Data Management Plan (DMP)

Managing data for potential sharing and reuse is a process that requires attention and planning. Researchers must therefore plan and allocate time for data management at the outset of their research project.

The DMP is a living document that must be completed and updated regularly. It can take different forms (e.g. electronic document, online template, paper form, etc.) and varies considerably depending on the discipline and research project.

In practice, the DMP is the natural instrument for the "FAIRification" of research, implementing data discovery, accessibility, interoperability and reuse.

De facto, the DMP has become a new standard requirement of funders, led by the SNSF, and of a growing number of publishers and university teaching and research institutes (see Unil's position below).

Performing a DMP is important and useful for several reasons :

• It saves time and anticipates certain issues (e. g. costs, data destruction or loss, infrastructure, etc.)
• It is sometimes mandatory and required by some public donors before funding is granted.
• It encourages the reuse of data
• It adds value to your data and research
• It supports and promotes research that is honest, accountable and transparent

It should be noted that since October 2017, the DMP has become mandatory to obtain funding from the SNSF. It is also mandatory since June 2019 to obtain secure storage space set up by the Ci's Calculation and Research Support Division (see below).

The main information contained in the DMP specifies the following aspects :

1. Context: The nature and context of the research project
2. Description: The type of research data collected and produced
   • How will new data be collected or produced and/or how will pre-existing data be reused?
   • What data (e.g. types, formats and volumes) will be collected or produced?
3. Documentation and quality: The formats, metadata and standards used
   • What metadata and documentation (e.g. data collection methodology and organization) will accompany the data?
   • What data quality control measures will be implemented?
4. Storage and Backup: Data storage, backup and security information
   • How will data and metadata be stored and backed up throughout the research process?
   • How will data security and the protection of sensitive data be ensured throughout the research process?
5. Legal and ethical requirements: Ethical, legal and ethical issues (code of conduct) that arise
   • If personal data are processed, how will compliance with the provisions of the legislation on personal data and data security be ensured?
   • How will other legal issues, such as ownership or intellectual property rights over the data, be addressed? What is the applicable legislation?
   • How will possible ethical issues be taken into account, ethical codes respected?
6. Long-term sharing and preservation: Access, sharing, reuse of data, as well as archiving and repository used
   • How and when will the data be shared? Are there any restrictions on data sharing or reasons for defining an embargo?
   • How will the data to be retained be selected and where will it be preserved over the long term (e. g. a data warehouse or an archive)?
   • What methods or software tools will be needed to access and use the data?
   • How will the application of a unique and sustainable identifier (such as DOI) be carried out for each dataset?
7. Responsibilities: The roles and responsibilities of the different research stakeholders
   • Who (e.g., role, position and reporting institution) will be responsible for data management (i.e., the data manager)?
   • What resources (budget and time allocated) will be dedicated to data management to ensure that data is FAIR (Easy to find, Accessible, Interoperable, Reusable)?

Source : Science Europe - Guide pratique pour une harmonisation internationale de la gestion des données de recherche, 2019