The INRIA DataLake: A Generic and Scalable Ecosystem of Pipelines for HAL Applied to Software Mentions Tracking
TL;DR
The INRIA DataLake project is presented, which provides an ecosystem of scalable and interconnected pipelines for preparing scientific literature, extracting structured information, and applying specialised treatments, and contributes to open science through improved visibility and tracking of research software.
Abstract
Research repositories contain a large amount of scientific knowledge, but access to structured articles and specialised information, such as datasets or software metadata, remains limited. In this paper, we present the INRIA DataLake project, which provides an ecosystem of scalable and interconnected pipelines for preparing scientific literature, extracting structured information, and applying specialised treatments. Using a large-scale shared infrastructure, Grid'5000/ABACA, we demonstrate our ecosystem through a concrete use case: extracting software mentions from scientific articles deposited daily and visualising them after validation in the HAL research portal. Our results show that the system can efficiently process large volumes of scientific literature while supporting user validation and interoperability with external systems. Designed to grow by integrating additional pipelines and sharing the preparation effort across research groups, this project already contributes to open science through improved visibility and tracking of research software.