I focus on contextualizing and harmonizing scientific research data. In multi-modal research, data often exists in silos, with each transient team of researchers concentrating on specific aspects of a problem. This frequently leads to fragmented storage and documentation, making it difficult to share or reuse valuable datasets. This fragmentation does not indicate a lack of skill among scientists—it underscores the need for more coordinated and intuitive data infrastructure and standards.
Complex and chaotic data systems, like those often found in academic research, present unique opportunities. These environments are ideal for developing new strategies for data management and knowledge transfer that benefit both niche organizations and the broader scientific community through a more rhizomic, bottom-up approach.
One promising solution is knowledge graphs—data structures that excel at managing complex data by enriching its context and structure through semantics and networks. These graphs hold significant potential in biomedical research, particularly in areas where data management and sharing face their greatest challenges. While industry can advance by enforcing top-down data standards within an organization, academic research presents a different challenge: addressing fragmented, bottom-up data ecosystems driven by community knowledge. Successfully managing these systems can help scale solutions to meet the complex needs of global data ecosystems that integrate diverse, localized data sources.