A SUMMARY ON SELECTION AND APPRAISAL OF DATA

A SUMMARY ON SELECTION AND APPRAISAL OF DATA

Selection and appraisal of data are important activities in research data management and digital curation, because they help institutions to identify, preserve, and provide access to variable and reliable research information for future use. In digital curation, selection involves determining which datasets, digital records, or research materials should be retained, while appraisal refers to evaluating their long-term significance based on factors such as research value, originality, authenticity, legal requirements, ethical issues, and potential for reuse (Higgins, 2008).

The continuous increase in digital research output, big data, and open science practices has made effective appraisal procedures more necessary in libraries, archives, and research institutions. Cox et...al. (2019) note that academic libraries are increasingly expected to support researchers throughout the research data lifecycle, including during the selection and appraisal stages. Proper appraisal enables institutions to minimise unnecessary storage costs, avoid duplication of data, and focus preservation effort on datasets that are considered valuable for future research and institutional purposes. In additions, the FAIR principles which are; Findable, Accessible, Interoperable, and Reusable play an importable role in guiding data selection because they promote data sharing, accessibility, and long-term usability in research environment (Wilkison, et...al., 2016).

Metadata creation is also essential in the appraisal process because it helps librarians and information professionals assess the origin, reliability, context, and usability of datasets before making preservation decisions (Johnston et...al., 2018). Furthermore, ethical and legal concerns are significant during appraisal, especially when research data contain confidential or sensitive information. Information professionals must therefore balance the need for open access with the protection of privacy and intellectual property rights (Johnston et...al., 2018). The advancement of artificial intelligence and machine learning technologies has also influenced appraisal practices by supporting automated classification and analysis of large volumes of digital records.

However, Acker and Kriesberg (2021) argue that professional human judgement remains essential because appraisal often requires contextual interpretation and ethical decision-making that automated systems can not fully provide. Collaborative appraisal has also become increasingly important in digital curation, as librarians, archivists, researchers, and information technology specialists work together to determine the long-term value of research data. Yoon and Schultz (2017) explain that such collaboration strengthens data stewardship and promote sustainable digital preservation practices within institutions. Moreover, appraisal decisions influence preservation planning because they determine which datasets will continue to receive institutional support for storage, migration, and long-term maintenance. Therefore, selection and appraisal of data remain central to digital curation and research data management as they promote the preservation of trustworthy and reusable research information, support research integrity, encourage open science, and ensure effective use of institutional resources in managing digital data.     

References

Acker, A., & Kriesberg, A. (2021). Practical appraisal of digital records: A review of challenges

 and emerging approaches in digital curation. Journal of Contemporary Archival Studies,

 8(1), 1–15.

Cox, A. M., Kennan, M. A., Lyon, L., & Pinfield, S. (2019). Developments in research data

 management in academic libraries: Towards an understanding of research data service

 maturity. Journal of the Association for Information Science and Technology, 70(9), 1–

14. https://doi.org/10.1002/asi.24171

Higgins, S. (2008). The DCC curation lifecycle model. International Journal of Digital

  Curation, 3(1), 134–140. https://doi.org/10.2218/ijdc.v3i1.48

Johnston, L. R., Carlson, J., Hswe, P., & Hudson-Vitale, C. (2018). Data curation network: How

 do we compare? A snapshot of current data curation services. Journal of eScience

 Librarianship, 7(1), Article e1122. https://doi.org/10.7191/jeslib.2018.1122

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A.,

 Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J.,

 Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T.,

 Finkers, R., … Mons, B. (2016). The FAIR guiding principles for scientific data

 management and stewardship. Scientific Data, 3, Article 160018.

 https://doi.org/10.1038/sdata.2016.18

Yoon, A., & Schultz, T. (2017). Research data management services in academic libraries in the

 US: A content analysis of libraries’ websites. College & Research Libraries, 78(7), 920–

933. https://doi.org/10.5860/crl.78.7.920 











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