Member
Matteo Cancellieri
Lead Developer - Open Research
Works on improving and and maintaining CORE (http://core.ac.uk) FOSTER (http://fosteropenscience.eu) and Stories Of Change (http://storiesofchange.ac.uk). Once he was a Super Mario plumber.
Team: Valeriy Budko, Petr Knoth, Nancy Pontika, David Pride, Mologadi Ramushu, Friedrich Summann, Besik Tsiklauri, Andrew Vasilyev, Anton Zhuk
Projects
Technologies
Frictionless Data Exchange Across Research Data, Software and Scientific Paper Repositories
News
02 Jul 2025
04 Apr 2025
20 Mar 2025
18 Dec 2024
20 Mar 2024
Publications
Cancellieri, M., El-Ebshihy, A., Fink, T., Galuščáková, P., Gonzalez-Saez, G., Goeuriot, L., Iommi, D., Keller, J., Knoth, P., Mulhem, P., Piroi, F., Pride, D. and Schaer, P. (2025) LongEval at CLEF 2025: Longitudinal Evaluation of IR Model Performance Advances in Information Retrieval: 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6-10, 2025, Proceedings, Part V, eds. Claudia Hauff,Craig Macdonald,Dietmar Jannach,Gabriella Kazai,Franco Maria Nardini,Fabio Pinelli,Fabrizio Silvestri,Nicola Tonellotto, 15576, pp. 382-388, Springer
Knoth, P., Lopez, P., Laurent, R., Cosmo, R., Smrz, P., Umerle, T., Harrison, M., Monteil, A., Cancellieri, M. and Pride, D. (2024) Making Software FAIR: A machine-assisted workflow for the research software lifecycle, 19th International Conference on Open Repositories (OR2024), Göteborg, Sweden
Knoth, P., Klein, M., Macgregor, G., Cancellieri, M. and Walk, P. (2024) How to make repository content indexed and discoverable, The 19th International Conference on Open Repositories, Göteborg, Sweden
Pride, D., Cancellieri, M. and Knoth, P. (2023) CORE-GPT: Combining Open Access Research and Large Language Models for Credible, Trustworthy Question Answering, International Conference on Theory and Practice of Digital Libraries TPDL 2023: Linking Theory and Practice of Digital Libraries, Zadar, Croatia
Thelwall, M., Kousha, K., Wilson, P., Makita, M., Abdoli, M., Stuart, E., Levitt, J., Knoth, P. and Cancellieri, M. (2023) Predicting article quality scores with machine learning: The UK Research Excellence Framework, Quantitative Science Studies, pp. (early access), MIT Press