--- 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.