citation-management

# Citation Management ## Overview Manage citations systematically throughout the research and writing process. This skill provides tools and strategies for searching academic databases (Google Scholar, PubMed), extracting accurate metadata from multiple sources (CrossRef, PubMed, arXiv), validating citation information, and generating properly formatted BibTeX entries. Critical for maintaining citation accuracy, avoiding reference errors, and ensuring reproducible research. Integrates seamlessly with the literature-review skill for comprehensive research workflows. ## When to Use This Skill Use this skill when: - Searching for specific papers on Google Scholar or PubMed - Converting DOIs, PMIDs, or arXiv IDs to properly formatted BibTeX - Extracting complete metadata for citations (authors, title, journal, year, etc.) - Validating existing citations for accuracy - Cleaning and formatting BibTeX files - Finding highly cited papers in a specific field - Verifying that citation information matches the actual publication - Building a bibliography for a manuscript or thesis - Checking for duplicate citations - Ensuring consistent citation formatting ## 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:** ```bash 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**: ```bash # 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**: ```bash # 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: ```bash # 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: ```bash # 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:** | Entry Type | Must Have | Should Have | |------------|-----------|-------------| | @article | author, title, journal, year | volume, pages, number, doi | | @inproceedings | author, title, booktitle, year | pages, doi | | @book | author/editor, title, publisher, year | isbn, doi | | @misc | author, title, year | doi or url | 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, use the **parallel-web skill** to search for the missing information: **Option A — Search by title and author** (best for finding DOI): ```bash parallel-cli search "FIRST_AUTHOR TITLE JOURNAL_NAME volume pages DOI" \ --json --max-results 10 \ -o sources/search_citation_CITATIONKEY.json ``` **Option B — Extract from DOI page** (best when DOI is known but volume/pages missing): ```bash parallel-cli extract "https://doi.org/10.XXXX/YYYY" --json \ --objective "extract complete citation metadata: volume, issue, pages, publication date" \ -o sources/extract_doi_CITATIONKEY.json ``` **Option C — Search CrossRef API directly** (programmatic, fast): ```bash parallel-cli search "crossref DOI metadata FIRST_AUTHOR TITLE" \ --json --max-results 10 \ -o sources/search_crossref_CITATIONKEY.json ``` **Option D — Search Google Scholar** (fallback for hard-to-find papers): ```bash parallel-cli search "google scholar FIRST_AUTHOR TITLE YEAR complete citation" \ --json --max-results 10 \ -o sources/search_scholar_CITATIONKEY.json ``` #### 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: ```bibtex 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 | Missing Field | Best Search Strategy | |---------------|---------------------| | DOI | Search "AUTHOR TITLE DOI" via parallel-cli search | | Volume | Extract from DOI page or search "JOURNAL YEAR TITLE volume" | | Pages | Extract from DOI page or search publisher website | | Issue/Number | Extract from DOI page or CrossRef | | Publisher | Search "JOURNAL publisher" or check journal website | --- ### 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**: ```bibtex @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: ```bash # 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 ```bash # Validate BibTeX file python scripts/validate_citations.py references.bib # Validate against a venue standard (e.g., Nature, NeurIPS, Literature Review) python scripts/validate_citations.py references.bib --venue nature python scripts/validate_citations.py references.bib --venue neurips python scripts/validate_citations.py references.bib --venue review # Validate with custom minimum citation count python scripts/validate_citations.py references.bib --min-count 40 # Check references against a written manuscript file (detect missing or unused citations) python scripts/validate_citations.py references.bib --manuscript paper.md # Generate detailed validation report python scripts/validate_citations.py references.bib \ --venue nature \ --manuscript paper.md \ --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**: ```json { "total_entries": 150, "valid_entries": 145, "errors": [ { "citation_key": "Smith2023", "error_type": "missing_field", "field": "journal", "severity": "high" },