A tool for assessing alignment of biomedical data repositories with open, FAIR, citation and trustworthy principles

Murphy¹,

Bar-Sinai

Martone

2021

Preprint

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Increasing attention is being paid to the operation of biomedical data repositories in light of efforts to improve how scientific data is handled and made available for the long term. Simultaneously, groups around the world have been coming together to formalize principles that govern different aspects of open science and data sharing.The most well known are the FAIR data principles. These are joined by principles and practices that govern openness, citation, credit and good stewardship (trustworthiness). Together, these define a framework for data repositories to support Open, FAIR, Citable and Trustworthy (OFCT) data. Here we developed an instrument using the open source PolicyModels toolkit that attempts to operationalize key aspects of OFCT principles and applied the instrument to eight biomedical community repositories listed by the NIDDK Information Network (dkNET.org). The evaluation was performed through inspection of documentation and interaction with the sites. Overall, there was little explicit acknowledgement of any of the OFCT principles, although the majority of repositories provided at least some support for their tenets.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A tool for assessing alignment of biomedical data repositories with open, FAIR, citation and trustworthy principles

Murphy¹,

Bar-Sinai

Martone

2021

Preprint

View full text Add to dashboard Cite

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“…In addition, by vertically stacking representations for different datasets, we can visually compare FAIRness levels for each maturity indicator. In the literature, another example of visualization is insignia , created for the platform FAIRshake [ 15 ]. It consists of multiple squares colored from blue (satisfactory) to red (unsatisfactory) for different levels of FAIRness.…”

Section: Discussionmentioning

confidence: 99%

“…Following these criteria, two platforms have recently been developed to automatically compute FAIR maturity indicators: FAIR Evaluation Services and FAIRshake. The first platform offers an evaluation of maturity indicators and compliance tests [ 14 ], whereas the second platform provides metrics, rubrics, and evaluators for registered digital resources [ 15 ]. Both platforms provide use cases for FAIRness assessment, however they do not provide systematic analysis of evaluated datasets and repositories.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…Sharing experimental data has major advantages for the scientific community. With the amount of biological data produced increasing each year, structured databases are a crucial tool to store, share and maintain data, improving quality and reproducibility ( 1 ). Being able to have this information aggregated in a single discipline-specific repository could spare a lot of time.…”

Section: Introductionmentioning

confidence: 99%

KiMoSys 2.0: an upgraded database for submitting, storing and accessing experimental data for kinetic modeling

2020

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The KiMoSys (https://kimosys.org), launched in 2014, is a public repository of published experimental data, which contains concentration data of metabolites, protein abundances and flux data. It offers a web-based interface and upload facility to share data, making it accessible in structured formats, while also integrating associated kinetic models related to the data. In addition, it also supplies tools to simplify the construction process of ODE (Ordinary Differential Equations)-based models of metabolic networks. In this release, we present an update of KiMoSys with new data and several new features, including (i) an improved web interface, (ii) a new multi-filter mechanism, (iii) introduction of data visualization tools, (iv) the addition of downloadable data in machine-readable formats, (v) an improved data submission tool, (vi) the integration of a kinetic model simulation environment and (vii) the introduction of a unique persistent identifier system. We believe that this new version will improve its role as a valuable resource for the systems biology community. Database URL: www.kimosys.org

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FAIRshake: Toolkit to Evaluate the FAIRness of Research Digital Resources

Cited by 41 publications

References 11 publications

A tool for assessing alignment of biomedical data repositories with open, FAIR, citation and trustworthy principles

A tool for assessing alignment of biomedical data repositories with open, FAIR, citation and trustworthy principles

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

KiMoSys 2.0: an upgraded database for submitting, storing and accessing experimental data for kinetic modeling

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