Awarded proposals Open eScience Call 2025 (OEC25)
We had planned on eight proposals to be granted for OEC 25, but we managed eleven grants instead. We’re very happy to announce the grants for the projects below (no particular order) and can’t wait to start working on these exiting new projects.
1. Online Reconstructions for Accelerated mri Exams (ORACLE)
Lead Applicant Dr. Alessandro Sbrizzi, UMC Utrecht
The ORACLE project aims to revolutionize MRI by enabling fast, quantitative, and standardized imaging through advanced computational techniques. Building on the success of MR-STAT, our team will develop new algorithms and open-source software to dramatically accelerate MRI data reconstruction, making real-time analysis possible during patient scans. By combining machine learning, high-performance GPU computing, and innovative mathematical methods, we will reduce scan times and improve diagnostic quality for a wide range of clinical applications. The project brings together experts in scientific computing, medical imaging, and software engineering, and will deliver sustainable, open-access tools for researchers, clinicians, and industry partners. ORACLE’s outcomes will support more efficient, patient-tailored MRI protocols, foster collaboration across institutions, and set new standards for reproducible and accessible medical imaging research.
2. Accelerating sea ice floe modelling for forecasting extreme loss events (AMP-ICE)
Lead applicant: Dr. Mukund Gupta, Delft University of Technology
Sea ice is a critically important component of the climate because it mitigates against global warming. In the last decades, sea ice extent has declined drastically over both poles, with consequences on global temperature and weather patterns. Current climate models cannot reliably predict these changes, in part because they cannot represent the individual pieces (floes) that make up the ice pack. As part of a multi-university initiative, we developed a unique model (SubZero) that resolves individual floes and their complex interactions with the ocean. This model revealed important new physics that control ice breakage and melt, but was constrained to small domains due to computational limitations of the model. This project will improve SubZero’s parallelization and coupling infrastructure to enable basin-wide climate simulations. We will use these developments to better understand and forecast the response of sea ice to extreme storm events and anthropogenic climate change.
3. OSIPI’s Open-source PYthon library for perfusion imaging (OSIPY)
Lead applicant: Dr. Ir. Petra van Houdt, Netherlands Cancer Institute
Blood flow and delivery of oxygen and nutrients to tissues, collectively referred to as tissue perfusion, are abnormal in many diseases. Therefore, non-invasive perfusion magnetic resonance imaging (MRI) measurements are a critical imaging biomarker for both diagnosis and treatment monitoring. Despite decades of innovation in image acquisition and implementation by all major MRI vendors, the process of converting images to a single number for clinical decision making is currently hindered by a poor reproducibility and accessibility (only for specialist sites) posing a major barrier to multicenter studies and widespread clinical use. This project – a partnership between the Open Science for Perfusion Imaging (OSIPI; perfusion MRI experts) and eScience (software engineers) – will develop a community-led software package, enabling reproducible, standardized, and straightforward analysis of perfusion MRI data. The tool will greatly reduce the barriers to perfusion MRI via easy data integration, click-and-play analysis, and automated batch processing options.
4. Nanoscale Descriptors of Materials & Electrons (NANO-DOME)
Lead applicant: Dr. Ir. Alexandros Vasileiadis, Delft University of Technology
NANO-DOME will deliver a new open-source software toolkit to accelerate materials discovery and design. Understanding how materials behave at the nanoscale is vital for advancing technologies such as batteries, catalysts, and electronic devices. Yet researchers often struggle to access and connect nanoscale thermodynamic and kinetic information with experimental data and larger-scale modelling, which limits progress toward real-world applications.
NANO-DOME addresses this challenge with three complementary tools: Phasefinder, which predicts phase stability and voltage profiles; Crystallizer, which produces crystallographic data from nanoscale modeling directly comparable to experiments; and Pathfinder, which explores charge-carrier mobility to quantify both ionic and electronic transport. Together, these tools provide accurate nanoscale descriptors that can feed into mesoscale and macroscale models, bridging scales in materials science.
By creating software that “speaks the language” of both experimentalists and computational scientists, NANO-DOME will foster cross-disciplinary collaboration, strengthen reproducibility, and accelerate innovation in materials research worldwide
5. Modelling Microtubules & Mechanics in Plants (3M-Plant)
Lead applicant: Dr. Eva Deinum Wageningen University & Research
Plant cells exhibit diverse shapes and structures due to their surrounding cell walls, which support essential functions such as protection, mechanical strength, and water transport. These properties benefit humans directly (e.g., material properties of wood and fibers used in clothing) and indirectly through their importance for plant (crop) health and resilience. The required cell wall structures arise with mutual feedback between cortical microtubules and the cell wall’s mechanical properties. This project aims to integrate and extend two established software tools—CorticalSim, for simulating plant cortical microtubules, and MorphoMechanX, for modeling cell wall mechanics and physically based growth. The combined software will provide insights into the fundamental processes of plant growth. The specific application for this project is the difference between straight and twisted plant growth. The software will be made easily accessible to both expert and novice users, including experimentalists, thus providing a valuable resource for the global plant community.
6. Scalable UNified Beam-tracing for Earth–Atmosphere Models (SUNBEAM)
Lead applicant: Dr. B. van Werkhoven, Leiden University
Understanding how sunlight and heat move through the atmosphere is essential for accurate climate predictions and the effective use of renewable energy. However, today’s weather and climate models rely on simplified one-dimensional radiation calculations, because it is too computationally expensive to model the complex three-dimensional (3D) interactions between sunlight, clouds, aerosols, and terrain. SUNBEAM tackles the long-standing challenge of realistic 3D radiative transfer by harnessing the power of GPU computing and artificial intelligence. By developing a cutting-edge Monte Carlo ray tracing model, to simulate the complex interactions between sunlight, clouds, and the Earth’s surface. This breakthrough, enabled by multiple advanced computing and artificial intelligence techniques, will allow high-resolution models to capture how radiation truly behaves in our atmosphere, leading to better forecasts, improved climate projections, and smarter renewable energy planning. By overcoming current computational limits, SUNBEAM paves the way for the next generation of atmospheric modeling.
7. Grand Unified Trajectory Sampling (GUTS)
Lead applicant: Prof. Dr. Peter Bolhuis, University of Amsterdam
Molecular processes such as chemical reactions and biological transitions often occur on time scales far too long to be captured with standard computer simulations. This limits our ability to address key questions, from energy conversion to drug design. Path sampling algorithms make it possible to reach these longer time scales. Until now, such algorithms have been developed at various research institutions, but a coordinated effort with a shared code base is needed to accelerate progress. With our successful application to the eScience Center–Lorentz competition, we took the first steps by establishing both a code base and a consortium: the Grand Unified Trajectory Sampling (GUTS) project. This Open eScience Call allows us to continue this collaborative effort. We will launch powerful path sampling methods that exploit high-performance computing, and, together with software engineers of the eScience Center, create robust, user-friendly tools that enable new insights in chemistry, biology, and materials science.
8. Faces of the Past: Facial Recognition for the Identification of Historical Portraits (Faces of the Past)
Lead applicant: Dr. Lisandra Costiner, Utrecht University
Faces of the Past applies state-of-the-art computer-vision algorithms to one of art history’s oldest questions: who are the people gazing back from unidentified historical portraits? Using the RKD – Netherlands Institute for Art History’s collection of 240,000 digitized artistic portraits, the vast majority which remain anonymous, the project will refine facial-recognition algorithms to identify recurring subjects across painting, print, and photography. Its results will enrich the RKD’s records, support art-historical discovery, and provide open-source tools for cultural heritage research worldwide. More broadly it will enable specialists and the public to discover hidden connections across centuries of Dutch and Flemish art, restoring lost identities, and giving names and stories back to the faces of the past.
9. TauREx4: atmospheric retrievals for modern exoplanet data (TauREx4)
Lead applicant: Dr. Quentin Changeat, University of Groningen
In the next decade, our understanding of exoplanet atmosphere will be revolutionized by the observations of 1000s of exoplanets thanks to a new generation of ultra-powerful telescopes: NASA-JWST, ESO-ETL, and ESA-Ariel. These novel observations are extremely complex and rich in information, pushing our current interpretation tools to their limits. The data reveals previously unseen physical processes that are difficult to jointly model, and require optimization over large free parameter spaces. Atmospheric retrievals, the most utilized inversion technique, are currently not equipped to handle the new scale of the datasets.
To meet these challenges, we propose to re-design one of the most utilized retrieval codes, TauREx. This project will incorporate recent community advancements, focusing on modernizing the computational and statistical framework to ensure performance and sustainability. Critically, technological advancements from the machine learning community will be deployed, establishing new standards for inversion problems in the field of exoplanet astronomy.
10. From rags to riches: A pipeline for processing semi-structured handwritten texts (Rags2Riches)
Lead applicant: Dr. Auke Rijpma, Utrecht University
Wealth inequality is a major socio-economic challenge. Yet long-term empirical evidence, particularly for the Netherlands, remains limited. This project unlocks a unique historical source: Dutch inheritance tax records known as the Memories van Successie. Hundreds of thousands of these semi-structured handwritten documents contain detailed accounts of deceased individuals’ assets, offering rich material for analyzing wealth patterns.
Manually processing these records is infeasible. Existing Handwritten Text Recognition (HTR) tools struggle with their structure. The Rags2Riches project leverages recent advances in Document AI to fine-tune models on a curated sample of inheritance records containing detailed transcriptions and classified assets. The resulting pipeline will automatically read, interpret, and structure these scans into research-ready data. 2
The project will generate the most extensive historical dataset on wealth holdings to date and deliver a reusable framework for extracting structured data from complex handwritten sources; an increasingly vital process given the rapid growth of digitized historical documents.
11. A GPU-Accelerated Integrated Framework for Resilient Water–Climate–Food System (SWIFT)
Lead applicant: Dr. Ir. Inge de Graaf, Wageningen University
Access to sufficient freshwater is crucial for humans and ecosystems, yet two to three billion people already suffer from shortages—a number likely to rise with population growth and climate change. Understanding how water demand increases, availability declines, and water use affects societies and ecosystems is vital for developing adaptation strategies. However, simulating future global-change scenarios remains computationally demanding. Large-scale water resource models are increasingly complex, making it difficult to run multiple scenarios or test model sensitivities due to high computational costs. This limits our capacity to assess uncertainty, adaptation options, and long-term sustainability under global change.
This project aims to overcome these limitations by developing an open-source, GPU-accelerated, high-resolution modelling toolkit that dynamically links groundwater, surface water, and crop-growth processes at the global scale. By optimizing computational bottlenecks, we will enable extensive scenario and sensitivity analyses to improve global water, food, and ecosystem assessments and support climate-resilient water management worldwide.
Who are we?
The eScience Center is a research organization dedicated to applying research software to answer research questions in any scientific domain through project collaborations. It has the largest concentration of dedicated, high-level research software expertise in the Netherlands. The eScience Center also has a Fellowship Programme and makes all of its software and training materials openly available online. For more information about what we offer, visit esciencecenter.nl.