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Scholarly datasets are increasingly recognized as vital research products. Ongoing discussions address data publication formats, peer review, and citation standards to advance data sharing.

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Area of Science:

  • Data Science
  • Scholarly Publishing
  • Research Data Management

Background:

  • The scholarly community is increasingly recognizing datasets as first-class research products, necessitating their validation, preservation, citation, and crediting.
  • The proliferation of data publication venues has accelerated the movement towards integrating datasets into the scholarly record, but significant debates persist regarding formats, processes, and terminology.

Purpose of the Study:

  • To provide an overview of current data publication initiatives and ongoing discussions within the scientific community.
  • To highlight areas of consensus and identify key issues that remain in contention regarding data publication standards and practices.

Main Methods:

  • Review of current data publication initiatives and their implementations.
  • Analysis of the ongoing discourse surrounding data formats, validation, documentation, and peer review processes.
  • Identification of common terminology and points of divergence in data publication practices.

Main Results:

  • Data publication implementations vary significantly in documentation, data validation, and presentation methods (e.g., supplemental material, data papers, independent publication).
  • The term "published" data implies public availability and citable status but does not consistently guarantee peer review.
  • Defining "data peer review" remains a challenge, with diverse standards and novel variations emerging.
  • While basic data citation is agreed upon, complexities arise with dynamic datasets or those integrated into larger data sets.

Conclusions:

  • There is a growing consensus on the importance of data publication, but standardization of formats, terminology, and peer review processes is still needed.
  • Addressing the nuances of data citation for dynamic or complex datasets is crucial for accurate crediting and reproducibility.
  • The scientific community is exploring advanced concepts like "data as software" to tackle persistent challenges in data publication and management.