/scientific-literature-search

name: scientific-literature-search
description: "Systematic strategies for searching scientific literature across PubMed, arXiv, Google Scholar, and AI-assisted tools. Covers PICO framework for clinical questions, three-tiered search (database-specific, AI-assisted, content extraction), PubMed field tags and MeSH, boolean query construction, and full-text extraction. Use when planning a literature search or choosing a search tier."
license: CC-BY-4.0

Scientific Literature Search

Overview

Scientific literature search is the foundation of evidence-based research. A well-executed search maximizes recall (finding all relevant papers) while maintaining precision (avoiding irrelevant results). This guide provides a systematic approach that combines database-specific query strategies, AI-assisted synthesis, and direct content extraction, organized into a three-tiered framework that scales from targeted lookups to comprehensive landscape reviews.

Key Concepts

The PICO Framework

For clinical and biomedical questions, structure queries using the PICO framework:

• **P** (Population): Who are you studying? (e.g., "Diabetes Mellitus"[MeSH])
• **I** (Intervention): What treatment or exposure? (e.g., "Metformin"[MeSH])
• **C** (Comparison): What is the alternative? (e.g., placebo, standard care)
• **O** (Outcome): What result are you measuring? (e.g., "Cardiovascular Diseases"[MeSH])

PICO queries can be combined with publication type filters to target specific evidence levels:

"Diabetes Mellitus"[MeSH] AND "Metformin"[MeSH] AND "Cardiovascular Diseases"[MeSH] AND ("clinical trial"[Publication Type] OR "meta-analysis"[Publication Type])

Three-Tiered Search Strategy

Literature search is most effective when approached in tiers of increasing breadth:

**Tier 1 -- Database-Specific Searches (Most Reliable)**

Query established academic databases (PubMed, arXiv, Google Scholar) for peer-reviewed, indexed content. This is the most reliable tier and should always be the starting point.

• PubMed (via Biopython `Bio.Entrez`): Primary database for biomedical and life science literature. Supports MeSH controlled vocabulary and advanced field tags.
• arXiv (via the `arxiv` package): Preprint server for physics, mathematics, computer science, and quantitative biology. Results appear faster than peer-reviewed journals.
• Google Scholar (via the `scholarly` package): Broadest coverage across all academic disciplines. Note: has aggressive rate limits on automated queries.

Best for: finding specific papers, systematic reviews, clinical evidence, preprints.

**Tier 2 -- AI-Assisted Web Search (Comprehensive)**

Use the Claude API with the `web_search_20250305` server-side tool to synthesize broader context, identify research trends, and surface recent developments not yet indexed in databases. Also use general web search (e.g. via the `duckduckgo-search` package) for protocols, tutorials, and software documentation.

Best for: understanding the research landscape, complex multi-faceted questions, finding recent developments, identifying key researchers.

Avoid for: specific paper lookups (use Tier 1), citation counts (use Google Scholar), systematic reviews requiring reproducibility, searches where exact query terms must be documented.

**Tier 3 -- Direct Content Extraction (Deep Dive)**

Extract and analyze full-text content, PDFs, and supplementary materials from identified papers using `trafilatura` (HTML article extraction), `pypdf` (PDF text), and the Crossref API (DOI → supplementary file URLs).

Best for: detailed methodology extraction, data retrieval, protocol identification, supplementary data access.

PubMed Field Tags

PubMed supports field-specific searching to improve precision:

| Tag | Description | Example | |-----|-------------|---------| | `[MeSH]` | Medical Subject Heading (controlled vocabulary) | `"Neoplasms"[MeSH]` | | `[Title]` | Title field only | `"CRISPR"[Title]` | | `[Title/Abstract]` | Title or abstract | `"gene therapy"[Title/Abstract]` | | `[Author]` | Author name | `"Zhang F"[Author]` | | `[Journal]` | Journal name | `"Nature"[Journal]` | | `[Publication Type]` | Article type filter | `"Review"[Publication Type]` | | `[Date - Publication]` | Publication date range | `"2020/01/01"[Date - Publication]:"2024/12/31"[Date - Publication]` | | `[MeSH Major Topic]` | MeSH term as major focus of the article | `"CRISPR-Cas Systems"[MeSH Major Topic]` |

Boolean Operators

Boolean operators control how search terms combine:

# AND: All terms must be present -- narrows results
results = query_pubmed("CRISPR AND cancer AND therapy")

# OR: Any term can be present -- broadens results (use for synonyms)
results = query_pubmed("(tumor OR tumour OR neoplasm) AND immunotherapy")

# NOT: Exclude terms -- use sparingly to avoid losing relevant papers
results = query_pubmed("cancer immunotherapy NOT review")

Use parentheses to group OR terms together before combining with AND.

arXiv Subject Categories

arXiv organizes preprints by subject category. Biology-related categories include:

| Category | Description | |----------|-------------| | `q-bio.BM` | Biomolecules | | `q-bio.CB` | Cell Behavior | | `q-bio.GN` | Genomics | | `q-bio.MN` | Molecular Networks | | `q-bio.NC` | Neurons and Cognition | | `q-bio.QM` | Quantitative Methods | | `cs.AI` | Artificial Intelligence | | `cs.LG` | Machine Learning |

Decision Framework

Use this tree to determine which search tier and database to start with:

What type of question are you answering?
├── Clinical / biomedical question
│   ├── Specific drug or treatment → Tier 1: PubMed with PICO query
│   ├── Disease mechanism → Tier 1: PubMed with MeSH terms
│   └── Clinical trial evidence → Tier 1: PubMed filtered by Publication Type
├── Computational / quantitative methods
│   ├── ML model or algorithm → Tier 1: arXiv (cs.LG, cs.AI)
│   ├── Computational biology method → Tier 1: arXiv (q-bio.*)