brycewang-stanford
brycewang-stanford/Auto-Empirical-Research-Skills
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill bibliometrix-guide命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
正在显示 SKILL.md
基于 SOC 职业分类
| name | bibliometrix-guide |
| description | Perform science mapping and bibliometric analysis with R bibliometrix |
| metadata | {"openclaw":{"emoji":"📉","category":"literature","subcategory":"metadata","keywords":["bibliometrix","bibliometrics","science mapping","R","citation analysis","research trends"],"source":"https://github.com/massimoaria/bibliometrix"}} |
Bibliometrix is an R package for comprehensive science mapping and bibliometric analysis. It imports data from Scopus, Web of Science, PubMed, and other databases, then performs co-citation analysis, keyword co-occurrence mapping, collaboration networks, thematic evolution tracking, and more. Includes Biblioshiny — a Shiny-based web interface for no-code analysis.
install.packages("bibliometrix")
# Or development version
devtools::install_github("massimoaria/bibliometrix")
library(bibliometrix)
# From Scopus CSV export
M <- convert2df("scopus_export.csv", dbsource = "scopus", format = "csv")
# From Web of Science
M <- convert2df("wos_export.txt", dbsource = "wos", format = "plaintext")
# From PubMed
M <- convert2df("pubmed_export.txt", dbsource = "pubmed", format = "pubmed")
# From multiple files
file_list <- c("data1.csv", "data2.csv")
M <- convert2df(file_list, dbsource = "scopus", format = "csv")
# Basic bibliometric summary
results <- biblioAnalysis(M)
summary(results, k = 10) # Top 10 in each category
# Key metrics produced:
# - Publication trends over time
# - Most productive authors
# - Most cited papers
# - Top journals/sources
# - Country/affiliation rankings
# - Keyword frequency
# Most cited documents
CR <- citations(M, field = "article", sep = ";")
head(CR$Cited, 20)
# Most cited first authors
CR_auth <- citations(M, field = "author", sep = ";")
# Local citations (within the dataset)
LC <- localCitations(M)
head(LC$Papers, 10)
# Co-citation network
NetMatrix <- biblioNetwork(M, analysis = "co-citation",
network = "references", sep = ";")
net <- networkPlot(NetMatrix, n = 30, type = "fruchterman",
Title = "Co-citation Network")
# Author collaboration network
NetMatrix <- biblioNetwork(M, analysis = "collaboration",
network = "authors", sep = ";")
net <- networkPlot(NetMatrix, n = 50, type = "kamada",
Title = "Collaboration Network")
# Keyword co-occurrence
NetMatrix <- biblioNetwork(M analysis
network sep
net networkPlotNetMatrix n type
Title
# Thematic map (strategic diagram)
Map <- thematicMap(M, field = "DE", n = 250, minfreq = 5)
plot(Map$map)
# Quadrants:
# Motor themes (high centrality, high density)
# Basic themes (high centrality, low density)
# Niche themes (low centrality, high density)
# Emerging/declining themes (low centrality, low density)
# Thematic evolution over time periods
nexus <- thematicEvolution(M,
field = "DE",
years = c(2015, 2019, 2023),
n = 100, minFreq = 3)
plotThematicEvolution(nexus$Nodes, nexus$Edges)
# Launch interactive web dashboard
biblioshiny()
# Opens browser with GUI for:
# - Data import from multiple sources
# - Descriptive analysis
# - Network visualization
# - Thematic mapping
# - All plots exportable
| Source | Format | Import function |
|---|---|---|
| Scopus | CSV/BibTeX | convert2df(..., dbsource="scopus") |
| Web of Science | Plain text/BibTeX | convert2df(..., dbsource="wos") |
| PubMed | PubMed format | convert2df(..., dbsource="pubmed") |
| Dimensions | CSV | convert2df(..., dbsource="dimensions") |
| Cochrane | Plain text | convert2df(..., dbsource="cochrane") |
| OpenAlex | JSON | Via API integration |
| Analysis | Function | Output |
|---|---|---|
| Descriptive | biblioAnalysis() | Summary statistics |
| Co-citation | biblioNetwork(analysis="co-citation") | Citation clusters |
| Collaboration | biblioNetwork(analysis="collaboration") | Author networks |
| Co-occurrence | biblioNetwork(analysis="co-occurrences") | Keyword maps |
| Thematic map | thematicMap() | Strategic quadrant diagram |
| Trend analysis | fieldByYear() | Topic evolution |
| Country collab | metaTagExtraction() + biblioNetwork() | Geo collaboration |