For best experience please turn on javascript and use a modern browser!
You are using a browser that is no longer supported by Microsoft. Please upgrade your browser. The site may not present itself correctly if you continue browsing.
uva.nl
Data Science Centre Data Science Centre

DSC outputs

Our members have produced a range of scientific outputs such as journal publications, conference papers, posters, and media interviews.
Journal publications
  • 2024
    • Dijkman, J., Dijkstra, M., van Roij, R., Welling, M., van de Meent, J. & Ensing, B. (2024). Learning Neural Free-Energy Functionals with Pair-Correlation Matching. Preprint. https://doi.org/10.48550/arXiv.2403.15007
    • Helm, P., Bella, G., Koch, G., & Giunchiglia, F. (2024). Diversity and Language Technology: How Techno-Linguistic Bias Can Cause Epistemic Injustice. Ethics and Information Technology, 26(8). doi: https://doi.org/10.1007/s10676-023-09742-6
  • 2023
    • Abegaz, F., Abedini, D., White, F., Guerrieri, A., Zancarini, A., Dong, L., Westerhuis, J. A., van Eeuwijk, F., Bouwmeester, H., & Smilde, A. K. (2023). A strategy for differential abundance analysis of sparse microbiome data with group-wise structured zeros. bioRxiv preprint. doi: https://doi.org/10.1101/2023.07.24.549296
    • Angelakis, A., Soulioti, I., & Filippakis, M. (2023). Diagnosis of acute myeloid leukaemia on microarray gene expression data using categorical gradient boosted trees. Heliyon, 9 (10). doi: https://doi.org/10.1016/j.heliyon.2023.e20530
    • Bella, G., Helm, P. Koch, G., & Giunchiglia, F. (2023). Towards Bridging the Digital Language Divide. arXiv preprint. doi: https://doi.org/10.48550/arXiv.2307.13405
    • Boelrijk, J., van Herwerden, D., Ensing, B., Forré, P., & Samanipour, S. (2023). Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data. Journal of Cheminformatics, 15 (28). doi: https://doi.org/10.1186/s13321-023-00699-8
    • Feraud, M., O'Brien, J. W., Samanipour, S., Dewapriya, P., van Herwerden, D., Kaserzon, S., Wood, I., Rauert, C., & Thomas, K. V. InSpectra – A platform for identifying emerging chemical threats (2023). Journal of Hazardous Materials, 455. doi:  https://doi.org/10.1016/j.jhazmat.2023.131486

    • Helm, P., De Götzen, A., Cernuzzi, L., Ruiz Correa, S., Diwakar, S., & Gatica-Perez, D (2023). Diversity and Neocolonialism in Big Data Research. Big Data & Society, 10(2). doi: https://doi.org/10.1177/20539517231206802

    • Helm, P. & Matzner, T. (2023). Co-addictive human–machine configurations: Relating design and algorithm studies to medical-psychiatric research on “problematic Internet use”. New Media & Society, https://doi.org/10.1177/1461444823116591

    • Guha, S., Khan, F. A., Stoyanovich, J., Schelter, S. (2023). Automated data cleaning can hurt fairness in machine learning-based decision making. 2023 IEEE 39th International Conference on Data Engineering. doi: https://doi.org/10.1109/ICDE55515.2023.00303
    • Liu, X., Karimi Nejadasl, F., van Gemert, J. C., Booij, O., & Pintea, S. L. (2023). Objects do not disappear: Video object detection by single-frame object location anticipation. Presented at the International Conference on Computer Vision. doi: https://doi.org/10.48550/arXiv.2308.04770
    • Lunansky, G., Hoekstra, R. H., & Blanken, T. (2023). Disentangling the role of affect in the evolution of depressive compalints using complex dynamical networks. Collabra:Psychology, 9 (1). doi: https://doi.org/10.1525/collabra.74841
    • Möller, M., Vermeer, S.A.M., & Baumgartner, S. (2023). Cutting through the comment chaos: A supervised machine learning approach to identifying relevant YouTube comments. Social Science Computer Review. doi: https://doi.org/10.1177/08944393231173895

    • Müller, N., Groen, I., & Scholte, S. (2023). Pre-training on high-quality natural image data reduces DCNN texture bias. 2023 Conference on Cognitive Comutational Neuroscience. doi: https://doi.org/10.32470/CCN.2023.1294-0

    • Rieder, B., Borra, E. K., Coromina, Ò., & Matamoros-Fernández, A. (2023). Making a living in the creator economy: A large-scale study of linking on YouTube. Social Media + Society, 9 (2). https://doi.org/10.1177/20563051231180628

    • van Spengler, M., Berkhout, E., & Mettes, P. (2023). Poincaré ResNet. Presented at International Conference on Computer Vision. doi: https://doi.org/10.48550/arXiv.2303.14027 
    • van Spengler, M., Wirth, P., & Mettes, P. (2023). HypLL: The Hyperbolic Learning Library. ACM MM 2023 Open-Source Software Competition. doi: https://doi.org/10.48550/arXiv.2306.06154 
    • Wei, W., Oswald, M. R., Karimi Nejadasl, F., & Gevers, T. (2023). APNet: Urban-level scene segmentation of aerial images and point clouds. Presented at the International Conference on Computer Vision. doi: https://doi.org/10.48550/arXiv.2309.17162
    • Haider, I., Yunmeng, Z., White, F., Li, C., Incitti, R., Alam, I., Gojobori, T., Ruyter-Spira, C., Al-Babili, S., & Bouwmeester, H. J. (2023). Transcriptome analysis of the phosphate starvation response sheds light on strigolactone biosynthesis in rice. The Plant Journal, 114. doi: https://doi.org/10.1111/tpj.16140
  • 2022

    • Baas, J., van Wissen, L., Reinders, J., Dastani, M., & Feelders, A. (2022). Adding Domain Knowledge to Improve Entity Resolution in 17th and 18th Century Amsterdam Archival Records. In Towards a Knowledge-Aware AI (pp. 90-104). IOS Press. https://doi.org/10.3233/SSW22001

    • Bracks, C., & Moss, F. C. (2022). Totoli’s art of lelegesan: Analyzing sociocultural context and musical content. OSF Preprints. https://doi.org/10.31219/osf.io/5tsxa

    • Burger, J., Isvoranu, A.M., Lunansky, G., Haslbeck, J., Epskamp, S., Hoekstra, R.H., Fried, E.I., Borsboom, D. and Blanken, T.F., (2022). Reporting standards for psychological network analyses in cross-sectional data. Psychological Methods. doi: https://doi.org/10.1037/met0000471

    • Harasim, D., Affatato, G., & Moss, F. C. (2022). midiVERTO: A Web Application to Visualize Tonality in Real Time. In M. Montiel, O. A. Agustín-Aquino, F. Gómez, J. Kastine, E. Lluis-Puebla, & B. Milam (Eds.), Mathematics and Computation in Music (pp. 363–368). Springer International Publishing. doi: https://doi.org/10.1007/978-3-031-07015-0_31

    • Kuiper, M.E., Chambon, M., de Bruijn, A.L., Reinders Folmer, C., Olthuis, E.H., Brownlee, M., Kooistra, E.B., Fine, A., van Harreveld, F., Lunansky, G. and van Rooij, B. (2022). A Network Approach to Compliance: A Complexity Science Understanding of How Rules Shape Behavior. Journal of Business Ethics, 1 - 26. doi: https://doi.org/10.1007/s10551-022-05128-8

    • Moss, F. C., Affatato, G., & Harasim, D. (2022). Phantom Curves: Scientific Discovery through Interactive Music Visualization. In L. Pugin (Ed.), DLfM '22: Proceedings of the 9th International Conference on Digital Libraries for Musicology, Association for Computing Machinery (pp.. https://doi.org/10.1145/3543882.3543886

    • Renai, L., Del Bubba, M., Samanipour, S., Stafford, R., & Gargano, A.F.G. (2022). Development of a comprehensive two-dimensional liquid chromatographic mass spectrometric method for the non-targeted identification of poly- and perfluoroalkyl substances in aqueous film-forming foams. Analytica Chimica Acta, 1232. doi: https://doi.org/10.1016/j.aca.2022.340485

    • Rieder, B., Peeters, S. C. J., & Borra, E. K. (2022). From tool to tool-making: Reflections on authorship in social media research software. Convergence : The International Journal of Research into New Media Technologies. https://doi.org/10.1177/13548565221127094

    • Samanipour, S., O'Brien, J., Reid, M., Thomas, K., & Praetorius, A. (2022). From molecular descriptors to intrinsic fish toxicity of chemicals: An alternative approach to chemical prioritization. Envrironmental Science & Technology. doi: https://doi.org/10.1021/acs.est.2c07353

    • Sano N., Lounifi I., Cueff G., Collet B., Clément G., Balzergue S., Huguet S., Valot B., Galland M., & Rajjou, L. (2022). Multi-Omics approaches unravel specific features of embryo and endosperm in rice seed germination. Frontiers in Plant Science, 13. doi:  https://doi.org/10.3389/fpls.2022.867263

    • Yang, F., van Herwerden, D., Preud’homme, H., & Samanipour, S. (2022). Collision Cross Section Prediction with Molecular Fingerprint Using Machine Learning. Molecules, 27(19), 6424. https://doi.org/10.3390/molecules27196424

In the media

2023:

Schadelijke stoffen zoals PFAS zijn topje van de ijsberg: ‘Slechts 2% van chemicaliën is bekend’
[English translation: Harmful substances such as PFAS are the tip of the iceberg: 'Only 2% of chemicals are known']
Saer Samanipour (Assistant Professor at the Van't Hoff Institute for Molecular Sciences) was interviewed in this article about the dangers posed by unknown synthetic substances. 
17 October 2023 - Folia [Dutch]

Lost in AI translation: Growing reliance on language apps jeopardizes some asylum applications.
Paula Helm (Assistant Professor in Data Science and Ethics) was interviewed in this article about the real-life implications of AI translation tools.
7 September 2023 - The Guardian

Data-gedreven reclame: is big brother watching me? 
Joanna Strycharz (Assistant Professor in Persuasive Communication) discusses 'dataveillance' in advertising.
7 March 2023 - SWOCC [Dutch]

Should computers be in charge? 
Shuai Yuan (Assistant Professor in People Analytics) was interviewed in this article about the application of AI to employment practices. 
5 January 2023 - Science Business

Books

Posters

Blog articles

Decomposing big goals into manageable tasks with ChatGPT
Authors: Viktoriya Degeler, Paul Groth, Boy Menist, Fina Yilmaz Polat, ChatGPT
25 May 2023 - DSC Away Day 2023

DSC GitHub

The DSC GitHub is a collaborative public workspace where the DSC collaborates on data repositories, code, and other related materials. 

For example, you can find materials from previous DSC activities and workshops on topics such as Dash, Shiny, Deep Learning, Conventional Machine Learning, and Data Visualisation.

The DSC supports open education and is committed to continuously updating the DSC GitHub.

Access DSC GitHub