A platform that turns scattered neuroscience knowledge into evidence you can query.
BrainKB structures and organizes scientific knowledge using knowledge graphs (KGs) — delivering evidence-based insights with provenance. Every assertion is traceable, every contribution reviewable, every dataset connected.
Structured Knowledge
Capture neuroscience findings as structured, queryable evidence in a shared knowledge graph.
Data Exploration
Search, visualize, and analyze the graph through accessible, interactive tools.
Community Contribution
Researchers contribute, review, and validate evidence with provenance.
Collaboration Hub
A common workbench for neuroscientists to build on each other's findings.
Browse the neuroscience landscape.
AbstractAtlas maps thousands of neuroscience abstracts — OHBM 2026 and NeuroScape PubMed 1999–2023 corpus — into an interactive 2D/3D UMAP projection. Search semantically, filter by facet, and see where any study sits within the wider literature.
Open AbstractAtlas →
Use Cases
Where the toolkit meets real research. Each use case is developed in the open — with public inspection, interaction, and feedback at every stage.
How use cases work
Three open phases combine into one transparent knowledge-management system.
Data-provision mechanisms — public or member-only — depending on the use case.
Accessible interfaces for exploring and interacting with the knowledge graph.
Community-driven validation mechanisms keep the graph trustworthy.
BrainKB Tools
Open, modular tools that integrate with BrainKB applications or operate independently within your own research pipelines.
View all tools →
A task-agnostic agentic framework for structured information extraction from text and documents — with human-in-the-loop evaluation and benchmarking.
PRISMA-guided literature review — from search strategy to screening, critical appraisal, and synthesis — orchestrated across multiple LLMs.
Turn meeting audio and notes into actionable knowledge graphs — extracting decisions, action items, and key concepts automatically.
Map terms and concepts to standardized ontologies for consistent, interoperable, and machine-readable knowledge representations.
Watch BrainKB's tools work in practice.
From a raw meeting recording to a structured, queryable knowledge graph in minutes — see how MeetGraph captures decisions, action items, and key concepts automatically.
Watch on YouTube →Structured Models
View all models →These models provide the shared structure behind BrainKB's graph, tools, and use cases.
Genome Annotation Schema
A data model designed to represent types and relationships of an organism's annotated genome.
Anatomical Structure Schema
A data model designed to represent types and relationships of anatomical brain structures.
Library Generation Schema
A schema designed to represent types and relationships of samples and digital data assets generated during multimodal genomic data processes.
Evidence Assertion Ontology
A data model designed to represent types and relationships of evidence and assertions.
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BrainKB runs on cutting-edge agentic frameworks. StructSense enables sophisticated structured-information extraction; SynthScholar extends it to PRISMA-guided literature synthesis and critical appraisal across multiple LLMs.
Chhetri, T.R., Chen, Y., Trivedi, P., Jarecka, D., Haobsh, S., Ray, P., Ng, L. and Ghosh, S.S., 2025. STRUCTSENSE: A Task-Agnostic Agentic Framework for Structured Information Extraction with Human-In-The-Loop Evaluation and Benchmarking. arXiv preprint arXiv:2507.03674.
See Research Paper →