Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+task_categories:
+- text-classification
+- tabular-classification
+- zero-shot-classification
+- text-generation
+size_categories:
+- 10K<n<100K
+language:
+- en
+tags:
+- chemistry
+- molecular-properties
+- hazard-prediction
+- GHS
+- safety
+- cheminformatics
+- llm-evaluation
+- multi-label-classification
+---
+
+# Dataset Card for SafeChem
+
+## Dataset Summary
+
+SafeChem is a regulatory-grounded benchmark dataset of 32,614
+chemical substances for multi-label GHS (Globally Harmonized System)
+hazard prediction and LLM safety reliability evaluation. Unlike prior
+molecular benchmarks constructed by querying pharmaceutical databases,
+SafeChem is seeded from a curated hazardous materials registry,
+ensuring coverage of real-world industrial and safety-critical chemicals
+including solvents, reactive gases, agrochemicals, and heavy metal
+compounds underrepresented in drug-focused benchmarks.
+
+Each substance is annotated with molecular structure representations
+(canonical SMILES, InChI, InChI key), eight physicochemical descriptors,
+free-text physical descriptions, and 30 binary GHS hazard classification
+labels spanning toxicological, physical, and environmental hazard
+categories derived from European regulatory sources (CLP Regulation and
+ECHA C&L Inventory as aggregated by PubChem).
+
+The dataset supports two benchmark tasks:
+- **Task 1 — Multi-label hazard prediction**: Scaffold-split evaluation
+  of GHS hazard classification from molecular structure across 17 retained
+  hazard classes with sufficient test-set support.
+- **Task 2 — LLM safety hallucination benchmark**: Evaluation of omission
+  hallucination rate, commission hallucination, and silent abstention
+  behavior across LLMs on a curated 500-substance high-hazard subset.
+
+## Dataset Details
+
+### Dataset Description
+
+- **Curated by:** Anonymous (camera-ready version will include full
+  author and institution information)
+- **License:** CC BY 4.0
+- **Language:** English
+- **Size:** 32,614 unique substances (deduplicated by PubChem CID)
+
+### Dataset Sources
+
+- **Primary source:** Hazardous Materials Management Information System
+  (HMMIS) regulatory registry (anonymized for double-blind review)
+- **Molecular enrichment:** PubChem Compound and GHS Classification
+  databases (https://pubchem.ncbi.nlm.nih.gov)
+- **GHS annotations:** ECHA CLP Regulation submissions and C&L Inventory,
+  Japan NITE, and other national regulatory bodies as aggregated by PubChem
+- **Paper:** Anonymous submission to NeurIPS 2026 Datasets and Benchmarks
+  Track (link to be added in camera-ready version)
+
+## Dataset Structure
+
+### Files
+
+| File | Description | Size |
+|---|---|---|
+| `full_dataset.csv` | Complete 32,614-substance master file with all 57 columns | 32,614 rows |
+| `split_train.csv` | Training partition (70%, scaffold split) | ~22,829 rows |
+| `split_val.csv` | Validation partition (15%, scaffold split) | ~4,892 rows |
+| `split_test.csv` | Test partition (15%, scaffold split) | ~4,893 rows |
+
+### Column Groups
+
+**Molecular identifiers**
+- `cas_number`, `cid`, `inchi`, `inchi_key`, `canonical_smiles`,
+  `isomeric_smiles`, `iupac_name`, `molecular_formula`
+
+**Molecular descriptors** (pre-computed via RDKit and PubChem)
+- `molecular_weight`, `xlogp`, `tpsa`, `complexity`,
+  `h_bond_donor_count`, `h_bond_acceptor_count`,
+  `rotatable_bond_count`, `heavy_atom_count`
+
+**Physical properties**
+- `boiling_point`, `flash_point`, `physical_description`
+
+**GHS hazard labels** (30 binary columns, 1 = hazard applies, 0 = not classified)
+- Health: `acute_toxicity_oral`, `acute_toxicity_dermal`,
+  `acute_toxicity_inhalation`, `carcinogenicity`,
+  `germ_cell_mutagenicity`, `reproductive_toxicity`,
+  `skin_corrosion_irritation`, `skin_sensitization`,
+  `eye_damage_irritation`, `respiratory_sensitization`,
+  `aspiration_hazard`, `stot_single_exposure`,
+  `stot_repeated_exposure`
+- Physical: `flammable_liquids`, `flammable_gases`, `flammable_solids`,
+  `flammable_aerosols`, `explosives`, `oxidizing_liquids`,
+  `oxidizing_solids`, `oxidizing_gases`, `organic_peroxides`,
+  `self_reactive_substances`, `pyrophoric_substances`,
+  `self_heating_substances`, `water_reactive_substances`,
+  `gases_under_pressure`, `metal_corrosion`
+- Environmental: `acute_aquatic_toxicity`, `chronic_aquatic_toxicity`
+
+### Dataset Splits
+
+Splits were generated using **Bemis-Murcko scaffold splitting** to
+prevent structural data leakage between train and test, combined with
+**iterative stratification** to preserve per-label positive rates across
+partitions. This is the standard evaluation protocol for molecular ML
+benchmarks. A 70/15/15 train/validation/test ratio was applied.
+
+**Task 1 benchmark uses 17 of the 30 labels** — those with at least 10
+positive instances in the test partition after scaffold splitting. The
+remaining 13 labels are retained in the full dataset and used in Task 2.
+
+## Dataset Creation
+
+### Curation Rationale
+
+Existing molecular ML benchmarks (MoleculeNet, Tox21) are populated
+primarily from pharmaceutical and bioassay databases, biasing their
+chemical space toward drug-like molecules. Real-world chemical safety
+workflows involve industrial solvents, reactive gases, agrochemicals,
+and heavy metal compounds that are systematically underrepresented in
+these benchmarks. SafeChem was created to fill this gap by
+grounding the substance population in a regulatory hazardous materials
+registry rather than database availability.
+
+### Data Collection and Processing
+
+1. **Seed corpus**: Substance identifiers (CAS numbers) were extracted
+   from a regulatory hazardous materials management information system
+   (HMMIS).
+2. **PubChem enrichment**: CAS numbers were resolved to PubChem CIDs
+   via the PubChem identifier exchange service. Records were retained
+   only if they had a valid canonical SMILES and at least one GHS
+   hazard classification entry.
+3. **Descriptor retrieval**: Molecular descriptors were retrieved from
+   PubChem's computed properties endpoint and cross-validated against
+   RDKit recomputation; discrepancies exceeding 5% used the RDKit value.
+4. **Deduplication**: Records were deduplicated by PubChem CID. Label
+   conflicts across duplicate records (20 CIDs, 23 label-level conflicts)
+   were resolved by union — a hazard class was assigned positive if any
+   source record carried a positive classification.
+5. **Filtering**: Substances with no positive GHS labels were excluded.
+   The final corpus contains 32,614 unique substances.
+
+### Source Data Producers
+
+GHS hazard annotations are regulatory submissions from government
+bodies and industry to ECHA, NITE, and other national authorities,
+as aggregated by PubChem. Molecular structure data is sourced from
+PubChem's Compound database.
+
+### Annotations
+
+GHS hazard labels are binary indicators derived from regulatory
+classification submissions rather than manual annotation by the dataset
+authors. Zero values indicate the absence of a positive GHS
+classification in PubChem's aggregated records; this predominantly
+reflects a regulatory determination that the hazard does not apply,
+though absence of data cannot be fully excluded for all substances.
+
+### Personal and Sensitive Information
+
+The dataset contains only chemical substance data (molecular structures,
+physicochemical properties, regulatory classifications). No personal or
+sensitive information about individuals is included.
+
+## Bias, Risks, and Limitations
+
+- **Jurisdictional scope**: GHS annotations are aggregated by PubChem
+  from multiple regulatory bodies. The majority derive from ECHA
+  submissions (CLP Regulation and C&L Inventory), with a minority from
+  Japan's NITE and other national authorities. The precise jurisdictional
+  distribution cannot be fully characterised as PubChem does not expose
+  per-record source provenance. Classifications may not align with US
+  OSHA HazCom 2012 or other national GHS implementations for all
+  substances.
+- **Label noise**: A subset of annotations derives from industry
+  self-notifications in the ECHA C&L Inventory, which are self-reported
+  and may exhibit inter-submitter inconsistency not fully resolved by
+  union merging.
+- **Scaffold clustering**: Four labels exhibit train/test prevalence
+  deviations due to scaffold clustering (`stot_repeated_exposure`,
+  `flammable_solids`, `metal_corrosion`, `stot_single_exposure`);
+  per-label metrics for these should be interpreted with caution.
+- **Single compounds only**: All substances are single compounds;
+  mixture toxicology is not captured.
+- **Temporal snapshot**: Labels reflect the state of regulatory
+  knowledge at the time of PubChem data collection and may not reflect
+  subsequent regulatory updates.
+
+
+
+## Glossary
+
+- **GHS**: Globally Harmonized System of Classification and Labelling
+  of Chemicals — the international standard for chemical hazard
+  communication.
+- **CLP Regulation**: EU Regulation (EC) No 1272/2008 on classification,
+  labelling and packaging of substances and mixtures, which implements
+  GHS in the European Union.
+- **ECHA C&L Inventory**: European Chemicals Agency Classification and
+  Labelling Inventory — a database of GHS classifications notified by
+  industry.
+- **Scaffold split**: A dataset partitioning strategy that separates
+  molecules by their Bemis-Murcko core ring system, preventing
+  structurally similar molecules from appearing in both train and test.
+- **OHR (Omission Hallucination Rate)**: The fraction of true positive
+  hazard labels that an LLM fails to predict — the safety-critical
+  false-negative rate.
+- **EPR (Empty Prediction Rate)**: The fraction of LLM responses that
+  return no hazard labels, indistinguishable from a confirmed negative
+  classification in automated pipelines.
+- **SMILES**: Simplified Molecular Input Line Entry System — a string
+  notation for representing molecular structures.
+- **AUPRC**: Area Under the Precision-Recall Curve — the primary
+  evaluation metric for Task 1, preferred over AUROC under class
+  imbalance.