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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- question-answering
+- text-retrieval
+language:
+- en
+tags:
+- scientific-literature
+- citation-prediction
+- biochemistry
+- chemistry
+- biotechnology
+size_categories:
+- n<1K
+---
+
+# BioChemCite
+
+**120 citation-prediction questions in biochemistry, chemistry and
+biotechnology.** Given a sentence from a paper with its citation blanked out,
+name the paper being cited.
+
+Built in the shape of [CiteME](https://huggingface.co/datasets/bethgelab/CiteME),
+for the same reason and in a different literature.
+
+## Why this exists
+
+CiteME measures *finding* rather than *reading*, which is the harder and less
+measured half of a research agent. But its excerpts are machine-learning papers,
+the densest region of any frontier model's training. A model can recognise the
+citation and search only to confirm it, which measures recall dressed as
+retrieval.
+
+These questions sit in wet-lab and process literature, where that advantage is
+much weaker and where the answer has to be found rather than remembered.
+
+## Where the answers come from
+
+Nowhere expensive. JATS articles link every in-text citation to its reference,
+and the reference carries a DOI:
+
+```xml
+<xref rid="B30" ref-type="bibr">30</xref>   →   <ref id="B30"> … 10.1016/j.…
+```
+
+So the ground truth ships with the question. No annotation, no LLM judge, no
+human labelling — and therefore nothing to disagree with.
+
+## Fields
+
+| | |
+|---|---|
+| `id` | `dq0000` … |
+| `excerpt` | the sentence, with the citation replaced by `[CITATION]` |
+| `target_doi` | DOI of the cited paper — the answer |
+| `target_title` | title of the cited paper |
+| `source_pmcid` | the article the excerpt came from |
+| `source_license` | always `cc by` |
+
+## How each question was filtered
+
+Roughly one sentence in twenty survives:
+
+- **Exactly one citation.** *"as shown previously [4,7,12]"* has three right
+  answers and no way to say which was meant.
+- **No author named.** *"Li et al. [CITATION]"* hands over the answer. CiteME
+  obfuscates these; here they are dropped.
+- **Makes a claim.** Methods sections cite reagent suppliers in passing. An
+  early build produced *"polymerase was obtained from Vazyme [CITATION]"*
+  pointing at a Gibson assembly paper — unfindable, and worthless as a question.
+  Methods, Materials and Protocol sections are excluded outright.
+- **120–600 characters.** Long enough to carry a specific claim, short enough to
+  stay a citation task rather than a reading one.
+- **Resolves to a DOI**, so scoring is exact rather than a title-matching
+  argument.
+
+Capped at four questions per source article, so no single paper dominates.
+
+## Licensing
+
+**Every source article is CC BY**, verified individually against Europe PMC
+rather than assumed. This matters: about half the open-access literature in
+these fields is CC BY-NC-ND, and lifting a sentence out of an article and
+blanking its citation makes a derivative, which ND forbids. Filtering happens at
+source selection, not afterwards.
+
+`source_pmcid` is on every row so each excerpt traces back to the article it
+came from.
+
+## Known limitations
+
+- **Recency.** Source articles are recent Europe PMC deposits, so the cited
+  papers skew toward work published before them. Nothing here tests finding very
+  new work.
+- **Europe PMC only**, which means life-sciences-indexed venues. Chemistry
+  published outside PMC is under-represented, and conference proceedings are
+  absent entirely.
+- **Difficulty is not calibrated.** Some excerpts describe the cited work
+  precisely; others gesture at it. There is no per-question difficulty label and
+  no human baseline — unlike CiteME, which has one at 69.7%.
+- **One citation per question** by construction, so nothing here tests
+  disentangling a multi-citation claim.
+
+## Building it yourself
+
+The generator is at
+[`benchmarks/build_domainqa.py`](https://github.com/jmbarrancoidener/vela) in
+the Vela repository. Re-running it produces a different draw from current
+Europe PMC content rather than this exact set.
+
+Built for [Vela](https://github.com/jmbarrancoidener/vela), a research agent at
+[Idener](https://idener.ai).