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 dataverse-api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.
基于 SOC 职业分类
正在显示 SKILL.md
| name | dataverse-api |
| description | Deposit and discover research datasets via Harvard Dataverse API |
| metadata | {"openclaw":{"emoji":"🗄️","category":"literature","subcategory":"fulltext","keywords":["Dataverse","research data","data repository","Harvard","dataset deposit","data sharing"],"source":"https://dataverse.org/"}} |
Dataverse is an open-source research data repository platform developed by Harvard IQSS, hosting 150K+ datasets across 80+ installations worldwide. The Harvard Dataverse alone has 130K+ datasets covering social science, natural science, and humanities. The API supports search, metadata retrieval, file download, and dataset deposit. Free, no authentication for read access.
https://dataverse.harvard.edu/api
# Search datasets
curl "https://dataverse.harvard.edu/api/search?q=climate+change&type=dataset&per_page=20"
# Search files within datasets
curl "https://dataverse.harvard.edu/api/search?q=temperature+data&type=file&per_page=20"
# Filter by subject
curl "https://dataverse.harvard.edu/api/search?q=survey+data&type=dataset&\
fq=subject_ss:\"Social Sciences\""
# Filter by publication date
curl "https://dataverse.harvard.edu/api/search?q=genomics&type=dataset&\
fq=dateSort:[2024-01-01T00:00:00Z TO *]"
# Sort by relevance or date
curl "https://dataverse.harvard.edu/api/search?q=machine+learning&type=dataset&\
sort=date&order=desc"
# By persistent ID (DOI)
curl "https://dataverse.harvard.edu/api/datasets/:persistentId/?persistentId=doi:10.7910/DVN/EXAMPLE"
# By dataset ID
curl "https://dataverse.harvard.edu/api/datasets/12345"
# Get dataset versions
curl "https://dataverse.harvard.edu/api/datasets/:persistentId/versions?persistentId=doi:10.7910/DVN/EXAMPLE"
# Download a specific file by ID
curl -O "https://dataverse.harvard.edu/api/access/datafile/67890"
# Download with original format
curl -O "https://dataverse.harvard.edu/api/access/datafile/67890?format=original"
# Download all files in a dataset (as zip)
curl -O "https://dataverse.harvard.edu/api/access/dataset/:persistentId/?persistentId=doi:10.7910/DVN/EXAMPLE"
| Parameter | Description | Example |
|---|---|---|
q | Search query | q=voter+turnout |
type | Item type | dataset, file, dataverse |
per_page | Results per page (max 1000) | per_page=50 |
start | Pagination offset | start=50 |
sort | Sort field | name, date |
order | Sort order | asc, desc |
fq | Filter query (Solr) | fq=subject_ss:"Medicine" |
{
"status": "OK",
"data": {
"q": "climate change",
"total_count": 2450,
"items": [
{
"name": "Global Temperature Dataset 2024",
"type": "dataset",
"url": "https://doi.org/10.7910/DVN/EXAMPLE",
"global_id": "doi:10.7910/DVN/EXAMPLE",
"description": "Monthly global temperature anomalies...",
"published_at": "2024-03-15",
"publisher": "Harvard Dataverse",
"subjects": ["Earth and Environmental Sciences"],
"fileCount"
import requests
BASE_URL = "https://dataverse.harvard.edu/api"
def search_datasets(query: str, per_page: int = 20,
subject: str = None) -> list:
"""Search Harvard Dataverse for datasets."""
params = {
"q": query,
"type": "dataset",
"per_page": per_page,
"sort": "date",
"order": "desc",
}
if subject:
params["fq"] = f'subject_ss:"{subject}"'
resp = requests.get(f"{BASE_URL}/search", params=params)
resp.raise_for_status()
data = resp.json()
results = []
for item in data.get("data", {}).get("items", []):
results.append({
"name": item.get("name"),
"doi": item.get("global_id"),
"description": item.get("description", "")[:300],
"published": item.get("published_at"),
"subjects": item.get("subjects", []),
"files": item.get("fileCount", 0),
"url": item.get("url"),
})
results
() -> :
resp = requests.get(
,
params={: doi},
)
resp.raise_for_status()
data = resp.json().get(, {})
files = []
version = data.get(, {})
f version.get(, []):
df = f.get(, {})
files.append({
: df.get(),
: df.get(),
: df.get(),
: df.get(),
: df.get(),
})
files
():
resp = requests.get(
,
stream=,
)
resp.raise_for_status()
(output_path, ) f:
chunk resp.iter_content(chunk_size=):
f.write(chunk)
datasets = search_datasets(,
subject=)
ds datasets:
()
()
| Installation | URL | Focus |
|---|---|---|
| Harvard Dataverse | dataverse.harvard.edu | Multi-discipline |
| UNC Dataverse | dataverse.unc.edu | Social science |
| AUSSDA | data.aussda.at | Austrian social science |
| Borealis (Canada) | borealisdata.ca | Canadian research |
| DataverseNL | dataverse.nl | Dutch research |