README.md

metadata

license: other
pretty_name: SciModelingBench Design-Bench Data
tags:
  - benchmark
  - scientific-machine-learning
  - black-box-optimization
  - agent
configs:
  - config_name: tfbind8
    data_files:
      - split: six6_ref_r1
        path: data/tfbind8/six6_ref_r1.parquet
  - config_name: cell_dag_nas
    data_files:
      - split: architectures
        path: data/cell_dag_nas/architectures.parquet
  - config_name: hopper_controller
    data_files:
      - split: policies
        path: data/hopper_controller/policies.parquet
  - config_name: superconductor
    data_files:
      - split: composition_groups
        path: data/superconductor/composition_groups.parquet
  - config_name: tfbind10_pho4
    data_files:
      - split: observations
        path: data/tfbind10_pho4/observations.parquet
  - config_name: utr_mrl_egfp_unmodified
    data_files:
      - split: measurements
        path: data/utr_mrl_egfp_unmodified/measurements.parquet
  - config_name: gfp
    data_files:
      - split: protein_genotypes
        path: data/gfp/protein_genotypes.parquet
  - config_name: drugmatrix_clinical_pathology
    data_files:
      - split: observations
        path: data/drugmatrix_clinical_pathology/observations.parquet

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.json maps 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.