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A data management plan will help you in the management of research data generated in your project. Put simply, a data management plan describes the data that is collected or generated in the course of your work and what happens to this data during its life-cycle (storage, publication, citation, long-term availability, anonymity, deletion, etc.). The goal of a data management plan is to meet the requirements of good scientific practice and to allow for reproducibility of research results.

It is recommended to start early with the preparations for the handling of research data and to update the procedures during the project. The following specifications guide you in this process.

In the ETH Zurich Guidelines on scientific integrity 1, some data management guidelines are specified in articles 10 and 11 and summarised in brief just below: "

  • The persons collaborating on a research project are responsible for the accuracy of the research data and for the compliance with relevant regulations.
  • Relevant research data and materials that underlie a publication should be shared in line with the FAIR Data Principles.
  • Such relevant research data which underpins a publication are securely stored in repositories and data archives.
  • All persons collaborating in the project should determine as early as possible how research data derived from this project is shared and can be used outside of the project team."


Funding organisations often state additional requirements. The SNSF funding regulations 2 (Art. 44) require that research data is made available to other researchers for secondary research and is integrated in recognised scientific data pools. Researchers who are applying for funding with the Swiss National Science Foundation (SNSF) will be required to submit a data management plan (DMP) with their funding application as of October 2017. SNSF is currently working out DMP guidelines. Further information will be provided continuously from Spring 2017 onwards on the SNSF website (external link).

The Research Data Management and Digital Curation Office is offering guidance on how to develop data management plans in their trainings on research data management. It also provides a checklist with further information on what needs to be considered regarding data management.


The following checklists and guides help you to write a data management plan:

Data management plans vary greatly depending on the research project or discipline. The Research Data Management and Digital Curation Office can assist you in your work on a data management plan5.



Footnotes (last retrieved on 5 April 2022)

1 ETH Zurich Guidelines on scientific integrity, RSETHZ 414 (as of 01.01.2022)
2 SNSF funding regulations (Version 1 July 2012)
3 Examples by the Digital Curation Centre 
4 Checklists by the Digital Curation Centre
5 Data Management Checklist from DLCM project

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