Data Management Plan - DMP

What is the Data Management Plan (DMP) ?

Managing data for their eventual sharing and reuse is a process that requires attention and planning, so researchers should plan and allocate time for data management early in their research project.

The Data Management Plan (DMP) helps you plan the management of your data (creation, collection, documentation, description, sharing and preservation) while specifying the legal issues related to their use or re-use (legal restrictions, data ownership, intellectual property, contractual obligations, sensitive data). This planning assistance is provided by the DMP before, during and after the research project.

It is a living document, completed and updated regularly, and can take different forms (e.g., electronic document, online template, paper form, etc.) and vary considerably across disciplines and research projects.

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

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

Why should you write a DMP ?

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).

What information does the DMP contain ?

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

DMPs FNS & H2020


Since October 2017, it has been mandatory to include a Data Management Plan (DMP) with each request. The SNSF provides a DMP in the account of each researcher on MyFNS.

Therefore, the SNSF asks all researchers it funds to :

  • archive the research data they have worked on and produced during their work
  • share this data with other researchers, unless they are bound by legal, ethical, copyright, confidentiality or other clauses
  • deposit their data and metadata in existing public archives (repositories), in formats that are accessible and reusable without restriction by everyone, and that comply with FAIR principles.

Data sharing

The SNSF considers the sharing of research data as a fundamental contribution to the impact, transparency and reproducibility of scientific research. Grant recipients must therefore ensure that the data generated by their project are publicly accessible in non-commercial data repositories and comply with FAIR principles. These are at least the data necessary to reproduce the published results. These data must be made available as quickly as possible, but at the latest at the time of the corresponding scientific publication.

Some data cannot be shared because the applicants are bound by legal, ethical, confidentiality or copyright clauses. In any case, one of these specific constraints that does not allow data sharing must be justified in the DMP.

Sections of the DMP - SNSF

1 Data collection and documentation

1.1 What data will you collect, observe, generate or reuse ?
1.2 How will data be collected, observed or generated ?
1.3 What documentation and metadata will you provide with the data ?

2 Ethical, legal and safety issues

2.1 How will ethical issues be addressed and treated ?
2.2 How will data access and security be managed ?
2.3 How will you deal with copyright and intellectual property rights ?

3 Data storage and preservation

3.1 How will your data be stored and backed up during the search ?
3.2 What is your data preservation plan ?

4 Data sharing and reuse

4.1 How and where will the data be shared ?
4.2 Are there any limitations necessary to protect sensitive data ?
4.3 I will choose digital repositories that comply with FAIR data principles.
4.4 I will choose digital repositories managed by a non-profit organization.

See the SNSF FAQ on the DMP and DMP FNS - example and content

Guide for the Social Sciences

To help you in writing your DMP-FNS, FORS, the Swiss Competence Centre for Social Sciences, has developed a guide entitled : How to draft a DMP from the perspective of the social sciences, using the SNSF template - june 2019.

What about the DMP at UNIL ?

According to UNIL directive 4.5, any research project using the resources of the Computing and Research Support Division (DCSR) of the Computer Centre must be accompanied by a data management plan (DMP) that will be collected, processed or generated during the different phases of the research.

A simplified DMP has been developed by UNIRIS and a technical interface related to storage demand will soon be developed by Ci (planned for early 2020). This interface will allow you to create your DMP when you request storage space at Ci, to edit it and complete it as the research project goes ahead.

For projects that do not use DCSR resources, the drafting of a DMP is strongly recommended. The DMP remains mandatory, notwithstanding the use of UNIL storage, when the conditions of the funding agencies require so (e.g. SNSF, H2020).

Need help or advice ? Contact us !

Sending data
Follow us:    

Videos on DMP

Checklists DMP

To avoid forgetting elements in your DMP, refer to the checklists below !

Checklist for Data Management Plan (v.4.0) (Digital Curation Centre, 2014) + flyer version

Checklist for Data Management Plan (Swedish National Data Service, 2017)

Models of DMP

Example DMPs and guidance (Digital Curation Centre)

Public DMPs (DMPTool)

Data Management Plan Examples (NC State University Library)

Online tools for writing your DMP

DMPonline (Digital Curation Center)

DMPTool (University of California)

DMP Canvas Generator (SIB Vital-IT)



Informative ressource

A curated, informative and educational resource on data and metadata standards, inter-related to databases and data policies :