Datasets:

TheJackBright
/

verisci-verified-science-math-code

license: apache-2.0
task_categories:
  - text-generation
  - question-answering
language:
  - en
tags:
  - adaption-labs
  - autoscientist
  - science
  - math-code
  - verifier-grounded
  - scientific-reasoning
  - code-generation
  - abstention
pretty_name: VeriSci Verified Science Math Code
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.jsonl
      - split: validation
        path: validation.jsonl
      - split: test
        path: test.jsonl

VeriSci Verified Science Math Code

Verifier-grounded dataset for the Adaption AutoScientist Challenge Part 2, targeting the Science category with secondary Math and Code coverage.

Summary

VeriSci trains models to solve scientific computations, finite-difference PDE updates, numerical ODE steps, unit-checked mechanics, thermodynamics, circuits, chemistry stoichiometry, molarity, unit conversion, vector decomposition, two-point linear modeling, small Python code-generation tasks, and explicit abstention when required variables are missing.

Every row is generated by a deterministic Python program and checked by a verifier. This makes the dataset suitable for supervised fine-tuning and for objective base-vs-adapted evaluation.

Fields

id: stable row id.
split: train, validation, or test.
prompt: instruction for the model.
completion: target response with reasoning and final JSON.
reasoning_trace: intermediate reasoning steps.
final_answer: machine-readable target answer.
verifier: verifier type and parameters.
variables: source variables used to generate the row.
dedupe_signature: canonical hash used for de-duplication and split assignment.
task_family: task family.
difficulty: easy, medium, or hard.
source: generation provenance.
license: row license.

Task Families

unit_checked_mechanics
thermodynamics
electric_circuits
exponential_decay
chemistry_stoichiometry
chemistry_solutions
numerical_ode
numerical_integration
finite_difference_pde
unit_conversion
vector_reasoning
linear_modeling
dimensional_analysis
python_code_generation
scientific_abstention
code_abstention

Reproduction

git clone <repository-url>
cd verisci-autoscientist
PYTHONPATH=src python3 -m verisci.generate \
  --rows 8000 \
  --out data/generated/verisci_8k.jsonl \
  --csv data/generated/verisci_8k.csv \
  --summary data/generated/verisci_8k_summary.json
PYTHONPATH=src python3 -m verisci.evaluate --data data/generated/verisci_8k.jsonl

Release Integrity

The current 8k public release is split-safe:

Metric	Value
Rows	8,000
Unique prompts	8,000
Duplicate prompts	0
Train/validation/test prompt leakage rows	0
Dedupe signatures	8,000
Gold verifier accuracy	100%

Adaptive Data And AutoScientist

This dataset has been run through a low-credit Adaption pilot and is prepared for AutoScientist training.

Current platform evidence:

Metric	Value
Pilot dataset ID	`09749657-7dde-4e20-8988-1d9ee53e9132`
50-row Adaptive Data estimate	1 credit, 11 minutes
Enhanced-completion audit	Failed: 0/42 rows preserved `Final: {...}`
Source-column audit	Passed: 42/42 rows preserved `Final: {...}`
12k source preflight dataset ID	`13b1c92a-a96b-4ce7-810b-4368ad6aa234`
12k source-column audit	Passed: 12,000/12,000 rows preserved `Final: {...}`
AutoScientist recommendation	`google/gemma-3-4b-it`, 1-epoch LoRA
Diagnostic 8k Llama run	`43b5486d-0bfc-4e9f-869e-a9892a679386`; best win rate 49.48%; not final
Clean 8k candidate run	`7ea2c71b-94d7-472d-8288-795ab5e0a2c3` on `mistralai/Mistral-7B-Instruct-v0.2`
Clean 8k candidate dataset	`3103c6ac-7d61-4271-af62-41cb023de85e`
Clean 8k latest metric	Succeeded, 5/5 iterations, best win rate 52.45%, checkpoint packaged

Fill after final AutoScientist run:

Metric	Value
Adaptive Data grade before	Pending
Adaptive Data grade after	Pending
Adaptive Data quality improvement	Pending
AutoScientist best win rate	52.45%; below the 75% publish gate
Domain augmentation rows	8,000 source rows in clean candidate
General augmentation rows	0 in clean candidate

Limitations

VeriSci is synthetic and deliberately narrow. It is designed to test exact scientific and code reasoning patterns, not to replace expert review for high-stakes engineering, laboratory, medical, financial, or safety decisions.

License

Apache-2.0.