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Compare major academic databases and when to use each for research
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公司金融实证研究的"漏斗式选题查找器"。互动开场先后询问 (1) 研究方向、(2) 候选标题数量 N， 再扫描全球文献（已出版英文学术期刊 + SSRN working paper + 全球高校 department seminar 1 年内日程），基于 Edmans (2024) "1000 Rejections" 红线生成 N 个候选标题，**通过并行 subagent（Agent 工具）批量生成计划书 + 查新；每个 subagent 必须强制调用 Skill 工具加载 econfin-proposal 与 novelty-check 两个预设 skill 完成各自模块**，**只有当 novelty score >= 9 时（即 JF/JFE/RFS 顶刊层次），subagent 才把 proposal + 查新报告合并的 md 写入 F:\Dropbox\CC\选题大全\<研究方向短名>\（以"简短选题名称-分数"命名，子文件夹名由 Step 0 从用户输入的研究方向派生）；< 9 分的选题在 subagent 内部直接丢弃，绝不写盘、绝不输出**。当用户说"找选题"、"帮我找选题"、"想做 X 方向"、 "empirical CF idea search"、"批量生成研究计划书"、"100 ideas"、"econfin-idea-finder" 时触发。
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| name | database-comparison-guide |
| description | Compare major academic databases and when to use each for research |
| metadata | {"openclaw":{"emoji":"🗄️","category":"literature","subcategory":"search","keywords":["academic database search","scholarly database","search strategy","field-specific search"],"source":"wentor-research-plugins"}} |
A comprehensive reference for choosing and querying the right academic database for your research domain, including coverage details, advanced operators, and cross-database strategies.
| 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 |
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"
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")
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")
"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]
A robust literature search should query multiple databases to maximize recall: