Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science
Bibliographic record
Abstract
Modern data-driven science often consists of iterative cycles of data discovery, acquisition, preparation, analysis, model building and validation leading to knowledge discovery as well as dissemination at scale. The unique challenges of building and simulating the whole rodent brain in the Swiss EPFL Blue Brain Project (BBP) required a solution to managing large-scale highly heterogeneous data, and tracking their provenance to ensure quality, reproducibility and attribution throughout these iterative cycles. Here, we describe Blue Brain Nexus (BBN), an ecosystem of open source, domain agnostic, scalable, extensible data and knowledge graph management systems built by BBP to address these challenges. BBN builds on open standards and interoperable semantic web technologies to enable the creation and management of secure RDF-based knowledge graphs validated by W3C SHACL. BBN supports a spectrum of (meta)data modeling and representation formats including JSON and JSON-LD as well as more formally specified SHACL-based schemas enabling domain model-driven runtime API. With its streaming event-based architecture, BBN supports asynchronous building and maintenance of multiple extensible indices to ensure high performance search capabilities and enable analytics. We present four use cases and applications of BBN to large-scale data integration and dissemination challenges in computational modeling, neuroscience, psychiatry and open linked data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".