# German State Extraction Pattern (Reusable Template)

**Last Updated**: 2025-11-20  
**Status**: Production-ready pattern validated on Saxony (411 institutions)

---

## Overview

This document provides a **copy-paste template** for extracting heritage institutions from any German state (Bundesland) using the proven Saxony pattern.

**Success Rate**: 99.8% ISIL coverage, 100% core metadata completeness  
**Time Required**: 1.5-2 hours per state (foundation + museums)  
**Difficulty**: Easy (automated scraping, minimal manual curation)

---

## Two-Phase Extraction Strategy

### Phase 1: Foundation Dataset (Archives + Libraries)
Extract **high-quality records** from major state institutions:
- State archives (Staatsarchiv, Landesarchiv)
- State/university libraries (Staatsbibliothek, Universitätsbibliothek)
- Research libraries (specialized subject libraries)

**Target**: 10-20 institutions with 80%+ completeness

### Phase 2: Museum Registry Extraction
Extract **comprehensive museum coverage** from isil.museum:
- Official German museum ISIL registry (Institut für Museumsforschung)
- ~6,300 total German museums
- Filter by state name (Bundesland)

**Target**: 200-1,500 museums per state (varies by state size)

---

## Quick Start Template

### Step 1: Create State Scraper (5 minutes)

```bash
# Replace STATE_NAME and state_name throughout
# Example: Bayern (Bavaria), Baden-Württemberg, etc.

# Copy Saxony museum scraper template
cp scripts/scrapers/harvest_isil_museum_sachsen.py scripts/scrapers/harvest_isil_museum_STATE_NAME.py

# Update state references
# macOS:
sed -i '' 's/Sachsen/STATE_NAME/g' scripts/scrapers/harvest_isil_museum_STATE_NAME.py
sed -i '' 's/sachsen/state_name/g' scripts/scrapers/harvest_isil_museum_STATE_NAME.py

# Linux:
sed -i 's/Sachsen/STATE_NAME/g' scripts/scrapers/harvest_isil_museum_STATE_NAME.py
sed -i 's/sachsen/state_name/g' scripts/scrapers/harvest_isil_museum_STATE_NAME.py
```

**Manual Edits Required**:
1. Update `SACHSEN_URL` to point to your state:
   ```python
   # Before:
   SACHSEN_URL = f"{BASE_URL}/?t=liste&mode=land&suchbegriff=Sachsen"
   
   # After:
   STATE_URL = f"{BASE_URL}/?t=liste&mode=land&suchbegriff=Bayern"  # Example: Bayern
   ```

2. Update region in `convert_to_linkml()`:
   ```python
   # Before:
   "region": "Sachsen"
   
   # After:
   "region": "Bayern"  # Use official German state name
   ```

### Step 2: Extract Museums (2 minutes)

```bash
# Run state museum scraper
python3 scripts/scrapers/harvest_isil_museum_STATE_NAME.py

# Output: data/isil/germany/state_name_museums_YYYYMMDD_HHMMSS.json
```

**Expected Results**:
- 200-1,500 museums (depends on state size)
- 100% ISIL coverage (DE-MUS-* codes)
- 100% name/city coverage
- 0% address coverage (requires detail page scraping)

### Step 3: Extract Foundation Dataset (30-60 minutes)

**Option A: Manual Web Research** (recommended for first iteration)
1. Search for state archives (e.g., "Bayerisches Hauptstaatsarchiv")
2. Visit official websites
3. Extract contact info, ISIL codes, descriptions
4. Create JSON file: `data/isil/germany/state_name_archives_YYYYMMDD_HHMMSS.json`

**Option B: Automated Scraping** (if API/structured data available)
1. Create custom scraper for state archive portal
2. Parse institution listings
3. Extract metadata
4. Export to JSON

**Foundation Institutions to Target**:
- State archives (Staatsarchiv, Landesarchiv)
- State library (Staatsbibliothek, if separate from state archive)
- Major university libraries (technical universities, research universities)
- Specialized research libraries

### Step 4: Merge Datasets (2 minutes)

```bash
# Copy merge template
cp scripts/merge_sachsen_complete.py scripts/merge_STATE_NAME_complete.py

# Update state references
# macOS:
sed -i '' 's/sachsen/state_name/g' scripts/merge_STATE_NAME_complete.py
sed -i '' 's/Sachsen/STATE_NAME/g' scripts/merge_STATE_NAME_complete.py

# Run merge
python3 scripts/merge_STATE_NAME_complete.py

# Output: data/isil/germany/state_name_complete_YYYYMMDD_HHMMSS.json
```

---

## German States Priority List

### High Priority (Large States, High Institution Count)

| State | German Name | Estimated Institutions | Difficulty |
|-------|-------------|------------------------|------------|
| Bavaria | Bayern | 1,200-1,500 | Medium |
| Baden-Württemberg | Baden-Württemberg | 1,000-1,200 | Medium |
| Lower Saxony | Niedersachsen | 800-1,000 | Medium |
| Hesse | Hessen | 500-700 | Easy |
| Rhineland-Palatinate | Rheinland-Pfalz | 400-600 | Easy |

### Medium Priority

| State | German Name | Estimated Institutions | Difficulty |
|-------|-------------|------------------------|------------|
| Berlin | Berlin | 300-400 | Easy |
| Brandenburg | Brandenburg | 300-400 | Easy |
| Schleswig-Holstein | Schleswig-Holstein | 250-350 | Easy |
| Mecklenburg-Vorpommern | Mecklenburg-Vorpommern | 200-300 | Easy |

### Already Complete ✅

| State | Status | Institutions | ISIL Coverage |
|-------|--------|-------------|---------------|
| Saxony | ✅ COMPLETE | 411 | 99.8% |
| Thuringia | ✅ COMPLETE | 1,061 | 97.8% |
| Saxony-Anhalt | ✅ COMPLETE | 317 | 98.4% |
| North Rhine-Westphalia | ✅ COMPLETE | 1,893 | 99.2% |

---

## Data Quality Expectations

### Phase 1 (Foundation Dataset)

| Field | Expected Coverage |
|-------|-------------------|
| Name | 100% |
| Institution Type | 100% |
| City | 100% |
| Street Address | 80-100% |
| Postal Code | 80-100% |
| Phone | 80-100% |
| Email | 60-80% |
| Website | 90-100% |
| ISIL Code | 90-100% |
| Description | 100% |

**Average Completeness**: 80-90%

### Phase 2 (Museum Registry)

| Field | Expected Coverage |
|-------|-------------------|
| Name | 100% |
| Institution Type | 100% |
| City | 100% |
| Street Address | 0% (requires detail page scraping) |
| Postal Code | 0% (requires detail page scraping) |
| Phone | 0% (requires detail page scraping) |
| Email | 0% (requires detail page scraping) |
| Website | 0% (requires detail page scraping) |
| ISIL Code | 100% |
| Description | 100% (generic) |

**Average Completeness**: 40-50% (basic extraction)

### Phase 3 (Optional Enrichment)

**Detail Page Scraping** (adds 2-3 hours):
- Extract addresses, phone, email, website from individual museum pages
- Expected completeness gain: 40% → 75%

**Wikidata Enrichment** (adds 1-2 hours):
- SPARQL query for state museums
- Fuzzy match to extracted museums
- Expected Wikidata coverage: 0% → 50-60%

---

## Validation Checklist

Before marking a state as "COMPLETE", verify:

- [ ] Foundation dataset created (10-20 institutions)
- [ ] Museums extracted from isil.museum (200+ institutions)
- [ ] Datasets merged into `state_name_complete_*.json`
- [ ] ISIL coverage >95%
- [ ] Core field completeness 100% (name, type, city)
- [ ] Geographic distribution analyzed (city counts)
- [ ] Metadata completeness report generated
- [ ] LinkML schema validation passed
- [ ] Session summary documented

---

## Example Workflow: Bavaria (Bayern)

### Step 1: Museum Extraction

```bash
# Create scraper
cp scripts/scrapers/harvest_isil_museum_sachsen.py scripts/scrapers/harvest_isil_museum_bayern.py

# Update references
sed -i '' 's/Sachsen/Bayern/g' scripts/scrapers/harvest_isil_museum_bayern.py
sed -i '' 's/sachsen/bayern/g' scripts/scrapers/harvest_isil_museum_bayern.py

# Manually update URL
# Line 27: SACHSEN_URL = f"{BASE_URL}/?t=liste&mode=land&suchbegriff=Bayern"
# Line 139: "region": "Bayern"

# Run extraction
python3 scripts/scrapers/harvest_isil_museum_bayern.py

# Expected output: ~1,200 Bavarian museums
```

### Step 2: Foundation Dataset

**Bavarian Foundation Institutions** (research manually):

1. **Bavarian State Archives**
   - Hauptstaatsarchiv München
   - Staatsarchiv Amberg
   - Staatsarchiv Augsburg
   - Staatsarchiv Bamberg
   - Staatsarchiv Coburg
   - Staatsarchiv Landshut
   - Staatsarchiv Nürnberg
   - Staatsarchiv Würzburg

2. **Major Libraries**
   - Bayerische Staatsbibliothek (Munich)
   - Universitätsbibliothek München (LMU)
   - Universitätsbibliothek der TU München
   - Universitätsbibliothek Würzburg
   - Universitätsbibliothek Erlangen-Nürnberg
   - Universitätsbibliothek Regensburg

**Total**: ~14 foundation institutions

### Step 3: Merge

```bash
# Create merge script
cp scripts/merge_sachsen_complete.py scripts/merge_bayern_complete.py

# Update references
sed -i '' 's/sachsen/bayern/g' scripts/merge_bayern_complete.py
sed -i '' 's/Sachsen/Bayern/g' scripts/merge_bayern_complete.py

# Run merge
python3 scripts/merge_bayern_complete.py

# Expected output: ~1,214 Bavarian institutions (14 + 1,200)
```

---

## Troubleshooting

### Problem: "No museums found in HTML"

**Cause**: State name not recognized by isil.museum registry

**Solution**:
1. Manually browse to http://www.museen-in-deutschland.de
2. Search for state name in dropdown menu
3. Copy exact search parameter from URL (e.g., `suchbegriff=Baden-W%C3%BCrttemberg`)
4. Update `STATE_URL` in scraper with correct URL-encoded parameter

### Problem: "ISIL coverage <95%"

**Cause**: Some foundation institutions may not have ISIL codes

**Solution**:
1. Check SIGEL database: https://sigel.staatsbibliothek-berlin.de
2. Search for missing institutions
3. Manually add ISIL codes if found
4. Mark as "ISIL_not_assigned" if genuinely missing

### Problem: "Merge fails with FileNotFoundError"

**Cause**: Museum extraction output not found

**Solution**:
1. Verify museum scraper ran successfully: `ls -la data/isil/germany/*museums*.json`
2. Check scraper output for errors
3. Ensure output directory exists: `mkdir -p data/isil/germany`
4. Re-run museum extraction if needed

### Problem: "City names have special characters"

**Cause**: German umlauts and special characters in city names

**Solution**:
- Keep original German names (don't transliterate)
- Ensure UTF-8 encoding: `encoding='utf-8'` in all file operations
- Example: München (not Muenchen), Köln (not Koln), Düsseldorf (not Duesseldorf)

---

## Performance Benchmarks

| State | Institutions | Extraction Time | Merge Time | Total Time |
|-------|-------------|-----------------|------------|------------|
| Saxony | 411 | 5 seconds | 3 seconds | 8 seconds |
| Bavaria (est.) | 1,214 | 15 seconds | 5 seconds | 20 seconds |
| Baden-Württemberg (est.) | 1,100 | 12 seconds | 5 seconds | 17 seconds |

**Note**: Foundation dataset research adds 30-60 minutes (manual web research)

---

## LinkML Schema Compliance

All extracted records MUST conform to:
- `schemas/core.yaml` - HeritageCustodian, Location, Identifier classes
- `schemas/enums.yaml` - InstitutionTypeEnum (ARCHIVE, LIBRARY, MUSEUM)
- `schemas/provenance.yaml` - Provenance, data_source, data_tier

**Required Fields**:
```yaml
id: https://w3id.org/heritage/custodian/de/...
name: <institution name>
institution_type: MUSEUM | LIBRARY | ARCHIVE
locations:
  - city: <city name>
    region: <state name>
    country: DE
identifiers:
  - identifier_scheme: ISIL
    identifier_value: DE-MUS-* | DE-* | ...
provenance:
  data_source: WEB_SCRAPING
  data_tier: TIER_2_VERIFIED
  extraction_date: <ISO 8601 timestamp>
  extraction_method: <description>
  confidence_score: 0.90
  source_url: <URL>
```

---

## Success Metrics

### Minimum Viable Dataset
- ✅ Foundation dataset: 10+ institutions at 80%+ completeness
- ✅ Museums: 200+ institutions at 40%+ completeness
- ✅ ISIL coverage: >95%
- ✅ Core fields: 100% (name, type, city)

### High-Quality Dataset
- ✅ Foundation dataset: 15+ institutions at 90%+ completeness
- ✅ Museums: 500+ institutions at 50%+ completeness
- ✅ ISIL coverage: >98%
- ✅ Core fields: 100%
- ✅ Wikidata enrichment: >50% for major institutions

---

## Related Documentation

- `SESSION_SUMMARY_20251120_SAXONY_MUSEUMS_COMPLETE.md` - Full Saxony extraction case study
- `SAXONY_HARVEST_STRATEGY.md` - Strategic planning document
- `SESSION_SUMMARY_20251120_THUERINGEN_V4_COMPLETE.md` - Thuringia case study (enrichment example)
- `AGENTS.md` - AI agent instructions for extraction

---

## Contact & Support

**Questions?** Check existing session summaries in project root for similar extraction patterns.

**Bugs in scraper?** The isil.museum HTML structure may change. If extraction fails:
1. Inspect current HTML: `curl http://www.museen-in-deutschland.de/?t=liste&mode=land&suchbegriff=YOUR_STATE > debug.html`
2. Open `debug.html` in browser
3. Update BeautifulSoup selectors in `parse_museum_table()` function
4. Test with small sample before full extraction

---

**Pattern Status**: ✅ Production-ready (validated on Saxony with 99.8% ISIL coverage)  
**Reusability**: High (copy-paste template with minimal edits)  
**Scalability**: Excellent (handles 200-1,500 institutions per state)  
**Maintenance**: Low (official registry rarely changes structure)