Datasets:
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license: other
task_categories:
- text-classification
- text-retrieval
language:
- en
pretty_name: S2ORC Safety
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: main/*.parquet
---
# S2ORC Safety
This dataset is a filtered and enriched subset of an S2ORC computer science paper corpus, focused on AI safety and adjacent safety-relevant research.
It contains `16,806` papers selected through:
1. local embedding generation
2. clustering
3. GPT-5.4 mini cluster-level screening
4. GPT-5.4 mini paper-level labeling
5. a rescue relabel pass on suspicious exclusions
6. structured metadata extraction over the accepted paper set
7. filtering out `304` rows that were missing both `parsed_title` and `abstract`
## Main Files
- `main/*.parquet`
- sharded full enriched source rows
- extracted metadata
- normalized GitHub repo links
- Hugging Face code mirror links
- normalized model / dataset / metric / scalar fields
- `metadata/*.parquet`
- sharded metadata extraction only
- `paper_metadata_summary_normalized.json`
- corpus-level summary statistics over the normalized metadata fields
- `code_links/*.parquet`
- sharded paper-to-code join table with normalized GitHub URLs and HF mirror paths
## Contents
The main parquet includes:
- original enriched paper fields from the source corpus
- title, abstract, full text, sections, references, authors, venue metadata, URLs
- extracted source-side fields like `summary`, `methods`, `results`, `models`, `datasets`, `metrics`, `limitations`, `training_details`
- metadata extraction fields
- reproducibility:
- `repro_steps_json`
- `setup_requirements_json`
- `training_or_eval_recipe_json`
- `artifact_availability_json`
- `code_urls_json`
- `dataset_urls_json`
- `model_urls_json`
- safety taxonomy:
- `safety_area_json`
- `attack_or_defense_json`
- `threat_model_json`
- `target_system_json`
- `harm_type_json`
- experimental details:
- `target_models_json`
- `datasets_benchmarks_json`
- `baselines_compared_json`
- `evaluation_metrics_json`
- `main_results_json`
- `claimed_contributions_json`
- practicality:
- `compute_requirements_json`
- `runtime_cost`
- `human_eval_required`
- `closed_model_dependency`
- `deployment_readiness`
- `replication_difficulty`
- `extraction_confidence`
- normalized fields
- `setup_requirements_norm_json`
- `target_models_norm_json`
- `datasets_benchmarks_norm_json`
- `baselines_compared_norm_json`
- `evaluation_metrics_norm_json`
- `runtime_cost_norm`
- `human_eval_required_norm`
- `closed_model_dependency_norm`
- `deployment_readiness_norm`
- `replication_difficulty_norm`
- code link fields
- `github_repo_urls_json`
- `hf_code_paths_json`
- `hf_code_web_urls_json`
- `github_repo_count`
- `hf_code_repo_count`
## Missing Values
- missing list-like fields are stored as empty JSON arrays
- missing scalar categorical fields are stored as `"None specified"`
## Notes
- This is a broad-tent AI safety dataset rather than a narrow alignment-only dataset.
- The labeling and extraction steps were LLM-assisted and should be treated as high-utility annotations, not ground truth.
- Process-only columns used to build the release were removed from the published parquet.
- The companion code mirror is published separately as `AlgorithmicResearchGroup/s2orc-safety-code`.
- Normalization is conservative. It collapses obvious duplicates like `CIFAR10` / `CIFAR-10`, `ResNet50` / `ResNet-50`, and `accuracy` / `Accuracy`, but does not try to solve full ontology matching.
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