Data sharing

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A. Background & Definitions

Publication bias: Studies with neutral and null results are more likely to end up in the file drawer than studies with statistically significant findings. This phenomenon (defined as “publication bias”) leaves scientists, funding agencies and clinicians with a distorted view of the scientific evidence, which can lead to poor decisions about what research directions are most promising and should be funded or what medical treatments should be recommended to patients.

B. Guidance & Expectations

  • For each multi-partner project, especially for Academia-Industry-collaborations, it should be agreed on - before project start - which data sets can be published and when: A dedicated plan already in place before/when creating the data can be helpful here.
  • For Academia-Industry collaborations and to provide PhD students with the possibility to defend and publish their PhD thesis, it is recommended that the academic institution establishes conditions so that students are able to at least submit and defend their PhD theses under certain secrecy conditions.
  • For provide a perspective for early career researchers at the beginning of a collaboration project, it is recommended to agree and checked which data from the project are non-IP relevant and can be published and/or uploaded to data repositories (ideally within an acceptable embargo timeframe).
  • Consider generating a ‘shadow publication’ including IP-relevant data sets and uploading to the private, non-public areas of repositories like the OSF. Later, after e.g. relevant patents are granted, the publication can be submitted in no time simply by pushing a button.

C. Resources

  • fiddle: a tool to combat publication bias by getting research out of the file drawer and into the scientific community


  • Data repository, for example the Open Science Framework