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+# Scandium-Dataset Paper Outline
+
+**Target Venues** (in priority order):
+1. NeurIPS Datasets & Benchmarks (if benchmark experiments are the core contribution)
+2. Scientific Data (if curation methodology is the core contribution)
+3. npj Computational Materials (if materials science insight is the core contribution)
+4. J. Chem. Inf. Model. (domain-specific data resource)
+
+---
+
+## Title
+
+**Scandium: A Curated Multi-Source Dataset with Quality Tiering for ML-Driven Solid-State Battery Discovery**
+
+---
+
+## Authors
+
+Scandium Labs (individual names if available)
+
+---
+
+## Abstract (draft)
+
+*We introduce Scandium-Dataset, a unified computational materials dataset of 266,732 DFT-computed entries aggregated from Materials Project, OQMD, and JARVIS-DFT. Unlike prior harmonization efforts, Scandium implements a calibrated 4-tier quality system (Strict Gold / Gold / Validated / Raw) with per-entry provenance tracking, 31,997 duplicates removed, and 137,405 repaired structures. We provide frozen benchmark splits across four held-out strategies (random, composition, family, chemistry), enabling reproducible evaluation of generalization to unseen compositions and material families. Baseline experiments with a composition-only model show that training on Gold-tier data (96,242 entries) consistently outperforms training on the full dataset, achieving FE MAE of 0.298 vs 0.392 on random splits and 1.384 vs 1.657 on chemistry held-out splits — validating the tiering strategy. Battery (82,925 entries) and electrolyte (41,665 entries) subsets are provided for domain-specific applications. The dataset is released with full documentation, audit reports, and known limitations. All code and data are publicly available.*
+
+---
+
+## 1. Introduction
+
+### Problem Statement
+- Solid-state battery discovery is bottlenecked by slow DFT screening
+- ML acceleration requires high-quality, well-characterized training data
+- Existing data is scattered (MP, OQMD, JARVIS differ in schema, quality, conventions)
+
+### Gaps in Prior Work
+- Prior harmonization (LeMat-Traj) unifies schema but doesn't tier by quality
+- Existing benchmarks (Matbench) use single-source data and lack OOD splits
+- Battery-specific datasets (Electrolyte Genome) are small and experimental-only
+
+### Contributions (numbered)
+1. Multi-source unification with 4-tier quality system and 11 validation gates
+2. Cross-source agreement quantification and systematic bias documentation
+3. Battery/electrolyte classification methodology with 10 material families
+4. Frozen benchmark splits (4 types) with OOD held-out configurations
+5. Baseline experiments validating the tiering system
+
+---
+
+## 2. Related Work
+
+(Expand from [related_work.md](related_work.md))
+
+- **Materials databases:** MP, OQMD, JARVIS, AFLOW, NOMAD
+- **Harmonized datasets:** LeMat-Traj (closest prior art — explicit comparison table)
+- **Benchmarks:** Matbench, JARVIS-Leaderboard
+- **Battery-specific:** Electrolyte Genome, BatteryHub
+- **Novelty positioning:** Tiering methodology + battery curation + OOD splits
+
+---
+
+## 3. Curation Methodology
+
+### 3.1 Source Selection
+- Why MP, OQMD, JARVIS? Coverage, complementarity, license variation
+- Extraction dates, API versions pinned for reproducibility
+
+### 3.2 Schema Unification
+- Field mapping across sources
+- Unit normalization
+- Structure standardization (pymatgen JSON)
+
+### 3.3 Deduplication
+- 31,997 duplicates removed across 21,140 groups
+- Structure matching method (pymatgen StructureMatcher)
+- Priority rules for choosing which entry to keep
+
+### 3.4 Repair Pipeline
+- OQMD coordinate artifacts (137,405 entries): `coords_are_cartesian=False`
+- OQMD space group determination (171,764 entries): spglib symmetry pass
+- Volume/density repair (73,480 entries): extraction from structure_json
+- JARVIS volume extraction (25,673 entries)
+
+### 3.5 Quality Scoring
+- 5 sub-scores (geometry, DFT, metadata, novelty, chemical)
+- Composite score range: 48–88 (intentionally conservative)
+- Calibration: score 80-90 predicts 85.2% validation rate, 0.18 eV FE MAE
+
+### 3.6 Tiering System
+- 4 tiers with cumulative gates:
+  - Strict Gold (56,966): 11 gates, quality ≥ 80, full provenance, no defects
+  - Gold (96,242): 8 gates, validated + unique + stable + complete metadata
+  - Validated (140,382): 5 gates, valid structure + targets + no critical issues
+  - Raw (30,108): source + formula present
+- Gate definitions and pass/fail criteria for each tier
+
+### 3.7 Provenance Tracking
+- Per-entry checksum, repair history, tier gate results, dedup resolution
+- Enables auditability and reproducibility
+
+### 3.8 Material Family Classification
+- 10 families based on element composition
+- Family distribution and battery relevance assessment
+- Known issue: garnet undercount (23 entries)
+
+---
+
+## 4. Dataset Analysis
+
+### 4.1 Overall Statistics
+(Table: entries per source, per tier, per family)
+
+### 4.2 Property Distributions
+(Figures: FE, EaH, BG histograms by source and tier)
+
+### 4.3 Cross-Source Agreement
+- 11,741 formulas overlapping between MP and JARVIS
+- FE MAE across overlaps: 0.20 eV/atom
+- Source-level biases documented (MP mean quality 87.4 vs OQMD 73.2)
+
+### 4.4 Quality Score Calibration
+- Monotonic relationship between score and downstream prediction error
+- Figure: score bin → validation rate, FE MAE, EaH MAE
+
+### 4.5 Tier Impact Analysis
+- Outlier removal effectiveness (Gold excludes 42 extreme-FE OQMD entries)
+- Defect distribution by tier
+- Missing data analysis (EaH for JARVIS: 100% missing by source limitation)
+
+### 4.6 Battery and Electrolyte Subset Statistics
+(Per-family breakdown, tier distribution within subsets)
+
+---
+
+## 5. Benchmark Baselines
+
+### 5.1 Experimental Setup
+- 4 split types (random, composition-held-out, family-held-out, chemistry-held-out)
+- 2 training configurations (full dataset vs Gold-tier only)
+- 8 experiments total
+
+### 5.2 Model
+- Composition-only: bag-of-elements + RF + Ridge ensemble
+- Rationale: fast, reproducible, structure-agnostic lower bound
+
+### 5.3 Results
+
+**Key table (8 rows):**
+
+| Training Data | Split | FE MAE | EaH MAE | BG MAE |
+|---------------|-------|--------|---------|--------|
+| Full | random | 0.392 | 0.224 | 0.494 |
+| Gold | random | **0.298** | **0.024** | 0.762 |
+| Full | composition | 0.390 | 0.223 | 0.497 |
+| Gold | composition | **0.306** | **0.024** | 0.772 |
+| Full | family | 0.739 | 0.169 | 1.105 |
+| Gold | family | **0.458** | **0.019** | 0.943 |
+| Full | chemistry | 1.657 | 0.355 | 1.966 |
+| Gold | chemistry | **1.384** | **0.027** | 2.129 |
+
+### 5.4 Analysis
+- **Gold-tier training consistently beats full-dataset** on FE and EaH despite 3× less data
+- **EaH dramatically benefits** from Gold filtering (excludes JARVIS with no EaH, filters OQMD outliers)
+- **Chemistry held-out is hardest** — negative R² for both configurations
+- **Band gap trade-off** — Gold has fewer metals (BG=0), making BG prediction harder
+- **Per-family analysis** reveals model weaknesses (intermetallic bias, garnet data scarcity)
+
+### 5.5 Discussion
+- The tiering system is empirically validated: cleaner data beats more data
+- OOD splits reveal limits of composition-only models
+- Roadmap for structure-aware models (CGCNN, MEGNet, ALIGNN)
+
+---
+
+## 6. Limitations
+
+(Lift directly from [KNOWN_ISSUES.md](../KNOWN_ISSUES.md))
+
+- JARVIS EaH missing (25,673 entries) — fundamental source limitation
+- MP vs OQMD quality score gap (87.4 vs 73.2 mean)
+- Family imbalance (~63% intermetallics)
+- Garnet family undercount (23 entries)
+- No experimental validation — purely computational
+- License complexity — 3 different licenses across sources
+- Conservative quality scoring (max score: 88)
+- No Docker container (since resolved in v0.1.0-rc.1)
+- No DOI assigned yet (Zenodo archival in progress)
+
+---
+
+## 7. Conclusion
+
+- Summary of contributions
+- Empirical validation of tiering system via benchmark experiments
+- Roadmap: JARVIS EaH computation, NOMAD/AFLOW integration, experimental data, ionic conductivity computation
+- Call for community adoption
+
+---
+
+## Figures Needed
+
+1. **Property distributions** — FE, EaH, BG histograms colored by source (3 panels)
+2. **Quality score calibration** — score bin → validation rate (line plot with confidence bands)
+3. **Cross-source agreement** — MP vs JARVIS FE scatter plot (11,741 overlapping formulas)
+4. **Tier impact** — FE MAE bar chart across tiers (4 bars)
+5. **Benchmark results** — FE MAE across 4 splits, full vs Gold (grouped bar chart)
+6. **Family distribution** — bar chart of 10 families with battery relevance labels
+7. **Battery subset composition** — pie chart or treemap
+
+---
+
+## Tables Needed
+
+1. Source comparison (MP, OQMD, JARVIS — size, properties, license)
+2. Tier system (4 tiers, counts, gate criteria)
+3. Family classification (10 families, criteria, battery relevance)
+4. Benchmark results (8 rows as above)
+5. Comparison with prior work (Scandium vs LeMat-Traj, Matbench, JARVIS)
+6. Dataset statistics per source (mean FE, EaH, BG, quality score)
+7. Cross-source overlap statistics (11,741 formulas, FE MAE by source pair)
+
+---
+
+## Submission Checklist
+
+- [ ] DOI obtained from Zenodo
+- [ ] License conflict resolved (LICENSE_BREAKDOWN.md added)
+- [ ] per-entry `license` field added to dataset
+- [ ] Baseline benchmark results computed (8 experiments)
+- [ ] Related work analyzed against LeMat-Traj, Matbench, JARVIS
+- [ ] Battery/electrolyte methodology documented
+- [ ] Version bumped to v0.1.0
+- [ ] Evaluation runner fixed (metrics module created)
+- [ ] Code and data archived for reproducibility
+- [ ] All known issues documented