BRAINWORKS is a tool to discover comprehensive scientific theories through knowledge integration across multiple fields, scales, units of analysis, and species. A theory is hereby defined as a testable set of mathematical or natural language statements that answers a why/what/how question, involving multiple variables, facts, or hypotheses. A scientific theory is here represented by a semantic triple codifying the theoretical statement in the form of subject–predicate–object expressions (e.g., “Hippocampal Long Term Potentiation”-“Correlate Of”-“Memory”).
The Need: The scientific knowledge landscape is vast, complex and rapidly expanding. In 2020, an additional 2 million new peer-reviewed papers were added to the scientific literature, which is now estimated to contain over 60 million works. At this volume, it would take a single individual almost 20 years (without breaks) to perform a 5-minute review of each paper written in 2020. Even narrow subdomains of scientific investigation now produce a level of output that is intractable for a single scholar to master: over 100,000 papers about the coronavirus pandemic were published in 2020, alone.
The Solution: As knowledge generation continues to outpace the ability of individual scientists to consume and integrate it, there is a critical need for technology tools that can organize, integrate, and represent the nuanced knowledge contained within the growing body of the scientific literature. BRAINWORKS is a web platform that addresses these needs by structuring the scientific literature as a dynamic and interactive knowledge graph. While development of the platform is ongoing, an alpha version of the tool is freely available online here.
The Innovation: BRAINWORKS is innovative because of its ability to represent scientific knowledge as well as the context governing its creation (funding, grants, authors, etc.). Furthermore, it provides a novel way to visualize the temporal evolution of scientific knowledge.
The technology stack for BRAINWORKS consists of three layers: information, algorithms, visualization. Each layer was designed to function independently to maximize extensions of the technology stack for other use cases. To learn more about the technology stack, please visit the GitHub repository.
Click the buttons below to view examples of possible search queries: