BZPEERReading, trust and collaboration for researchResearcher profile
Bridges dataset-centric ML with reproducibility tooling.
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We combine publication metadata, author neighborhoods and dataset signals to improve retrieval quality for computational biology teams working across papers, code and experiments.
We trace dataset reuse across scholarly graphs to improve auditability, discovery and replication planning.
We align code repositories, datasets and papers with provenance-aware linking that survives version churn and renamed assets.
We study open-weight assistants that combine paper summaries, dataset cards and experiment context for biology teams.
We study how follow edges, review quality, graph proximity and freshness can be blended into an explainable feed optimized for high-signal research discovery.