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Good health and well-being is among key issues in the United Nations 2030 Sustainable Development Goals. The rising prevalence of large-scale infectious diseases and the accelerated aging of the global population are driving the…
To reproduce eScience, several challenges need to be solved: scientific workflows need to be automated; the involved software versions need to be provided in an unambiguous way; input data needs to be easily accessible; High-Performance…
The increasing use of 3D imaging technologies in biological sciences is generating vast repositories of anatomical data, yet significant barriers prevent this data from reaching its full potential in educational and collaborative contexts.…
With the advancement in the technology sector spanning over every field, a huge influx of information is inevitable. Among all the opportunities that the advancements in the technology have brought, one of them is to propose efficient…
Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems, offering powerful tools for innovation. Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy,…
In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools…
Research increasingly relies on computational methods to analyze experimental data and predict molecular properties. Current approaches often require researchers to use a variety of tools for statistical analysis and machine learning,…
Just like the scientific data they generate, simulation workflows for research should be findable, accessible, interoperable, and reusable (FAIR). However, while significant progress has been made towards FAIR data, the majority of science…
Healthcare disparities persist across socioeconomic boundaries, often attributed to unequal access to screening, diagnostics, and therapeutics. However, this perspective highlights that critical biases can emerge much earlier, during data…
This report summarizes insights from the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science, which convened more than 40 experts from national…
Reproducibility of computationally-derived scientific discoveries should be a certainty. As the product of several person-years' worth of effort, results -- whether disseminated through academic journals, conferences or exploited through…
With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, and extract new…
Medical image computing software is essential for identifying imaging biomarkers that can support diagnosis, prognosis, treatment planning, and clinical research. However, the lack of standardized, user-friendly, and reproducible software…
Clinicians are interested in better understanding complex diseases, such as cancer or rare diseases, so they need to produce and exchange data to mutualize sources and join forces. To do so and ensure privacy, a natural way consists in…
The Horizon 2020 project EOSC-Life brings together the 13 Life Science 'ESFRI' research infrastructures to create an open, digital and collaborative space for biological and medical research. Sharing sensitive data is a specific challenge…
The development of a knowledge repository for climate science data is a multidisciplinary effort between the domain experts (climate scientists), data engineers whos skills include design and building a knowledge repository, and machine…
Biomedical research results are being published at a high rate, and with existing search engines, the vast amount of published work is usually easily accessible. However, reproducing published results, either experimental data or…
Cancer registries collect multisource data and provide valuable information that can lead to unique research opportunities. In the Netherlands, a registry and model-based approach (MBA) are used for the selection of patients that are…
Despite the excitement behind biomedical artificial intelligence (AI), access to high-quality, diverse, and large-scale data - the foundation for modern AI systems - is still a bottleneck to unlocking its full potential. To address this…
In modern information retrieval (IR). achieving more than just accuracy is essential to sustaining a healthy ecosystem, especially when addressing fairness and diversity considerations. To meet these needs, various datasets, algorithms, and…