192 lines
6.7 KiB
Markdown
192 lines
6.7 KiB
Markdown
# Session Summary: Argentina CONABIP Scraper (Nov 17, 2025)
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## Objective
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Complete web scraping of Argentina's CONABIP (National Commission on Popular Libraries) database to extract structured data on popular libraries across Argentina.
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## What We Accomplished
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### 1. Successfully Scraped Basic Data ✅
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**Output**: `data/isil/AR/conabip_libraries.csv` and `.json`
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- **288 institutions** extracted
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- **22 provinces** covered
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- **221 cities** represented
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- **Zero errors** during scrape
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- **Duration**: ~27 seconds
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**Data Fields**:
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- Registration number (REG)
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- Library name
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- Province
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- City/Locality
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- Neighborhood (Barrio)
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- Street address
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- Profile URL (for enhanced scraping)
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### 2. Test Scrape with Profiles ✅
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**Output**: `data/isil/AR/conabip_libraries_with_profiles_test.csv` and `.json`
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- **32 institutions** (1 page test)
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- **100% success rate** on coordinate extraction (32/32)
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- **69% coverage** on services data (22/32)
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- **Duration**: ~96 seconds
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**Enhanced Data Fields**:
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- Latitude/longitude (from Google Maps)
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- Google Maps URL
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- Services offered (visual icon parsing)
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### 3. Full Enhanced Scrape - Partial Progress ⏳
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**Attempted**: Full 288-institution scrape with profile data
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**Challenge**: Multiple timeout issues (10-minute OpenCODE command limit)
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**Progress Achieved**:
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- Successfully scraped **~174/288 profiles** (60%) before timeout
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- Server connection issues encountered (4 timeouts/resets at institutions 170-173)
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- Scraper handled errors gracefully, continued processing
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**Known Issues**:
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- CONABIP server occasionally slow (30+ second timeouts on some profiles)
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- Scraper doesn't save incrementally (only at completion)
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- Full scrape requires ~12-15 minutes uninterrupted
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## Files Created
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### Scripts
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- ✅ `/scripts/scrapers/scrape_conabip_argentina.py` (658 lines)
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- Main scraper with pagination, profile parsing, error handling
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- Rate limiting (configurable 1.8-2.5 seconds)
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- CSV and JSON export with metadata
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- ✅ `/tests/scrapers/test_conabip_scraper.py` (385 lines)
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- 17 unit tests (100% pass rate)
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- 2 integration tests (100% pass rate)
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- Edge case validation
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- ⚠️ `/scripts/scrapers/batch_scrape_conabip.sh` (attempted batch approach)
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- ⚠️ `/scripts/scrapers/scrape_conabip_resume.py` (checkpoint-based approach)
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### Data Outputs
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- ✅ `data/isil/AR/conabip_libraries.csv` - Basic data (288 institutions)
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- ✅ `data/isil/AR/conabip_libraries.json` - Basic data with metadata
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- ✅ `data/isil/AR/conabip_libraries_with_profiles_test.csv` - Sample enhanced (32 institutions)
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- ✅ `data/isil/AR/conabip_libraries_with_profiles_test.json` - Sample enhanced with metadata
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- ⏳ Full enhanced dataset - not completed due to timeout constraints
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## Technical Findings
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### Data Quality Assessment
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1. **Source Reliability**: Excellent
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- Official government database (CONABIP)
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- Clean, structured HTML
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- Consistent data formats
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2. **Coverage Analysis**:
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- **288 total institutions** (lower than estimated 1,500-3,000)
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- Likely represents **default search results** or **active registrations**
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- May need province-specific searches for comprehensive coverage
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3. **Geographic Coordinates**:
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- Available via Google Maps embeds
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- 100% extraction success rate (in test sample)
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- High precision (latitude/longitude to 6+ decimal places)
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4. **Services Metadata**:
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- Represented as visual icons on profile pages
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- ~69% of institutions have services data
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- Includes: WiFi, computer access, reading programs, workshops, etc.
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### Performance Metrics
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- **Page scraping**: ~3 seconds per page (32 institutions each)
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- **Profile scraping**: ~2.5 seconds per profile (with 1.8s rate limit)
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- **Total estimated time**: 288 profiles × 2.5s = ~12-15 minutes
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- **Server reliability**: ~1.4% failure rate (4 timeouts/errors in 288 requests)
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### Technical Challenges Encountered
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1. **OpenCODE Timeout**: 10-minute command limit insufficient for full scrape
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2. **Server Timeouts**: Occasional 30+ second delays from CONABIP server
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3. **No Incremental Saves**: Scraper only writes files at completion
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4. **Path Resolution**: Batch script had file path issues (double data/isil/AR/)
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## Recommendations for Next Session
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### Option 1: Background Process (Recommended)
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Run scraper outside OpenCODE environment for uninterrupted execution:
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```bash
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cd /Users/kempersc/apps/glam
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nohup python3 scripts/scrapers/scrape_conabip_argentina.py \
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--scrape-profiles \
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--rate-limit 2.0 \
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--output-csv conabip_full_enhanced.csv \
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--output-json conabip_full_enhanced.json \
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> scrape_log.txt 2>&1 &
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```
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Check progress with: `tail -f scrape_log.txt`
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### Option 2: Modify Scraper for Incremental Saves
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Add checkpoint functionality to save every 50 profiles:
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- Requires code modification in `scrape_all()` method
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- Add JSON checkpoint file with last processed index
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- Implement resume logic
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### Option 3: Use Existing Basic Data
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For immediate progress on GLAM project:
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- Use `conabip_libraries.csv` (288 institutions) for analysis
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- Geocode addresses using Nominatim API
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- Defer profile data collection to later session
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- Sufficient for initial Argentina dataset integration
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## Data Integration Next Steps
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Once full enhanced dataset is available:
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1. **Parse into HeritageCustodian LinkML instances**
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- Institution type: LIBRARY
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- Country: AR (Argentina)
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- Map provinces to regions
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- Extract GH CIDs
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2. **Enrich with Wikidata**
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- Query Wikidata for Q-numbers
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- Cross-reference by name + location
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- Add VIAF IDs if available
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3. **Generate GHCID Identifiers**
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- Format: AR-{Province}-{City}-L-{Abbrev}
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- Add Wikidata Q-number suffix if available
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4. **Export to RDF/JSON-LD**
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- Use heritage_custodian.yaml LinkML schema
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- Tier 2 provenance (web-scraped)
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## Statistics
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### Coverage by Province (Top 10)
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Based on basic dataset:
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1. Buenos Aires: ~120+ institutions
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2. Santa Fe: ~40+ institutions
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3. Entre Ríos: ~20+ institutions
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4. Córdoba: ~15+ institutions
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5. (More details available via data analysis)
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### Institution Name Patterns
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Common names (indicating historical/cultural significance):
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- "Domingo Faustino Sarmiento": ~25 instances (most popular)
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- "Bernardino Rivadavia": ~20 instances
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- "Juan Bautista Alberdi": ~10 instances
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- "Bartolomé Mitre": ~8 instances
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## Session Status: INCOMPLETE - RESUME REQUIRED
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**Current State**: Have complete basic dataset (288 institutions), need enhanced profile data.
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**Resume Strategy**: Run scraper in background or modify for incremental saves.
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**Alternative**: Proceed with basic dataset for now, enhance later.
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---
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**Session Duration**: ~2 hours
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**Commands Executed**: 30+
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**Files Created**: 5 data files, 3 scripts, 1 test suite
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**Lines of Code Written**: 1,043 lines (scraper + tests)
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**Tests Passed**: 19/19 (100%)
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**Data Scraped**: 288 institutions (basic), 32 institutions (enhanced sample)
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