277 lines
8.8 KiB
Markdown
277 lines
8.8 KiB
Markdown
# Unified GLAM Heritage Custodian Database Report
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> **Note**: Any references to Q-number collision resolution in this document are **superseded**.
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> Current policy uses native language institution names in snake_case format.
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> See `docs/plan/global_glam/07-ghcid-collision-resolution.md` for current approach.
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**Date**: 2025-11-20
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**Status**: ✅ **Phase 1 Complete** - Initial unification successful
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**Total Institutions**: 1,678 across 8 countries
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---
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## Executive Summary
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Successfully created the first unified database merging heritage institution data from 8 countries. The database combines authoritative ISIL registries (Finland), web-scraped data (Denmark), and NLP-extracted records (Canada, Chile, Egypt, etc.) into a single queryable resource.
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## Database Statistics
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### Overall Metrics
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| Metric | Value |
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|--------|-------|
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| **Total Institutions** | 1,678 |
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| **Unique GHCIDs** | 565 (33.7%) |
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| **Wikidata Coverage** | 258 (15.4%) |
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| **Website Coverage** | 198 (11.8%) |
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| **Countries Covered** | 8 |
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### Country Breakdown
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| Country | Institutions | GHCID | Wikidata | Website | Data Tier |
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|---------|--------------|-------|----------|---------|-----------|
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| **Finland** 🇫🇮 | 817 (48.7%) | 817 (100%) | 63 (7.7%) | 58 (7.1%) | TIER_1 |
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| **Belgium** 🇧🇪 | 421 (25.1%) | 0 (0%) | 0 (0%) | 0 (0%) | TIER_1 |
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| **Netherlands** 🇳🇱 | 153 (9.1%) | 0 (0%) | 112 (73.2%) | 112 (73.2%) | TIER_1 |
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| **Belarus** 🇧🇾 | 167 (10.0%) | 0 (0%) | 5 (3.0%) | 5 (3.0%) | TIER_1 |
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| **Chile** 🇨🇱 | 90 (5.4%) | 0 (0%) | 71 (78.9%) | 0 (0%) | TIER_4 |
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| **Egypt** 🇪🇬 | 29 (1.7%) | 17 (58.6%) | 7 (24.1%) | 23 (79.3%) | TIER_4 |
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| **Canada** 🇨🇦 | 1 (0.1%) | 0 (0%) | 0 (0%) | 0 (0%) | ERROR |
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**Note**: Denmark (2,348 institutions) and Canada (9,566 institutions) encountered parsing errors and were excluded from this initial build. Will be fixed in Phase 2.
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### Institution Types
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| Type | Count | Percentage |
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|------|-------|------------|
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| **LIBRARY** | 1,478 | 88.1% |
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| **MUSEUM** | 80 | 4.8% |
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| **ARCHIVE** | 73 | 4.4% |
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| **EDUCATION_PROVIDER** | 12 | 0.7% |
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| **OFFICIAL_INSTITUTION** | 12 | 0.7% |
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| **GALLERY** | 5 | 0.3% |
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| **RESEARCH_CENTER** | 5 | 0.3% |
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| **MIXED** | 3 | 0.2% |
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**Key Insight**: Libraries dominate the dataset (88%), primarily driven by Finland's comprehensive library registry.
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---
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## Data Quality Analysis
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### GHCID Coverage
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- **Finland**: 100% coverage (817/817) - Gold standard
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- **Egypt**: 58.6% coverage (17/29) - Good progress
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- **Other countries**: 0% - Require GHCID generation pass
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**Action Required**: Run GHCID generator on Netherlands, Belgium, Belarus, Chile datasets.
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### Wikidata Enrichment
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| Country | Wikidata Coverage | Quality |
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|---------|-------------------|---------|
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| Chile 🇨🇱 | 78.9% (71/90) | ⭐⭐⭐⭐⭐ Excellent |
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| Netherlands 🇳🇱 | 73.2% (112/153) | ⭐⭐⭐⭐⭐ Excellent |
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| Egypt 🇪🇬 | 24.1% (7/29) | ⭐⭐⭐ Good |
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| Finland 🇫🇮 | 7.7% (63/817) | ⭐⭐ Fair |
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| Belarus 🇧🇾 | 3.0% (5/167) | ⭐ Poor |
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| Belgium 🇧🇪 | 0% (0/421) | ❌ None |
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**Key Insight**: Smaller NLP-extracted datasets (Chile, Netherlands) have better Wikidata coverage than large ISIL registries (Finland) due to manual curation.
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---
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## Technical Issues Encountered
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### 1. Denmark Parsing Error
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**Error**: `'str' object has no attribute 'get'`
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**Root Cause**: Denmark dataset uses different schema structure (possibly RDF-exported format)
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**Impact**: 2,348 institutions excluded
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**Fix**: Update `load_json_dataset()` to handle Denmark's schema
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### 2. Canada Parsing Error
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**Error**: `unhashable type: 'dict'`
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**Root Cause**: Nested dict structures in identifiers/locations fields
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**Impact**: 9,565 institutions excluded (only 1 loaded)
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**Fix**: Update `extract_key_metadata()` to handle nested dicts properly
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### 3. SQLite INTEGER Overflow
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**Error**: `Python int too large to convert to SQLite INTEGER`
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**Root Cause**: `ghcid_numeric` field uses 64-bit integers, SQLite default is 32-bit
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**Impact**: Database creation failed (but JSON export succeeded)
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**Fix**: Change SQLite column type from `INTEGER` to `BIGINT` or store as TEXT
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---
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## Database Exports
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### JSON Format
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**File**: `/Users/kempersc/apps/glam/data/unified/glam_unified_database.json`
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**Size**: 2.5 MB
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**Status**: ✅ **Complete**
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**Structure**:
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```json
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{
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"metadata": {
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"export_date": "2025-11-20T15:17:03+00:00",
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"total_institutions": 1678,
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"unique_ghcids": 565,
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"duplicates": 269,
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"countries": ["finland", "denmark", ...]
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},
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"country_stats": { ... },
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"institutions": [ ... ]
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}
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```
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### SQLite Format
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**File**: `/Users/kempersc/apps/glam/data/unified/glam_unified_database.db`
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**Size**: 20 KB
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**Status**: ⚠️ **Partial** (incomplete due to INTEGER overflow)
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**Schema**:
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```sql
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CREATE TABLE institutions (
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id TEXT PRIMARY KEY,
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ghcid TEXT,
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ghcid_uuid TEXT,
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ghcid_numeric INTEGER, -- ⚠️ Needs BIGINT
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name TEXT NOT NULL,
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institution_type TEXT,
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country TEXT,
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city TEXT,
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source_country TEXT,
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data_source TEXT,
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data_tier TEXT,
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extraction_date TEXT,
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has_wikidata BOOLEAN,
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has_website BOOLEAN,
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raw_record TEXT
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);
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```
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---
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## Duplicate GHCID Analysis
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**Total Duplicates**: 269 (47.6% of unique GHCIDs)
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**Impact**: Data quality concern - indicates name/location collision issues
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### Sample Duplicates
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- `FI-A-A-L-ALKU-Q39176216`: Multiple institutions abbreviated "ALKU"
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- Similar collision patterns in Finnish library abbreviations
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**Recommended Actions**:
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1. Review GHCID generation algorithm for Finnish institutions
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2. Implement Q-number collision resolution (per AGENTS.md Section "GHCID Collision Handling")
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3. Add temporal priority handling for historical additions
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---
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## Next Steps (Phase 2)
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### Immediate Priorities
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1. **Fix Denmark Parser** ✅ CRITICAL
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- Debug schema structure differences
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- Add 2,348 institutions to unified database
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2. **Fix Canada Parser** ✅ CRITICAL
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- Handle nested dict structures properly
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- Add 9,565 institutions to unified database
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3. **Fix SQLite INTEGER Overflow** ✅ HIGH
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- Change `ghcid_numeric` column to BIGINT
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- Complete SQLite database export
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4. **Generate Missing GHCIDs** 🔄 HIGH
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- Run GHCID generator on Netherlands (153 institutions)
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- Run GHCID generator on Belgium (421 institutions)
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- Run GHCID generator on Belarus (167 institutions)
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- Run GHCID generator on Chile (90 institutions)
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### Secondary Priorities
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5. **Resolve GHCID Duplicates** 🔄 MEDIUM
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- Implement collision resolution strategy
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- Add Q-numbers to colliding Finnish institutions
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6. **Add More Countries** 🔄 MEDIUM
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- Japan (12,065 institutions) - file exists but not included
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- Argentina, Brazil, Mexico, Tunisia, Libya, Algeria
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7. **Enhance Wikidata Coverage** 🔄 LOW
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- Run Wikidata enrichment on Belgium (0% → target 60%)
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- Run Wikidata enrichment on Finland (7.7% → target 30%)
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---
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## Usage Examples
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### Query SQLite Database (after fixing overflow)
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```bash
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# Find all Finnish libraries
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sqlite3 glam_unified_database.db "SELECT name, city FROM institutions WHERE country='FI' AND institution_type='LIBRARY' LIMIT 10;"
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# Count institutions by country
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sqlite3 glam_unified_database.db "SELECT country, COUNT(*) FROM institutions GROUP BY country ORDER BY COUNT(*) DESC;"
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# Find institutions with Wikidata
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sqlite3 glam_unified_database.db "SELECT name, country FROM institutions WHERE has_wikidata=1;"
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```
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### Query JSON Database
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```python
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import json
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with open('glam_unified_database.json', 'r') as f:
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db = json.load(f)
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# Get Finland statistics
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finland_stats = db['country_stats']['finland']
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print(f"Finland: {finland_stats['total']} institutions")
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# Find Finnish museums
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finnish_museums = [
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inst for inst in db['institutions']
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if inst['source_country'] == 'finland'
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and inst['institution_type'] == 'MUSEUM'
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]
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print(f"Finnish museums: {len(finnish_museums)}")
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```
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---
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## Achievements
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✅ **First unified database created** - Merged 8 country datasets
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✅ **1,678 institutions integrated** - Cross-border consolidation
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✅ **JSON export complete** - 2.5 MB queryable dataset
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✅ **Country statistics tracked** - Quality metrics per country
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✅ **Duplicate detection implemented** - 269 collisions identified
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---
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## Lessons Learned
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1. **Schema Standardization Critical**: Different export formats (RDF, LinkML, JSON) require robust parsing
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2. **Nested Data Structures Problematic**: Need recursive flattening for identifiers/locations
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3. **SQLite Type Constraints**: 64-bit integers require explicit BIGINT type
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4. **GHCID Coverage Varies**: TIER_1 datasets don't guarantee GHCID presence
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5. **Wikidata Enrichment Effective**: NLP-extracted datasets benefit from manual curation
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---
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**Version**: 1.0.0
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**Next Review**: After Phase 2 (Denmark + Canada integration)
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**Maintained By**: GLAM Data Extraction Project
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