PaN-Finder

The PaN-Finder project aims to enhance the capabilities of the existing PaNOSC Data Portal, which provides researchers access to a wealth of data from Europe’s Photon and Neutron (PaN) Research Infrastructures (RIs). Originally developed as part of the EU Horizon 2020 project PaNOSC, the portal has already improved the visibility and accessibility of scientific data. PaN-Finder will introduce an AI-powered search tool that simplifies user interaction, enabling intelligent, prompt-based searches. By leveraging state-of-the-art AI technologies, such as Large Language Models and Natural Language Processing, this tool will make it easier for researchers, journalists, and the general public to navigate and utilise valuable PaN data effectively. This talk was held in the course of the DAPHNE4NFDI TA1 Data for science lecture series on October, 28 2025

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-28
DOI
https://doi.org/10.5281/zenodo.17471791
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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PaN-Finder

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

PaN-Finder

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Abstract

The PaN-Finder project aims to enhance the capabilities of the existing PaNOSC Data Portal, which provides researchers access to a wealth of data from Europe’s Photon and Neutron (PaN) Research Infrastructures (RIs). Originally developed as part of the EU Horizon 2020 project PaNOSC, the portal has already improved the visibility and accessibility of scientific data. PaN-Finder will introduce an AI-powered search tool that simplifies user interaction, enabling intelligent, prompt-based searches. By leveraging state-of-the-art AI technologies, such as Large Language Models and Natural Language Processing, this tool will make it easier for researchers, journalists, and the general public to navigate and utilise valuable PaN data effectively. This talk was held in the course of the DAPHNE4NFDI TA1 Data for science lecture series on October, 28 2025

Zenodo (CERN European Organization for Nuclear Research)
European Spallation Source (SE)
Industry, innovation and infrastructure
Openalex Percentile: Top 56%
Machine Learning in Materials Science
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