Data sharing

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A. Background & Definitions

Open Science: From the development to the dissemination of knowledge, ‘Open Science’ (OS) aims to make scientific research, data and their dissemination available to any interested person, from professionals to citizens - with the ultimate goal to make it easier to publish and communicate scientific knowledge. OS fosters sharing and collaboration, introducing a systemic change to the way scientific research is done.

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.

Preregistration: A time-stamped, read-only version of the study protocol and data analysis plan created before the study (see 2.1.11 Preregistration).

B. Guidance & Expectations

Open Science:

  • Open Data: It is advisable to make data underlying reported results openly available - to the greatest extent permissible by legal and ethical constraints.
  • Open Materials: It is advisable to make research materials or analytical code available for others to use and reuse.
  • TOP Guidelines: Open Science can be improved by increasing transparency of the research process and products. Here, the Transparency and Openness Promotion (TOP) Guidelines provide guidance on how to enhance transparency in the science that journals publish: 8 standards within the TOP guidelines move scientific communication toward greater openness. Moreover, the guidelines are sensitive to openness barriers by articulating a process for exceptions to sharing because of ethical issues, intellectual property concerns, or availability of necessary resources.

Multi-partner projects:

  • 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 [1]
  • Data repositories, for example the Open Science Framework [2]
  • TOP Guidelines [3]

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