Frontiers in digital health · 2026
Selecting medical research data platforms for translational biomedical research: a five-tier overview and requirement-weighted assessment framework.
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Jacobs Marc, Goudarzi Samad, Stücke Jan, Röhm Robin, Kanninen Timo, Oinonen Tero, Ritter Petra, Schirner Michael, Prasser Fabian, Bhagwagar Arman, Matzenbach Bernd, Schultze Hartmut, Schultze Joachim, Göschl Mathias, Matthies Henrik, Evans Kenneth R, Dharsee Moyez, Stephan Christian, Belz Marvin, Gribbon Philip, Herzog Katja, Chen Chester, Ruau David, Dayan Ittai, Sannyasi Adrish, Kranzlmüller Dieter, Zhou Naweiluo, Warfsmann Jens, Hoffmann Martin, Schmidt Rudi, Hofmann-Apitius Martin
Аннотация
INTRODUCTION: Translational biomedical research is increasingly collaborative and multimodal, making secure, high-quality data capture, curation, and analytics a major challenge. This work aims to provide an overview of existing medical research data platforms to support informed platform selection for translational biomedical research.
METHODS: As part of an ongoing Fraunhofer Request for Proposal (RFP) process, we developed a requirements assessment tool for users across the Fraunhofer ecosystem. In parallel, we compiled a structured overview of medical research data platforms through an open collaboration between academic and industry experts, who supplemented our market screening by identifying additional relevant platforms. Using a standardized questionnaire on key aspects of distributed data collaboration, we collected harmonized platform descriptions and organized them into a side-by-side overview with an accompanying feature weighting matrix.
RESULTS: The study yielded a structured, comparative characterization of medical research data platforms across five functional classes, highlighting common strengths in security, interoperability, data quality, and multimodal data support. We devised (developed) a platform feature-partner weight matrix that enables context-sensitive platform scoring without imposing a predefined global ranking. In this way, users can align platform scoring with their specific translational research requirements.
DISCUSSION: This structured, overview is intended to accelerate decision-making in the medical research community when choosing data platforms. By supporting context-sensitive, feature-weighted selection rather than one-size-fits-all comparisons, it acknowledges diversified research needs and can be updated as technologies and practices evolve.
METHODS: As part of an ongoing Fraunhofer Request for Proposal (RFP) process, we developed a requirements assessment tool for users across the Fraunhofer ecosystem. In parallel, we compiled a structured overview of medical research data platforms through an open collaboration between academic and industry experts, who supplemented our market screening by identifying additional relevant platforms. Using a standardized questionnaire on key aspects of distributed data collaboration, we collected harmonized platform descriptions and organized them into a side-by-side overview with an accompanying feature weighting matrix.
RESULTS: The study yielded a structured, comparative characterization of medical research data platforms across five functional classes, highlighting common strengths in security, interoperability, data quality, and multimodal data support. We devised (developed) a platform feature-partner weight matrix that enables context-sensitive platform scoring without imposing a predefined global ranking. In this way, users can align platform scoring with their specific translational research requirements.
DISCUSSION: This structured, overview is intended to accelerate decision-making in the medical research community when choosing data platforms. By supporting context-sensitive, feature-weighted selection rather than one-size-fits-all comparisons, it acknowledges diversified research needs and can be updated as technologies and practices evolve.