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+# Overview
+
+## Data inputs
+
+Mostly JSON, but each one different in form and quality.
+
+Core inputs:
+
+* refs schema, from metadata or grobid (1-4B)
+* fatcat release entities (100-200M)
+* open library solr export (10-50M)
+
+Other inputs:
+
+* researchgate sitemap, titles (10-30M)
+* oai-pmh harvest metadata (50-200M)
+* sim (serials in microfilm, "microfilm") metadata
+
+Inputs related to evaluation:
+
+* BASE md dump (200-300M)
+* Microsoft Academic, MAG (100-300M)
+
+Casually:
+
+* a single title, e.g. ILL related (1)
+* lists of titles (1-1M)
+
+## Targets
+
+### BiblioRef
+
+Most important high level target; basic schema for current setup; elasticsearch
+indexable, small JSON docs, allowing basic aggregations and lookups.
+
+This is not just a conversion, but may involve clustering, verification, etc.
+
+## Approach
+
+We may call it "local map-reduce", and we try to do it all in a single MR setup, e.g.
+
+* extract relevant fields and sort (map)
+* apply computation on groups (reduce)
+
+As we want performance and sometimes custom code (e.g. for finding information
+in unstructured data), we try to group code into a Go library with a suite of
+command line tools. Easy to build and deploy.
+
+If the scaffoling is good, we can plug in mappers and reducers as we go, and
+expose them in the tools.
+
+