Introduction
Data science research at Maastricht University aims to accelerate scientific discovery through the development of powerful Artificial Intelligence (AI) platforms coupled with FAIR data and services to systematically unlock knowledge about the world we live in. Furthermore the improvement of clinical care and well-being through the creation of intelligent systems to bring the science of medicine back into the practice of medicine. Also the empowering of communities to characterize, implement, and monitor data-driven solutions that optimize their investments to maximize their quality of life through data mining and machine learning. You can find out more about us on our Maastricht University department webpage
Github projects
- Large-scale RDF-based Data Quality Assessment Pipeline
- FAIRsharing metrics
- Autodrill RML converter
- DrugCentral Indication Quality Analysis
- XML to RDF converter
- Descriptive statistics for Health Care and the Life Sciences datasets
- RDFUnit - RDF Unit Testing Suite
- CrowdED: Guideline for designing optimal crowdsourcing experiments
Courses
We highlight the various materials in the form of github repositories for the diverse workshops that has been organized by IDS since our inception. It is excepted to be used locally. Browse over to resources for materials meant for online use.
Statistics/machine learning/data science
- data-driven-justice
- Machine Learning for Research
- Docker for Data Science
- Business Analytics for Data Science
- Ontology Workshop
- Crowdsourcing-Workshop