Update README.md

README.md

@@ -1,20 +1,167 @@
 ---
 license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - tabular-regression
 language:
-- en
+  - en
 tags:
-- oligonucleotide
-- toxicity
-- hepatotoxicity
-- synthetic-data
-- siRNA
-- antisense-oligonucleotide
-- GalNAc
-- RNAi
-- ASO
-- drug-safety
-- tabular
-task_categories:
-- tabular-classification
-- tabular-regression
----
+  - oligonucleotide
+  - hepatotoxicity
+  - antisense
+  - siRNA
+  - GalNAc
+  - in-silico
+  - synthetic
+  - toxicology
+  - liver
+size_categories:
+  - 10K<n<100K
+---
+
+# OligoTox Phase 2 Dataset
+
+A computationally generated, AI-ready, literature-informed dataset for
+modeling oligonucleotide-associated hepatotoxicity. Released by DBbun
+LLC as part of a submission to the NIH/NCATS OligoTox Open Data Challenge
+Phase 2.
+
+## Summary
+
+The dataset connects oligonucleotide sequence, chemical modification
+pattern, delivery platform, dose/exposure context, in vitro or
+translational liver-relevant assay context, controls, and toxicity
+readouts in a structured set of tables. The final dataset contains:
+
+- **1,120 oligo records** (1,000 generated non-control oligos + 120 controls)
+- **5,600 assay instances** across multiple time points and replicates
+- **16,800 replicate-level toxicity readouts**
+- **128 tables** including 8 core modeling tables (oligo metadata, modifications, position-level modifications, biophysical, dose, assays, readouts, controls) plus per-source
+  evidence modules and supporting metadata files
+
+The dataset is intended for use in developing, training, benchmarking,
+and stress-testing in silico predictive models of oligonucleotide
+toxicity. It is not presented as experimentally measured wet-lab data;
+all values are computationally generated, with row-level provenance
+metadata distinguishing literature-grounded generated values from
+inferred and reported values.
+
+## How to use
+
+```python
+import pandas as pd
+
+# Core modeling tables
+oligos = pd.read_csv("oligos.csv")
+mods = pd.read_csv("chemical_modifications.csv")
+biophys = pd.read_csv("biophysical_properties.csv")
+dose = pd.read_csv("dose_exposure.csv")
+assays = pd.read_csv("assays.csv")
+readouts = pd.read_csv("toxicity_readouts.csv")
+controls = pd.read_csv("controls.csv")
+
+# Join into a model-ready table
+model_ready = (readouts
+    .merge(assays, on="assay_id")
+    .merge(oligos, on="oligo_id")
+    .merge(mods, on="oligo_id")
+    .merge(biophys, on="oligo_id")
+    .merge(dose, on="oligo_id"))
+```
+
+A baseline RandomForest classifier on standard predictors achieves
+overall accuracy of approximately 0.84 on the calibrated dataset, with
+per-class F1 scores of 0.95 (low-risk), 0.74 (moderate), and 0.49
+(high-risk).
+
+## Dataset structure
+
+### Core modeling tables
+
+| Table | Description |
+|-------|-------------|
+| `oligos.csv` | Oligonucleotide identifiers, target genes, modality, 5'-to-3' sequence, sequence length, control assignment, source role |
+| `chemical_modifications.csv` | Aggregate chemistry per oligo: sugar chemistry, backbone class, phosphorothioate fraction, modification pattern, GalNAc conjugation, purity, characterization-method metadata |
+| `oligo_modification_positions.csv` | **Position-level** chemical modifications: per-position base, sugar modification, backbone linkage, region (e.g., gapmer wing/gap, siRNA seed region), terminal conjugate, and provenance |
+| `biophysical_properties.csv` | Predicted Tm, ΔG, GC content, off-target hybridization burden, sequence-derived risk fields |
+| `dose_exposure.csv` | Dose/concentration, exposure duration, treatment frequency, exposure normalization |
+| `assays.csv` | Model system, cell model, species/human-proxy flag, organoid/MPS status, replicate count, assay type |
+| `toxicity_readouts.csv` | Replicate-level readouts: viability, ALT/AST proxy fold change, apoptosis, stress response, transcriptomic perturbation, immune activation, inflammatory context, overall risk score, hepatotoxicity label |
+| `controls.csv` | Positive, negative, vehicle, and platform-specific control oligos with rationale, derived from public literature |
+
+### Supporting documentation tables
+
+| File | Description |
+|------|-------------|
+| `schema.json` | Programmatic schema describing every table, column types, value ranges, and categorical values |
+| `data_dictionary.csv` | Curated variable-level definitions for the core modeling tables |
+| `data_dictionary_full.csv` | Auto-generated comprehensive column-level documentation across all dataset tables |
+| `dataset_manifest.json` | Full inventory of dataset files with sizes |
+| `reproducibility_manifest.json` | Reproducibility configuration: pipeline parameters, seeds, calibration step description |
+| `validation_summary.csv` | Automated validation checks: row counts, identifier mapping, range checks, schema consistency |
+| `assumptions.csv` | Explicit list of biological and modeling assumptions |
+| `provenance.csv` | Provenance category definitions |
+| `sources.csv` | Literature source metadata: paper titles, source IDs, relevance, license |
+| `source_triage_log.csv` | Documentation of source-level inclusion, deferral, exclusion decisions |
+| `information_gap_map.csv` | Identification of fields where future experimental data would have highest value |
+| `model_readiness_report.csv` | Modeling-readiness summary across required predictor and outcome fields |
+| `challenge_alignment_scorecard.csv` | Mapping of dataset components to challenge judging factors |
+| `toxicity_readouts_calibration_audit.json` | Full specification of the stochastic readout calibration step (random seed, per-readout sigmas, score weights, label fractions) |
+| `figures/` | Five reference figures: label distribution before/after calibration, classifier performance, risk score distribution, predictor distributions, top source contributors |
+
+### Per-source evidence modules
+
+The dataset includes per-paper or per-context evidence tables (e.g.,
+`hsd17b13_genetics_aso_translation.csv`, `pnpla3_azd2693_precision_mash.csv`,
+`galnac_sirna_offtarget_rat_hepatotoxicity.csv`) that record the
+literature-grounded generation logic applied for each source. These
+tables document which design rationale, control logic, or readout
+constraint was contributed by each source.
+
+## Provenance
+
+Every generated value carries a provenance label, which can take one of
+five values:
+
+- `reported_in_paper` — value reported directly in a source paper
+- `extracted_from_paper` — value extracted from a source paper's tables/figures
+- `inferred_from_paper` — value inferred from source-level reasoning
+- `literature_grounded_generated` — value generated within
+  literature-informed bounds
+- `not_reported` — value not available
+
+Toxicity readouts are stochastically calibrated with assay-specific
+noise to produce realistic predictive structure for in silico modeling.
+The full calibration specification is recorded in
+`toxicity_readouts_calibration_audit.json`.
+
+## Limitations
+
+- This dataset is computationally generated; no physical oligonucleotides
+  were synthesized, purified, administered, or assayed.
+- The dataset is intended for in silico model development, schema
+  evaluation, and benchmarking. It is not intended as direct evidence
+  for regulatory decision-making.
+- The hepatotoxicity label distribution (60% low / 30% moderate / 10%
+  high) is a deliberate benchmark stratification chosen to produce a
+  non-trivial classification problem with both common and rare strata;
+  it is not a claim about wet-lab oligonucleotide toxicity prevalence.
+- Original source PDFs are not redistributed; sources are identified
+  through title-level metadata only.
+
+## Citation
+
+If you use this dataset, please cite:
+
+```
+DBbun LLC. OligoTox Phase 2 Dataset: A computationally generated,
+literature-informed dataset for oligonucleotide-associated hepatotoxicity
+modeling. Hugging Face Datasets, 2026.
+```
+
+## Related work
+
+DBbun LLC has previously released open synthetic biomedical datasets
+including the MELD-Plus 1M cohort, the UK Biobank ASCVD 10M cohort, and
+the Insomnia 1M cohort, all available under the DBbun namespace on
+Hugging Face.