# Table display helper for HTML and PDF export
from IPython.display import display, HTML, Markdown

def show_table(df, caption=None, index=False):
    """
    Display DataFrame as HTML table with optional caption.
    For PDF export via markdown, tables are converted by nbconvert.
    Links in cells should use HTML <a> tags for proper rendering.
    """
    html = df.to_html(escape=False, index=index, classes='dataframe table')
    if caption:
        html = f"<p><strong>{caption}</strong></p>\n{html}"
    display(HTML(html))

# Report metadata
last_updated = timeline["metadata"].get("last_updated", "Never")
last_updated_str = last_updated[:19].replace("T", " ") if last_updated and last_updated != "Never" else "Never"

display(Markdown(f'''
**Report Information**

| Field | Value |
|-------|-------|
| Generated | {report_timestamp[:19].replace("T", " ")} UTC |
| Timeline Version | {timeline["metadata"].get("version", "1.0")} |
| Last Updated | {last_updated_str} UTC |
| Update Frequency | Monthly (1st of each month) |
'''))

# Display P1343 findings
p1343_display = p1343_count if p1343_count is not None else "Query failed"
komet_prov_count = len(komet_provenance) if komet_provenance else 0

display(Markdown(f'''
### P1343 Limitation Finding

| Metric | Value |
|--------|------:|
| Scholarly articles with P1343 | **{p1343_display}** |
| Total scholarly articles in Wikidata | ~37,000,000 |
| Items referencing KOMET as source | {komet_prov_count} |

**Conclusion:** P1343 is not suitable for tracking KOMET contributions.
'''))

# Display partner journal statistics as interactive table
df_journals = pd.DataFrame([{
    'Journal': s['name'],
    'Partner': s['partner'],
    'Wikidata QID': f'<a href="https://www.wikidata.org/wiki/{s["qid"]}" target="_blank">{s["qid"]}</a>',
    'Articles': s['wikidata_articles'],
    'Citations (P2860)': s['wikidata_citations_p2860']
} for s in journal_stats])

display(Markdown(f'''
#### Partner Journal Wikidata Statistics

**Total journals tracked:** {len(journal_stats)} | **Total articles:** {total_articles} | **Total citations:** {total_citations}
'''))

show_table(df_journals)

# Create comparison table
comparison_data = []
for s in journal_stats:
    baseline = s.get('baseline_2022', {})
    baseline_art = baseline.get('wikidata_articles', 0)
    current_art = s['wikidata_articles']
    change = current_art - baseline_art
    
    comparison_data.append({
        'Journal': s['name'][:40] + ('...' if len(s['name']) > 40 else ''),
        '2022 Articles': baseline_art,
        'Current Articles': current_art,
        'Change': f"+{change}" if change >= 0 else str(change),
        'OpenAlex (2022)': baseline.get('openalex', 'N/A')
    })

df_comparison = pd.DataFrame(comparison_data)

display(Markdown('#### 2022 Baseline vs Current Wikidata Coverage'))
show_table(df_comparison)

# Analyze and display key observations
active_journals = [s for s in journal_stats if s['wikidata_articles'] > 0]
journals_with_citations = [s for s in journal_stats if s['wikidata_citations_p2860'] > 0]

# Find journals with significant OpenAlex but no Wikidata
gap_journals = []
for s in journal_stats:
    baseline = s.get('baseline_2022', {})
    openalex = baseline.get('openalex', 0)
    if openalex and openalex > 100 and s['wikidata_articles'] == 0:
        gap_journals.append({'name': s['name'], 'openalex': openalex, 'wikidata': s['wikidata_articles']})

# Calculate total OpenAlex coverage
total_openalex = sum(s.get('baseline_2022', {}).get('openalex', 0) or 0 for s in journal_stats)
coverage_ratio = (total_articles / total_openalex * 100) if total_openalex > 0 else 0

# Get specific journal stats
info_praxis = next((s for s in journal_stats if s['qid'] == 'Q46478422'), None)
itdb = next((s for s in journal_stats if s['qid'] == 'Q107074231'), None)

# Display key findings using markdown (works in both HTML and PDF)
display(Markdown(f'''
### Key Findings

| Metric | Value |
|--------|-------|
| **Active Journals in Wikidata** | {len(active_journals)} of {len(journal_stats)} journals have articles indexed |
| **Wikidata vs OpenAlex Gap** | {coverage_ratio:.1f}% Wikidata coverage of OpenAlex articles |


**Informationspraxis** (Q46478422) leads with {info_praxis['wikidata_articles'] if info_praxis else 0} articles and {info_praxis['wikidata_citations_p2860'] if info_praxis else 0} citation relationships — the only journal with P2860 data.
**{len(gap_journals)} journals** have significant OpenAlex coverage but zero Wikidata presence.
'''))

# Display the gap analysis table
if gap_journals:
    gap_df = pd.DataFrame(gap_journals)
    gap_df.columns = ['Journal', 'OpenAlex Articles', 'Wikidata Articles']
    gap_df = gap_df.sort_values('OpenAlex Articles', ascending=False)
    show_table(gap_df)

# Visualization: Coverage gap chart
fig, ax = plt.subplots(figsize=(12, 6))

# Prepare data for visualization
viz_data = []
for s in journal_stats:
    baseline = s.get('baseline_2022', {})
    openalex = baseline.get('openalex', 0) or 0
    if openalex > 0 or s['wikidata_articles'] > 0:
        viz_data.append({
            'name': s['name'][:25] + ('...' if len(s['name']) > 25 else ''),
            'openalex': openalex,
            'wikidata': s['wikidata_articles']
        })

viz_df = pd.DataFrame(viz_data).sort_values('openalex', ascending=True)

y_pos = range(len(viz_df))
ax.barh(y_pos, viz_df['openalex'], height=0.4, label='OpenAlex (2022)', color='#3498db', alpha=0.7)
ax.barh([y + 0.4 for y in y_pos], viz_df['wikidata'], height=0.4, label='Wikidata (Current)', color='#e74c3c', alpha=0.7)

ax.set_yticks([y + 0.2 for y in y_pos])
ax.set_yticklabels(viz_df['name'])
ax.set_xlabel('Number of Articles')
ax.set_title('Coverage Gap: OpenAlex vs Wikidata for Partner Journals', fontsize=14, fontweight='bold')
ax.legend(loc='lower right')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.show()

display(HTML('<p style="font-size: 0.9em; color: #666;"><em>Figure 1: The gap between OpenAlex coverage and Wikidata presence demonstrates the opportunity for KOMET to enrich the metadata commons.</em></p>'))

# Display OpenCitations summary
status_breakdown = summary['status_breakdown']

display(Markdown(f'''
### OpenCitations Crowdsourcing Overview

#### All Crowdsourcing Activity

**Total submissions: {summary["total_issues"]}**

| Status | Count |
|--------|------:|
''' + '\n'.join([f'| {status} | {count} |' for status, count in sorted(status_breakdown.items(), key=lambda x: -x[1])]) + f'''

#### KOMET Contributions

**Total KOMET submissions: {komet_summary["total_issues"]}**

| Status | Count |
|--------|------:|
| Successful | {successful} |
| Pending | {pending} |
| Invalid | {invalid} |**Funding**: BMBFTR (German Federal Ministry of Education and Research)  

'''))

# Visualization: OpenCitations status breakdown
if summary['status_breakdown']:
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
    
    # Pie chart for all issues
    statuses = list(summary['status_breakdown'].keys())
    counts = list(summary['status_breakdown'].values())
    colors = ['#2ecc71', '#3498db', '#e74c3c', '#f39c12', '#9b59b6']
    
    ax1.pie(counts, labels=statuses, autopct='%1.0f%%', colors=colors[:len(statuses)], startangle=90)
    ax1.set_title('All OpenCitations Submissions\nby Status', fontweight='bold')
    
    # Bar chart for KOMET vs total
    categories = ['Total Issues', 'KOMET Issues', 'KOMET Successful']
    values = [summary['total_issues'], komet_summary['total_issues'], successful]
    bar_colors = ['#3498db', '#27ae60', '#2ecc71']
    
    bars = ax2.bar(categories, values, color=bar_colors)
    ax2.set_ylabel('Number of Issues')
    ax2.set_title('KOMET Contribution Overview', fontweight='bold')
    ax2.spines['top'].set_visible(False)
    ax2.spines['right'].set_visible(False)
    
    # Add value labels on bars
    for bar, val in zip(bars, values):
        ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.3, str(val), 
                 ha='center', va='bottom', fontweight='bold')
    
    plt.tight_layout()
    plt.show()
    
    display(HTML('<p style="font-size: 0.9em; color: #666;"><em>Figure 2: OpenCitations crowdsourcing activity breakdown showing KOMET\'s contribution share.</em></p>'))

# Display KOMET contributions table
if komet_issues:
    komet_table_data = []
    for issue in komet_issues:
        parsed = kh.parse_opencitations_issue(issue)
        komet_table_data.append({
            'Issue': f'<a href="{parsed["url"]}" target="_blank">#{parsed["issue_number"]}</a>',
            'Title': parsed['title'][:50] + ('...' if len(parsed['title']) > 50 else ''),
            'Status': parsed['status'],
            'Created': parsed['created_at'][:10],
            'Creator': parsed['creator']
        })
    
    df_komet = pd.DataFrame(komet_table_data)
    display(Markdown(f'''
#### KOMET Software Contributions

*Contributors tracked: {", ".join(kh.KOMET_CONTRIBUTORS)}*
'''))
    show_table(df_komet)
else:
    display(Markdown('*No KOMET contributions found yet.*'))

# Fetch OpenCitations Index statistics for partner journals (hidden - data fetching)
# Uses the venue-citation-count API endpoint

oc_index_stats = []

for journal in partner_journals:
    qid = journal["wikidata_qid"]
    name = journal["name"]
    
    # Get ISSN from Wikidata
    meta = kh.get_journal_metadata_wikidata(qid)
    issn = meta.get("issn")
    
    oc_citations = None
    if issn:
        oc_citations = kh.get_journal_citation_count_opencitations(issn)
        time.sleep(0.5)  # Rate limiting (180 req/min)
    
    oc_index_stats.append({
        "name": name,
        "qid": qid,
        "issn": issn,
        "oc_citations": oc_citations
    })
    
    # Log to timeline
    if oc_citations is not None:
        kh.add_observation(
            timeline, f"opencitations.index.{qid}.citations", oc_citations, 
            "opencitations_index",
            metric_name=f"OC Index Citations ({name[:20]})",
            metric_description=f"Citations in OpenCitations Index for {name}"
        )

# Calculate totals
total_oc_citations = sum(s['oc_citations'] or 0 for s in oc_index_stats)
journals_with_issn = sum(1 for s in oc_index_stats if s['issn'])
journals_with_oc_data = sum(1 for s in oc_index_stats if s['oc_citations'] and s['oc_citations'] > 0)

# Display OpenCitations Index statistics
df_oc_index = pd.DataFrame([{
    'Journal': s['name'][:40] + ('...' if len(s['name']) > 40 else ''),
    'ISSN': f'<a href="https://portal.issn.org/resource/ISSN/{s["issn"]}" target="_blank">{s["issn"]}</a>' if s['issn'] else '—',
    'Citations in OC Index': s['oc_citations'] if s['oc_citations'] is not None else '—'
} for s in oc_index_stats])

display(Markdown(f"""
**Summary:** {journals_with_issn} journals have ISSNs | {journals_with_oc_data} have citations in OpenCitations | **Total citations: {total_oc_citations}**
"""))

show_table(df_oc_index)

display(Markdown("""
**Note**: Citation counts are retrieved from the [OpenCitations Index API](https://api.opencitations.net/index/v2). 
A count of 0 means the journal's articles are not yet indexed in OpenCitations, representing an opportunity for KOMET contributions.
"""))

# Display all partner journals with links
all_journals = kh.COLLABORATION_PARTNERS["journals"]
journals_without_wd = kh.COLLABORATION_PARTNERS.get("journals_without_wikidata", [])

partner_table = [{
    'Journal': j['name'],
    'Partner Organization': j.get('partner', 'N/A'),
    'Website': f'<a href="{j.get("url", "#")}" target="_blank">Link</a>' if j.get('url') else 'N/A',
    'Wikidata': f'<a href="https://www.wikidata.org/wiki/{j["wikidata_qid"]}" target="_blank">{j["wikidata_qid"]}</a>',
    'OJS Version': j.get('ojs_version', 'N/A')
} for j in all_journals]

df_partners = pd.DataFrame(partner_table)

display(Markdown(f'''
**Summary:** {len(all_journals)} journals with Wikidata entries | {len(journals_without_wd)} journals pending Wikidata creation

#### Journals with Wikidata Entries
'''))

show_table(df_partners)

# Journals without Wikidata
if journals_without_wd:
    pending_list = '\n'.join([f'- {j["name"]} ({j.get("partner", "N/A")})' for j in journals_without_wd])
    display(Markdown(f'''
#### Journals Pending Wikidata Creation

{pending_list}
'''))

# Display publishing platforms
platforms = kh.COLLABORATION_PARTNERS.get("platforms", [])
if platforms:
    platform_list = '\n'.join([f'- [{p["name"]}]({p["url"]})' for p in platforms])
    display(Markdown(f'''
#### Publishing Platforms

{platform_list}
'''))

# Display key metrics summary
display(Markdown(f'''
### Summary Metrics

| Metric | Value |
|--------|------:|
| Partner Journals | {len(partner_journals)} |
| Wikidata Articles | {total_articles} |
| OpenCitations Submissions | {komet_summary["total_issues"]} |
| Successful Deposits | {successful} |
'''))

# Display metrics timeline table
latest_metrics = kh.get_all_latest_metrics(timeline)

# Group metrics by category
wikidata_metrics = []
opencitations_metrics = []

for path, data in sorted(latest_metrics.items()):
    entry = {
        'Metric': data.get('name', path.split('.')[-1]),
        'Value': data['value'],
        'Last Updated': data['timestamp'][:10]
    }
    if path.startswith('wikidata'):
        wikidata_metrics.append(entry)
    elif path.startswith('opencitations'):
        opencitations_metrics.append(entry)

if opencitations_metrics:
    display(Markdown('#### OpenCitations Metrics Timeline'))
    show_table(pd.DataFrame(opencitations_metrics))

# Fetch geospatial metadata statistics from Wikidata (hidden - data fetching)
# Uses the scholarly-specific Wikidata endpoint for better performance

geo_stats = {
    "direct_coordinates_p625": kh.count_scholarly_articles_with_coordinates(),
    "bounding_box_p1332_p1335": kh.count_scholarly_articles_with_bounding_box(),
    "geoshape_p3896": kh.count_scholarly_articles_with_geoshape(),
    "geographic_main_subject": kh.count_scholarly_articles_with_geographic_subject(),
    "temporal_start_p580": kh.count_scholarly_articles_with_start_time(),
    "temporal_end_p582": kh.count_scholarly_articles_with_end_time(),
}

# Log geospatial metrics to timeline
kh.add_observation(
    timeline, "wikidata.geospatial.direct_coordinates_p625", 
    geo_stats["direct_coordinates_p625"], "wikidata",
    metric_name="Direct Coordinates (P625)",
    metric_description="Scholarly articles with coordinate location property"
)
kh.add_observation(
    timeline, "wikidata.geospatial.bounding_box", 
    geo_stats["bounding_box_p1332_p1335"], "wikidata",
    metric_name="Bounding Box (P1332-P1335)",
    metric_description="Scholarly articles with bounding box properties"
)
kh.add_observation(
    timeline, "wikidata.geospatial.geoshape_p3896", 
    geo_stats["geoshape_p3896"], "wikidata",
    metric_name="GeoShape (P3896)",
    metric_description="Scholarly articles with geoshape (GeoJSON polygon/line)"
)
kh.add_observation(
    timeline, "wikidata.geospatial.geographic_main_subject", 
    geo_stats["geographic_main_subject"], "wikidata",
    metric_name="Geographic Main Subject",
    metric_description="Scholarly articles with main subject linking to geographic entity"
)
kh.add_observation(
    timeline, "wikidata.temporal.start_time_p580", 
    geo_stats["temporal_start_p580"], "wikidata",
    metric_name="Start Time (P580)",
    metric_description="Scholarly articles with study period start time"
)
kh.add_observation(
    timeline, "wikidata.temporal.end_time_p582", 
    geo_stats["temporal_end_p582"], "wikidata",
    metric_name="End Time (P582)",
    metric_description="Scholarly articles with study period end time"
)

# Get sample of articles with coordinates for context
geo_samples = kh.get_scholarly_articles_with_coordinates_sample(limit=10)

# Display geospatial metadata statistics
p625_count = geo_stats["direct_coordinates_p625"] if geo_stats["direct_coordinates_p625"] is not None else "Query failed"
bbox_count = geo_stats["bounding_box_p1332_p1335"] if geo_stats["bounding_box_p1332_p1335"] is not None else "Query failed"
geoshape_count = geo_stats["geoshape_p3896"] if geo_stats["geoshape_p3896"] is not None else "Query failed"
geo_subj_count = geo_stats["geographic_main_subject"] if geo_stats["geographic_main_subject"] is not None else "Query failed"
start_count = geo_stats["temporal_start_p580"] if geo_stats["temporal_start_p580"] is not None else "Query failed"
end_count = geo_stats["temporal_end_p582"] if geo_stats["temporal_end_p582"] is not None else "Query failed"

display(Markdown(f'''
### 5.2 Current Statistics

#### Geospatial Properties on Scholarly Articles

| Property | ID | Description | Count |
|----------|-----|-------------|------:|
| **Coordinate location** | [P625](https://www.wikidata.org/wiki/Property:P625) | Direct lat/lon coordinates | **{p625_count}** |
| **Bounding box (any)** | [P1332](https://www.wikidata.org/wiki/Property:P1332)-[P1335](https://www.wikidata.org/wiki/Property:P1335) | Articles with any N/S/E/W extent property | **{bbox_count}** |
| **GeoShape** | [P3896](https://www.wikidata.org/wiki/Property:P3896) | GeoJSON polygon/line data (Commons) | **{geoshape_count}** |
| **Geographic main subject** | [P921](https://www.wikidata.org/wiki/Property:P921) → geo item | Subject links to item with P625 | **{geo_subj_count}** |

#### Temporal Properties on Scholarly Articles

| Property | ID | Description | Count |
|----------|-----|-------------|------:|
| **Start time** | [P580](https://www.wikidata.org/wiki/Property:P580) | Research period start | **{start_count}** |
| **End time** | [P582](https://www.wikidata.org/wiki/Property:P582) | Research period end | **{end_count}** |
'''))

# Display links to Wikidata Query Service with pre-filled queries
# Uses scholarly-specific endpoint and efficient UNION queries

query_links = {
    "P625 (Coordinates)": "https://query-scholarly.wikidata.org/#%23%20Scholarly%20articles%20with%20coordinate%20location%20%28P625%29%0ASELECT%20%3Farticle%20%3FarticleLabel%20%3Fcoord%20%3FjournalLabel%20WHERE%20%7B%0A%20%20%3Farticle%20wdt%3AP31%20wd%3AQ13442814%20.%0A%20%20%3Farticle%20wdt%3AP625%20%3Fcoord%20.%0A%20%20OPTIONAL%20%7B%20%3Farticle%20wdt%3AP1433%20%3Fjournal%20.%20%7D%0A%20%20SERVICE%20wikibase%3Alabel%20%7B%20bd%3AserviceParam%20wikibase%3Alanguage%20%22en%22%20.%20%7D%0A%7D%0ALIMIT%20100",
    "P1332-P1335 (Bounding Box)": "https://query-scholarly.wikidata.org/#%23%20Scholarly%20articles%20with%20ANY%20bounding%20box%20property%20%28P1332-P1335%29%0ASELECT%20DISTINCT%20%3Farticle%20%3FarticleLabel%20WHERE%20%7B%0A%20%20%3Farticle%20wdt%3AP31%20wd%3AQ13442814%20.%0A%20%20%7B%20%3Farticle%20wdt%3AP1332%20%5B%5D%20.%20%7D%0A%20%20UNION%20%7B%20%3Farticle%20wdt%3AP1333%20%5B%5D%20.%20%7D%0A%20%20UNION%20%7B%20%3Farticle%20wdt%3AP1334%20%5B%5D%20.%20%7D%0A%20%20UNION%20%7B%20%3Farticle%20wdt%3AP1335%20%5B%5D%20.%20%7D%0A%20%20SERVICE%20wikibase%3Alabel%20%7B%20bd%3AserviceParam%20wikibase%3Alanguage%20%22en%22%20.%20%7D%0A%7D%0ALIMIT%20100",
    "P3896 (GeoShape)": "https://query-scholarly.wikidata.org/#%23%20Scholarly%20articles%20with%20geoshape%20%28P3896%29%0ASELECT%20%3Farticle%20%3FarticleLabel%20%3Fgeoshape%20WHERE%20%7B%0A%20%20%3Farticle%20wdt%3AP31%20wd%3AQ13442814%20.%0A%20%20%3Farticle%20wdt%3AP3896%20%3Fgeoshape%20.%0A%20%20SERVICE%20wikibase%3Alabel%20%7B%20bd%3AserviceParam%20wikibase%3Alanguage%20%22en%22%20.%20%7D%0A%7D%0ALIMIT%20100",
    "P921 → Geographic Subject": "https://query-scholarly.wikidata.org/#%23%20Scholarly%20articles%20with%20geographic%20main%20subject%0ASELECT%20%3Farticle%20%3FarticleLabel%20%3Fsubject%20%3FsubjectLabel%20WHERE%20%7B%0A%20%20%3Farticle%20wdt%3AP31%20wd%3AQ13442814%20.%0A%20%20%3Farticle%20wdt%3AP921%20%3Fsubject%20.%0A%20%20%3Fsubject%20wdt%3AP625%20%3Fcoord%20.%0A%20%20SERVICE%20wikibase%3Alabel%20%7B%20bd%3AserviceParam%20wikibase%3Alanguage%20%22en%22%20.%20%7D%0A%7D%0ALIMIT%20100",
    "P580/P582 (Temporal Scope)": "https://query-scholarly.wikidata.org/#%23%20Scholarly%20articles%20with%20temporal%20scope%20%28P580%20or%20P582%29%0ASELECT%20DISTINCT%20%3Farticle%20%3FarticleLabel%20WHERE%20%7B%0A%20%20%3Farticle%20wdt%3AP31%20wd%3AQ13442814%20.%0A%20%20%7B%20%3Farticle%20wdt%3AP580%20%5B%5D%20.%20%7D%0A%20%20UNION%20%7B%20%3Farticle%20wdt%3AP582%20%5B%5D%20.%20%7D%0A%20%20SERVICE%20wikibase%3Alabel%20%7B%20bd%3AserviceParam%20wikibase%3Alanguage%20%22en%22%20.%20%7D%0A%7D%0ALIMIT%20100"
}

display(Markdown("""
#### Explore Geospatial Scholarly Articles

Click the links below to run pre-filled queries in the **Wikidata Scholarly Query Service**:
"""))

links_df = pd.DataFrame([
    {"Property": prop, "Query Link": f'<a href="{url}" target="_blank">Run Query</a>'}
    for prop, url in query_links.items()
])
show_table(links_df)

display(Markdown("""
> **Note**: Queries use the [scholarly-specific endpoint](https://query-scholarly.wikidata.org/) optimized for ~41M scholarly articles. 
> Bounding box and temporal queries use UNION to find articles with **any** of the properties.
"""))

# Visualization: World map of scholarly articles with geospatial metadata
# Uses Cartopy with Equal Earth (equal-area) projection

try:
    import cartopy.crs as ccrs
    import cartopy.feature as cfeature
    from shapely.geometry import Point, box
    import geopandas as gpd
    import numpy as np
    CARTOPY_AVAILABLE = True
except ImportError:
    CARTOPY_AVAILABLE = False
    print("Cartopy not available - skipping map visualization")

if CARTOPY_AVAILABLE:
    # Fetch all geometries from Wikidata
    all_geoms = kh.get_all_scholarly_geometries(limit=500)
    
    if all_geoms:
        # Create GeoDataFrame
        gdf = kh.create_geometry_geodataframe(all_geoms)
        
        if gdf is not None and len(gdf) > 0:
            # Separate by geometry type
            points_gdf = gdf[gdf['geom_type'] == 'point']
            bbox_gdf = gdf[gdf['geom_type'] == 'bbox']
            shape_gdf = gdf[gdf['geom_type'] == 'geoshape']
            
            # Equal Earth projection (modern equal-area)
            projection = ccrs.EqualEarth()
            
            fig = plt.figure(figsize=(14, 7))
            ax = fig.add_subplot(1, 1, 1, projection=projection)
            ax.set_global()
            
            # Basemap features
            ax.add_feature(cfeature.LAND, facecolor='#f5f5f5', edgecolor='none')
            ax.add_feature(cfeature.OCEAN, facecolor='#e8f4f8')
            ax.add_feature(cfeature.COASTLINE, linewidth=0.4, edgecolor='#888888')
            ax.add_feature(cfeature.BORDERS, linewidth=0.2, edgecolor='#cccccc', linestyle=':')
            
            # Plot bounding boxes - polygon + centroid marker for visibility
            if len(bbox_gdf) > 0:
                bbox_lons = []
                bbox_lats = []
                for idx, row in bbox_gdf.iterrows():
                    geom = row.geometry
                    # Draw the polygon (may be too small to see at global scale)
                    ax.add_geometries([geom], crs=ccrs.PlateCarree(),
                                     facecolor='#3498db', alpha=0.4,
                                     edgecolor='#2171b5', linewidth=2)
                    # Collect centroid for marker
                    centroid = geom.centroid
                    bbox_lons.append(centroid.x)
                    bbox_lats.append(centroid.y)
                
                # Add square markers at centroids (always visible at any scale)
                ax.scatter(bbox_lons, bbox_lats, transform=ccrs.PlateCarree(),
                          c='#3498db', s=80, zorder=4, marker='s',
                          edgecolor='#2171b5', linewidth=1.5, alpha=0.9)
            
            # Plot geoshapes - polygon + centroid marker
            if len(shape_gdf) > 0:
                shape_lons = []
                shape_lats = []
                for idx, row in shape_gdf.iterrows():
                    geom = row.geometry
                    ax.add_geometries([geom], crs=ccrs.PlateCarree(),
                                     facecolor='#9b59b6', alpha=0.4,
                                     edgecolor='#7b4397', linewidth=2)
                    centroid = geom.centroid
                    shape_lons.append(centroid.x)
                    shape_lats.append(centroid.y)
                
                # Diamond markers for geoshapes
                ax.scatter(shape_lons, shape_lats, transform=ccrs.PlateCarree(),
                          c='#9b59b6', s=80, zorder=4, marker='d',
                          edgecolor='#7b4397', linewidth=1.5, alpha=0.9)
            
            # Plot points (circles)
            if len(points_gdf) > 0:
                lons = [p.x for p in points_gdf.geometry]
                lats = [p.y for p in points_gdf.geometry]
                ax.scatter(lons, lats, transform=ccrs.PlateCarree(),
                          c='#e74c3c', s=40, zorder=5, marker='o',
                          edgecolor='white', linewidth=0.8, alpha=0.9)
            
            # Gridlines
            gl = ax.gridlines(draw_labels=True, linewidth=0.3, color='gray',
                             alpha=0.5, linestyle='--')
            gl.top_labels = False
            gl.right_labels = False
            
            # Title (use text instead of Unicode symbols to avoid font warnings)
            ax.set_title(
                f'Scholarly Articles with Geospatial Metadata in Wikidata\n'
                f'Points: {len(points_gdf)} | Bounding Boxes: {len(bbox_gdf)} | GeoShapes: {len(shape_gdf)}',
                fontsize=11, fontweight='bold', pad=12
            )
            
            # Legend with markers (matplotlib handles these without Unicode issues)
            from matplotlib.lines import Line2D
            legend_elements = []
            if len(points_gdf) > 0:
                legend_elements.append(Line2D([0], [0], marker='o', color='w',
                                             markerfacecolor='#e74c3c', markersize=8,
                                             markeredgecolor='white', markeredgewidth=0.8,
                                             label=f'P625 Coordinates ({len(points_gdf)})'))
            if len(bbox_gdf) > 0:
                legend_elements.append(Line2D([0], [0], marker='s', color='w',
                                             markerfacecolor='#3498db', markersize=10,
                                             markeredgecolor='#2171b5', markeredgewidth=1.5,
                                             label=f'P1332-P1335 Bounding Box ({len(bbox_gdf)})'))
            if len(shape_gdf) > 0:
                legend_elements.append(Line2D([0], [0], marker='d', color='w',
                                             markerfacecolor='#9b59b6', markersize=10,
                                             markeredgecolor='#7b4397', markeredgewidth=1.5,
                                             label=f'P3896 GeoShape ({len(shape_gdf)})'))
            
            if legend_elements:
                ax.legend(handles=legend_elements, loc='lower left', 
                         fontsize=9, framealpha=0.95)
            
            plt.tight_layout()
            #plt.savefig('geospatial_map.pdf', dpi=150, bbox_inches='tight', facecolor='white')
            plt.show()
            
            display(HTML(
                '<p style="font-size: 0.9em; color: #666;"><em>'
                'Figure 3: Global distribution of scholarly articles with geospatial metadata. '
                'Small polygons (bounding boxes, geoshapes) are marked with symbols at their centroids for visibility. '
                'Note: The bounding box in Mongolia/China region has incorrect coordinates in Wikidata (should be in Nevada, USA).'
                '</em></p>'
            ))
        else:
            display(Markdown('*No valid geometries could be created from the Wikidata results.*'))
    else:
        display(Markdown('*No scholarly articles with geospatial properties found in Wikidata.*'))
else:
    display(Markdown(
        '*Map visualization requires cartopy and geopandas. '
        'Install with: `pip install cartopy geopandas`*'
    ))

# Fetch geospatial/temporal stats per partner journal with metadata (hidden - data fetching)
# This queries each journal for articles with geo/temporal properties and enhances with ISSN/publisher

journal_geo_stats = []

for journal in partner_journals:
    qid = journal["wikidata_qid"]
    name = journal["name"]
    
    # Get metadata from Wikidata (ISSN, publisher)
    meta = kh.get_journal_metadata_wikidata(qid)
    
    # Get geospatial/temporal counts
    coords = kh.count_journal_articles_with_coordinates(qid)
    bbox = kh.count_journal_articles_with_bounding_box(qid)
    geoshape = kh.count_journal_articles_with_geoshape(qid)
    temporal = kh.count_journal_articles_with_temporal_scope(qid)
    geo_subj = kh.count_journal_articles_with_geographic_subject(qid)
    
    # Try Crossref for publisher if not in Wikidata
    publisher = meta.get("publisher")
    publisher_src = "wikidata" if publisher else None
    if not publisher and meta.get("issn"):
        cr_meta = kh.get_journal_metadata_crossref(meta["issn"])
        if cr_meta and cr_meta.get("publisher"):
            publisher = cr_meta["publisher"]
            publisher_src = "crossref"
    
    journal_geo_stats.append({
        "name": name,
        "qid": qid,
        "issn": meta.get("issn"),
        "publisher": publisher,
        "publisher_source": publisher_src,
        "total_articles": next((s["wikidata_articles"] for s in journal_stats if s["qid"] == qid), 0),
        "with_coordinates": coords or 0,
        "with_bounding_box": bbox or 0,
        "with_geoshape": geoshape or 0,
        "with_temporal_scope": temporal or 0,
        "with_geo_subject": geo_subj or 0
    })
    
    time.sleep(0.5)  # Rate limiting

# Display journal geospatial/temporal properties table
df_journal_geo = pd.DataFrame([{
    'Journal': s['name'][:35] + ('...' if len(s['name']) > 35 else ''),
    'ISSN': s['issn'] or '—',
    'Publisher': (s['publisher'][:25] + '...' if s['publisher'] and len(s['publisher']) > 25 else s['publisher']) or '—',
    'Articles': s['total_articles'],
    'P625': s['with_coordinates'],
    'Bbox': s['with_bounding_box'],
    'Shape': s['with_geoshape'],
    'Temporal': s['with_temporal_scope'],
    'Geo Subj': s['with_geo_subject']
} for s in journal_geo_stats])

# Calculate totals
total_with_any_geo = sum(1 for s in journal_geo_stats if s['with_coordinates'] > 0 or s['with_bounding_box'] > 0 or s['with_geoshape'] > 0 or s['with_geo_subject'] > 0)
total_with_temporal = sum(1 for s in journal_geo_stats if s['with_temporal_scope'] > 0)

display(Markdown(f'''
#### Partner Journal Geospatial & Temporal Coverage

**Journals with any geospatial data:** {total_with_any_geo} | **Journals with temporal data:** {total_with_temporal}

*Columns: P625 = direct coordinates, Bbox = bounding box (P1332-P1335), Shape = geoshape/GeoJSON (P3896), Temporal = start/end time (P580/P582), Geo Subj = geographic main subject*
'''))

show_table(df_journal_geo)

# Add geospatial stats to report data and save (hidden)
report_data["geospatial"] = {
    "direct_coordinates_p625": geo_stats["direct_coordinates_p625"],
    "bounding_box_p1332_p1335": geo_stats["bounding_box_p1332_p1335"],
    "geoshape_p3896": geo_stats["geoshape_p3896"],
    "geographic_main_subject": geo_stats["geographic_main_subject"],
    "temporal_start_p580": geo_stats["temporal_start_p580"],
    "temporal_end_p582": geo_stats["temporal_end_p582"],
    "properties_tracked": {
        "P625": "coordinate location",
        "P1332": "northernmost point",
        "P1333": "southernmost point", 
        "P1334": "easternmost point",
        "P1335": "westernmost point",
        "P3896": "geoshape (GeoJSON polygon/line)",
        "P921": "main subject (linked to geo items)",
        "P580": "start time",
        "P582": "end time"
    },
    "notes": "Data from scholarly-specific Wikidata endpoint (query-scholarly.wikidata.org)"
}

# Add journal geospatial stats to report data
report_data["journal_geospatial"] = journal_geo_stats

# Save timeline with all metrics including geospatial
kh.save_timeline(timeline)
kh.save_json(report_data, "komet_report_data.json")

@dataset{komet_report_2026,
  author       = {Nüst, Daniel and Niers, Tom and Hauschke, Christian and Yücel, Gazi},
  title        = {{KOMET Project - Open Metadata Evaluation Report}},
  year         = 2026,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.18479191},
  url          = {https://doi.org/10.5281/zenodo.18479191}
}

Source	Purpose	Priority
OpenCitations	Citation metadata contributions	Primary
Wikidata	Scholarly graph baseline & specific properties	Monitoring

Journal	Partner	Wikidata QID	Articles	Citations (P2860)
Journal of South Asian Linguistics	KIM Universität Konstanz	Q122948152	0	0
Free Neuropathology	WWU Münster	Q108455809	0	0
Jahrbuch für Christliche Sozialwissenschaften	WWU Münster	Q1678617	0	0
Journal für Kulturpflanzen	Julius Kühn-Institut	Q1455822	0	0
VITIS - Journal of Grapevine Research	Julius Kühn-Institut	Q15756080	0	0
Journal of Applied Botany and Food Quality	Julius Kühn-Institut	Q15764825	0	0
Francia-Recensio	heiJOURNALS Heidelberg	Q101247086	0	0
Heidelberger Beiträge zum Finanz- und Steuerrecht	heiJOURNALS Heidelberg	Q105103105	0	0
Informationspraxis	heiJOURNALS Heidelberg	Q46478422	28	2
International Journal of Dream Research	heiJOURNALS Heidelberg	Q96332444	0	0
Journal of Dynamic Decision Making	heiJOURNALS Heidelberg	Q50817185	0	0
Archäologischer Anzeiger	Deutsches Archäologisches Institut	Q636752	0	0
Journal of Spatial Information Science	JOSIS / TU Dresden	Q50814880	0	0
Cognitio	ZHB Luzern	Q111049844	0	0
itdb - inter- und transdisziplinäre Bildung	ZHB Luzern	Q107074231	14	0

Journal	2022 Articles	Current Articles	Change	OpenAlex (2022)
Journal of South Asian Linguistics	0	0	+0	0
Free Neuropathology	0	0	+0	0
Jahrbuch für Christliche Sozialwissensch...	0	0	+0	850
Journal für Kulturpflanzen	0	0	+0	558
VITIS - Journal of Grapevine Research	0	0	+0	1762
Journal of Applied Botany and Food Quali...	3	0	-3	419
Francia-Recensio	1	0	-1	1096
Heidelberger Beiträge zum Finanz- und St...	0	0	+0	0
Informationspraxis	55	28	-27	43
International Journal of Dream Research	0	0	+0	352
Journal of Dynamic Decision Making	0	0	+0	18
Archäologischer Anzeiger	0	0	+0	224
Journal of Spatial Information Science	25	0	-25	201
Cognitio	31	0	-31	0
itdb - inter- und transdisziplinäre Bild...	13	14	+1	0

Metric	Value
Active Journals in Wikidata	2 of 15 journals have articles indexed
Wikidata vs OpenAlex Gap	0.8% Wikidata coverage of OpenAlex articles

Journal	OpenAlex Articles	Wikidata Articles
VITIS - Journal of Grapevine Research	1762	0
Francia-Recensio	1096	0
Jahrbuch für Christliche Sozialwissenschaften	850	0
Journal für Kulturpflanzen	558	0
Journal of Applied Botany and Food Quality	419	0
International Journal of Dream Research	352	0
Archäologischer Anzeiger	224	0
Journal of Spatial Information Science	201	0

Field	Value
Generated	2026-02-04 09:10:02 UTC
Timeline Version	2.0
Last Updated	2026-02-03 13:56:01 UTC
Update Frequency	Monthly (1st of each month)

Metric	Value
Scholarly articles with P1343	14
Total scholarly articles in Wikidata	~37,000,000
Items referencing KOMET as source	0

Issue	Title	Status	Created	Creator
#6	deposit localhost doi:10.1234/37fs2v66	invalid	2025-10-10	GaziYucel
#5	deposit localhost doi:10.1234/37fs2v66	invalid	2025-10-09	GaziYucel
#4	deposit localhost doi:10.1234/37fs2v66	invalid	2025-10-09	GaziYucel
#3	deposit localhost doi:10.0000/1rjphk47	invalid	2025-03-27	GaziYucel
#2	Add GaziYucel to whitelist.txt	rejected	2023-04-04	GaziYucel

Journal	ISSN	Citations in OC Index
Journal of South Asian Linguistics	1947-8232	0
Free Neuropathology	2699-4445	0
Jahrbuch für Christliche Sozialwissensch...	—	—
Journal für Kulturpflanzen	1867-0911	0
VITIS - Journal of Grapevine Research	0042-7500	0
Journal of Applied Botany and Food Quali...	0066-1759	0
Francia-Recensio	2425-3510	0
Heidelberger Beiträge zum Finanz- und St...	—	—
Informationspraxis	2297-3249	0
International Journal of Dream Research	1866-7953	0
Journal of Dynamic Decision Making	2365-8037	0
Archäologischer Anzeiger	0003-8105	0
Journal of Spatial Information Science	1948-660X	0
Cognitio	2624-8417	0
itdb - inter- und transdisziplinäre Bild...	2673-7671	0

Journal	Partner Organization	Website	Wikidata	OJS Version
Journal of South Asian Linguistics	KIM Universität Konstanz	Link	Q122948152	3.3.0.14
Free Neuropathology	WWU Münster	Link	Q108455809	3.3.0.13
Jahrbuch für Christliche Sozialwissenschaften	WWU Münster	Link	Q1678617	3.3.0.13
Journal für Kulturpflanzen	Julius Kühn-Institut	Link	Q1455822	3.3.0.8
VITIS - Journal of Grapevine Research	Julius Kühn-Institut	Link	Q15756080	3.3.0.8
Journal of Applied Botany and Food Quality	Julius Kühn-Institut	Link	Q15764825	3.3.0.8
Francia-Recensio	heiJOURNALS Heidelberg	Link	Q101247086	3.2.1.4
Heidelberger Beiträge zum Finanz- und Steuerrecht	heiJOURNALS Heidelberg	Link	Q105103105	3.2.1.4
Informationspraxis	heiJOURNALS Heidelberg	Link	Q46478422	3.2.1.4
International Journal of Dream Research	heiJOURNALS Heidelberg	Link	Q96332444	3.2.1.4
Journal of Dynamic Decision Making	heiJOURNALS Heidelberg	Link	Q50817185	3.2.1.4
Archäologischer Anzeiger	Deutsches Archäologisches Institut	Link	Q636752	3.4.0.6
Journal of Spatial Information Science	JOSIS / TU Dresden	Link	Q50814880	3.3.0.6
Cognitio	ZHB Luzern	Link	Q111049844	3.3.0.12
itdb - inter- und transdisziplinäre Bildung	ZHB Luzern	Link	Q107074231	3.3.0.12

Metric	Value
Partner Journals	15
Wikidata Articles	42
OpenCitations Submissions	4
Successful Deposits	0

Metric	Value	Last Updated
OC Index Citations (Francia-Recensio)	0	2026-02-03
OC Index Citations (itdb - inter- und tr)	0	2026-02-03
OC Index Citations (Free Neuropathology)	0	2026-02-03
OC Index Citations (Cognitio)	0	2026-02-03
OC Index Citations (Journal of South Asi)	0	2026-02-03
OC Index Citations (Journal für Kulturpf)	0	2026-02-03
OC Index Citations (VITIS - Journal of G)	0	2026-02-03
OC Index Citations (Journal of Applied B)	0	2026-02-03
OC Index Citations (Informationspraxis)	0	2026-02-03
OC Index Citations (Journal of Spatial I)	0	2026-02-03
OC Index Citations (Journal of Dynamic D)	0	2026-02-03
OC Index Citations (Archäologischer Anze)	0	2026-02-03
OC Index Citations (International Journa)	0	2026-02-03
KOMET Successful	0	2026-01-30
KOMET Invalid	4	2026-01-30
KOMET Issues	5	2026-01-30
KOMET Pending	0	2026-01-30
Total Crowdsourcing Issues	16	2026-01-30

Approach	Properties	Description
Direct coordinates	P625	Attach coordinates directly to the article
Bounding box	P1332, P1333, P1334, P1335	North/South/East/West extent of study area
GeoShape	P3896	GeoJSON polygon/line data stored on Wikimedia Commons
Subject-based linking	P921 → geo item	Link article to geographic entity with coordinates
Temporal scope	P580, P582	Start/end time of the research period

Property	ID	Description	Count
Coordinate location	P625	Direct lat/lon coordinates	46
Bounding box (any)	P1332-P1335	Articles with any N/S/E/W extent property	1
GeoShape	P3896	GeoJSON polygon/line data (Commons)	0
Geographic main subject	P921 → geo item	Subject links to item with P625	5

Property	ID	Description	Count
Start time	P580	Research period start	11
End time	P582	Research period end	9

Status	Count
invalid	11
unknown	2
done	1
to_be_processed	1

Source	License	Terms
Wikidata	CC0 1.0	Wikidata Licensing
OpenCitations	CC0 1.0	OpenCitations Licensing

Property	Query Link
P625 (Coordinates)	Run Query
P1332-P1335 (Bounding Box)	Run Query
P3896 (GeoShape)	Run Query
P921 → Geographic Subject	Run Query
P580/P582 (Temporal Scope)	Run Query

KOMET Project Evaluation Report

About KOMET

Data Sources

1. Wikidata Analysis

1.1 P1343 (Described by Source) - Limited Reach

P1343 Limitation Finding

1.2 P2860 (Cites Work) - Partner Journal Baseline

Partner Journal Wikidata Statistics

1.3 Comparison with 2022 Baseline

2022 Baseline vs Current Wikidata Coverage

1.4 Observations

Key Findings

1.5 Discussion

2. OpenCitations

2.1 OpenCitations Crowdsourcing Analysis

OpenCitations Crowdsourcing Overview

All Crowdsourcing Activity

KOMET Contributions

KOMET Software Contributions

2.2 OpenCitations Index - Partner Journal Coverage

3. Partner Journal Overview

Journals with Wikidata Entries

Journals Pending Wikidata Creation

Publishing Platforms

4. Summary & Key Metrics

Summary Metrics

OpenCitations Metrics Timeline

5. Geospatial & Temporal Metadata Analysis

5.1 Wikidata Mechanisms for Geospatial Metadata

5.2 Current Statistics

Geospatial Properties on Scholarly Articles

Temporal Properties on Scholarly Articles

Explore Geospatial Scholarly Articles

Global Distribution Map

Partner Journal Geospatial & Temporal Coverage

5.3 Discussion

6. Status and Next Steps

License & Data Sources

This Notebook

External Data Sources

Citation

Funding