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A Data-Driven Approach to Appraisal and Selection at a Domain Data Repository.

Amy Pienta1, Dharma Akmon1, Justin Noble1

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Analyzing user search data in a social science data repository helps optimize data curation. A new search-to-study ratio technique identifies collection gaps, ensuring valuable data is findable and usable for researchers.

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Area of Science:

  • Social Sciences
  • Data Curation
  • Information Science

Background:

  • Growing volume of social science data presents challenges for data appraisal and selection for reuse.
  • Finite resources necessitate strategic allocation for processing and curating research data.

Purpose of the Study:

  • To analyze user search activity in a social science data repository to understand data demand.
  • To guide collection development and ensure curation resources are used effectively.
  • To improve data findability, understandability, accessibility, and usability.

Main Methods:

  • Analysis of user search activity data from a social science domain repository (over 500,000 annual searches in 2014-2015).
  • Application of a novel search-to-study ratio technique to identify repository holdings gaps.
  • Data-driven approach to inform collection development and curation policies.

Main Results:

  • Identified trends in user search behavior within the social science data repository.
  • The search-to-study ratio technique revealed specific gaps in the repository's data holdings.
  • Analysis provided actionable insights for collection and curation practices.

Conclusions:

  • The proposed evaluative technique serves as a baseline for future trend analysis in user demand.
  • Findings have broader implications for collection development and curation policies in other data repositories.
  • A data-driven approach enhances the value and accessibility of social science data.