ADS grant calls
The focus area Applied Data Science has funding to financially support small research projects within the Special Interest Groups. The aim is to foster the application of data science techniques in research areas where they are not yet applied and to accelerate the development of data science techniques by working in an interdisciplinary way.
Examples
- Interfaculty research projects aimed at the application or development of data science techniques.
- Seed money for the preparation of interdisciplinary (interfaculty) subsidy applications, for a pilot study to prepare a subsidy application.
- Consultation costs when a researcher from one faculty helps a researcher from another faculty with the application/development of data science techniques.
| Title project | Main applicant | Faculties involved | SIG |
| Prediction of Vertebral Collapse using Artificial Intelligence in Multiple Myeloma patients | Kenneth Gijlhuis | Medicine, Science | Imaging |
| Bringing problems and solutions together: applying AI methods to understand the healthy living process | Artola Arita, V.A. (Vicente) | Medicine, Science | Clinical Data Applications |
| Optimising trauma treatment: Predicting treatment effects by content and prosody analyses of imagery rescripting sessions | Hagenaars, M.A. (Muriel) | Social and Behavioural Sciences, Humanities | Clinical Data Applications |
| I’M YOUR TYPE: usIng heMocYtometry data for identificatiOn of neUtRophil immunophenoTYPEs | Saskia Haitjema | Medicine | Clinical Data Applications |
| Enhancing Synthetic Data Generation for Distributed Healthcare Research: A discussion on the utility of metadata information | Andaur Navarro, C.L. (Constanza) | Medicine, Social and Behavioural Sciences | Clinical Data Applications |
| Leveraging Large Language Models for Stance Detection and Analysis of Political Polarization in Social Media | Javier Garcia-Bernardo | Social and Behavioural Sciences, Humanities | NLP@UU+UMCU |
| Playing for Cognition: working toward better statistical models predicting cognitive outcome in participation using machine learning | Nijboer, T.C.W. (Tanja) | Social and Behavioural Sciences, Science, Medicine | Machine Learning |
| Efficient and reliable cell tracking in complex 3D microscopy datasets using AI enhanced image restoration | Suijkerbuijk, S.J.E. (Saskia) | Medicine, Science | Imaging |
| Title project | Main applicant | Faculties involved | SIG |
| To err is human, but how to compute the Inter-rater reliability with active learning models? | Dr. Beth Grandfield | Social and Behavioural Sciences, Science | Active Learning |
| Exploration of Hidden Links in ECG-Data: An Artificial Intelligence Approach to Prognostication in LVAD-Patients | Pim van der Harst | Medicine, Social and Behavioural Sciences | Clinical Data Applications |
| Introducing prediction methods for sociological theory assessment | Javier Garcia-Bernardo | Social and Behavioural Sciences, Science | Network Science, Machine Learning Applications |
| Taking the bull by the horns: using token classification to study historical attitudes towards plants and animals | Ayoub Bagheri | Social and Behavioural Sciences, Humanities | Text Mining |
| Assessing Reliability of Annotations in the Context of Model Predictions and Explanations | Social and Behavioural Sciences, Science | Text Mining |
| Title project | Main applicant | Faculties involved | SIG |
| Segmentation of Rhabdomyosarcoma in pediatric diffusion MRI data | Donno, G. de (Giulia) | Medicine (Image Sciences Institute, Psychiatry), Princess Maxima Center for Pediatric Oncology | Imaging |
| Identifying Long COVID: A Machine Learning Approach to Diagnosis | Lopez Rincon, A. (Alejandro) | Science (Pharmacology), Nutricia Research, Social and Behavioural Sciences (Methodology & Statistics), Medicine (Julius Centrum) | Clinical Data Applications |
| Development of a Medical Language Model Grounded to Medical Ontologies and Thesauri | Es, B. van (Bram) | Medicine, Science (Information and Computing Sciences) | Clinical Data Applications |
| LVAD-LVAD: Left Ventricular Assist Devices provide Loads of Valuable Additional Data | Aarts, E. (Emmeke) | Social and Behavioural Sciences (Methodology & Statistics), Medicine (Cardiology, Cardiothoracic Surgery) | Clinical Data Applications |
| Change point detection to anticipate unexpected changes in standardized healthcare data | Andaur Navarro, C.L. (Constanza) | Medicine (Julius Center, Data Science & Biostatistics), Social and Behavioural Sciences (Methodology & Statistics) | Machine Learning Applications |
| Are the teens alright?: A novel computational model for predicting excessive avoidance learning in teenagers | Giachanou, A. (Anastasia) | Social and Behavioural Sciences (Methodology & Statistics, Clinical Psychology), Medicine (Data Science & Biostatistics, Julius Center) | Text Mining |
| Machine learning for the prediction of drug release profiles | Feelders, A.J. (Ad) | Medicine (Regenerative Medicine), Science (Information and Computing Sciences) | Machine Learning Applications |
| Finding Sentiments in Factual Texts: Evaluation of Approaches for Analyzing Sentiments in Historical Newspapers | Huijnen, P. (Pim) | Humanities (History and Art History), University Corporate Offices (Research IT), Social and Behavioural Sciences (Methodology & Statistics) | Text Mining |
| In the aftermath of the Earthquake: Collecting data for a paralinguistic analysis of mass trauma | Akdag, A.A. (Almila) | Science (Information and Computing Science), Social and Behavioural Sciences (Interdisciplinary Social Science) | Machine Learning Applications |
| Title project | Main applicant | Faculties involved | SIG |
|---|---|---|---|
| Machine learning for automated discovery of clinical deterioration in pediatric intensive care patients with congenital heart disease: aberration detection. | Joppe Nijman | Medicine (Pediatric Intensive Care), Science (Information and computing sciences) | Machine Learning Applications |
| Creating the benchmark for automated assessment of pediatric Wilms’ tumors by organizing a grand challenge. | Bas van der Velden | Medicine (Image Sciences Institute), Princess Máxima Center for Pediatric Oncology | Imaging |
| Computer vision-based analysis of visual facial features driving health and beauty perception. | Monique Smeets | Social and Behavioral Sciences (Social Health & Organizational Psychology), Science (Information and computing sciences) | Machine Learning Applications |
| What’s going on? Deep learning based detection of human interactions to distinguish the procedures and processes that matter in psychotherapy. | Marcus Huibers | Social and Behavioral Sciences (Clinical Psychology; Methodology & Statistics), Science (Information and computing sciences) | Machine Learning Applications |
| Machine learning based quantification of intracranial arteries. | Ynte Ruigrok | Medicine (Neurology & Neurosurgery; Image Sciences Institute; Radiology), Science (Information and computing sciences) | Imaging |
| Detecting Categorical Outliers in Relational Datasets. | Hakim Qahtan | Science (Information and computing sciences), Medicine | Machine Learning Applications |
| Title project | Main applicant | Faculties involved | SIG |
| Correction of drift artifacts in neonatal diffusion MRI data | Alexander Leemans | Medicine (Image Sciences Institute; Neonatology), Women and children's hospital Isala | Imaging |
| Social bias detection in text using NLP and their associations with people’s opinions | Anastasia Giachanou | Social and Behavioural Sciences (Methodology and Statistics), Humanities (Media and Culture Studies) | Text mining |
| AI-based prediction of pseudoprogression in patients with metastatic renal cell carcinoma treated with immunotherapy | Bas H. M. van der Velden | Medicine(Image Sciences institute; Medical Oncology; Radiology/Nuclear Medicine) | Imaging |
| Identifying Group of Relevant Antibodies in predicting Kidney Transplantation Risk using Text Mining | Danial M. Senejohnny | Medicine (Center for Translational Immunology), Social and Behavioural Sciences (Methodology and Statistics) | Machine learning applications |
| Title project | Main Applicant | Faculties involved | SIG |
| Identifying medication trajectories among individuals living with multiple chronic conditions using machine learning | dr. Daniala Weir | Science: Pharmacoepidemiology, Pharmaceutical Sciences, Information and Computing Sciences | Machine Learning Applications |
| Text mining as imputation model for clinical prediction research: does it have to be perfect to be useful? | dr. Artuur Leeuwenberg | Medicine (Julius Centre), Social and Behavioural Sciences (Methodology and Statistics) | Machine Learning Applications |
| Optimization of the Data Donation Workflow for Location History Data | dr. Bella Struminskaya | Social and Behavioural Sciences (Methodology and Statistics), Geosciences (Health geography), Science (Human-Computer Interaction) | Sensors |
| Sleep stage Analysis of Newborns Through Imaging (SANTI) | dr. Ronald Poppe | Science (Information and Computing Sciences), Medicine (Neonatology) | Imaging |
| Individual patient expression profiles of disease activity and relation with structural disease progression in rheumatoid arthritis | dr. Paco M.J. Welsing | Medicine (Rheumatology and Clinical Immunology, Central Diagnostic Laboratory), Science (Pharmaceutical Sciences) | Clinical Data Applications |
| Feasibility study of Dutch clinical language support system using state of the art language modeling techniques and open medical texts | dr. ir. Bram van Es | Clinical Data Applications | |
| SAVANT SARS-CoV-2 Variant | Prof. dr. Aletta D. Kraneveld | Science (Pharmacology), Social and Behavioural Sciences (Methodology and Statistics) | Clinical Data Applications |
| Accelerating transition towards data-driven agriculture using real time data from the first LoRa application from “De Tolakker farm” @Utrecht University | dr. ing. Miel Hostens | Veterinary Medicine (Population Health Sciences), ITS | Sensors |
| Title project | Main applicant | Faculties / Departments involved | SIG |
| Prediction of prognosis of patients with heart failure using deep learning on electrocardiograms and text mining | Prof. Dr. Folkert Asselbergs | Medicine (Cardiovascular genetics), Social and Behavioural Sciences (Methodology and Statistics) | Clinical Data Applications |
| An evaluation of ‘real-time’ missing data handling in machine learning and prevailing statistical models | Dr. Thomas Debray | Medicine (Julius Centre, Epidemiology), Social and Behavioural Sciences (Methodology and Statistics) | Clinical Data Applications |
| Improving Health Policy Research through Automated Knowledge Extraction from Regulatory and Reimbursement Reports | Prof. dr. Yannis Velegrakis | Science (Information and Computing Sciences), Medicine, Pharmacoepidemiology, Clinical Pharmacology | Text mining |
| CALL-me-BABY . Linking research and ClinicAL data of heaLthy and diseased BABies for sYnergistic big data research into brain development | Dr. Saskia Haitjema | Medicine (Neonatology, Gynaecology, Neuroscience), Social and Behavioural Sciences (Developmental Psychology) | Clinical Data Applications |
| Precision Nudging: Using Machine Learning to tailor behavior change techniques to the personal characteristics of the user | Prof. dr. Lars Tummers | Law, Economics and Governance (Utrecht University School of Governance), Social and Behavioural Sciences (Experimental Psychology), Research IT (ITS) | Machine Learning Applications |
| Streaming comparisons of real-world outcomes with trial results of anti-cancer drugs | Prof. Ewoudt van de Garde | Science (Clinical Pharmacology), Medicine (Julius Centre, Epidemiology) | Machine Learning Applications |
| Mining deeper meaning from text: Developing an automated cognitive mapping text analysis tool | Dr. Femke van Esch | Law, Economics and Governance (Utrecht University School of Governance), Humanities (Computational Linguistics) | Text mining |
| Data-driven optimisation of multi-contrast MRI acquisitions | Dr. ir. Chantal Tax | Medicine (Image Sciences Institute), Science (Information and Computing Sciences) | Imaging |
| Black-box analysis and transfer learning for increased generalizability of pseudo-CT generating neural networks | Prof. dr. ir. Hugo de Jong | Medicine (Radiology and Nuclear Medicine), Science (Information and Computing Sciences) | Imaging |
| Digital platform for sharing brain vulnerability maps for vascular cognitive impairment | Dr. Hugo J. Kuijf | Medicine (Image Sciences Institute, Neurology and Neurosurgery), Science (Information and Computing Sciences) | Imaging |
| How Biased is the Linked Open Data? Towards Bias Profiling Web Knowledge Graphs | Dr. Mel Chekol | Science (Information and Computing Sciences), Humanities (Utrecht Data School) | Machine Learning Applications |
| Feature Selection and Machine Learning as a tool to understand visual search behavior: An application and extension of the Protosc toolkit | Dr. Sjoerd Stuit | Social and Behavioural Sciences (Experimental Psychology), Science (Information and Computing Science) | Machine Learning Applications |
| Title project | Main Applicant | Faculties/Departments involved | SIG |
| Can you see it in the eyes? Relating eye tracking features to brain damage in children with perinatal asphyxia | Niek van der Aa | Medicine (Neonatology, Wilhelmina Children's Hospital UMCU), Social Sciences (Experimental Psychology) | Clinical Data Applications |
| Move it: Development of an Interdisciplinary Setup for Innovative Human Behavior Research | Ronald Poppe | Science (Information and Computing Sciences), Social Sciences (Clinical Psychology, Methodology and Statistics) | Machine Learning Applicatons |
| Explainable AI for patient‐doctor decision making in metastatic breast cancer | Bas van der Velden | Medicine (Image Sciences Institute, UMCU), Julius Center (Epidemiology), Medical Oncology | Imaging |
| Title project | Main Applicant | Faculties/Departments involved | SIG |
|---|---|---|---|
| ELSIE - EvoLutionary Selection In Epigenetics | Aletta Kraneveld | Science, Pharmacology, Social Sciences, Methodology and Statistics | Clinical Data Applications |
| Call me if you can: Machine learning assisted acoustic monitoring of tropical forest wildlife | Marijke van Kuijk | Science, Environmental Biology, Information and Computing Sciences | Machine Learning Applicatons |
| SILICON: The search for a Systemic Immune profile responsible for the Local Inflammation in the keratoCONus eye | Robert Wisse | Medicine, Ophthalmology, Social Sciences, Methodology and Statistics | Clinical Data Applications |
| DEEP-ENIGMA: a DEEP nEural Network for Image seGMentation to classify and quantify Atherosclerotic disease based on high-resolution scanned histological slides | Sander van der Laan | Medicine, Circulatory Health, Social Sciences, Methodology and Statistics | Clinical Data Applications |
| Real-time holographic data visualization with augmented reality display for improving patient workflow during MR-HIFU therapy | Cyril Ferrer | Medicine, Image Sciences Institute, TU Eindhoven, Electrical Engineering | Imaging |
| Machine learning models for predicting gait patterns in human-computer interaction | Stella Donker | Social Sciences, Experimental Psychology, Science, Human-Centred Computing | Machine Learning Applicatons |
| Title project | Main applicant | Faculties involved | SIG |
|---|---|---|---|
| Machine learning based quantification of intracranial aneurysms | Hugo J. Kuijf | Medicine, Image Sciences Institute | Imaging |
| Using active learning to reduce the costs of population-based neuroimaging studies | Hugo Schnack | Medicine (Psychiatry, Brain Division), Science (Information & Computing Sciences) | Machine Learning Applications |
| CONVOCALS: a CONVOlutional neural network to predict symptoms and major secondary CArdiovascuLar events based on high-resolution scanned histological Slides | Sander W. van der Laan | Medicine (Circulatory Health), Social Sciences (Methodology & Statistics) | Clinical Data Applications |
| A text mining approach to interest development | Sanne Akkerman | Social Sciences (Educational Sciences), Science (Information & Computing Sciences) | Text Mining |
| Applying machine learning techniques to the study of polarization | Frank van Tubergen | Social Sciences (Sociology), Humanities (Media & Culture Studies) | Machine Learning Applications |
| Machine learning choice models: with application to bike sharing in Beijing, China | Anae Sobhani | Geosciences (Human Geography & Planning), Social Sciences (Methodology & Statistics) | Machine Learning Fundamentals |
| Identification of cells in a digital microscope environment: toward automated recognition of pollen grains | Koen Vincken | Medicine, Image Sciences Institute, Geosciences (Palaeoecology) | Imaging |
| Title project | Main applicant | Faculties involved | SIG |
|---|---|---|---|
| Deep Neural Networks for ICD-10 Classification of Diagnosis Registration in Cardiovascular Notes to allow data mining in electronic health records. | Folkert Asselbergs, Cardiology, Circulatory Health | Medicine, Social Sciences | Clinical Data Applications |
| Eye movement signatures of dysfunctions in visual attention during virtual reality simulations. | Tanja Nijboer, Brain Center Rudolf Magnus | Medicine, Social Sciences | Clinical Data Applications |
| Training a neural network for the automatic detection of peritoneal metastases from ovarian (and other) cancers. | Ronald Zweemer, Oncologic Gynaecology | Medicine, Image Sciences Institute/QIA | Imaging |
| How formal information infrastructures solicit discourse – a text mining approach. | Gertjan Plets, Department of History and Art History | Humanities, Social Sciences | Text Mining |
| What drives automatic reactions to emotion facial expressions? | Sjoerd Stuit, Experimental Psychology | Social Sciences, Humanities | Machine Learning Applications |
| Machine learning techniques to predict worsening of diastolic dysfunction in patients from an outpatient clinic. | Hester den Ruijter, Experimental Cardiology | Medicine, Social Sciences | Clinical Data Applications |
| Data-mining Hate-Speech. Analysing speech, images and interactions on Gab.ai, the ’Twitter of the alt right’. | Mirko Schäfer, Utrecht Data School | Humanities, Social Sciences | Text Mining |
More information
Please send an email to Applied Data Science.