Training structure
Faculty of Law and Political Science
Presentation
The interdisciplinary “Scientific Data Steward” degree program is based on the recognition of a training need expressed by the scientific communities, a need that stems from the guidelines of the National Open Science Plan (2018) and the European Commission. The program is designed to serve all scientific communities (faculty members, researchers, engineers, doctoral students, and undergraduates).
The open science policy is also driven by the challenges currently posed by the “data-driven economy” (IoT, AI, etc.). It is therefore now essential for research institutions to produce data that can be reused not only by researchers themselves but also by the private sector. This, however, requires that the data produced be shareable and disseminable—and thus “machine-readable.” With this in mind, the use of specific formats has been recommended by public authorities, with the support of part of the research community (the FAIR format: findable, accessible, interoperable, reusable). These requirements now determine the funding of research projects and are mandatory for the implementation of LabEx programs (staff training, development of Data Management Plans).
The program, which has been running for four consecutive years (2020–2024) and has already produced about 100 graduates, aims to meet the strong demand for training in “data engineering.” A program that addresses the aspects of research data management (Scientific Data Steward) from technical, legal, and economic perspectives is therefore essential today.
The training is intended for scientific communities across all disciplines and at all levels.
The Scientific Data Steward University Diploma is based on two pillars:
• Excellence – Professors, research engineers, research directors, and professionals in the fields of healthcare and data are part of the teaching team for the Montpellier University Diploma program.
• Online – The university diploma program will be digital, and classes will be held online. The goal is to promote flexibility in knowledge transfer and increase the number of students.
Objectives
The goal will be to present the scientific data environment from two perspectives:
1/ A doctrinal approach, grounded in fundamental knowledge, aimed at awarding a degree of excellence:
The first part of the training helps participants understand the data environment in science
The following will be developed: collaborative research, funding for research focused on data collection and analysis, and strategies for the development of
data science projects as well as the governance rules that apply to data.
2/ A practical perspective on "Scientific Data Steward" research
These two approaches will be organized into modules comprising 186 hours of classes and online educational content.
The aim will be to present the key principles of research data governance (I), explain the tools for analyzing research data (II), and understand how to manage open research data (III) (see attached outline).
I. Understanding data governance requires an overview of the major challenges facing open science.
II. Mastering scientific data analysis tools requires understanding not only how data is extracted, contextualized, mined, and explored, but also how it can be secured (both technically and legally) and protected.
III. Managing open scientific data requires knowing how and when to share data, how to preserve, store, and archive it, and how to make the most of it (both scientifically and legally).
Program
Training volume: 125 hours
¬ Program:
Module 1: The Key Principles of Research Data Governance
Module 2: Tools for Analyzing Research Data
1. Extract the data
2. Manage Data
3. Explore and analyze the data
4. Secure the data
Module 3: Understanding How to Manage Open Research Data
1. Develop a data management plan
2. Store and archive data
3. Disseminate and share data
4. Make the Most of Data
Admission
Admission requirements
For students in their initial training program, a completed master's degree.
For continuing education, as determined by the academic director.
Tuition fees
- Initial training: 700€
- Continuing education: 3,500 €
- Special Rates:
200 €: Scholarship Recipients
350 €: Doctoral students
2,000 €: Self-financing
1,500 €: Job seekers registered with Pôle Emploi following a referral for a case review committee meeting
35 € / h: Registration for a single module (hourly rate increased by 28%)
€0: Re-enrollment in the second year to complete the degree (spanning two academic years)
Mandatory prerequisites
For students in their initial training program, a completed master's degree.
For continuing education, as determined by the academic director.
And after
Professional integration
The role of “Data Management Officer” is expected to eventually emerge in laboratories, as researchers and faculty members cannot always take on this task. It is therefore important to anticipate this need. Data management tasks can sometimes also be assigned to doctoral students. They must therefore be able to understand what happens to the data they handle. Finally, researchers and faculty members must understand the challenges and techniques involved in optimally storing and archiving data produced within a laboratory, for example, as well as how they can or must design Data Management Plans. They must also be able to determine what they can and cannot do with datasets when partnering with private-sector companies. Finally, doctoral students—to whom the training is open—may benefit from acquiring a “culture” of scientific data management if they wish to pursue a career in research, or simply because they are already handling large datasets in their laboratories, or because they have an innovative project to develop.