1,202 search results for “from data to insight” in the Public website
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Europe
For most of the past ten years, Europe has been in a state of ‘crisis’. The bank crisis mutated seamlessly via the Euro crisis to the present migrant crisis. Whereas previously the general assumption was that even closer cooperation within the European Union was a foregone conclusion, the EU is now…
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Language Diversity
Language offers new insights into our history, cultural differences, migration, and the way in which our brain processes information. This knowledge can in turn help us understand what it means to be human, as well as opening the way to many practical applications. In order to realise these goals, linguists…
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Collaborative and effective drug development
There are many complex links in the chain that provides patients with new drugs: from fundamental science, to clinical tests, to production. The entire chain can be found in Leiden. Leiden University, the Leiden University Medical Center (LUMC) and the businesses at the Leiden Bio Science Park (LBSP)…
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Public International Law
We would all like to live in a world in which individuals feel safe, conflicts are resolved peacefully and the interests of future generations are taken into consideration. At Leiden University legal scholars investigate to what extent public international law meets the needs of a globalised society.…
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Sustainable futures
How can we organise society so as to keep our planet habitable for us and for all other life forms around us? To answer this question, Leiden researchers collaborate across disciplines, from biology to data science, and from environmental economy to archaeology.
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Cancer chess: molecular insights into PARP inhibitor resistance
The clinical potential of applying synthetic lethality to cancer treatment is famously demonstrated by the BRCA1/PARP1 paradigm: a tumor specific defect in BRCA1 – a component of the DNA double-strand break (DSB) repair pathway homologous recombination (HR) – results in a remarkable sensitivity to PARP1…
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When data compression and statistics disagree: two frequentist challenges for the minimum description length principle
Promotor: P.D. Grünwald
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Victims as Stakeholders: Insights from the Intersection of Psychosocial, Ethical, and Crisis Communication Paths
This article examines the position of victims and those affected within communication theory. Current research has broadly been skewed toward reputation management and protecting brand value as primary goals of crisis communication efforts. The authors offer recommendations for crisis communication…
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Countering Lone Actor Terrorism: Data Collection & Analysis
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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Harold Nefs
Faculteit der Sociale Wetenschappen
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Ralph Rippe
Faculteit der Sociale Wetenschappen
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Sarah Plukaard
Faculteit der Sociale Wetenschappen
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Fatma Çapkurt
Faculteit Rechtsgeleerdheid
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Data-driven Predictive Maintenance and Time-Series Applications
Predictive maintenance (PdM) is a maintenance policy that uses the past, current, and prognosticated health condition of an asset to predict when timely maintenance should occur.
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Accountability and data-driven urban climate governance
The use of increasingly large and diverse datasets to guide urban climate action has implications for how, and by whom, local governments are held accountable.
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Interaction with sound for participatory systems and data sonification
This thesis deals with the use of sound in interactions in the context of participatory systems and data sonification. We investigate an interactive environment where participants perceive information of the data through sound elements.
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A large-scale crop protection bioassay data set
ChEMBL is a large-scale drug discovery database containing bioactivity information primarily extracted from scientific literature.
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Applied statistics as a pillar of data science
Data science is now growing fast in many places, but scholars at Leiden University have been developing data science techniques for a long time already. Thanks to their broad-based expertise, Leiden statisticians are currently combining the achievements in statistics with the latest methods of statistical…
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EU Erasmus+ Curriculum Development in Data Science and Artificial Intelligence
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.
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Exploring big data approaches in the context of early stage clinical
Als gevolg van de grote technologische vooruitgang in de gezondheidszorg worden in toenemende mate gegevens verzameld tijdens de uitvoering van klinische onderzoeken.
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Cheminformatics: Analyzing small-molecule activity data
While bioinformatics methods deal with the analysis of sequence information (be it proteins or DNA), the field of cheminformatics is concerned with the analysis of small-molecule datasets.
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Data use, online consumer needs, business strategies and regulatory response
This project aims to explore and examine the factors that impact upon the efficacy of information disclosure duties pertaining to customer data use in online business.
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Data governance: from open governmental data, to data commons
VVI Research Meeting 2023-2024
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chemistry and biochemistry of glycosphingolipids: new developments and insights
Advanced mass spectrometry of glycosphingolipids takes the central stage in this thesis. Investigations focus on characterization of glycosphingolipid metabolism in health and disease with emphasis to the detection and accurate quantitation of known and so far unknown glycosphingolipids and closely…
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Making big data meaningful for a promising start
All children deserve a promising start. Most children are doing fine. But some need extra support, because of problems during pregnancy or because they grow up in disadvantaged circumstances, e.g. due to poverty, parental addictions or psychological problems.
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Wouter van Loon
Faculteit der Sociale Wetenschappen
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Deep learning for tomographic reconstruction with limited data
Tomography is a powerful technique to non-destructively determine the interior structure of an object.Usually, a series of projection images (e.g.\ X-ray images) is acquired from a range of different positions.
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of Crane and Wetland Conservation in African Rural Landscapes: Insights from Kenya, Uganda and Zimbabwe
This thesis focuses on the social dimensions of crane and wetland conservation in rural landscapes in Kenya, Uganda and Zimbabwe.
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to make Personalized Predictions for Migraine and Stroke from E-Health Sensor Data
The research of this PhD project can be subdivided into two main disease areas: migraine and stroke. For both we will be investigating how artificial intelligence (AI) and machine learning (ML) techniques can be used to study these afflictions, their (early) detection, and their potential treatment.
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Anxiety and cognitive performance: better insights and new treatments
How does stress influence cognitive performance? What is the role of selective attention to threatening information in this effect? Could we prevent stress-induced decline of cognitive performance with pharmacological interventions? Could we use resting state theta/beta ratio as a biomarker for cognitive…
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Robust rules for prediction and description.
In this work, we attempt to answer the question:
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From scarcity to abundance: big data in archaeology
New digital methods and a data explosion are radically changing archaeological research. Karsten Lambers, Associate Professor of Archaeological Computer Science, tells us all about it.
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Insights into the mechanism of electrocatalytic CO2 reduction and concomitant catalyst degradation pathways
This work describes several studies into the electroreduction of carbon dioxide (CO2RR), both regarding mechanistical aspects and catalyst stability considerations. Mechanistic insights into carbon-carbon bond formation on a silver catalyst are described in Ch 2, were we find an acetaldehyde-like surface…
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SCALES project
How to strike a balance between the sometimes conflicting stakes of individual, public and private data-producers and data-processors?
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Modelling the role of mycorrhizal associations in soil carbon cycling: insights from global analyses of mycorrhizal vegetation
In this PhD study, I aim to deepen our understanding of the influence of major mycorrhizal types, namely arbuscular mycorrhizae (AM) and ectomycorrhizae (EM), on the global soil carbon cycle and their potential distribution changes under future environmental shifts.
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The flux and flow of data: connecting large datasets with machine learning in a drug discovery envirionment
This thesis focuses on data found in the field of computational drug discovery. New insight can be obtained by applying machine learning in various ways and in a variety of domains. Two studies delved into the application of proteochemometrics (PCM), a machine learning technique that can be used to…
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Based Clustering of Objects on Subsets of Attributes in High-Dimensional Data
This monograph focuses on clustering of objects in high-dimensional data, given the restriction that the objects do not cluster on all the attributes, not even on a single subset of attributes, but often on different subsets of attributes in the data.
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Staying healthy with big data
By analysing the metabolism using big data techniques, we can identify health risks at an earlier stage. Thomas Hankemeier, professor of Analytical Biosciences at the Leiden Academic Centre for Drug Research, explains how that works.
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VODAN Africa – FAIR Covid-19 Data across Africa and Asia
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…
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Cell-autonomous and host-dependent CXCR4 signaling in cancer metastasis: insights from a zebrafish xenograft model
Promotor: A.H. Meijer, Co-promotor: B.E. Snaar-Jagalska
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Project SENSYN
Making sensitive data reusable through synthetic data generation
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citizens’ participation in public service delivery: the possibilities of data dashboards
Data science offers exciting new instruments for governments to reach out to citizens, for example by using data-driven information channels, providing real-time simulations, or personalizing services based on citizen data. At the same time, the possibilities and use of data science methods can have…
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Data-Driven Machine Learning and Optimization Pipelines for Real- World Applications
Machine Learning is becoming a more and more substantial technology for industry.
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Ebifananyi. On photographs and telling histories from and about Uganda
In Luganda, the widest spoken minority language in East African country Uganda, the word for photographs is Ebifananyi. However, ebifananyi does not, contrary to the etymology of the word photographs, relate to light writings. Ebifananyi instead means things that look like something else. Ebifananyi…
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Design and development of a comprehensive data management platform for cytomics: cytomicsDB
Promotor: J.N. Kok, Co-promotor: F.J. Verbeek
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the automated detection of archaeological objects in remotely sensed data
Generally the data from remote sensing surveys - the scanning of the earth by satellite or aircraft in order to obtain information about it - is screened manually in archaeology. However, constant monitoring of the earth's surface causes a huge influx of data of high complexity and high quality. To…
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Monitoring drug-related homicides: An assessment of existing data sources and potential for future monitoring
This project’s aim is to critically assess current homicide data sources in order to develop a proposal for long-term EU-level monitoring of DRH.
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Stacked Domain Learning for multi-domain data: a new ensemble method
The aim of this project is to develop accurate but interpretable ensemble learning methods for high-dimensional multi-domain data.
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Modelling the dynamics of the innovation process: a data-driven agent-based approach
Promotor: Prof.dr. B.R. Katzy, Co-promotor: R. Ortt
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Making policy with big data
Governments have increasing amounts of data at their disposal. How can big data be used in policymaking? And are governments ready to deal with all this data? That is what Sarah Giest, Assistant Professor at the Institute of Public Administration, is interested in.