Literature Review

--------|-------------------|----------------| | 0-3 years | 20+ citations | Noteworthy | | 0-3 years | 100+ citations | Highly Influential | | 3-7 years | 100+ citations | Significant | | 3-7 years | 500+ citations | Landmark Paper | | 7+ years | 500+ citations | Seminal Work | | 7+ years | 1000+ citations | Foundational |

Journal and Venue Tiers

Prioritize papers from higher-tier venues:

• Tier 1 (Always Prefer): Nature, Science, Cell, NEJM, Lancet, JAMA, PNAS, Nature Medicine, Nature Biotechnology
• Tier 2 (Strong Preference): High-impact specialized journals (IF>10), top conferences (NeurIPS, ICML for ML/AI)
• Tier 3 (Include When Relevant): Respected specialized journals (IF 5-10)
• Tier 4 (Use Sparingly): Lower-impact peer-reviewed venues

Author Reputation Assessment

Prefer papers from:

• Senior researchers with high h-index (>40 in established fields)
• Leading research groups at recognized institutions (Harvard, Stanford, MIT, Oxford, etc.)
• Authors with multiple Tier-1 publications in the relevant field
• Researchers with recognized expertise (awards, editorial positions, society fellows)

Identifying Seminal Papers

For any topic, identify foundational work by:

1. High citation count (typically 500+ for papers 5+ years old)
2. Frequently cited by other included studies (appears in many reference lists)
3. Published in Tier-1 venues (Nature, Science, Cell family)
4. Written by field pioneers (often cited as establishing concepts)

Best Practices

Search Strategy

1. Use multiple databases (minimum 3): Ensures comprehensive coverage
2. Include preprint servers: Captures latest unpublished findings
3. Document everything: Search strings, dates, result counts for reproducibility
4. Test and refine: Run pilot searches, review results, adjust search terms
5. Sort by citations: When available, sort search results by citation count to surface influential work first

Screening and Selection

1. Use multiple databases (minimum 3): Ensures comprehensive coverage
2. Include preprint servers: Captures latest unpublished findings
3. Document everything: Search strings, dates, result counts for reproducibility
4. Test and refine: Run pilot searches, review results, adjust search terms

Screening and Selection

1. Use clear criteria: Document inclusion/exclusion criteria before screening
2. Screen systematically: Title → Abstract → Full text
3. Document exclusions: Record reasons for excluding studies
4. Consider dual screening: For systematic reviews, have two reviewers screen independently

Synthesis

1. Organize thematically: Group by themes, NOT by individual studies
2. Synthesize across studies: Compare, contrast, identify patterns
3. Be critical: Evaluate quality and consistency of evidence
4. Identify gaps: Note what's missing or understudied

Quality and Reproducibility

1. Assess study quality: Use appropriate quality assessment tools
2. Verify all citations: Run verify_citations.py script
3. Document methodology: Provide enough detail for others to reproduce
4. Follow guidelines: Use PRISMA for systematic reviews

Writing

1. Be objective: Present evidence fairly, acknowledge limitations
2. Be systematic: Follow structured template
3. Be specific: Include numbers, statistics, effect sizes where available
4. Be clear: Use clear headings, logical flow, thematic organization

Common Pitfalls to Avoid

1. Single database search: Misses relevant papers; always search multiple databases
2. No search documentation: Makes review irreproducible; document all searches
3. Study-by-study summary: Lacks synthesis; organize thematically instead
4. Unverified citations: Leads to errors; always run verify_citations.py
5. Too broad search: Yields thousands of irrelevant results; refine with specific terms
6. Too narrow search: Misses relevant papers; include synonyms and related terms
7. Ignoring preprints: Misses latest findings; include bioRxiv, medRxiv, arXiv
8. No quality assessment: Treats all evidence equally; assess and report quality
9. Publication bias: Only positive results published; note potential bias
10. Outdated search: Field evolves rapidly; clearly state search date

Example Workflow

Complete workflow for a biomedical literature review:

# 1. Create review document from template
cp assets/review_template.md crispr_sickle_cell_review.md

# 2. Search multiple databases using appropriate skills
# - Use gget skill for PubMed, bioRxiv
# - Use direct API access for arXiv, Semantic Scholar
# - Export results in JSON format

# 3. Aggregate and process results
python scripts/search_databases.py combined_results.json \
  --deduplicate \
  --rank citations \
  --year-start 2015 \
  --year-end 2024 \
  --format markdown \
  --output search_results.md \
  --summary

# 4. Screen results and extract data
# - Manually screen titles, abstracts, full texts
# - Extract key data into the review document
# - Organize by themes

# 5. Write the review following template structure
# - Introduction with clear objectives
# - Detailed methodology section
# - Results organized thematically
# - Critical discussion
# - Clear conclusions

# 6. Verify all citations
python scripts/verify_citations.py crispr_sickle_cell_review.md

# Review the citation report
cat crispr_sickle_cell_review_citation_report.json

# Fix any failed citations and re-verify
python scripts/verify_citations.py crispr_sickle_cell_review.md

# 7. Generate professional PDF
python scripts/generate_pdf.py crispr_sickle_cell_review.md \
  --citation-style nature \
  --output crispr_sickle_cell_review.pdf

# 8. Review final PDF and markdown outputs

Integration with Other Skills

This skill works seamlessly with other scientific skills:

Database Access Skills

• gget: PubMed, bioRxiv, COSMIC, AlphaFold, Ensembl, UniProt
• bioservices: ChEMBL, KEGG, Reactome, UniProt, PubChem
• datacommons-client: Demographics, economics, health statistics

Analysis Skills

• pydeseq2: RNA-seq differential expression (for methods sections)
• scanpy: Single-cell analysis (for methods sections)
• anndata: Single-cell data (for methods sections)
• biopython: Sequence analysis (for background sections)

Visualization Skills

• matplotlib: Generate figures and plots for review
• seaborn: Statistical visualizations

Writing Skills

• brand-guidelines: Apply institutional branding to PDF
• internal-comms: Adapt review for different audiences

Resources

Bundled Resources

Scripts:

• scripts/verify_citations.py: Verify DOIs and generate formatted citations
• scripts/generate_pdf.py: Convert markdown to professional PDF
• scripts/search_databases.py: Process, deduplicate, and format search results

References:

• references/citation_styles.md: Detailed citation formatting guide (APA, Nature, Vancouver, Chicago, IEEE)
• references/database_strategies.md: Comprehensive database search strategies

Assets:

• assets/review_template.md: Complete literature review template with all sections

External Resources

Guidelines:

• PRISMA (Systematic Reviews): http://www.prisma-statement.org/
• Cochrane Handbook: https://training.cochrane.org/handbook
• AMSTAR 2 (Review Quality): https://amstar.ca/

Tools:

• MeSH Browser: https://meshb.nlm.nih.gov/search
• PubMed Advanced Search: https://pubmed.ncbi.nlm.nih.gov/advanced/
• Boolean Search Guide: https://www.ncbi.nlm.nih.gov/books/NBK3827/

Citation Styles:

• APA Style: https://apastyle.apa.org/
• Nature Portfolio: https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards
• NLM/Vancouver: https://www.nlm.nih.gov/bsd/uniform_requirements.html

Dependencies

Required Python Packages

pip install requests  # For citation verification

Required System Tools

# For PDF generation
brew install pandoc  # macOS
apt-get install pandoc  # Linux

# For LaTeX (PDF generation)
brew install --cask mactex  # macOS
apt-get install texlive-xetex  # Linux

Check dependencies:

python scripts/generate_pdf.py --check-deps

Summary

This literature-review skill provides:

1. Systematic methodology following academic best practices
2. Multi-database integration via existing scientific skills
3. Citation verification ensuring accuracy and credibility
4. Professional output in markdown and PDF formats
5. Comprehensive guidance covering the entire review process
6. Quality assurance with verification and validation tools
7. Reproducibility through detailed documentation requirements

Conduct thorough, rigorous literature reviews that meet academic standards and provide comprehensive synthesis of current knowledge in any domain.