---
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).