• Training structure

    University of Montpellier

  • Language(s) of instruction



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

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The aim of the course is to raise awareness and provide training in open science techniques, but also to explain the meaning and the challenges. Thus, the course offers a wide range of courses in computer science applied to data management, data engineering (anonymisation, storage, archiving), project engineering, intellectual property and digital law, etc. For this reason, the course also offers knowledge and know-how (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 lead to a certificate and not a diploma.

It is open to both initial and continuing education.

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

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Control of knowledge

Report to be handed in, followed by a defence at the end of the year for students who wish to graduate

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Understanding the data environment of science

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

  • 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


Mastering the tools of data analysis in science

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

5 modules for part 2


Managing open science data

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

4 modules for part 3



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Conditions of access

  • Motivation to be demonstrated
  • Minimum Master's level

Applications are subject to review by the educational team

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How to register

  • Initial and continuing education
  • Doctoral student: participation in the training will give rise to a certificate for the corresponding number of hours. However, you must first ensure that the hours of this training will be taken into account by your Doctoral School. Similarly, registration for the doctoral training course is not equivalent to registration for the DU. The two registrations are distinct.

Teaching methods

  • Distance learning
  • Mandatory attendance at all live sessions
  • Possibility to follow modules of your choice. Beware, however, that some modules are interdependent.

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Target audience

  • Researchers
  • Teacher-researchers
  • Post-doctoral fellows
  • PhD students
  • Engineers
  • Innovative project holders (incubated or not)
  • Master students
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Expected results

Validation of the diploma

  • compulsory attendance: having followed the lessons of all the modules of the course
  • defence: 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


Training follow-up certificate

issued on request, for people who have completed a minimum of 5 modules, bearing in mind that some modules are essential prerequisites for other modules

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