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Why data citation isn't working, and what to do about it.

Peter Buneman1, Greig Christie2, Jamie A Davies3

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This study introduces an automated system for generating citation summaries from curated databases. This system ensures proper credit for database contributors and cited works, addressing challenges in granularity and evolution.

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Area of Science:

  • Bibliometrics and Scientometrics
  • Data Management and Curation
  • Scholarly Communication

Background:

  • Current citation practices often fail to adequately credit contributions within curated databases.
  • Existing metrics like h-indexes do not fully capture the impact of database components.
  • The dynamic nature of databases presents challenges for consistent citation and credit attribution.

Purpose of the Study:

  • To develop a system for automatically generating citation summaries from curated databases.
  • To ensure accurate credit attribution for database contributors and cited works.
  • To address the challenges of granularity and evolution in data citation.

Main Methods:

  • Development of an automated system to generate citation summaries.
  • Utilizing a curated database as the source for summary generation.
  • Establishing a dedicated journal for hosting these citation summaries.

Main Results:

  • A system capable of producing conventional publication-style citation summaries from database components.
  • Demonstration of how these summaries can be used for credit attribution (e.g., h-indexes).
  • Identification of key challenges related to granularity and the evolving nature of databases in citation.

Conclusions:

  • Automated citation summaries can effectively credit database contributors and cited works.
  • A tailored journal facilitates the dissemination and citation of these summaries.
  • Current citation mechanisms have limitations that impact the process and value of data citation.