Recommendations for the Adoption of Persistent Identifiers in Higher Education and Research in France
Bibliographic record
Abstract
The current research landscape produces an increasing volume of publications, data and, more broadly, diverse scientific results and objects (digital and/or physical). In this context, the ability to uniquely and reliably identify different elements of the scientific ecosystem - researchers, publications, datasets, software, etc. - across multiple information systems has become a central challenge for structuring and enhancing scientific activity, particularly to enable traceability of scientific objects. Persistent Identifiers (PIDs) address this challenge. These are unique and permanent digital or alphanumeric codes that are readable by both humans and machines. Unlike URLs, which can change or become obsolete, PIDs are designed to provide lasting references, ensuring stable access to an entity (digital or otherwise). They enable identification, discovery, traceability, and standardized citation throughout the research lifecycle. The use of PIDs directly supports the implementation of FAIR principles (Findable, Accessible, Interoperable, Reusable) by making digital objects more easily discoverable, accessible, interoperable, and reusable. Furthermore, they promote automation of data exchange between information systems, contributing to administrative simplification through a logic of reuse and non-duplication of information ("tell us once" principle). As such, PIDs are essential for ensuring sustainability, consistency, and interoperability of data in digital environments for higher education and research. Recognition of PIDs as structural instruments of open science is part of an international movement. Several initiatives converge in this direction, notably the Canadian federal government's roadmap for open science , guidelines from the Office of Science and Technology Policy (OSTP) in the United States , and the PID policy developed within the European Open Science Cloud (EOSC) framework . The United Kingdom and Australia have measured the benefits of adopting PIDs in terms of the number of days of administrative work saved for researchers . These countries, as well as Finland, Canada, the Netherlands, Germany, the Czech Republic, South Korea and New Zealand, have implemented policies or roadmaps in this area to improve the quality and efficiency of research . The G7 Research Compact (2021) also commits member countries to strengthening the availability, sustainability, interoperability, and accessibility of scientific data, technologies, and infrastructures . Finally, PIDs are explicitly mentioned in UNESCO recommendations on open science as fundamental elements for open, reliable, and sustainable research governance . This document is part of the work launched in 2024 by the MESR on the roadmap “Data for simplification and research management”, whose guiding principles aim to ensure the circulation and interoperability of data while respecting the autonomy of institutions. The roadmap is based on an action plan developed collectively by stakeholders and follows the principle of “tell us once”, reflecting the commitment to reduce the administrative burden on research teams. It relies on a set of qualified data to be shared across information systems, according to common quality standards and principles, under a framework of collective governance. The objectives are to strengthen interoperability between systems, consolidate and improve the reliability of shared data, enhance coordination between supervisory bodies, and reduce repeated data collections and surveys. In this context, persistent identifiers play a key role in ensuring the interoperability of data across heterogeneous higher education and research systems, by guaranteeing the traceability, reliability, and reusability of information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.135 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.031 | 0.027 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.037 | 0.015 |
| Insufficient payload (model declined to judge) | 0.046 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".