Collecting data in sports increased in importance the last few years.

This thesis applies data mining in commercial settings in the telecommunications industry.

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…

Due to the modern techniques of combinatorial chemistry and high-throughput screening, data on the biological activity of many millions of compounds is known. However, it is still very difficult to transfer this data into knowledge: if we know that compounds A and B bind to a certain protein with high…

Modern computer-aided simulation tools used by various industries produce gigabytes of data. But currently, they take days and even up to weeks of computation effort. To make the best use of all these data, the DAMIOSO project focuses on developing algorithms and tools for managing, mining, and optimizing…

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

In this thesis we explore machine and deep learning approaches that address keychallenges in high dimensional problem areas and also in improving accuracy in wellknown problems. In high dimensional contexts, we have focused on computational fluid dynamics (CFD) simulations.

This project develops a scientific method to extract clinically relevant new information from patient forum websites that discuss patient experiences concerning e.g. medication, nutrition, co-morbidities, genetic factors etc.

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

Latest technological developments in data mining and profiling.

With the rise of information and communication technologies, large amounts of data are being generated and stored in databases. In order to get a better grip on these large amounts of data, serious efforts are being made to discover patterns and relations in the data with the help of new techniques.…

The SAPPAO project aims to optimise the accuracy and reliability of predicting scheduled flight times. The full name of the project is 'A Systems Approach towards Data Mining and Prediction in Airlines Operations'.

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

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

Using positional data to determine which tactics lead to a successful attack? Rens Meerhoff, researcher at the Sports Data Center of the Leiden Institute of Advanced Computer Science (LIACS), has been using data-mining to analyse football tactics for several years. In Friesch Dagblad he explains the…

The Data-Driven Drug Discovery Network (D4N) is an initiative by researchers from Leiden University and collaborators to join efforts in applying and developing novel techniques from data science to drug discovery and related topics from bioinformatics.

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…

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…

Promotor: J.N. Kok, Co-promotor: C.M. Soares, A.J. Knobbe

An effective and equitable approach to resource conflicts?

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

Industrial manufacturing processes, such as the production of steel or the stamping of car body parts, are complex semi-batch processes with many process steps, machine parameters and quality indicators.

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 elicitation of mentor teachers' practical knowledge by prospective teachers

PUMA aims at composing a geological map of the urban mine of Amsterdam for a selection of metals. Where are main deposits of copper, iron and aluminium located, when will they become available for secondary production, in what state and shape are they?

In this work, we attempt to answer the question:

Text Mining and Retrieval Leiden (TMRL) focusses on text mining and retrieval problems in complex domains.

‘Tell me something I don’t know,’ said skating coach Jac Orie to Leiden data scientist Arno Knobbe. And he did. Knobbe and his colleagues now assist athletes in all kinds of ways with the help of data mining.

Mining time series is a machine learning subfield that focuses on a particular data structure, where variables are measured over (short or long) periods of time.

The Astronomy and Data Science master’s specialisation at Leiden University combines advanced Astronomy courses with relevant courses from the Computer Science programme at Leiden University.

On dinsdag 26 juni 2018, Linlin Sun defended her doctoral thesis ‘International Environmental Obligations and Liabilities in Deep Seabed Mining’. The doctoral research was supervised by Prof. dr. N.J. Schrijver en Prof. dr. E.C.P.D.C. De Brabandere.

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

Abdolreza Alamdar Baghini defended his thesis on 5 December 2019.

The ability to collect and interpret huge quantities of data has become indispensable to society and academia. Leiden University is a knowledge and expertise centre for data science that places the emphasis on interdisciplinary collaboration and innovation.

The Collective Experience of Empathic Data Systems (CEEDs) consortium developed novel integrated technologies that support experiencing, analysing and understanding of very large datasets.

LIACS is a partner in the EU Erasmus+ Curriculum Development for the Asian education system. The knowledge available in the field of Data Science and Artificial Intelligence education will be shared and adapted for the Asian market.

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.

In this PhD-thesis several new and existing data science application are described that are particularly focused on applications for tax administrations.

Open Educational Resources (OER) have the potential to change teaching in Higher Education, but adoption is low despite the growing amount of resources available. The current project aims to investigate if and how teacher communities can foster adoption of OER in curricula.

Read-Urban was a first project to investigate whether policy recommendations can be made with the aid of linked data collections and data science and to gain experience with the success factors for such a process.

Policy Note Insecurity in Burkina Faso – beyond conflict minerals: The complex links between artisanal gold mining and violence

By comparing individual health data with population data, doctors can provide personalized health advice and patients can learn from each other's experiences. Wessel Kraaij, professor of Applied Data Analytics shows how personal data can have predictive value.

Data reuse and big data: a taxonomy for personal data reuse.

The archaeology domain produces large amounts of texts, too much to effectively read or manually search through for research. To alleviate this problem, we created a search system (called AGNES), which combines full text search with entity and geographical search.