database-comparison-guide

Compare major academic databases and when to use each for research

191 stars

Best use case

database-comparison-guide is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Compare major academic databases and when to use each for research

Teams using database-comparison-guide should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/database-comparison-guide/SKILL.md --create-dirs "https://raw.githubusercontent.com/wentorai/research-plugins/main/skills/literature/search/database-comparison-guide/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/database-comparison-guide/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How database-comparison-guide Compares

Feature / Agentdatabase-comparison-guideStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Compare major academic databases and when to use each for research

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Database Comparison Guide

A comprehensive reference for choosing and querying the right academic database for your research domain, including coverage details, advanced operators, and cross-database strategies.

## Overview of Major Academic Databases

| Database | Coverage | Disciplines | Access Model | Unique Strength |
|----------|----------|-------------|--------------|-----------------|
| Web of Science | 1900-present, 21,000+ journals | Multidisciplinary | Subscription | Citation indexing, Journal Impact Factor |
| Scopus | 1970-present, 27,000+ journals | Multidisciplinary | Subscription | Largest abstract/citation DB, CiteScore |
| PubMed | 1946-present, 35M+ records | Biomedical, life sciences | Free | MeSH controlled vocabulary, clinical filters |
| IEEE Xplore | 1872-present, 6M+ docs | Engineering, CS | Subscription | Conference proceedings, standards |
| Google Scholar | Broad, undisclosed | All fields | Free | Widest coverage, full-text indexing |
| JSTOR | Historical archives | Humanities, social sciences | Subscription | Historical journal runs, primary sources |
| arXiv | 1991-present, 2.4M+ papers | Physics, CS, Math, Bio | Free | Preprints, no peer-review delay |
| SSRN | 1994-present | Social sciences, law | Free | Working papers, early-stage research |

## Field-Specific Database Selection

### STEM Fields

For physics, computer science, and mathematics, combine arXiv preprints with Web of Science indexed journals:

```
# arXiv API query for recent ML papers
curl "http://export.arxiv.org/api/query?search_query=cat:cs.LG+AND+ti:transformer&start=0&max_results=25&sortBy=submittedDate&sortOrder=descending"
```

For biomedical research, PubMed with MeSH terms provides the most precise retrieval:

```
# PubMed E-utilities search with MeSH
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=%22machine+learning%22[MeSH]+AND+%22drug+discovery%22[MeSH]&retmax=50&sort=date"
```

### Social Sciences and Humanities

- **Economics/Business**: Scopus + SSRN + RePEc (for working papers)
- **Psychology**: PsycINFO (APA) + PubMed
- **Law**: Westlaw + SSRN + HeinOnline
- **History/Literature**: JSTOR + Project MUSE + MLA International Bibliography

## Advanced Search Operators by Database

### Web of Science

```
TS=("deep learning" AND "drug discovery") AND PY=(2020-2025)
# TS = Topic (title + abstract + keywords)
# PY = Publication Year
# Use NEAR/x for proximity: TS=("climate" NEAR/3 "adaptation")
```

### Scopus

```
TITLE-ABS-KEY("deep learning" AND "drug discovery") AND PUBYEAR > 2019
# Additional operators:
# AUTHLASTNAME(smith) AND AUTHFIRST(j*)
# AFFIL("MIT" OR "Massachusetts Institute of Technology")
# REF("seminal paper title")
```

### PubMed

```
"deep learning"[Title/Abstract] AND "drug discovery"[Title/Abstract]
AND ("2020/01/01"[Date - Publication] : "2025/12/31"[Date - Publication])
# Use filters: Clinical Trial[pt], Review[pt], Free Full Text[Filter]
```

## Cross-Database Search Strategy

A robust literature search should query multiple databases to maximize recall:

1. **Define your research question** using PICO (Population, Intervention, Comparison, Outcome) or PCC (Population, Concept, Context) frameworks.
2. **Identify controlled vocabulary** for each database (MeSH for PubMed, Emtree for Embase, Thesaurus for PsycINFO).
3. **Build search strings** combining controlled vocabulary with free-text synonyms using Boolean operators.
4. **Execute searches** across at least 2-3 databases relevant to your field.
5. **Deduplicate results** using reference managers (Zotero, EndNote) or tools like Covidence.
6. **Document your search** with database, date, exact query string, and result count for reproducibility.

## Practical Tips

- **Scopus vs. Web of Science**: Scopus has broader coverage (especially post-2000 and non-English journals); WoS has deeper historical archives and the Journal Impact Factor.
- **Google Scholar** finds the most results but lacks advanced filtering. Use it for snowball searches and finding grey literature, not as your primary systematic search tool.
- **API access**: PubMed (E-utilities), OpenAlex, and Crossref all offer free APIs for programmatic searching. Scopus and WoS require institutional API keys.
- **Alert services**: Set up saved search alerts on PubMed, Scopus, and Google Scholar to stay current in fast-moving fields.