• Training structure

    University of Montpellier

  • Language(s) of instruction



The "Scientific Data Management" (SDM) training aims to train a wide audience to the issues, practices and tools of data management in scientific research.

Read more


The objective of the training is to raise awareness and train in open science techniques, but also to explain the meaning and the stakes. Thus, the program offers a wide range of courses in computer science applied to data management, data engineering (anonymization, storage, archiving), project engineering, intellectual property and digital law, etc. This is why the training also offers knowledge and know-how courses (practical work).

This is a diploma course of the University Diploma (DU) type. However, some modules can be taken independently of each other. In this case, the training will only be certifying and not leading to a degree.

It is open to both initial and continuing education.

The training is attached to theData Science Institute of Montpellier and theUniversity of Montpellier. It is the result of the CommonData research program, now the Platform of the Maison des Sciences de l'Homme SUD.

Read more


Knowledge control

Report due, followed by a defense at the end of the year for students who wish to graduate

Read more


Understanding the data environment of science

The first part of the course provides an understanding of the data science environment:

  • What is collaborative research?
  • How to fund research oriented towards data collection and analysis?
  • What are the strategies for developing data science projects?
  • What legal and/or governance rules apply to data?

3 modules for part 1


Master the tools of data analysis in science

The second part of the course provides training in the mastery of data analysis tools for science, i.e. tools for extracting, contextualizing, mining, securing and protecting data.

5 modules for part 2


Managing open science data

The third part proposes to train to the opening of scientific data. It consists in learning how to share, publish, but also store and archive data in a secure way so that they can eventually be reused and/or valorized.

4 modules for part 3



Read more


Conditions of access

  • Motivation to be demonstrated
  • Minimum Master level

Applications are submitted to the study of the pedagogical team

Read more

How to register

  • Initial and continuing education
  • Doctoral student: participation in the training will result in a certificate for the corresponding number of hours. However, you must first make sure that the hours of this training will be taken into account by your Doctoral School. Similarly, registration for the doctoral program is not the same as registration for the DU. The two registrations are distinct.

Teaching methods

  • Distance learning
  • Attendance at all live sessions is mandatory
  • Possibility of taking modules of your choice. However, beware of certain modules that are interdependent.

Read more

Target audience

  • Researchers
  • Teacher-researchers
  • Post-doctoral fellows
  • Doctoral students
  • Engineers
  • Innovative project holders (incubated or not)
  • Master students
Read more

Expected results

Validation of the diploma

  • compulsory attendance: to have followed the lessons of all the modules of the training
  • defense: in the form of a presentation of a scientific data management project before a jury
  • have obtained a mark ≥ 10/20 for the preparation and presentation of the project


Certificate of training follow-up

delivered upon request, for people who have completed a minimum of 5 modules, knowing that some modules are essential prerequisites for other modules

Read more