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Tuesday, July 21 • 2:00pm - 3:30pm
Credit for research artifacts – working session of the Research Object Citation Cluster

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ESIP has published guidelines for citing data and for citing software and services. These have been important and influential ESIP products. Now the Research Object Citation Cluster is working to address the issues of “research artifact” citation writ large. The cluster has been working to identify the various types of research artifacts that could or should be cited such as samples, instruments, annotations, and other artifacts. We have also been examining the various concerns that may be addressed in citing the objects such as access, credit or attribution, and scientific reproducibility. We find that citation of different types of objects may need to address different concerns and that different approaches may be necessary for different concerns and artifacts. The particular citation use case matters a lot. For example, reproducibility demands different considerations from credit.

At the previous ESIP meeting we assessed the “reproducibility use case”— when do different research objects need to be identified to ensure reproducibility or validity of a result? In this session, we want to begin to address the “credit use case.”

We will open the session with a brief overview of the cluster's work to date and introduce people to the Contribution Roles Taxonomy (CRediT): https://casrai.org/credit/. We will then work in small groups to identify whether the roles of CRediT apply and and where they should be captured for different clusters of research artifacts: Data, software, instruments & facilities, physical artifacts, semantics.

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View Session Notes
View Presentations and Outputs: See attached and session notes above

  • Defining the artifact to be credited is essential for assigning credit, but it is also difficult. “Model” is especially fraught.
  • The Contributor Roles Taxonomy (CRediT) may not apply well for artifacts other than papers. We need to consider other taxonomies as well.
  • The credit use case is harder than the reproducibility use case. People are more difficult than computers.

avatar for Ruth Duerr

Ruth Duerr

Research Scholar, Ronin Institute for Independent Scholarship
avatar for Mark Parsons

Mark Parsons

Editor in Chief, Data Science Journal

Tuesday July 21, 2020 2:00pm - 3:30pm EDT
Room 2
  Working Session, Room 2