Citation Management

Visual Enhancement with Scientific Schematics

When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.

If your document does not already contain schematics or diagrams:

• Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
• Simply describe your desired diagram in natural language
• Nano Banana Pro will automatically generate, review, and refine the schematic

For new documents: Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.

How to generate schematics:

python scripts/generate_schematic.py "your diagram description" -o figures/output.png

The AI will automatically:

• Create publication-quality images with proper formatting
• Review and refine through multiple iterations
• Ensure accessibility (colorblind-friendly, high contrast)
• Save outputs in the figures/ directory

When to add schematics:

• Citation workflow diagrams
• Literature search methodology flowcharts
• Reference management system architectures
• Citation style decision trees
• Database integration diagrams
• Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

Core Workflow

Citation management follows a systematic process:

Phase 1: Paper Discovery and Search

Goal: Find relevant papers using academic search engines.

Google Scholar Search

Google Scholar provides the most comprehensive coverage across disciplines.

Basic Search:

# Search for papers on a topic
python scripts/search_google_scholar.py "CRISPR gene editing" \
  --limit 50 \
  --output results.json

# Search with year filter
python scripts/search_google_scholar.py "machine learning protein folding" \
  --year-start 2020 \
  --year-end 2024 \
  --limit 100 \
  --output ml_proteins.json

Advanced Search Strategies (see references/google_scholar_search.md):

• Use quotation marks for exact phrases: "deep learning"
• Search by author: author:LeCun
• Search in title: intitle:"neural networks"
• Exclude terms: machine learning -survey
• Find highly cited papers using sort options
• Filter by date ranges to get recent work

Best Practices:

• Use specific, targeted search terms
• Include key technical terms and acronyms
• Filter by recent years for fast-moving fields
• Check "Cited by" to find seminal papers
• Export top results for further analysis

PubMed Search

PubMed specializes in biomedical and life sciences literature (35+ million citations).

Basic Search:

# Search PubMed
python scripts/search_pubmed.py "Alzheimer's disease treatment" \
  --limit 100 \
  --output alzheimers.json

# Search with MeSH terms and filters
python scripts/search_pubmed.py \
  --query '"Alzheimer Disease"[MeSH] AND "Drug Therapy"[MeSH]' \
  --date-start 2020 \
  --date-end 2024 \
  --publication-types "Clinical Trial,Review" \
  --output alzheimers_trials.json

Advanced PubMed Queries (see references/pubmed_search.md):

• Use MeSH terms: "Diabetes Mellitus"[MeSH]
• Field tags: "cancer"[Title], "Smith J"[Author]
• Boolean operators: AND, OR, NOT
• Date filters: 2020:2024[Publication Date]
• Publication types: "Review"[Publication Type]
• Combine with E-utilities API for automation

Best Practices:

• Use MeSH Browser to find correct controlled vocabulary
• Construct complex queries in PubMed Advanced Search Builder first
• Include multiple synonyms with OR
• Retrieve PMIDs for easy metadata extraction
• Export to JSON or directly to BibTeX

Phase 2: Metadata Extraction

Goal: Convert paper identifiers (DOI, PMID, arXiv ID) to complete, accurate metadata.

Quick DOI to BibTeX Conversion

For single DOIs, use the quick conversion tool:

# Convert single DOI
python scripts/doi_to_bibtex.py 10.1038/s41586-021-03819-2

# Convert multiple DOIs from a file
python scripts/doi_to_bibtex.py --input dois.txt --output references.bib

# Different output formats
python scripts/doi_to_bibtex.py 10.1038/nature12345 --format json

Comprehensive Metadata Extraction

For DOIs, PMIDs, arXiv IDs, or URLs:

# Extract from DOI
python scripts/extract_metadata.py --doi 10.1038/s41586-021-03819-2

# Extract from PMID
python scripts/extract_metadata.py --pmid 34265844

# Extract from arXiv ID
python scripts/extract_metadata.py --arxiv 2103.14030

# Extract from URL
python scripts/extract_metadata.py --url "https://www.nature.com/articles/s41586-021-03819-2"

# Batch extraction from file (mixed identifiers)
python scripts/extract_metadata.py --input identifiers.txt --output citations.bib

Metadata Sources (see references/metadata_extraction.md):

1. CrossRef API: Primary source for DOIs

   • Comprehensive metadata for journal articles
   • Publisher-provided information
   • Includes authors, title, journal, volume, pages, dates
   • Free, no API key required

2. PubMed E-utilities: Biomedical literature

   • Official NCBI metadata
   • Includes MeSH terms, abstracts
   • PMID and PMCID identifiers
   • Free, API key recommended for high volume

3. arXiv API: Preprints in physics, math, CS, q-bio

   • Complete metadata for preprints
   • Version tracking
   • Author affiliations
   • Free, open access

4. DataCite API: Research datasets, software, other resources

   • Metadata for non-traditional scholarly outputs
   • DOIs for datasets and code
   • Free access

What Gets Extracted:

• Required fields: author, title, year
• Journal articles: journal, volume, number, pages, DOI
• Books: publisher, ISBN, edition
• Conference papers: booktitle, conference location, pages
• Preprints: repository (arXiv, bioRxiv), preprint ID
• Additional: abstract, keywords, URL

Phase 2.5: Metadata Enrichment via Web Search (MANDATORY)

Goal: Detect and fill in any missing metadata fields using web search. This phase runs AFTER extraction and BEFORE formatting to ensure every BibTeX entry is complete.

Why This Is Critical: Metadata extraction from APIs (CrossRef, PubMed, arXiv) sometimes returns incomplete records — missing volume, pages, issue number, or DOI. These gaps must be filled before the bibliography is considered ready.

Step 1: Scan for Incomplete Entries

After extracting metadata, scan the BibTeX file for entries missing key fields:

Fields to check per entry type:

Any @article entry missing volume, pages, or doi is considered incomplete and must be enriched.

Step 2: Web Search for Missing Metadata

For each incomplete entry, search for the missing information:

Option A — Search by title and author (best for finding DOI):

python scripts/parallel_web.py search \
  "FIRST_AUTHOR TITLE JOURNAL_NAME volume pages DOI" \
  -o sources/search_YYYYMMDD_HHMMSS_citation_CITATIONKEY.md

Option B — Extract from DOI page (best when DOI is known but volume/pages missing):

python scripts/parallel_web.py extract \
  "https://doi.org/10.XXXX/YYYY" \
  --objective "extract complete citation metadata: volume, issue, pages, publication date" \
  -o sources/extract_YYYYMMDD_HHMMSS_doi_CITATIONKEY.md

Option C — Search CrossRef API directly (programmatic, fast):

python scripts/parallel_web.py search \
  "crossref DOI metadata FIRST_AUTHOR TITLE" \
  -o sources/search_YYYYMMDD_HHMMSS_crossref_CITATIONKEY.md

Option D — Search Google Scholar (fallback for hard-to-find papers):

python scripts/parallel_web.py search \
  "google scholar FIRST_AUTHOR TITLE YEAR complete citation" \
  -o sources/search_YYYYMMDD_HHMMSS_scholar_CITATIONKEY.md

Step 3: Update BibTeX Entries

After finding the missing metadata:

1. Open references.bib
2. Add the missing fields to the incomplete entry
3. Verify the found metadata is consistent with existing fields (same author, title, year)
4. Log each fix:

   [HH:MM:SS] METADATA ENRICHED: [CitationKey] - added volume={X}, pages={Y--Z}, doi={10.XXX/YYY} ✅
   

Step 4: Handle Unfindable Metadata

If metadata genuinely cannot be found after web search (very old paper, obscure conference, etc.):

1. Add a note field to the BibTeX entry explaining the gap:

   note = {Volume and pages not available — published online only}
   

2. Log the exception:

   [HH:MM:SS] METADATA INCOMPLETE: [CitationKey] - pages unavailable (online-only publication) ⚠️
   

3. These exceptions should be rare — most modern papers have complete metadata findable via web search.

Quick Reference: Common Missing Fields and Where to Find Them

Phase 3: BibTeX Formatting

Goal: Generate clean, properly formatted BibTeX entries.

Understanding BibTeX Entry Types

See references/bibtex_formatting.md for complete guide.

Common Entry Types:

• @article: Journal articles (most common)
• @book: Books
• @inproceedings: Conference papers
• @incollection: Book chapters
• @phdthesis: Dissertations
• @misc: Preprints, software, datasets

Required Fields by Type:

@article{citationkey,
  author  = {Last1, First1 and Last2, First2},
  title   = {Article Title},
  journal = {Journal Name},
  year    = {2024},
  volume  = {10},
  number  = {3},
  pages   = {123--145},
  doi     = {10.1234/example}
}

@inproceedings{citationkey,
  author    = {Last, First},
  title     = {Paper Title},
  booktitle = {Conference Name},
  year      = {2024},
  pages     = {1--10}
}

@book{citationkey,
  author    = {Last, First},
  title     = {Book Title},
  publisher = {Publisher Name},
  year      = {2024}
}

Formatting and Cleaning

Use the formatter to standardize BibTeX files:

# Format and clean BibTeX file
python scripts/format_bibtex.py references.bib \
  --output formatted_references.bib

# Sort entries by citation key
python scripts/format_bibtex.py references.bib \
  --sort key \
  --output sorted_references.bib

# Sort by year (newest first)
python scripts/format_bibtex.py references.bib \
  --sort year \
  --descending \
  --output sorted_references.bib

# Remove duplicates
python scripts/format_bibtex.py references.bib \
  --deduplicate \
  --output clean_references.bib

# Validate and report issues
python scripts/format_bibtex.py references.bib \
  --validate \
  --report validation_report.txt

Formatting Operations:

• Standardize field order
• Consistent indentation and spacing
• Proper capitalization in titles (protected with {})
• Standardized author name format
• Consistent citation key format
• Remove unnecessary fields
• Fix common errors (missing commas, braces)

Phase 4: Citation Validation

Goal: Verify all citations are accurate and complete.

Comprehensive Validation

# Validate BibTeX file
python scripts/validate_citations.py references.bib

# Validate and fix common issues
python scripts/validate_citations.py references.bib \
  --auto-fix \
  --output validated_references.bib

# Generate detailed validation report
python scripts/validate_citations.py references.bib \
  --report validation_report.json \
  --verbose

Validation Checks (see references/citation_validation.md):

1. DOI Verification:

   • DOI resolves correctly via doi.org
   • Metadata matches between BibTeX and CrossRef
   • No broken or invalid DOIs

2. Required Fields:

   • All required fields present for entry type
   • No empty or missing critical information
   • Author names properly formatted

3. Data Consistency:

   • Year is valid (4 digits, reasonable range)
   • Volume/number are numeric
   • Pages formatted correctly (e.g., 123--145)
   • URLs are accessible

4. Duplicate Detection:

   • Same DOI used multiple times
   • Similar titles (possible duplicates)
   • Same author/year/title combinations

5. Format Compliance:

   • Valid BibTeX syntax
   • Proper bracing and quoting
   • Citation keys are unique
   • Special characters handled correctly

Validation Output:

{
  "total_entries": 150,
  "valid_entries": 145,
  "errors": [
    {
      "citation_key": "Smith2023",
      "error_type": "missing_field",
      "field": "journal",
      "severity": "high"
    },
    {
      "citation_key": "Jones2022",
      "error_type": "invalid_doi",
      "doi": "10.1234/broken",
      "severity": "high"
    }
  ],
  "warnings": [
    {
      "citation_key": "Brown2021",
      "warning_type": "possible_duplicate",
      "duplicate_of": "Brown2021a",
      "severity": "medium"
    }
  ]
}

Phase 5: Integration with Writing Workflow

Building References for Manuscripts

Complete workflow for creating a bibliography:

# 1. Search for papers on your topic
python scripts/search_pubmed.py \
  '"CRISPR-Cas Systems"[MeSH] AND "Gene Editing"[MeSH]' \
  --date-start 2020 \
  --limit 200 \
  --output crispr_papers.json

# 2. Extract DOIs from search results and convert to BibTeX
python scripts/extract_metadata.py \
  --input crispr_papers.json \
  --output crispr_refs.bib

# 3. Add specific papers by DOI
python scripts/doi_to_bibtex.py 10.1038/nature12345 >> crispr_refs.bib
python scripts/doi_to_bibtex.py 10.1126/science.abcd1234 >> crispr_refs.bib

# 4. Format and clean the BibTeX file
python scripts/format_bibtex.py crispr_refs.bib \
  --deduplicate \
  --sort year \
  --descending \
  --output references.bib

# 5. Validate all citations
python scripts/validate_citations.py references.bib \
  --auto-fix \
  --report validation.json \
  --output final_references.bib

# 6. Review validation report and fix any remaining issues
cat validation.json

# 7. Use in your LaTeX document
# \bibliography{final_references}

Integration with Literature Review Skill

This skill complements the literature-review skill:

Literature Review Skill → Systematic search and synthesis Citation Management Skill → Technical citation handling

Combined Workflow:

1. Use literature-review for comprehensive multi-database search
2. Use citation-management to extract and validate all citations
3. Use literature-review to synthesize findings thematically
4. Use citation-management to verify final bibliography accuracy

# After completing literature review
# Verify all citations in the review document
python scripts/validate_citations.py my_review_references.bib --report review_validation.json

# Format for specific citation style if needed
python scripts/format_bibtex.py my_review_references.bib \
  --style nature \
  --output formatted_refs.bib

Search Strategies

Google Scholar Best Practices

Finding Seminal and High-Impact Papers (CRITICAL):

Always prioritize papers based on citation count, venue quality, and author reputation:

Citation Count Thresholds:

Venue Quality Tiers:

• Tier 1 (Prefer): Nature, Science, Cell, NEJM, Lancet, JAMA, PNAS
• Tier 2 (High Priority): Impact Factor >10, top conferences (NeurIPS, ICML, ICLR)
• Tier 3 (Good): Specialized journals (IF 5-10)
• Tier 4 (Sparingly): Lower-impact peer-reviewed venues

Author Reputation Indicators:

• Senior researchers with h-index >40
• Multiple publications in Tier-1 venues
• Leadership at recognized institutions
• Awards and editorial positions

Search Strategies for High-Impact Papers:

• Sort by citation count (most cited first)
• Look for review articles from Tier-1 journals for overview
• Check "Cited by" for impact assessment and recent follow-up work
• Use citation alerts for tracking new citations to key papers
• Filter by top venues using source:Nature or source:Science
• Search for papers by known field leaders using author:LastName

Advanced Operators (full list in references/google_scholar_search.md):

"exact phrase"           # Exact phrase matching