FAIRsFAIR Data Object Assessment Metrics

Devaraju, Anusuriya; Huber, Robert; Mokrane, Mahdi; Čepinskas, Linas; Davidson, Joy; Herterich, Patricia; L’Hours, Hervé; Vries, Jerry de; White, Angus

doi:10.5281/zenodo.3934401

Cited by 3 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Naturally, this can be best tested with an algorithmic framework. Indeed, the plethora of FAIRness claims and assessment tools led to stating the FAIR principle more precisely on the one hand 57 , and the development of automated tools on the other hand 2 , 21 , 24 , 58 .…”

Section: Methodsmentioning

confidence: 99%

“…We select the ARDC DAIR data self-assessment tool 46 for manual assessment and the FAIR indicator maturity test 2 , 44 as one of the two available machine-actionable tests. The other one is the rapidly evolving F-UJI test 21 . A recently published third semi-automated assessment tool 24 is specific for life sciences and therefore not considered in our context.…”

Section: Methodsmentioning

confidence: 99%

“…Independent of a specific domain, several evaluation tools have been developed to test the compliance of data resources with the FAIR principles and to offer guidance in improving the FAIR status. These tools range from checklists 16 – 18 and 19 , 20 to (semi-)automated evaluators 2 , 21 – 24 .

Figure 1 Selected milestones toward the implementation and testing of FAIR guiding principles along with efforts undertaken in the energy domain.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data

Schwanitz

Wierling

Biresselioğlu

et al. 2022

Sci Rep

View full text Add to dashboard Cite

With the continued digitization of the energy sector, the problem of sunken scholarly data investments and forgone opportunities of harvesting existing data is exacerbating. It compounds the problem that the reproduction of knowledge is incomplete, impeding the transparency of science-based targets for the choices made in the energy transition. The FAIR data guiding principles are widely acknowledged as a way forward, but their operationalization is yet to be agreed upon within different research domains. We comprehensively test FAIR data practices in the low carbon energy research domain. 80 databases representative for data needed to support the low carbon energy transition are screened. Automated and manual tests are used to document the state-of-the art and provide insights on bottlenecks from the human and machine perspectives. We propose action items for overcoming the problem with FAIR energy data and suggest how to prioritize activities.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Figure 1 Selected milestones toward the implementation and testing of FAIR guiding principles along with efforts undertaken in the energy domain.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data

Schwanitz

Wierling

Biresselioğlu

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The F-UJI tool was used to assess the FAIRness level of datasets. This tool evaluates all FAIR elements using the URL or persistent identifier of a dataset [7]. Using tools to assess the FAIRness of data helps the future improvement of data FAIRness and, therefore, their reuse [8].…”

Section: Introductionmentioning

confidence: 99%

FAIRness of Research Data in the European Humanities Landscape

Poljak Bilić,

Posavec

2024

Publications

View full text Add to dashboard Cite

This paper explores the landscape of research data in the humanities in the European context, delving into their diversity and the challenges of defining and sharing them. It investigates three aspects: the types of data in the humanities, their representation in repositories, and their alignment with the FAIR principles (Findable, Accessible, Interoperable, Reusable). By reviewing datasets in repositories, this research determines the dominant data types, their openness, licensing, and compliance with the FAIR principles. This research provides important insight into the heterogeneous nature of humanities data, their representation in the repository, and their alignment with FAIR principles, highlighting the need for improved accessibility and reusability to improve the overall quality and utility of humanities research data.

show abstract

Can LCA be FAIR? Assessing the status quo and opportunities for FAIR data sharing

Ghose

2024

Int J Life Cycle Assess

View full text Add to dashboard Cite

Aim The purpose of this study is to assess the status quo of data sharing in LCA in relation to the FAIR (findability, accessibility, interoperability, and reuse) data principles. Methods This study investigates how is LCA data from publicly funded research currently shared. Firstly, the focus is on life cycle inventory data shared in journal articles. Given that FAIR data sharing is not only the responsibility of the LCA practitioner, this study further investigates guidelines (e.g., data sharing standards and data management plans) and infrastructure (repositories, data formats, and nomenclature) to identify the tools and services available to LCA community which are essential to enable FAIR data sharing. Results The study identifies that although there is growing awareness to improve data sharing practices, implementation of FAIR guidelines for data sharing is seldom seen in practice. LCA studies that adhere to FAIR principles are primarily due to use of generic data repositories which provide tools to support data sharing. However, there is no guidance on how LCA specific data should be shared to ensure its findability, accessibility, interoperability, and reusability. This study suggests a workflow to enable FAIRification of LCA data. In addition, the study recommends further efforts within the LCA community on skill and technology development, strategic funding, and recognition of the best practices in relation to data sharing. Conclusion In conclusion, this study highlights the necessity of data sharing incentives, guidelines, and platforms/repositories specific for the LCA community.

show abstract

FAIRsFAIR Data Object Assessment Metrics

Cited by 3 publications

References 0 publications

Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data

Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data

FAIRness of Research Data in the European Humanities Landscape

Can LCA be FAIR? Assessing the status quo and opportunities for FAIR data sharing

Contact Info

Product

Resources

About