LLM-Based Semantic Metadata Enrichment for Findable Scientific Data Collections

Abstract

Scientific data repositories can be FAIR yet unsearchable: column names such as NEE_CUT_REF, SSA637, or lel carry meaning only for specialists, so a non-expert cannot find, filter, or plot the data at the variable level. We generate natural-language descriptions for opaque columns with a language model at ingestion, from their names, statistics, and sample values, and store them as curator-reviewable catalog metadata for search and a natural-language visual-discovery interface. Our clearest result is on findability: across three collections from a live repository, the descriptions make opaque columns retrievable by natural-language query, raising pooled mean reciprocal rank from 0.46 to 0.79, and a control shows the gain comes from the descriptions’ content—generic or shuffled text does not reproduce it. Downstream, in a within-model NL2Vis ablation, the benefit is conditional: enrichment raises translation accuracy in two of six model–collection cells and leaves the rest flat or slightly negative, in a pattern that tracks a column’s opacity to the model and the model’s accuracy headroom, so the single-collection lesson that “enrichment helps weaker models” does not generalize. A quality evaluation against curator references finds description accuracy governed by how familiar the model is with a naming convention; how opaque the name looks to a person does not predict it, so a human-unreadable but standard schema is described well while errors concentrate on unfamiliar specialized vocabulary, where curator review should focus. We release the questions, descriptions, and evaluation harness as a benchmark.