By Audrey Knauf, Associate Professor, Crem (University of Lorraine) – Open Science Ambassador, University of Lorraine – Co-deputy director Data & Corpus – the journal of data in SHS – Co-leader of the ANR SoSHS project
As part of Love Data Week at the University of Lorraine, a study day dedicated to research data brought together speakers on March 19 to share experiences, surveys, methods, tools, and reflections on an increasingly important issue for us researchers: training in research data.
One of the major contributions of this day was to remind us that data are not just a side aspect of research. On the contrary, they play a central role in our practices, at the core of our most ordinary gestures, sometimes invisible, while engaging in something fundamental: our way of producing knowledge, making it intelligible, transmitting it, and ultimately responding to it. They are part of proof, method, scientific discussion, work memory, and the very possibility of their circulation.
The starting point of this day was clear: to reflect on skills, learning formats, their integration into teaching curricula, and their adaptation to disciplinary specificities, making training a decisive lever. The transformation of practices does not happen on its own and requires a real practical culture of data beyond just adhering to open science. Among the training opportunities presented are those offered by the ADOC Lorraine data workshop, designed throughout the data lifecycle, as well as DoRANum as a self-training platform, and the FLSO project providing training for doctoral students in open science through peer transmission.
At the heart of the discussions, the Open Science Barometer indicates a lower level of open access in the humanities and social sciences, without implying a lack of openness practices. The insight from the SoSHS project invites us to nuance this: it highlights existing practices that are more discreet, less standardized, and less visible in the indicators. This leads to moving beyond the dichotomy of disciplines being “behind” or “ahead.”
In other words, data remind us that research always occurs within specific conditions with objects, methods, audiences, and legal, ethical, and scientific responsibilities, an essential nuance that applies to both disciplines and types of data. The afternoon examples illustrate this: the ArchiMed platform in clinical research involves issues of structuring, traceability, and security, while the “Les Vocaux” corpus emphasizes issues of openness, uses, and participatory frameworks for linguistic data. Despite their differences, a consistent requirement emerges: to consider data in their context of production, circulation, and reuse.
This question resonates with researchers because it carries a responsibility, as openness cannot be envisioned without attention to individuals, rights, fields, and production contexts. It leads to moving beyond an approach focused solely on ownership: with the principle of open access for public data, the question shifts to their nature, legal status, and the concrete conditions of their openness. It also challenges the very organization of scientific work, showing that research can no longer be seen as an individual and isolated activity.
Research data represent a collective challenge for the academic ecosystem. Open practices remain more limited without data engineers, despite being central socio-technical mediators in the redefined roles brought by open science. Behind data sharing and FAIR principles lies considerable, often invisible but essential work, requiring support, recognition, training, resources, and dialogue between stakeholders.
This day highlighted a convergence of worlds that had sometimes worked side by side without considering themselves together (researchers, librarians, support staff, legal experts, data engineers, information specialists, infrastructures, and platforms) all contributing, in their respective roles, to the quality of what we produce and transmit.
Finally, the day revealed that some data need to be protected or restricted, without contradicting open science, hence the concept of being “as open as possible, as closed as necessary.” The round table also emphasized the importance of instilling a genuine data culture from the undergraduate level. In conclusion, it was expressed that these exchanges should continue in laboratories, projects, doctoral programs, feed team discussions, and that the idea that “the main obstacle is ourselves” should belong to the past. Taking care of research data is also taking care of research itself.
