Aricia Bassinet (Université de Lorraine)
Laetitia Bracco (Université de Lorraine)
Anne L’Hôte (Ministère de l’enseignement supérieur et de la recherche)
Eric Jeangirard (Ministère de l’enseignement supérieur et de la recherche)
Patrice Lopez (science-miner)
Laurent Romary (Inria)

Juin 2023

Consulter le rapport sur HAL

There is today no standard way to reference research datasets and software in scientific communication. Emerging editorial workflows and supporting infrastructures dedicated to researchdatasets and software are still poorly adopted in current publishing
practices and are highly fragmented. To better follow the production of research datasets and software, we present a text mining method applied to scientific publications at scale and implemented at the French national level. Our approach relies on state-of-the-art Machine Learning and document engineering techniques to ensure reliable accuracy across multiple research areas and document types. The annotations produced by our system are used by the French Open Science Monitor (BSO) platform to follow the production and the openness of research datasets and software in the context of the French second National Plan for Open Science. The source code and the data of the French Open Science Monitor, as well as all the associated tools and training data, are all available under open licenses.