403 lines
13 KiB
Markdown
403 lines
13 KiB
Markdown
# Global GLAM Dataset Unification - Complete Summary
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**Completed**: 2025-11-11 15:17 UTC
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**Script**: `scripts/unify_all_datasets.py`
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## ✅ MISSION ACCOMPLISHED
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Successfully unified all heritage institution datasets into a single comprehensive global dataset.
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---
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## 📊 Final Statistics
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### Overall Coverage
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- **Total Institutions**: 13,502 (from 25,963 raw records)
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- **Countries Covered**: 18
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- **Wikidata Coverage**: 7,520/13,502 (55.7%)
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- **Geocoding Coverage**: 8,178/13,502 (60.6%)
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- **Duplicates Removed**: 12,461 (48.0% deduplication rate)
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### Data Quality Metrics
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- **Records Needing Enrichment**: 13,461 (99.7%)
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- **Missing Wikidata Q-numbers**: 5,982 institutions
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- **Missing Coordinates**: 5,324 institutions
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- **Missing Website URLs**: 2,085 institutions
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- **Missing/Incomplete Descriptions**: 13,036 institutions
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---
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## 🌍 Geographic Distribution
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### Top 10 Countries by Institution Count
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| Rank | Country | Code | Count | Wikidata % | Geocode % | Status |
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|------|---------|------|-------|------------|-----------|--------|
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| 1 | Japan | JP | 12,065 | 58.8% | 58.8% | ✅ Good |
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| 2 | Netherlands | NL | 622 | 31.0% | 99.8% | ⚠️ Needs Wikidata |
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| 3 | Mexico | MX | 226 | 15.0% | 73.9% | 🔴 Priority |
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| 4 | Brazil | BR | 212 | 13.7% | 45.8% | 🔴 Priority |
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| 5 | Chile | CL | 180 | 53.9% | 93.3% | ✅ Good |
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| 6 | Libya | LY | 50 | 16.0% | 0.0% | 🔴 Needs Geocoding |
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| 7 | Tunisia | TN | 69 | 1.4% | 13.0% | 🔴 Critical |
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| 8 | Vietnam | VN | 21 | 38.1% | 0.0% | ⚠️ Needs Geocoding |
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| 9 | Algeria | DZ | 19 | 5.3% | 0.0% | 🔴 Critical |
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| 10 | Georgia | GE | 14 | 0.0% | 0.0% | 🔴 Critical |
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### Countries with 0% Wikidata Coverage (HIGHEST PRIORITY)
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1. **Georgia** (GE): 14 institutions - NO data
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2. **Great Britain** (GB): 4 institutions - NO data
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3. **Belgium** (BE): 7 institutions - Geocoded but no Wikidata
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4. **United States** (US): 7 institutions - Geocoded but no Wikidata
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5. **Luxembourg** (LU): 1 institution - Geocoded but no Wikidata
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---
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## 📁 Files Generated
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### Output Location: `/data/instances/all/`
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1. **globalglam-20251111.yaml** (24 MB)
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- Complete unified dataset
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- 13,502 unique institutions
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- Provenance tracking for each record
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2. **ENRICHMENT_CANDIDATES.yaml** (2.8 MB)
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- 13,461 institutions needing enrichment
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- Sorted by priority (4 = most urgent, 1 = least urgent)
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- Detailed field-level gap analysis
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3. **UNIFICATION_REPORT.md** (11 KB)
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- Comprehensive statistics by country and source
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- Top 50 enrichment candidates
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- Duplicate detection results
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4. **DATASET_STATISTICS.yaml** (3 KB)
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- Machine-readable metrics
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- Country-by-country breakdown
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- Quality indicators
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---
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## 🔍 Data Sources Merged
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| Source | Count | Wikidata % | Geocode % | Notes |
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|--------|-------|------------|-----------|-------|
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| **Global Merged** | 13,396 | 55.6% | 100.0% | Base dataset from previous work |
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| **Japan** | 12,065 | 0.0% | 0.0% | Largest single-country dataset |
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| **Chile** | 90 | 78.9% | 86.7% | **Best quality** - enriched in Batch 19 |
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| **Brazil** | 115 | 6.1% | 0.0% | Batch 6 enriched |
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| **Mexico** | 117 | 0.0% | 49.6% | Geocoded only |
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| **Libya** | 54 | 14.8% | 0.0% | Needs geocoding |
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| **Tunisia** | 69 | 1.4% | 13.0% | Minimal data |
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| **Vietnam** | 21 | 38.1% | 0.0% | Needs geocoding |
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| **Algeria** | 19 | 5.3% | 0.0% | Minimal data |
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| **Georgia** | 14 | 0.0% | 0.0% | **Critical** - no enrichment |
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| **Historical** | 5 | 100.0% | 100.0% | Validation dataset |
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---
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## 🎯 Enrichment Priority Matrix
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### Priority 4 (4 Missing Fields): 855 Institutions
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**ALL need**: Wikidata, Coordinates, Website, Description
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**Geographic Focus**:
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- Brazil: 108 institutions (Museu dos Povos Acreanos, UFAC Repository, etc.)
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- Algeria: 19 institutions (all institutions)
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- Georgia: 14 institutions (all institutions)
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- Libya: 47 institutions (most institutions)
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**Action**: Batch Wikidata query + Nominatim geocoding + website scraping
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---
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### Priority 3 (3 Missing Fields): 4,875 Institutions
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**Typical pattern**: Missing Wikidata, Coordinates, Description (have website) OR missing Wikidata, Website, Description (have coordinates)
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**Geographic Focus**:
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- Japan: Majority of 12,065 institutions (missing Wikidata)
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- Netherlands: 430 institutions (have geocoding, need Wikidata)
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- Mexico: 170 institutions (partial data)
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**Action**: Focus on Wikidata enrichment via SPARQL
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---
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### Priority 2 (2 Missing Fields): 665 Institutions
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**Typical pattern**: Missing Wikidata + one other field
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**Action**: Targeted enrichment for specific gaps
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---
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### Priority 1 (1 Missing Field): 7,042 Institutions
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**Typical pattern**: Only missing description OR only missing website
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**Action**: Lower priority - can defer
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---
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## 🚀 Next Steps - Global Enrichment Workflow
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### Phase 1: Critical Countries (0% Wikidata Coverage)
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**Target**: 33 institutions across 5 countries (GE, GB, BE, US, LU)
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**Workflow**:
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1. Create enrichment script: `scripts/enrich_critical_countries.py`
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2. Query Wikidata SPARQL endpoint by country + institution type
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3. Fuzzy match institution names (threshold > 0.85)
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4. Geocode missing coordinates via Nominatim
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5. Validate and update records
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**Expected Outcome**: Bring all 5 countries to 50%+ Wikidata coverage
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---
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### Phase 2: North Africa (Tunisia, Algeria, Libya)
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**Target**: 112 institutions with <16% Wikidata coverage
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**Challenges**:
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- Limited Wikidata entries for North African institutions
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- Multilingual names (Arabic/French/English)
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- Missing coordinates
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**Workflow**:
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1. Wikidata enrichment with Arabic name variants
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2. Batch geocoding for all institutions
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3. Cross-reference with UNESCO heritage sites
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4. Manual validation of fuzzy matches
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**Expected Outcome**: 40%+ Wikidata coverage, 80%+ geocoding
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---
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### Phase 3: Latin America (Brazil, Mexico)
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**Target**: 438 institutions (212 BR + 226 MX)
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**Current State**:
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- Brazil: 13.7% Wikidata, 45.8% geocoded
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- Mexico: 15.0% Wikidata, 73.9% geocoded
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**Workflow**:
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1. Reuse Chile enrichment scripts (proven 78.9% success rate)
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2. Batch SPARQL queries for Brazilian/Mexican institutions
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3. Enhance geocoding for Brazil (currently 45.8%)
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4. Website crawling for missing descriptions
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**Expected Outcome**:
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- Brazil → 50%+ Wikidata, 80%+ geocoding
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- Mexico → 50%+ Wikidata, 90%+ geocoding
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---
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### Phase 4: Netherlands Deep Enrichment
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**Target**: 622 institutions, currently 31.0% Wikidata
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**Advantages**:
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- Already 99.8% geocoded
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- Rich metadata available (ISIL codes, KvK numbers)
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- Many institutions have websites
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**Workflow**:
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1. Cross-reference with Dutch ISIL registry (TIER_1 data)
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2. Query Wikidata using ISIL codes as identifiers
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3. Crawl institutional websites for descriptions
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4. Leverage existing digital platform metadata
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**Expected Outcome**: 70%+ Wikidata coverage (431 institutions)
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---
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### Phase 5: Japan Mass Enrichment
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**Target**: 12,065 institutions (89.5% of total dataset!)
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**Current State**: 0% Wikidata from local dataset, but global merge shows 58.8%
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**Analysis**: Japan data appears split between:
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- Local Japanese dataset (12,065 records, 0% enriched)
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- Global dataset (includes ~7,091 Japanese institutions with Wikidata)
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**Workflow**:
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1. Investigate duplicate detection logic (why 12,461 duplicates removed?)
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2. Verify Japanese institution deduplication by name + coordinates
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3. Run batch Wikidata enrichment for remaining institutions
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4. Consider Japanese-language Wikidata queries
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**Expected Outcome**: Maintain 58.8% coverage, improve to 70%+
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---
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## 📋 Enrichment Scripts to Create
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### 1. `scripts/enrich_critical_countries_batch.py`
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**Purpose**: Enrich Georgia, Great Britain, Belgium, US, Luxembourg institutions
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**Target**: 33 institutions (0% → 50%+ Wikidata)
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### 2. `scripts/enrich_north_africa_batch.py`
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**Purpose**: Enrich Tunisia, Algeria, Libya institutions
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**Target**: 112 institutions (<16% → 40%+ Wikidata)
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### 3. `scripts/enrich_brazil_comprehensive.py`
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**Purpose**: Full Brazil enrichment (Wikidata + geocoding + websites)
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**Target**: 212 institutions (13.7% → 50%+ Wikidata, 45.8% → 80%+ geocoding)
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### 4. `scripts/enrich_mexico_comprehensive.py`
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**Purpose**: Mexico Wikidata enrichment (geocoding already good)
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**Target**: 226 institutions (15.0% → 50%+ Wikidata)
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### 5. `scripts/enrich_netherlands_isil.py`
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**Purpose**: Netherlands enrichment using ISIL codes
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**Target**: 622 institutions (31.0% → 70%+ Wikidata)
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### 6. `scripts/enrich_japan_mass.py`
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**Purpose**: Japan mass enrichment + deduplication analysis
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**Target**: 12,065 institutions (maintain/improve 58.8% coverage)
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---
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## 🏆 Success Criteria
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### Minimum Viable Dataset (MVP)
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- ✅ **Total institutions**: 13,000+ (ACHIEVED: 13,502)
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- ✅ **Wikidata coverage**: 50%+ (ACHIEVED: 55.7%)
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- ✅ **Geocoding coverage**: 50%+ (ACHIEVED: 60.6%)
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- ❌ **All countries**: 30%+ Wikidata (NOT MET: 5 countries at 0%)
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### Target Goals (Post-Enrichment)
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- **Total institutions**: 15,000+ (add more countries)
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- **Wikidata coverage**: 70%+ globally
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- **Geocoding coverage**: 80%+ globally
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- **All countries**: 50%+ Wikidata minimum
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- **Description coverage**: 80%+ (currently 3.5%)
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---
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## 🔧 Technical Notes
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### Deduplication Strategy
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- **12,461 duplicates removed** (48% duplicate rate!)
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- Prioritized records with Wikidata Q-numbers
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- Kept most complete version when duplicates found
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- ID-based deduplication (exact match on `id` field)
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**Investigation Needed**: Why such a high duplicate rate?
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- Likely overlap between "global" dataset and country-specific datasets
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- Japan institutions may be duplicated between sources
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### Data Quality Issues
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1. **Description Completeness**: 96.5% of records missing/incomplete descriptions
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- Most urgent data quality issue
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- Affects usability and discoverability
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- Can be addressed via website crawling
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2. **Coordinate Precision**: 39.3% missing coordinates
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- Libya: 100% missing (50 institutions)
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- Algeria: 100% missing (19 institutions)
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- Vietnam: 100% missing (21 institutions)
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- Georgia: 100% missing (14 institutions)
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- Brazil: 54.2% missing (115 institutions)
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3. **Website URLs**: 15.5% missing
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- Lower priority (institutions may not have websites)
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- Focus on institutional websites vs. social media
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---
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## 📚 Chile Success Story - Benchmark for Quality
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**Chile Enrichment Results** (Completed in Batch 19):
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- **Total**: 90 institutions
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- **Wikidata**: 71/90 (78.9%) - **EXCEEDS 70% target by 8.9 points**
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- **Geocoding**: 78/90 (86.7%)
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- **Method**: Iterative batch enrichment with fuzzy matching
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- **Scripts**: `enrich_chile_batch[1-19].py`
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**Key Success Factors**:
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1. Iterative approach (19 batches, gradual refinement)
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2. Fuzzy matching threshold optimization (0.85+)
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3. Manual validation of uncertain matches
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4. Parent organization fallback (when direct match fails)
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**Replication Strategy**: Use Chile's approach as template for other countries
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---
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## 🎉 Achievements
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### Data Integration
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✅ Unified 11 separate datasets into single comprehensive file
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✅ Merged 25,963 raw records → 13,502 unique institutions (48.0% deduplication)
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✅ Covered 18 countries across 4 continents
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✅ Preserved provenance tracking for all records
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### Quality Metrics
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✅ Exceeded 50% Wikidata coverage globally (55.7%)
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✅ Exceeded 50% geocoding coverage globally (60.6%)
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✅ Generated comprehensive enrichment candidates list (13,461 records)
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✅ Automated priority scoring (4-level system)
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### Documentation
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✅ Created detailed unification report (UNIFICATION_REPORT.md)
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✅ Machine-readable statistics (DATASET_STATISTICS.yaml)
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✅ Enrichment roadmap with 6 phase plan
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✅ Country-by-country breakdown with quality indicators
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---
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## 💡 Recommendations
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### Immediate Priorities (This Week)
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1. **Enrich Critical Countries** (GE, GB, BE, US, LU) - 33 institutions, 0% coverage
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2. **Fix North Africa Geocoding** (DZ, LY, TN) - 112 institutions, 0% coordinates
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3. **Boost Brazil Coverage** - 212 institutions, only 13.7% Wikidata
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### Short-term Goals (This Month)
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4. **Netherlands Deep Dive** - 622 institutions, leverage ISIL codes
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5. **Mexico Enhancement** - 226 institutions, build on existing geocoding
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6. **Japan Deduplication Analysis** - Investigate high duplicate rate
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### Long-term Vision (Next Quarter)
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7. **Add New Countries** - Target 25 countries total
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8. **Semantic Web Integration** - Generate RDF/Turtle exports
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9. **API Development** - Create SPARQL endpoint for querying
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10. **Collection-Level Enrichment** - Extract collection metadata from websites
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---
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## 📊 Progress Tracking
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**Overall Progress**:
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- ✅ Phase 0: Dataset Unification (COMPLETE)
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- ⏳ Phase 1: Critical Countries Enrichment (READY TO START)
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- ⏳ Phase 2: North Africa Enrichment (READY TO START)
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- 📋 Phase 3: Latin America Enrichment (PLANNED)
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- 📋 Phase 4: Netherlands Enrichment (PLANNED)
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- 📋 Phase 5: Japan Mass Enrichment (PLANNED)
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**Files Ready for Use**:
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- ✅ `globalglam-20251111.yaml` - Master dataset
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- ✅ `ENRICHMENT_CANDIDATES.yaml` - Prioritized enrichment list
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- ✅ `UNIFICATION_REPORT.md` - Detailed statistics
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- ✅ `DATASET_STATISTICS.yaml` - Machine-readable metrics
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**Scripts to Create**:
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- 📝 `enrich_critical_countries_batch.py`
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- 📝 `enrich_north_africa_batch.py`
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- 📝 `enrich_brazil_comprehensive.py`
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- 📝 `enrich_mexico_comprehensive.py`
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- 📝 `enrich_netherlands_isil.py`
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- 📝 `enrich_japan_mass.py`
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---
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**Status**: ✅ **READY FOR GLOBAL ENRICHMENT WORKFLOW**
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**Next Command**: Create first enrichment script for critical countries (GE, GB, BE, US, LU)
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