A framework of data curation competencies

Over the last decade, the library and information science (LIS) field has driven data curation education, research, and practice. As the field continues to grow and new techniques and technologies are developed, there is a need to evaluate current data curation practices to understand the important data curation competencies that should be taught to LIS students. This research expands our understanding of data curation competencies, skills, and technical tools and provides a data curation competencies framework. This data curation competencies framework was developed through 1) a systematic literature review and 2) focus groups with strategic stakeholders. For the systematic literature review, over 1,400 documents were reviewed, with 43 deemed relevant. Relevant documents described the competencies needed to conduct data curation activities, and from these findings, the initial competencies framework was created. The framework consists of twenty specific competencies organized into data curation activities, technical skills, and soft skills related to data curation. Secondly, focus groups were held with strategic stakeholders (educators, practitioners, and community organizations) to provide feedback on the overall structure of the competencies framework and input on the competencies necessary to working with community data. As this framework extends our current understanding of data curation competencies, it can also be utilized in both research and curriculum development. Currently, this framework is being utilized to create and test a pilot community data curation curriculum with LIS students and will be disseminated for LIS educators to incorporate into their curriculum.

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Publication Details

Journal
Proceedings of the ALISE Annual Conference
Published
2024-10-16
DOI
https://doi.org/10.21900/j.alise.2024.1756
Primary Topic
Research Data Management Practices
Type
article
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article

A framework of data curation competencies

Ayoung Yoon, Angela P. Murillo
Proceedings of the ALISE Annual Conference
Research Data Management Practices
article

A framework of data curation competencies

Ayoung Yoon, Angela P. Murillo
article en

Abstract

Over the last decade, the library and information science (LIS) field has driven data curation education, research, and practice. As the field continues to grow and new techniques and technologies are developed, there is a need to evaluate current data curation practices to understand the important data curation competencies that should be taught to LIS students. This research expands our understanding of data curation competencies, skills, and technical tools and provides a data curation competencies framework. This data curation competencies framework was developed through 1) a systematic literature review and 2) focus groups with strategic stakeholders. For the systematic literature review, over 1,400 documents were reviewed, with 43 deemed relevant. Relevant documents described the competencies needed to conduct data curation activities, and from these findings, the initial competencies framework was created. The framework consists of twenty specific competencies organized into data curation activities, technical skills, and soft skills related to data curation. Secondly, focus groups were held with strategic stakeholders (educators, practitioners, and community organizations) to provide feedback on the overall structure of the competencies framework and input on the competencies necessary to working with community data. As this framework extends our current understanding of data curation competencies, it can also be utilized in both research and curriculum development. Currently, this framework is being utilized to create and test a pilot community data curation curriculum with LIS students and will be disseminated for LIS educators to incorporate into their curriculum.

Proceedings of the ALISE Annual Conference
Indiana University Indianapolis (US), Indiana University – Purdue University Indianapolis (US), Indiana University (US)
Quality Education
Openalex Percentile: Top 72%
Research Data Management Practices
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