B-P.49: Why Think Multimodal? Data Treatment for Integrated Structural Biology
Integrated structural biology increasingly depends on combining data from multiple experimental and computational approaches to answer biological questions that cannot be resolved by any single technique alone. However, multimodal projects are still often organized around individual methods, which creates fragmentation in data handling, metadata capture, quality control, and downstream reuse. This slows scientific progress, makes cross-technique interpretation more difficult, and increases the risk that valuable intermediate data, context, and decisions are lost before publication or deposition.
At the SciLifeLab Integrated Structural Biology (ISB) platform, multimodal data treatment is being developed as a way to shift focus from techniques to scientific questions. By treating structural biology data as connected evidence streams rather than isolated outputs, it becomes possible to use one modality to support, validate, or troubleshoot another, and to follow the sample and data journey from production to integrated interpretation. Such an approach is also essential for making data FAIR, reusable, and increasingly AI-ready, with sufficiently rich metadata, validation, and provenance to support future method development and cross-study analysis.
Here we describe the rationale, challenges, and practical principles for multimodal data treatment at the SciLifeLab ISB platform, with a focus on data integration across techniques, preservation of experimental context, and preparation of high-quality datasets for sharing, reuse, and AI-driven analysis.
Co-authors: Filip Arman, Joao Figueira, Sayyed Jalil Mahdizadeh, Anton Sellerberg, Simon Ekstrom, Hanna Kultima, Johan Rung, Nicholas Pearce
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