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Publication Checklist

GDD has a commitment to open science, transparency, and high quality research. The lead authors (first and corresponding/senior authors) are responsible for validating that the below checklist is complete before a project is submitted to a peer-reviewed journal.

Warning

This is a work in progress and subject to improvement. Check back as you start to wrap up your project.

During the project

  • Elicit feedback from co-authors throughout the conception, analysis, and manuscript writing phases of the project, as appropriate. Consult with the project lead on when and how feedback should be elicited.
  • Discuss authorship, author order, and expectations of author contributions explicitly. Do not wait until the end to discuss these things!
  • Ensure the project and scope of analyses was approved by the appropriate ethical review board(s). In Geneva usually it will be either CUREG or CCER but work outside Switzerland will also require local ethics approval.
  • Code written with AI assistance should be disclosed in commit statements or commit authorship on Github.

Towards the end of the project

  • Perform an AI-assisted code review. Researchers should carefully examine any errors or potential issues identified by the AI-assisted code review and address them.
  • Prepare a version of the analysis code base for public release. You will either convert your existing Github repository to a public repository or transfer the relevant code to a new public repository linked to the Geneva IDD Github account.
  • Prepare datasets for public release, as appropriate. Preparing any datasets for public release should be performed in consultations with the project lead. Many of the principles discussed in the handbook section on AI will can be used here when thinking about how to share data.

While writing

  • Ensure that accurate reporting guidelines are identified and followed, as appropriate (e.g., STROBE, CONSORT, PRISMA).
  • Set up a reference management system for updating citations (e.g., Zotero, Paperpile).
  • Record any meaningful use of generative AI as you go — see AI Use & Disclosure.

Before submission

  • Ensure that all co-author comments have been addressed.
  • Confirm that all named authors have seen and approved the version being submitted.
  • Check that ethical approval and consent statements accurate, with approval numbers correct.
  • Validate funding, acknowledgements, and affiliations sections.
  • Confirm that all individuals in the acknowledgments have agreed to be listed.
  • Write author contributions statement. This tool is quite helpful and follows the CRediT system.
  • Write a data availability statement, and make sure it is consistent with the public data that was prepared, what was approved, and with any data agreements.
  • Disclose AI use as needed and in agreement with journal requirements.
  • The public code repository should be citable, with a reproducible (or minimally reproducible) example from the public repository. Exceptions due to data sensitivity should be discussed with the project lead.
  • Decide whether a preprint will be posted, in consultation with the project lead and after review of the target journal's policy.
  • Open access route, fees, and funder requirements confirmed. Check which journals have no or subsidized publication fees due to agreements with UNIGE (link).

After submission

  • Reviewer responses drafted and agreed by co-authors.
  • Any changes to analysis re-run from the deposited code and the deposit updated.
  • Final release of data and code with a DOI obtained. (more details on this to come)
  • Project files backed up to a location accessible to GDD leadership and left in a state that a future reader can follow — see Organizing your research project.