--- license: cc-by-4.0 language: - en pretty_name: EDB-Bio size_categories: - 10K 0.5, matching direction | | `depmap` | DepMap 24Q4 + PRISM 24Q2 | gene *G* is a dependency in cell line *C* | Chronos gene effect ≤ −0.5 and dependency probability ≥ 0.5 | | `gwas` | NHGRI-EBI GWAS Catalog | SNP *S* (risk allele *A*) influences trait *T* in a stated direction | p < 5×10⁻⁸ with matching effect direction | ## Configs Nine configs in three families. The domain configs hold the episodes; the two testset families are **frozen evaluation instances** cut from them, so that scores are comparable across runs and papers. | family | config | train | validation | test | what it is | |---|---|---|---|---|---| | domain | `gwas` | 29,744 | 256 | 2,000 | the episodes, each with its **complete** 16-stub `context_pool` | | domain | `depmap` | 29,741 | 256 | 2,000 | " | | domain | `tahoe` | 29,741 | 256 | 2,000 | " | | primary | `testset-primary-{gwas,depmap,tahoe}` | — | — | 2,000 | the same 2,000 test episodes, each cut to **one** sampled `k` (2–16, seed 0) | | sensitivity | `testset-sensitivity-{gwas,depmap,tahoe}` | — | — | 4,352 | **256** episodes, each repeated **17 times**, at `k` = 0,1,2,…,16 | Why the testsets exist: how much evidence a model sees drives its score, so an evaluation that re-samples `k` is not reproducible. The testsets freeze that choice. `testsets/provenance.json` records the builder, seed, revision and `k` ranges. **The pools are physically truncated — no special loader is needed.** In both testset families `len(context_pool) == k` for every row (verified: 2000/2000 and 4352/4352). To run an evidence sweep, load the sensitivity config and group by `episode_id`; the 17 rows are the 17 values of `k`. ```python from datasets import load_dataset ds = load_dataset("EdisonScientific/EDB-Bio", "testset-sensitivity-gwas", revision="v1.0.0") # 4,352 rows = 256 episodes x k in 0..16; one row per (episode, k) ``` ## Splits | Config | train | validation | test | Evidence pool per episode | |---|---:|---:|---:|---| | `gwas` | 29,744 | 256 | 2,000 | 16 stubs | | `depmap` | 29,741 | 256 | 2,000 | 15–16 stubs | | `tahoe` | 29,741 | 256 | 2,000 | up to 32 stubs | Labels are balanced (50% positive). Each episode's `context_pool` is pre-shuffled; rendering its first *k* stubs gives nested contexts. The paper trains with *k* drawn uniformly from 2–16. ## Evaluation sets The paper evaluates on two fixed sets per domain, drawn from the test split, in `testsets/`. One row is one model call: `context_pool` is already cut to that row's context length `k`. | Config | Rows per domain | Episodes | Use | |---|---:|---:|---| | `testset-primary-` | 2,000 | 2,000 | Headline metrics. Each episode at one `k`, balanced over 2–16. | | `testset-sensitivity-` | 4,352 | 256 | Evidence-sensitivity analyses. Each episode at every `k` = 0…16. | Added columns: `domain`, `testset`, `k` (requested length) and `n_stubs` (actual stub count). The sets are built by `scripts/analysis/multidomain/build_static_testset.py` at default arguments; a rebuild from this revision is byte-identical. `testsets/provenance.json` records the arguments and row counts. ## Schema Shared by all three domains: | column | type | description | |---|---|---| | `episode_id` | string | stable identifier; the join key across configs | | `label` | int | the answer key: 1 if the held-out measurement supports the claim, else 0 | | `context_pool` | list | the evidence stubs revealed to the model, oldest first | | `target_gene` | string | gene the episode is about | | `target_gene_meta` | struct | annotations for that gene | | `target_direction` | string | direction of the claimed effect | | `negative_type` | string | null for positives; for negatives, why the claim fails (see below) | | `sufficiency_tier` | string | `rich` / `moderate` / `poor` — how much *supporting* evidence the pool was built to contain (see below) | Testset configs add `domain`, `testset`, `k`, `n_stubs` (`k` == `n_stubs` == `len(context_pool)`). Domain-specific: | domain | columns | |---|---| | `gwas` | `target_snp`, `target_snp_meta`, `target_trait`, `target_trait_meta`, `target_allele`, `target_efo_parent`, `ood_group`, `ground_truth` | | `depmap` | `target_cell`, `target_cell_id`, `target_cell_meta`, `lineage`, `ood_group`, `ground_truth` | | `tahoe` | `target_drug`, `target_drug_meta`, `target_cell`, `target_cell_meta`, `lineage`, `lfc_threshold`, `ground_truth_de` | `ground_truth` / `ground_truth_de` hold the held-out measurement that `label` is derived from. They are **not** shown to a model being evaluated; they are published so the label can be audited. **Columns provided for downstream analysis, not used by the paper's models.** `negative_type`, `sufficiency_tier` and `target_efo_parent` are episode strata: the reward environments and the evaluation code do not read them (only the test suite does). ### `sufficiency_tier` A **designed** variable, not a measured one (principle P4, Appendix A of the paper). Each episode is assigned a stratum by quota *before* its pool is built; the pool is then filled with exactly that many supporting relevant stubs and capped there, the rest of the budget going to non-supporting relevant stubs. A `poor` pool therefore contains **zero** supporting stubs by construction. Episodes are split in roughly equal thirds. What counts as "supporting" is domain-specific: | domain | what the tier counts | |---|---| | `tahoe` | conclusive (significant) related stubs: `rich` ≥ 4, `moderate` 1–3, `poor` none. Rich pools average 5.7 conclusive stubs against 0.8 for poor; 88% of all stubs are inconclusive | | `depmap` | evidence *supporting* a dependency — related cell lines that are themselves dependencies, over-expression, amplification, damaging mutation, drug sensitivity | | `gwas` | genome-wide-significant related stubs, by capping. Because the catalog holds only significant hits, non-significant *related* evidence is scarce, so `poor` pools fill largely with unrelated controls | **Not to be confused with the paper's "relevance tier".** That is a different axis — the expert hierarchy of how *directly* a stub bears on the target — and it is the one the belief-revision results are reported against. ### `negative_type` Null for every positive episode. For negatives it records which test the held-out measurement failed, and the categories are domain-specific: | domain | values | meaning | |---|---|---| | `gwas` | `sub_threshold` | held-out association did not reach genome-wide significance | | | `direction_inconsistent` | significant, but the effect allele or direction disagrees with the context | | `depmap`, `tahoe` | `inconclusive` | the measurement does not settle the claim | | | `refuted` | the measurement contradicts it | ## Use ```python from datasets import load_dataset ds = load_dataset("EdisonScientific/EDB-Bio", "tahoe", revision="v1.0.0") test = load_dataset("EdisonScientific/EDB-Bio", "testset-primary-tahoe", revision="v1.0.0") ``` The reward environments in the code repository render episodes into prompts exactly as in the paper (`envs/{gwas,depmap,tahoe}_meta`). ## Construction Each domain is built by one seeded script in the code repository (`scripts/data_pipeline/build_{gwas,depmap,tahoe_meta}_dataset.py`). Every build writes `manifest.json` recording its parameters and summary statistics. The published files are the canonical version: the GWAS Catalog is updated weekly, so rebuilding `gwas` from a later download gives a different set of associations. Appendix A of the paper describes the construction, the evidence-relevance hierarchy of each domain, and the design principles. **Known limitation.** Splits are assigned i.i.d., so one episode's held-out measurement can appear as an evidence stub in another episode on the other side of the split. This affects 0.6% (Tahoe), 10.1% (DepMap) and 53.2% (GWAS) of test episodes. The paper measures the effect on reported metrics, which is small (at most 0.005 macro Brier for the trained models). ## Sources and licences EDB-Bio is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). It contains values copied or computed from the public resources below. Our licence covers our compilation and derived annotations; upstream content remains subject to its original terms, listed here. All sources were modified (filtered, joined, re-scored, summarised). | Source | Version / files | Upstream licence | |---|---|---| | NHGRI-EBI GWAS Catalog | All associations (with ontology annotations) and EFO trait mappings, downloaded July 2026 | [EMBL-EBI Terms of Use](https://www.ebi.ac.uk/about/terms-of-use/) | | DepMap Public 24Q4 (Broad Institute) | CRISPRGeneEffect, CRISPRGeneDependency, Model, Gene, OmicsCNGene, OmicsExpressionProteinCodingGenesTPMLogp1, OmicsSomaticMutations*, Achilles common-essential / nonessential controls, PortalCompounds | [CC BY 4.0](https://doi.org/10.25452/figshare.plus.27993248.v1) | | PRISM Repurposing Public 24Q2 (Broad Institute) | Extended Primary Data Matrix, Extended Primary Compound List | [CC BY 4.0](https://doi.org/10.6084/m9.figshare.25917643.v1) | | Tahoe-100M (Tahoe Therapeutics) | Pseudobulk differential expression, drug and cell-line metadata | [CC0 1.0](https://huggingface.co/datasets/tahoebio/Tahoe-100M) | | GTEx v8 | Gene median TPM (top-3 tissues per gene, `gwas` config) | [GTEx Portal Data License](https://gtexportal.org/home/license) | | Reactome | Ensembl2Reactome.txt (pathway names, `gwas` config) | [CC0](https://reactome.org/license) | **Please also cite the sources:** - Cerezo M, et al. The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity. *Nucleic Acids Res.* 2025;53(D1):D998–D1005. doi:10.1093/nar/gkae1070 - DepMap, Broad (2024). DepMap 24Q4 Public. Figshare+. doi:10.25452/figshare.plus.27993248.v1 - DepMap, Broad; Kocak M (2024). Repurposing Public 24Q2. figshare. doi:10.6084/m9.figshare.25917643.v1 - Corsello SM, et al. Discovering the anticancer potential of non-oncology drugs by systematic viability profiling. *Nat Cancer* 2020. doi:10.1038/s43018-019-0018-6 - Zhang J, et al. Tahoe-100M: A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling. bioRxiv 2025. doi:10.1101/2025.02.20.639398 - GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. *Science* 2020;369:1318–1330. doi:10.1126/science.aaz1776 - Ragueneau E, et al. The Reactome Knowledgebase 2026. *Nucleic Acids Res.* 2025. doi:10.1093/nar/gkaf1223 ### Attribution required by the sources None of the upstream providers endorses this dataset. **GTEx.** The data used to build this dataset were obtained from the GTEx Portal in July 2026 (GTEx Analysis Release V8, dbGaP accession phs000424.v8.p2). The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The GTEx-derived fields come from release V8 and do not reflect the most current data available from the GTEx Portal. **GWAS Catalog.** Associations are the curated top hits reported by published studies, not full summary statistics. Each row carries its GCST study accession; please also cite the original study where relevant. ## Citation ```bibtex @article{ibis0, title = {Do Language Models Know What Compelling Scientific Evidence Looks Like?}, author = {Igoe, Conor and Grand, Gabriel and Koch, Zane and Bou, Albert and Moss, Eli and Hinks, Michaela M. and Rodriques, Samuel G. and White, Andrew and Narayanan, Siddharth}, journal = {arXiv preprint arXiv:ARXIV_ID}, year = {2026} } ```