Dataset for project MINESTRONE, ai-atoms (Deep learning in materials electron microscopy: models, applications and emerging trends)

Brief description This dataset contains the data needed to reproduce results from the paper: "Deep learning in materials electron microscopy: models, applications and emerging trends", by Camilo A. F. Salvador et al., 2026. Summary: A bibliographic analysis of more than 1,800 peer-reviewed articles, collected from the Web of Science (WoS) and classified using an LLM-augmented regex methodology, provides co-occurrence matrices and publication trends at the intersection of materials science, microscopy, and deep learning. Contributors: Camilo A. F. Salvador, Clovis Lapointe, Thomas Bilyk, Estelle Meslin, Mihai-Cosmin MarinicaThis work has been carried out within the Cross-disciplinary Initiative for Digital Science of the French Alternative Energies and Atomic Energy Commission (CEA). For more information, please drop us a note at https://github.com/ai-atoms/minestrone. A brief description of each file can be found in the table of contents below; Table of contents - "data/2026b/savedrecs*.xls": source WoS records used to generate the dataset. - "savedrecs1" to "savedrecs6": initial search. - "savedrecs10" to "savedrecs26": additional search.