license: cc-by-sa-4.0
language:
- en
tags:
- regalfire
- evaluation
- reproducible
configs:
- config_name: default
data_files:
- split: train
path: train.jsonl
- split: validation
path: validation.jsonl
- split: test
path: test.jsonl
- split: holdout
path: holdout.jsonl
Scientific-Code-Troubleshooting
RegalFire — AI Data Foundry · Omar Soliman · ootiris@gmail.com
Problem
Real scientific troubleshooting with literal code and attributed community answers. 50 records in this source-derived release. Do not sum it with its public ancestors as unique examples.
Sources and provenance
SOURCE_LOCK.json pins approved releases and file hashes. Per-record source IDs and hashes link to source rows. Selected normalized native scientific-computing sources accompany the affected products in native_scicomp_source.jsonl; raw API scrape files are not included. Source URLs, author attribution and revision contributors are retained. Unverified Bionic inputs are excluded.
Schema and extraction
See schema.json for required fields/types. Nested source content and labels are independently validated. Question troubleshooting lexeme plus literal pre block. Language/tools require explicit mentions; missing error/method fields are null. No invented root causes, fixes or error messages.
What labels mean
Community answers, acceptance and scores are not scientific truth or executable success labels.
Validation and reproduction
Install Python 3.10+ dependencies: pip install jsonschema huggingface_hub. Run in the repository directory: python validator/validate.py . Pinned public ancestors require network or a populated HF cache; credentials are unnecessary. The validator does not import the builder. Source computational checkers were implemented independently from source generators. See validation_report.json, robustness_report.json and QA_REPORT.md.
Splits and leakage
{"holdout": 5, "test": 6, "train": 36, "validation": 3}. Scientific threads, normalized shared text/code and near questions (5-shingle Jaccard >=0.85) are globally grouped across the two scientific-derived products. Synthetic task families are kept in one split, with repeated source grid groups united. Source master seeds identify an entire generation run, not independent per-task seeds. These are template-held-out splits, not fresh-generation-seed experiments. Uneven splits are intentional. Public holdout labels are visible and are not secret or contamination-free. Exhaustive semantic near-duplicate detection is not claimed.
Intended uses and limitations
Research, regression and scoped private adaptation. No measured model performance, clients, ROI or clinical claims. Scientific code is not executed; community answers are not expert labels. Clinical flags and email-like content are excluded heuristically, not certified absent. Agent errors are controlled harness operations, not real-agent logs; counterfactual recovery is prediction correction, not physical intervention. Validation checks the specified contracts, not universal scientific correctness.
License
cc-by-sa-4.0. Preserve attribution, revision contributors and source notices. Scientific content remains CC BY-SA 4.0; MIT synthetic rows retain MIT notices. See THIRD_PARTY_LICENSES.md. No private exclusivity is promised for public source adaptations.
Custom / Private Dataset Work
RegalFire builds custom AI datasets, evaluation sets and reproducible data pipelines for research and production systems.
Available:
- custom schemas
- private sources
- private holdout sets
- custom validators
- domain-specific hard cases
- continuous generation
- regression datasets
Contact: ootiris@gmail.com Hugging Face: RegalFire
Public license obligations remain applicable. Confidential private work requires client-owned or otherwise permitted sources.
Volume limitation
Approved inputs yield 50 explicit troubleshooting threads, below the requested 500. This is a bounded seed release without invented or repeated filler. Expansion requires further verified licensed raw input.