At Radboudumc, we envision open research software training and data interoperability as vital elements of research, that would empower researchers for impactful and reproducible work. Currently, we face gaps in these areas, with a lack of capacity for open software training and limited coverage of research data interoperability initiatives. To bridge these gaps, we propose expanding the capacity of the Digital Competence Center of the Radboudumc with Open Software Trainer and Data Interoperability Community Manager. The open software trainer will develop a knowledgebase and courses. The community manager will raise awareness, connect researchers, and facilitate knowledge sharing on data interoperability.

Radboudumc has recently implemented a scientific career path that provides transparent career prospects for research leaders, emphasizing academic quality, collaboration and leadership. However, gaps exist in recognizing Open and Team Science (OS/TS) contributions, with unclear expectations and assessment methods. In dialogue with experts, this project will concretize and implement OS/TS evaluation criteria. In addition, recommendations will be made on how to recognize the OS/TS contributions of researchers in the new staff scientist and academic profiles. Finally, training in OS/TS will be inventoried and, if necessary, developed. This will also benefit PhD candidates, postdocs and support staff..

Malaria is a devastating disease caused by Plasmodium parasites and still leads to 600,000 deaths each year. Current treatments are failing and new interventions are urgently needed. Interventions that kill malaria parasites in the mosquito hold particular promise but good drug targets are currently unknown. The researcher will develop a powerful tool to silence gene expression in Plasmodium parasites which will help in rapidly identifying new drug targets important for transmission of the malaria parasite to the mosquito host. This will aid in the development of new treatments to block and eliminate malaria.

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In the Netherlands, three million CT and MRI scans are made annually. These scans can reveal cancer at an early stage when treatment is most effective. LesionLens is an innovative AI startup that identifies potential abnormalities in these scans and estimates the malignancy risk in each suspicious region using advanced 3D deep learning techniques. Our technology is proven effective for detecting lung cancer in CT scans and prostate cancer in MRI scans. We are actively partnering with various radiology departments to broaden our application range, ensuring radiologists have consistent support and making early and precise cancer detection more accessible.