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Enhancing Data Citation Practices Across Research Domains

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This discussion, facilitated by Erin Robinson and presented by Julia Collins, focuses on the critical aspects of data citation, including the challenges researchers face in citing their own datasets. Key topics include the differentiation between original and processed data, the purpose and importance of citation in research, and the role of data identifiers. Participants explore outreach strategies to promote best practices in data citation, encouraging collaboration with journals and education for funding agencies. The session aims to elevate awareness and improve citation practices across various scientific communities.

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Enhancing Data Citation Practices Across Research Domains

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  1. Spruce Group Notes by Julia Collins Facilitated by Erin Robinsonhttp://ietherpad.com/s6guK7fpru Data Citation Breakout

  2. Potential Datasets for Citation • Biologic species • Dataset views • Field campaigns • Automated observing systems

  3. Can you cite your own data now? • No.

  4. Citation Issues & Research Questions • What artifacts do you cite? • Ambiguous title/author (data prepared without citation in mind) • Original data vs. Processed data • What is the citation purpose? • Just to give credit? • To find the exact data used and replicate analysis • Data Identifiers • What level of granularity is ID (collection, files)? • Who has the authority to give an ID? • ID vs. Locator

  5. Longer-term Research Questions • Provenance and citation • Workflow citation • Long-term preservation • Where does the authoritative copy reside? • Mirroring issues

  6. Outreach Strategies • Partner with a journal to require authors to try to cite their data • Use cases that come from this could define best practices • Different domain communities will have different practices • GenBank required for publication • Educate funding agencies and universities on data citation practices

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