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README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+pretty_name: >-
+  Topic model data from Finding Long-COVID: temporal topic modeling of
+  electronic health records from the N3C and RECOVER programs
+---
+
+These data represent medical concept probabilities for 300 topics generated via Latent Direchlet Allocation applied 
+to 387M electronic health record conditions for 7.9M patients as described in 
+[Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs
+](https://www.nature.com/articles/s41746-024-01286-3#Sec7). Terms are encoded as OMOP CDM standard `concept_id` values;
+see details at [OHDSI](https://ohdsi.org/) and [OHDSI ATHENA](https://athena.ohdsi.org).
+
+Column summary:
+ - `topic_name`: topics are named T-1 to T-300, in order of weighted usage; also included in topic name are `U` (aggregate usage of topic in data), `H` (a measure of topic usage uniformity across data contributing sites), and `C` (a z-score normalized coherence value indicating relative topic quality)
+ - `concept_name`: human-readable name of the OMOP CDM concept
+ - `concept_id`: OMOP CDM standard concept ID
+ - `term_weight`: the probability of the `concept_id` being generated by the topic `topic_name`
+ - `relevance`: A measure of topic-relative weight - positive values indicate concepts more specific to the topic than background, negative values less specific