geoscope

I built GEOscope, a discovery layer for NCBI GEO’s 288,904 public functional-genomics studies. GEO metadata is largely free text, so relevant datasets are often missed when researchers and submitters use different vocabulary. GEOscope processes the full corpus into canonical study records, normalizes organisms, sex, and assay concepts, generates embeddings, and indexes everything in Elasticsearch. Search combines BM25, semantic retrieval, live NCBI candidates, and Claude reranking. The same search powers a side-by-side website and a three-tool MCP server for Claude and other agents. I found that semantic retrieval can surface highly relevant studies absent from the first 100 candidates displayed by GEO’s literal keyword search. I also found that embeddings alone were insufficient: normalized metadata and hybrid retrieval were essential for precise, trustworthy discovery. This matters because researchers can find reusable public datasets using natural-language scientific questions instead of guessing the exact terminology used by submitters. This helps valuable existing data get discovered and reused.