ReviewFrontiers in digital health2026
Selecting medical research data platforms for translational biomedical research: a five-tier overview and requirement-weighted assessment framework.
Review in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
31 authors.
Funding
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.