Recommendations for the Adoption of Persistent Identifiers in Higher Education and Research in France
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».