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Data Science Journal Call for Papers: Data Management Planning across Disciplines and Infrastructures

The international Data Science Journal ( is seeking papers for a special collection devoted to “Data Management Planning across Disciplines and Infrastructures”.

The special collection aims to profile papers describing practical experiences, concepts, and future directions on the design and deployment of effective of Data Management Plans. These could focus on DMP templates, discipline-specific guidance, machine-actionable tools, or integration of DMP tools with other elements of the research lifecycle, etc.

The Data Science Journal welcomes papers from researchers, data professionals, data managers and curators, data providers, founders, IT specialists, software developers, and others, who are using, developing, or experimenting with the effective use of data management planning. Submissions from lab researchers and practitioners are particularly encouraged, as contributions from open-source movements.

Submissions can be made in one or several of the following categories:

  1. Implementation of DMPs in project management strategies
  2. Data management across disciplines
  3. Discipline-specific data management planning
  4. Data management planning integrating infrastructures
  5. Future directions in the development of Data Management Plans

Deadline for submissions: November 15th, 2022.

Further information is available at:

The CODATA Data Science Journal is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data and databases across all research domains, including science, technology, the humanities and the arts. The scope of the journal includes descriptions of data systems, their implementations and their publication, applications, infrastructures, software, legal, reproducibility and transparency issues, the availability and usability of complex datasets, and with a particular focus on the principles, policies and practices for open data.

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