The Explainatory Data Analysis group develops algorithms and theory that enable domain experts to explain data by finding interpretable patterns and models.

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How does the cluster bootstrap procedure in combination with various types of linear models perform in the analysis of longitudinal data, compared to other methods.

Understanding how data derived from wearable technology can be used to identify effective changes in behaviour that are likely to result in health improvements

This project aims to improve understanding of, and responses to, the phenomenon of lone actors through analysis of comprehensive data on cases from across Europe.

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This article provides an in‐depth assessment of lone actor terrorists’ attack planning and preparation.

In this thesis we will explore the use of fuzzy systems theory for applications in bioinformatics.

This dissertation focuses on developing new mathematical and statistical methods to properly represent time-varying covariates and model them within the context of time-to-event analysis.

The research programme Statistical Science is concerned with the analysis and interpretation of masses of data, the quantification of uncertainty using probability models, and the development and benchmarking of algorithms and methods with these aims.

We explored how to morphologically classify well-resolved jetted radio-loud active galactic nuclei (RLAGN) in the LOw Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) using machine learning.

Online data breaches are recurrent and damaging cyber incidents fors organizations worldwide. This study examines how organizations can effectively mitigate reputational damages in the aftermath of data breaches by hacking, through situational crisis communication strategies.

Promotores: J.C. van Houwelingen, S.A. van de Geer

In this dissertation, we study partition-based models that can be used both for interpretable predictive modeling and for understanding data via interpretable patterns.

Athletes always strive for the top. How can data scientists assist them in improving their performance? During the seminar Data Science and Sports, the possibilities and challenges of collaboration between these two worlds were discussed.

Overlevingsanalyse is een onderzoeksgebied dat zich richt op het bestuderen van de tijd tot het optreden van een specifieke uitkomst.

The majority of scientists, from archaeologists through to zoologists, collect huge volumes of data. Their massive databases contain large amounts of information which is difficult for humans to filter. With a solid grounding in statistics, we can develop algorithms for analysing and identifying patterns…

D1.2 reports on the findings of an analysis of framework conditions relevant in the context of the data economy from a legal, a socio-economic and a technological perspective. The analysis is a key foundation for the creation of an initial, heuristic model of the European data economy. The deliverable…

The majority of scientists, from archaeologists through to zoologists, collect enormous volumes of data. Their massive databases contain large amounts of information which is difficult for humans to filter. With a solid grounding in statistics and computer science, we can develop algorithms for analyzing…

Obtaining imaging data is nowadays only the start of your project. Once the data has been acquired there are many things that still need to be done.

D2.2 comprises the in-depth legal analysis of the initial heuristic model, focused on addressing, in more detail, the main legal concerns for data reusers in the European data economy. This detailed analysis of the legal propositions presented in D2.1 is supplemented by an analysis of the technological,…

What workflow of non-destructive techniques provides accurate, valuable data to improve our understanding of Caribbean archaeological landscapes? How were Amerindian settlements configured?

How sustainable are open data infrastructures? Will the data we need in the future be available and usable? This project takes COVID-19 data/infrastructures as a case to study the sustainability and dynamics of open data infrastructures.

Analysis of neuroimaging data requires multiple steps where statistics play a crucial role. The MRI methods research group develops new statistical methods that are accurate, transparent and easy to use.

The master's specialisation Data Science: Computer Science at Leiden University provides students thorough knowledge and understanding of statistical and computational aspects of data analysis, including their application in databases, advances in data mining, networks, pattern recognition, and deep…

The change from a closed institution to an open living environment for patients with late stages of dementia will give the patients more freedom in their day-to-day life. The effect of this change on the patients’ mobility, activity and interaction with others will be assessed with sensor technology…

In this thesis,

Our group is part of the Metabolomics and Analytics Centre where we accompany the data from its acquisition all the way to the publication of identified associations and biomarkers for a range of human diseases. The generated data of the metabolic measurements are assessed using an in-house quality…

Promotor: Prof.dr. J.N. Kok, Co-promotor: Dr. A.J. Knobbe

Promotor: J.N. Kok, Co-promotor: W.A. Kosters

Sarah Giest and Annemarie Samuels, both Assistant Professors at Leiden University, researched the quality and coverage of the data being collected for policiymakers to be used, specifically pertaining to minority groups.

This project is a collaboration between Sanquin, LUMC and LIACS. It focuses on the development of meta-modelling methods and tools for the exchange, analysis, and publication of anonymised patient data. Measures are being constructed to evaluate both the level of privacy and the extent to which an anonymized…

Sport Data Center

This thesis describes the Multi-site All-Sky CAmeRA (MASCARA), which consists of two small robotic telescope designed to detect exoplanets around the brightest stars in the sky.

Promotor: J.N. Kok, Co-Promotor: A.J. Knobbe

With textnets it is possible to visualize and analyze textual data in novel ways.

Explaining treatment response variability between and within patients can support treatment and dosing optimization, to improve treatment of individual patients.