architecture list | test_accuracies list | mean_test_accuracy float64 0.1 0.94 | canonical_hash stringlengths 32 32 | aliases listlengths 1 60 | train_accuracies list | validation_accuracies list | training_times list |
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End of preview. Expand in Data Studio
SciModelingBench Design-Bench Data
Canonical, provenance-tracked observations for scientific modeling and design Tasks.
GitHub · Python Package · Documentation · Organization
This repository stores the scientific observation layer used by the SciModelingBench Design-Bench suite. The Python package supplies validators, Agent-visible Protocols, trusted Objectives, submission contracts, and Task metrics. Data and evaluation logic are versioned separately so experiments can pin both.
Quick Start
Load one canonical table with Hugging Face Datasets:
from datasets import load_dataset
observations = load_dataset(
"sci-modeling-bench/design-bench",
name="drugmatrix_clinical_pathology",
split="observations",
)
Or construct an end-to-end benchmark Task from the current package release:
from sci_modeling_bench.suites.design_bench import TFBind8BlackBoxOptimizationTask
task = TFBind8BlackBoxOptimizationTask.from_hub()
agent_input = task.build_input()
For reproducible work, pass an immutable Hub commit through the suite's
revision= argument rather than relying on the current default branch.
Available Configs
| Config | Scientific object | Canonical rows | Evaluation setting |
|---|---|---|---|
tfbind8 |
Complete SIX6 DNA 8-mer binding landscape | 65,536 | Exact lookup; free-form black-box optimization |
tfbind10_pho4 |
Pho4 BET-seq raw count observations | 4,160,533 | Replicate-count posterior; black-box optimization |
utr_mrl_egfp_unmodified |
Synthetic 50-nt 5' UTRs | 318,468 | Measured MRL; compositional pool ranking |
gfp |
Sarkisyan GFP protein genotypes | 51,715 | Measured median brightness; pool ranking |
superconductor |
Normalized elemental compositions | 15,164 | Measured group-median critical temperature; pool ranking |
drugmatrix_clinical_pathology |
Individual-animal rat toxicology observations | 10,605 | Matched-control measured endpoints; pool ranking |
cell_dag_nas |
Canonical NASBench-101 cell DAGs | 423,624 | Official repeated NAS records; black-box optimization |
hopper_controller |
Structured PPO policy checkpoints | 3,200 | 500 frozen Hopper-v5 rollouts per policy; pool ranking |
Canonical rows are not always the final candidate-pool size. Protocols derive Agent-visible observations and label-hidden candidates from the pinned table without persisting candidate ranks or evaluator-only labels.
Trust Model
SciModelingBench does not use one evaluator type for every scientific domain:
- Exact: complete tabulated or analytic mappings, such as TFBind8.
- Measured: retained experimental observations or repeated simulator outcomes, such as GFP, Superconductor, DrugMatrix, and Hopper Controller.
- Posterior-derived: deterministic aggregation grounded in raw replicate counts, such as TFBind10 Pho4.
- Legacy learned surrogate: documented when relevant, but not silently treated as experimental truth when more reliable source measurements exist.
An Objective can be exact with respect to a frozen aggregation rule while the underlying scientific measurement remains noisy. Each config's documentation states that distinction explicitly.
Repository Layout
README.md
scimodelingbench.json
data/<config>/<split>.parquet
manifests/<config>.json
provenance/<config>/*.json
scimodelingbench.jsonmaps config names to strict semantic manifests.manifests/defines inputs, targets, context, units, constraints, splits, sources, citations, versions, and license identity.provenance/records source hashes, transformations, release statistics, artifact hashes, and setting-specific audits.data/contains only canonical benchmark tables, not Python code or model checkpoints used by the package.
Documentation And Provenance
| Config | Task documentation | Machine-readable provenance |
|---|---|---|
tfbind8 |
TFBind8 | six6_ref_r1.json |
tfbind10_pho4 |
TFBind10 Pho4 | observations.json |
utr_mrl_egfp_unmodified |
Not yet published | measurements.json |
gfp |
Not yet published | protein_genotypes.json |
superconductor |
Superconductor | composition_groups.json |
drugmatrix_clinical_pathology |
Not yet published | observations.json |
cell_dag_nas |
CellDAG-NAS | architectures.json |
hopper_controller |
Hopper Controller | build.json |
Licensing
The shared repository contains artifacts with different upstream terms, so the
root card uses license: other. Every config manifest records its own license
identity and source references.
| Config | Manifest license |
|---|---|
cell_dag_nas |
Apache-2.0 |
gfp |
CC BY 4.0 |
hopper_controller |
MIT for source policies; generated rollout provenance is recorded separately |
superconductor |
CC BY 4.0 |
tfbind10_pho4 |
CC BY 4.0 |
tfbind8 |
Source-specific terms; see manifest and provenance |
utr_mrl_egfp_unmodified |
Unknown in the upstream redistribution |
drugmatrix_clinical_pathology |
Unknown; the CEBS page does not state a simple artifact redistribution license |
Do not infer a Dataset config's license from the MIT license of the Python package.
Evaluation Boundary
Protocols hide evaluator labels through the package API, but these are public scientific artifacts and may be discoverable outside that API. Controlled Agent evaluations require an external harness to isolate the full Dataset, provenance, caches, source checkout, and network according to the intended setting. Query budgets and iterative feedback policy also belong to that external harness rather than this data repository.
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