FAIRsFAIR Data Object Assessment Metrics

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

doi:10.5281/zenodo.4081213

Cited by 8 publications

(6 citation statements)

References 1 publication

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“…The FAIRification framework provides a valuable tool to guide FAIRification efforts in a range of communities and for a variety of data types. There already exists a wide range of tools, standards, indicators and measures developed to improve data FAIRification practice, such as FAIRness assessment frameworks proposed by the RDA 18 or FAIRsFAIR 19 , the Data Use Ontology (DUO) 20 standard for encoding data reuse conditions or the biosciences specific resource markup framework, Bioschemas 21 . The framework is agnostic of any specific indicators or implementation and any of these can be plugged into the framework in the relevant places.…”

Section: Discussionmentioning

confidence: 99%

FAIR in action - a flexible framework to guide FAIRification

et al. 2023

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The COVID-19 pandemic has highlighted the need for FAIR (Findable, Accessible, Interoperable, and Reusable) data more than any other scientific challenge to date. We developed a flexible, multi-level, domain-agnostic FAIRification framework, providing practical guidance to improve the FAIRness for both existing and future clinical and molecular datasets. We validated the framework in collaboration with several major public-private partnership projects, demonstrating and delivering improvements across all aspects of FAIR and across a variety of datasets and their contexts. We therefore managed to establish the reproducibility and far-reaching applicability of our approach to FAIRification tasks.

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Section: Discussionmentioning

confidence: 99%

FAIR in action - a flexible framework to guide FAIRification

et al. 2023

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“…The measures undertaken by PANGAEA have been recently evaluated by the European Commission in their European Research Data Landscape report 11 using the F-UJI FAIR assessment tool 17 . It turned out that according to this report, PANGAEA, reached the highest FAIR scores for its datasets among 31 investigated European data archives.…”

Section: Discussionmentioning

confidence: 99%

PANGAEA - Data Publisher for Earth & Environmental Science

et al. 2023

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The information system PANGAEA provides targeted support for research data management as well as long-term data archiving and publication. PANGAEA is operated as an open access library for archiving, publishing, and distributing georeferenced data from earth and environmental sciences. It focuses on observational and experimental data. Citability, comprehensive metadata descriptions, interoperability of data and metadata, a high degree of structural and semantic harmonization of the data inventory as well as the commitment of the hosting institutions ensures the long-term usability of archived data. PANGAEA is a pioneer of FAIR and open data infrastructures to enable data intensive science and an integral component of national and international science and technology activities. This paper provides an overview of the recent organisational, structural, and technological advancements in developing and operating the information system.

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“…28 Representative examples include FAIR Metrics, 29 the data maturity model of the RDA (Research Data Alliance) working group, 30 and FAIRsFAIR. 31 FAIR Metrics presents an evaluation framework that can measure FAIR indices using an automated tool. Discussions on the FAIR principle are also expanding in digital archives and libraries.…”

Section: Reuse Of Ontology Vocabularies and Fair Data Principlesmentioning

confidence: 99%

Japanese Military “Comfort Women” Knowledge Graph

Park¹,

Kim²

2023

ITAL

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Materials related to Japanese military “comfort women” in Korea are managed by several institutions. Each digital archive has their own metadata schema and management policies. So far, a standard or a common guideline for describing digital records is not formalized. We propose a Japanese military “comfort women” knowledge graph to semantically interlink the digital records from distributed digital archives. To build a Japanese military “comfort women” knowledge graph, digital records and descriptive metadata were collected from existing digital archives. A list of metadata was defined by analyzing commonly used properties and a knowledge model designed by reusing standard vocabularies. Knowledge was constructed by interlinking the collected records, external data sources, and enriching data. The knowledge graph was evaluated using the FAIR data maturity model.

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FAIRsFAIR Data Object Assessment Metrics

Cited by 8 publications

References 1 publication

FAIR in action - a flexible framework to guide FAIRification

FAIR in action - a flexible framework to guide FAIRification

PANGAEA - Data Publisher for Earth & Environmental Science

Japanese Military “Comfort Women” Knowledge Graph

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