Please meet our colleague and postdoctoral researcher Lianne Ippel, who joined IDS last April. She is project manager of the educational (sub) team at IDS. Furthermore, she coordinates the ‘pilots’ workgroup for the Community for Data-Driven Insights (CDDI). Lianne aims to build bridges between different research areas, such as machine learning and social sciences. Studying the fundaments of research methods makes her work applicable in very diverse fields of research. These fields include Sociology, Psychology and Educational research but these methods are also relevant for the health domain and computer sciences.
Lianne's recently accepted paper "Estimating Multilevel models on Data Streams" details a much more efficient fitting approach for statistical models to analyze data coming from repeatedly observed individuals. This paper will be published in Psychometrika at the start of 2019. Code to fit the model in a data stream can be found on Lianne’s github page github.com/L-Ippel/SEMA and the article will be publicly available early next year! Congratulations on behalf of the IDS team!
#UMDataScience #DataScience
Research institutes
Institute of Data Science
Vast amounts of data being generated across all segments of society. If taken advantage of, these data offer an unprecedented opportunity to accelerate scientific discovery, to improve healthcare and wellbeing, and to strengthen our communities. Data Science is an interdisciplinary field concerning the scientific methods, systems and workflows to obtain insights from data.
Data Science has the potential to affect all aspects of human activity. We embrace this development and are preparing a new generation of data scientists, scholars and entrepreneurs who work in a collaborative manner to tackle the world’s most pressing problems.
Fast facts
- interfaculty institute
- fosters a collaborative environment for cross-disciplinary data science research and training
- develops standards to facilitate navigating the knowledge landscape
- led by prof. Michel Dumontier
- hiring for diverse data science positions
Research
Research at IDS focuses on:
- accelerating scientific discovery through powerful platforms for scalable, collaborative and reproducible discovery science
- improving clinical care and wellbeing by developing a learning health system to understand the health of diverse populations and to maximise the wellness and health outcomes for individuals.
- empowering communities so that they can learn what works, what doesn’t, and how to enact change to improve the quality of life for themselves and their neighbours
IDS aims to tackle impactful research problems through multidisciplinary teams involving students, researchers, partners, and stakeholders inside and out of the university. We aim to train the next generation of data scientists to be even more collaborative and interdisciplinary.
News
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UM and Dutch Embassy host Berlin Science Week event
Monday, November 5, 2018Read moreMaastricht University (UM) is the only Dutch university to have organised an event together with the Dutch Embassy during the famous Berlin Science Week.
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Postdoc part of ESOF Panel on AI
Wednesday, July 18, 2018Read moreAmrapali Zaveri, postdoc at IDS, was part of a panel on “Increasing Scientific Productivity through Artificial Intelligence” of the EuroScience Open Forum conference.
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The future of data in academia
Wednesday, January 24, 2018Read moreFrom Apple's personal assistant Siri helping you out to Netflix recommending content for you, artificial intelligence and big-data technologies are increasingly affecting our daily lives. Is society prepared for the data revolution we are experiencing? And what will the data-filled world of the future look like? These questions are being explored on 26 January during Maastricht University’s 42nd Dies Natalis. Keynote speakers Sally Wyatt and Michel Dumontier share their thoughts on data in academia.
Calendar
Social media
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42 min 2 sec ago -
RT @macborgers: Presenting our ambitions and results to become a FAIR University @MaastrichtU with IDS @UM_IDS , UL @WijkIngrid, ICTS, dat…
UMIDS via3 days 1 hour ago -
Today, Michel Dumontier will talk about ‘The FAIR concept’ at the BigData4Imaging course that is held at the Maastricht School of Management from 9 to 12 December. The Big Data 4 Imaging course is a hands-on course about Radiomics, Deep Learning and Distributed Learning. Check out more info here: https://bigdata4imaging.info/
#UMDataScience #DataScience #bigdata #distributedlearning #medicalimaging #radiomics5 days 22 hours ago
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RT @amrapaliz: Submit your research ideas on how to assess and improve #knowledgegraph #dataquality to the @TheOfficialACM Journal of Data…
UMIDS via6 days 47 min ago -
RT @_DigitalSociety: #DigitalSociety Interview Sally Wyatt: “Technical choices are always ethical choices too” https://t.co/oWuY57owDC
UMIDS via6 days 47 min ago -
Please meet our colleague Johan van Soest, postdoctoral researcher and clinical Data Scientist, who joined IDS this December. Johan recently obtained his PhD at Maastricht University (Department of Radiotherapy / Maastro Clinic) on centralized and distributed learning of prognostic or predictive models in radiation oncology, especially in the field of rectal cancer. His research focus is on knowledge presentation, methods for validation of existing models and translating these models into clinical practice in both a central and decentral database. In addition, he has a Master’s degree in Medical Informatics.
Within both IDS and the Maastro Clinic, his focus is on distributed learning and the setting up and organizing of the infrastructure for the Personal Health
Train, aiming to connect distributed health data and create value by increasing the use of existing health data for citizens, healthcare, and scientific research. In his spare time, Johan also enjoys cycling and playing trumpet. His knowledge continues to be a very valuable addition to the IDS team!
#UMDataScience #Datascience #Maastroclinic6 days 20 hours ago
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Prof. Michel Dumontier
Professor Data Science
"Data science offers a solid foundation to examine and understand the world we live in. Importantly, it has never been so easy for young researchers to tackle important issues facing communities using cheap computing power and somebody else's data."
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