Drawing up a management plan

What is a DMP?

A Data Management Plan (DMP) is a document that explains the different stages in the data life cycle, indicating how data will be managed, from production to publication. The DMP helps researchers to organize and anticipate the various stages by asking themselves the right questions about the management of their data. It generally takes the form of a form organized into a series of questions. The answers to these questions make up the DMP, and help to explain what is planned for each dataset at each stage of the lifecycle.
The DMP addresses legal, ethical and budgetary issues, as well as aspects relating to responsibility and data security.

Why write a DMP ?

• The DMP is a deliverable requested by funding bodies (ANR, Horizon Europe, etc.) as part of the evaluation of research projects.
• It is a guarantee of research quality.
• It supports the FAIRization of data by facilitating data discovery, accessibility, interoperability and reuse.

The aim of the DMP is to show that data is produced and managed according to "good practices", from collection to publication, within an "ethical and legal framework" in line with the FAIR principle (Easy to Find, Accessible, Interoperable and Reusable). However, it is important to note that the principle of "as open as possible, as closed as necessary" prevails: not all data is necessarily intended for publication.

Who can help me draw up a SMP?

If you have any questions about drafting a PGD, please contact: donnees-recherche@universite-paris-saclay.fr (send us your PGD for expert review).

Filling in your PGD: what questions should you ask yourself?

The SMP is an evolving document, with several versions during the course of a project. It can also be presented in different forms or templates. Some funding agencies offer their own DMP template (see template in DMP-OPIDoR). Whatever the model used, the DMP must address / specify the following aspects:

1- Data description :
This requires a description of the data used in the project, whatever its origin. What data (type, nature, volume, etc.) will be produced or collected? How will pre-existing data be re-used? Which data are worth keeping for the long term? Which data processing software (preferably open source)?

2- Documentation and quality of data and metadata:
Metadata is data that describes other data, and is an essential part of its architecture. It involves a detailed description of the data (context of acquisition, type of data, unit of measurement, file format, language, contributors, etc.). What procedures should be used to control data quality? Which metadata standards should be used: disciplinary (Darwin Core, EML, etc.), general (Dublin Core, Datacite, etc.)? Wherever possible, we recommend the use of co

3- Data storage and backup during the project :
Storage consists of depositing data on a digital medium to make it accessible. Backing up involves duplicating the data on a medium other than the one in which it is stored. How and where will data and metadata be stored and backed up during the research project? How will data be secured, and how will personal, sensitive or strategic data be protected? From the outset, it's essential to think about storage requirements (volume of data) and backup resources (available infrastructures). It's also important to anticipate the management of unforeseen events (computer system failure, virus, theft, loss, etc.).
system failure, virus, theft, loss, etc.) by planning secure data recovery methods. The 3-2-1 rule is highly recommended: 3 copies on 2 different media, including 1 on a remote location.

4- Ethical and legal aspects:
The content of a DMP must address the main legal and ethical issues raised by the processing of personal and sensitive data (voice, medical status, sexual orientation, etc.). How is personal data handled? How are aspects relating to data ownership or intellectual property handled? What is the relevant legislation? Is there a confidentiality clause? How will data be affected by ethical and deontological issues? How will data be processed (data anonymization, ethics commission approval
approval, official consent of data subjects, etc.)?

5- Sharing and publishing:
Opening up data promotes the transparency and reproducibility of research work. The aim of this stage is to define how data will be exchanged between partners during the project. It also defines how data will be accessed, published and shared during and after the project. Questions to ask include: What data will be published (see decision aid here)? Are there any restrictions on data sharing, or reasons for embargoes? How and when will the data be shared? Define the data-sharing mechanism (on-demand or other process etc.).
process etc.). Which warehouse (disciplinary or general) should be chosen? It is advisable to share data in a FAIR, non-commercial warehouse.

6- Responsibilities and resources :
This part consists of identifying all the people who will be responsible for managing the data, from production to publication, both during and after the project. Questions to ask include: What human, material and financial resources are in place to ensure proper data management? Who is/are the person(s) responsible for each data management activity? What are the requirements in terms of expertise and training?