glam/data/isil/finland/QUICK_START.md
2025-11-21 22:12:33 +01:00

2.1 KiB

Finnish ISIL Database - Quick Start Guide

Files in This Directory

  1. finland_isil_complete_20251120.json (104 KB)

    • Raw JSON data from API
    • 817 total records
    • Complete dataset
  2. finland_isil_complete_20251120.csv (55 KB)

    • CSV export for spreadsheet analysis
    • Headers: isil, name, linda, cities, former, active
  3. FINLAND_ISIL_HARVEST_REPORT.md (12 KB)

    • Comprehensive analysis report
    • Institution type breakdown
    • Geographic distribution
    • API documentation
  4. QUICK_START.md (this file)

    • Quick reference guide

Quick Stats

  • Total: 817 institutions
  • Active: 750 (91.8%)
  • Inactive: 67 (8.2%)
  • Coverage: 200+ cities

Institution Types

  • Public Libraries: 479 (58.6%)
  • Academic Libraries: 232 (28.4%)
  • Museums: 15 (1.8%)
  • Archives: 4 (0.5%)
  • Special Libraries: 9 (1.1%)
  • Other: 78 (9.5%)

Top Cities

  1. Helsinki: 127 institutions
  2. Turku: 35
  3. Espoo: 23
  4. Tampere: 19
  5. Kuopio: 15

Data Source

Usage Examples

Load JSON in Python

import json

with open('finland_isil_complete_20251120.json', 'r') as f:
    data = json.load(f)
    records = data['data']
    print(f"Loaded {len(records)} institutions")

Load CSV in Python

import pandas as pd

df = pd.read_csv('finland_isil_complete_20251120.csv')
print(df.head())

Query API Directly

# Get all records
curl "https://isil.kansalliskirjasto.fi/api/query"

# Search by city
curl "https://isil.kansalliskirjasto.fi/api/query?cities=helsinki"

# Search by name
curl "https://isil.kansalliskirjasto.fi/api/query?name=kansalliskirjasto"

Next Steps

  1. Convert to LinkML HeritageCustodian format
  2. Classify institution types (LIBRARY/ARCHIVE/MUSEUM)
  3. Geocode city names to coordinates
  4. Cross-link with Wikidata Q-numbers
  5. Add to GLAM master database

See Also

  • Full analysis: FINLAND_ISIL_HARVEST_REPORT.md
  • GLAM project: /Users/kempersc/apps/glam/
  • Schema: /Users/kempersc/apps/glam/schemas/