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Recommendations for Discipline-Specific FAIRness Evaluation Derived from Applying an Ensemble of Evaluation Tools

  • Published:2022 Issue: Volume:21 Page:
  • ISSN:1683-1470
  • Container-title:Data Science Journal
  • language:en
  • Short-container-title:

Author:

Gehlen Karsten Peters-vonORCID,Höck HeinkeORCID,Fast AndrejORCID,Heydebreck DanielORCID,Lammert AndreaORCID,Thiemann HannesORCID

Publisher

Ubiquity Press, Ltd.

Subject

Computer Science Applications,Computer Science (miscellaneous)

Reference64 articles.

1. ANDS. 2021. FAIR data self-assessment tool. https://www.ands-nectar-rds.org.au/fair-tool, accessed: 2022-02-01.

2. WDS/RDA Assessment of Data Fitness for Use WG Outputs and Recommendations,2019

3. The FAIR Data Maturity Model: An Approach to Harmonise FAIR Assessments;Data Sci. J,2020

4. Results of an Analysis of Existing FAIR assessment tools,2019

5. Requirements for a global data infrastructure in support of CMIP6;Geosci. Model Dev,2018

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2. A Novel Approach to Outlining Research Data Management Life Cycle: A Case Study;Communications in Computer and Information Science;2024

3. CkanFAIR: a digital tool for assessing the FAIR principles;2023 IEEE International Conference on Big Data (BigData);2023-12-15

4. Which FAIR are you?;Proceedings of the Conference on Research Data Infrastructure;2023-09-07

5. euFAIR: A Digital Tool for Assessing the FAIR Principles;Lecture Notes in Computer Science;2023
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