Have a personal or library account? Click to login
Data Quality Assurance at Research Data Repositories Cover

Data Quality Assurance at Research Data Repositories

Open Access
|Nov 2022

References

  1. 1Assante, M, et al. 2016. Are scientific data repositories coping with research data publishing? Data Science Journal, 15(6). DOI: 10.5334/dsj-2016-006
  2. 2Austin, C, et al. 2016. Research data repositories: Review of current features, gap analysis, and recommendations for minimum requirements. IASSIST Quarterly, 39(4): 24. DOI: 10.29173/iq904
  3. 3Austin, C, et al. 2017. Key components of data publishing: Using current best practices to develop a reference model for data publishing. International Journal on Digital Libraries, 18(2): 7792. DOI: 10.1007/s00799-016-0178-2
  4. 4Batini, C and Scannapieco, M. 2016. Data quality dimensions. In: Batini, C and Scannapieco, M (eds.), Data and information quality: Dimensions, principles and techniques. Berlin: Springer. pp. 2151. DOI: 10.1007/978-3-319-24106-7_2
  5. 5Cai, L and Zhu, Y. 2015. The challenges of data quality and data quality assessment in the big data era. Data Science Journal, 14(2). DOI: 10.5334/dsj-2015-002
  6. 6Canali, S. 2020. Towards a contextual approach to data quality. Data, 5(4): 90. DOI: 10.3390/data5040090
  7. 7CASRAI (Consortia Advancing Standards in Research Administration Information). 2022a. Curation. Available at https://casrai.org/term/curation/ [Last accessed 30 August 2022].
  8. 8CASRAI (Consortia Advancing Standards in Research Administration Information). 2022b. Data quality. Available at https://casrai.org/term/data-quality/ [Last accessed 30 August 2022].
  9. 9CTS (CoreTrustSeal Standards and Certification Board). 2019. CoreTrustSeal trustworthy data repositories requirements 2020–2022. DOI: 10.5281/zenodo.3638211
  10. 10Downs, R, et al. 2021. Perspectives on citizen science data quality. Frontiers in Climate, 3. DOI: 10.3389/fclim.2021.615032
  11. 11ISO (International Organization for Standardization). 2015. Quality management systems—Fundamentals and vocabulary (ISO 9000:2015). Available at https://www.iso.org/standard/45481.html [Last accessed 30 August 2022].
  12. 12Johnston, L, et al. 2018. How important are data curation activities to researchers? Gaps and opportunities for academic libraries. Journal of Librarianship and Scholarly Communication, 6(1). DOI: 10.7710/2162-3309.2198
  13. 13Juran, JM. 1951. Quality-control handbook. New York: McGraw-Hill.
  14. 14Kindling, M and Strecker, D. 2021. How to ensure ‘good’ data? A presentation at Open Repositories 2021. Available at https://coref.project.re3data.org/blog/how-to-ensure-good-data-a-presentation-at-open-repositories-2021 [Last accessed 30 August 2022].
  15. 15Kindling, M, Strecker, D and Wang, Y. 2022. Data quality assurance at research data repositories: Survey data (1.0) [data set]. Zenodo. DOI: 10.5281/ZENODO.6457849
  16. 16Kindling, M, et al. 2017. The landscape of research data repositories in 2015: A re3data analysis. D-Lib Magazine, 23(3/4). DOI: 10.1045/march2017-kindling
  17. 17Kindling, M, et al. 2022. Data quality assurance at research data repositories—Results from a survey. In: International Digital Curation Conference on 13–16 June 2022. DOI: 10.5281/ZENODO.6638409
  18. 18Koltay, T. 2020. Quality of open research data: Values, convergences and governance. Information, 11(4): 175. DOI: 10.3390/info11040175
  19. 19Koshoffer, A, et al. 2018. Giving datasets context: A comparison study of institutional repositories that apply varying degrees of curation. International Journal of Digital Curation, 13(1): 1534. DOI: 10.2218/ijdc.v13i1.632
  20. 20Lafia, S, et al. 2021. Leveraging machine learning to detect data curation activities. In: IEEE 17th International Conference on eScience, Innsbruck, Austria, 20–23 September 2021. DOI: 10.1109/eScience51609.2021.00025
  21. 21Lawrence, B, et al. 2011. Citation and peer review of data: Moving towards formal data publication. International Journal of Digital Curation, 6(2). DOI: 10.2218/ijdc.v6i2.205
  22. 22Lee, DJ and Stvilia, B. 2017. Practices of research data curation in institutional repositories: A qualitative view from repository staff. PLoS ONE: e0173987. DOI: 10.1371/journal.pone.0173987
  23. 23Lee, Y, et al. 2002. AIMQ: A methodology for information quality assessment. Information & Management, 40(2): 133146. DOI: 10.1016/S0378-7206(02)00043-5
  24. 24Madnick, S, et al. 2009. Overview and framework for data and information quality research. Journal of Data and Information Quality, 1(1): 122. DOI: 10.1145/1515693.1516680
  25. 25Mangione, D, Candela, L and Castelli, D. 2022. A taxonomy of tools and approaches for FAIRification. In: CEUR Workshop Proceedings, Padova, Italy, 24–25 February 2022. Available at http://ircdl2022.dei.unipd.it/downloads/papers/IRCDL_2022_paper_18.pdf [Last accessed 30 August 2022].
  26. 26Mayernik, M, et al. 2015. Peer review of datasets: When, why, and how. Bulletin of the American Meteorological Society, 96(2): 191201. DOI: 10.1175/BAMS-D-13-00083.1
  27. 27Merriam-Webster. 2022. Quality. Available at https://www.merriam-webster.com/dictionary/quality [Last accessed 30 August 2022].
  28. 28OKF (Open Knowledge Foundation). n.d. Open Definition: Version 2.1. Available at http://opendefinition.org/.
  29. 29Palmer, C, Weber, N and Cragin, M. 2011. The analytic potential of scientific data: Understanding re-use value. Proceedings of the American Society for Information Science and Technology, 48(1): 110. DOI: 10.1002/meet.2011.14504801174
  30. 30Parr, C, et al. 2019. A discussion of value metrics for data repositories in earth and environmental sciences. Data Science Journal, 18: 58. DOI: 10.5334/dsj-2019-058
  31. 31Parsons, M and Fox, P. 2013. Is data publication the right metaphor? Data Science Journal, 12: WDS32WDS46. DOI: 10.2481/dsj.WDS-042
  32. 32Peer, L, Stephenson, E and Green, A. 2014. Committing to data quality review. International Journal of Digital Curation, 9(1): 263291. DOI: 10.2218/ijdc.v9i1.317
  33. 33Peng, G, et al. 2015. A unified framework for measuring stewardship practices applied to digital environmental datasets. Data Science Journal, 13: 231253. DOI: 10.2481/dsj.14-049
  34. 34Peng, G, et al. 2022. Global community guidelines for documenting, sharing, and reusing quality information of individual digital datasets. Data Science Journal, 21: 8. DOI: 10.5334/dsj-2022-008
  35. 35Plantin, J -C. 2019. Data cleaners for pristine datasets: Visibility and invisibility of data processors in social science. Science, Technology, & Human Values, 44(1): 5273. DOI: 10.1177/0162243918781268
  36. 36RfII (German Council for Scientific Information Infrastructures). 2020. The data quality challenge: Recommendations for sustainable research in the digital turn. Göttingen. Available at https://nbn-resolving.org/urn:nbn:de:101:1-2020041412321918717265
  37. 37Strecker, D, et al. 2021. Metadata schema for the description of research data repositories: Version 3.1. DOI: 10.48440/re3.010
  38. 38Trisovic, A, et al. 2021. Repository approaches to improving the quality of shared data and code. Data, 6(2): 15. DOI: 10.3390/data6020015
  39. 39Wang, R and Strong, D. 1996. Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4): 533. DOI: 10.1080/07421222.1996.11518099
  40. 40Wilkinson, M, et al. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1): 160018. DOI: 10.1038/sdata.2016.18
Language: English
Submitted on: May 10, 2022
Accepted on: Oct 28, 2022
Published on: Nov 22, 2022
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2022 Maxi Kindling, Dorothea Strecker, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.