Acknowledgments

The DSRI is an initiative, which was made possible by the joint efforts of different collaborating parties who have also provided funding for establishing the cluster:

• Maastricht University I-Board
• Maastricht University - Institute of Data Science (IDS)
• Maastricht University - ICT Service Centre (ICTS)
• Maastricht University - School of Business and Economics (SBE)
• Maastricht University - Maastricht Centre for Systems Biology (MaCSBio)
• Maastricht University - Department of Bioinformatics (BiGCaT)
• MAASTRO
• DELL
• NVIDIA

Acknowledging the DSRI in your publications

If you are planning to present or publish your work which was made possible by using the DSRI, we encourage you to acknowledge the use of DSRI. For this purpose we propose to add the following sentence to your publication:

"This research was made possible, in part, using the Data Science Research Infrastructure (DSRI) hosted at Maastricht University."

Citations

• Diffusion connectivity disparities across Parkinson’s disease motor subtypes and non-demented controls: a postmortem ultra high field MRI study, Boonstra JT, Michielse S, Temel Y, Jahanshahi A, and Roebroeck A, Neuroanatomical Variation In Parkinson’s Disease Motor Subtypes p67, 2023.

• Generating synthetic personal health data using conditional generative adversarial networks combining with differential privacy, Chang Sun, Johan van Soest, Michel Dumontier, Journal of Biomedical Informatics, 2023.

• Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks, Antoine Louis, Gijs van Dijck, and Gerasimos Spanakis, Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2761–2776, Dubrovnik, Croatia. Association for Computational Linguistics, 2023.

• VendorLink: An NLP approach for Identifying & Linking Vendor Migrants & Potential Aliases on Darknet Markets, Vageesh Saxena, Nils Rethmeier, Gijs Van Dijck, Gerasimos Spanakis, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, pages to appear, Toronto, Canada. Association for Computational Linguistics, 2023.

• The development of an automatic speech recognition model using interview data from long-term care for older adults, Coen Hacking, Hilde Verbeek, Jan P H Hamers, Sil Aarts, Journal of the American Medical Informatics Association, Volume 30, Issue 3, March 2023, Pages 411–417, 2023.

• Semantically-Informed Deep Neural Networks For Sound Recognition,, M. Esposito, G. Valente, Y. Plasencia-Calaña, M. Dumontier, B. L. Giordano and E. Formisano, International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, 2023.

• The value of maintaining cognition in patients with mild cognitive impairment: The innovation headroom and potential cost-effectiveness of roflumilast,, Ron Handels, Sabine Grimm, Arjan Blokland, Nina Possemis, Inez Ramakers, Anke Sambeth, Frans Verhey, Stephanie Vos, Manuela Joore, Jos Prickaerts, Linus Jönsson, Alzheimer's & Dementia, 2023.

• Image based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimization, Pedro Mateus, Leroy Volmer, Leonard Wee et al. PREPRINT (Version 1), april 2023.

• PSnpBind: a database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow, A Ammar, R Cavill, C Evelo, E Willighagen, Journal of Cheminformatics, 2022.

• Knowledge Base Construction from Pre-trained Language Models by Prompt learning, Ning, X., and Celebi, R., CEUR Workshop Proceedings, 2022

• A Statutory Article Retrieval Dataset in French, Antoine Louis and Gerasimos Spanakis, In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, May 2022.

• Improving correlation capture in generating imbalanced data using differentially private conditional GANs, Sun, C., van Soest, J., & Dumontier, M.. arXiv preprint arXiv:2206.13787, 2022.

• ciTIzen-centric DAta pLatform (TIDAL): Sharing distributed personal data in a privacy-preserving manner for health research, Sun, C., Gallofré Ocaña, M., van Soest, J., & Dumontier, M., Semantic Web, (Preprint), 1-20, 2022.

• Oropharyngeal Tumour Segmentation Using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge, Bontempi, D., Dekker, A., Teuwen, J. and Traverso, A, Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 12603 (2021): 65, 2021.