If researchers make their data FAIR, computers can link large quantities of data and identify patterns, thus greatly accelerating the process of arriving at new insights. In Leiden, the birthplace of ‘FAIR data’, Professor Barend Mons explains the meaning of this term.

This project aims to contribute to the improvement of documentation and archiving standards (conform the FAIR principles) for systematic Mediterranean archaeological field survey.

On 10 March 2017, Professor Karel Luyben (TU Delft) welcomed the ‘Metabolomics GO FAIR Implementation Network’ as community initiative to start the European Open Science Cloud. The network aims to implement the FAIR data principles in the field of metabolomics in Europe.

Webinar

VODAN Africa started as a platform to enable access to critical data needed from Africa to fight the novel COVID-19. The initiative was inspired by the experience from the Liberia Ebola Virus outbreak in 2014: early detection requires contact tracing. Inclusion of the most vulnerable is critical to…

On 22 August, the GO-FAIR IN Africa was launched: an implementation network under the GO FAIR efforts to train, build and change the next generation of the Internet of FAIR Data and Services (IFDS).

Leiden University joind in the second edition of the Impact Fair. This was for anyone who wants to contribute to the world of tomorrow and is seeking inspiration.

Welcome to the Fair Educational Assessment in the Age of AI (FAIR-ASSESS) project!

If you've only just come to live in the Netherlands, life can at times be both exciting and difficult. To help you feel at home more quickly, a Feel at Home Fair is being organised in city hall, The Hague on 3 February. Leiden University will be present at the fair. It's a good opportunity to attend…

online workshop

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.

Today, March 15 2016, the FAIR Guiding Principles for scientific data management and stewardship were formally published in the Nature Publishing Group journal Scientific Data.

On 1 November 2019, international Master's students Data Science from Leiden University presented the elaboration of their data concepts to senior officials of the House of Representatives and Rijks I-Ecosysteem. With the presentation, they successfully completed this part of the Data Science master…

Welcome to the Fair Educational Assessment in the Age of AI (FAIR-ASSESS) project!

Anticipation on the promises and challenges that AI-assisted assessments present.

Workshop

How do laws and procedures for police vetting unfold in practice?

The focus of this thesis is on the technical methods which help promote the movement towards Trustworthy AI, specifically within the Inspectorate of the Netherlands.

Computers are becoming so smart that in the future they will perhaps take over the role of judges. In the meantime, experts at Leiden University are examining the question of which standards responsible data science should meet.

Data Science seminar at the Faculty of Governance and Global Affairs

In this article, Barrie Sander together with his colleagues explore whether fairness in digital governance inadvertently entrenches structural inequalities

FAIRDOM consortium is an open initiative of various partners including funding programmes, large-consortia, institutes, small groups as well as individuals.

On 7 February 2019, Helena Ursic-Vrabec defended her thesis 'Uncontrollable: Data subject rights and the data- driven economy'. The doctoral research was supervised by Prof. dr. S. van der Hof.

The extensive use of electronic communication channels and other devices has opened new possibilities for collecting data on human behavior. This information is sometimes openly accessible, but largely part of administrative registration systems that are not open to the broader public. The data provides…

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…

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.

Advanced microscopy to understand life and fight disease: that’s the goal of the new NL-BioImaging network that will develop and integrate state-of-the-art microscopy technologies and services. Researchers from all Dutch universities, including Leiden University and the Leiden University Medical Centre,…

This article by Honorata Mazepus and Florian van Leeuwen in the journal Governance: An International Journal of Policy, Administration and Institutions looks at how evaluations of authorities were influenced by four aspects of a governmental response to a hypothetical disaster.

Seminars, workshops

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.

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

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.

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…

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'.

How can the Indonesian National Ombudsman make the transition from autonomous to responsive justice in view of the day-to-day challenges it is faced with?

Data and information should be stored in a way that computers can understand, says Barend Mons, professor of Biosemantics at the Leiden University Medical Center and Chair of the High Level Expert Group for the European Open Science Cloud. We speak with him about FAIR data, knowlets and nanopublicat…

Researchers are pulled in various, sometimes contradictory directions by the multiplication of performance metrics and new incentives to align with societal needs. Management structures, funding systems, and publication practices are increasingly influenced by pressures to promote only the highest quality…

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.

Data Sets Department of Economics

Today, everybody and everything produces data. People produce large amounts of data in social networks and in commercial transactions. Medical, corporate, and government databases continue to grow. Sensors continue to get cheaper and are increasingly connected, creating an Internet of Things, and generating…

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

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…

Netherlands Statistics (CBS), Leiden University and the University of Amsterdam have started a collaboration in the form of a research project titled 'Enriching official economic statistics using data-driven modelling techniques'.

How can teachers be motivated by their students’ data in making stepwise changes to their teaching practices?