skills/43-wentorai-research-plugins/skills/domains/geoscience/pangaea-data-api/SKILL.md
Access earth and environmental science datasets via PANGAEA API
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research pangaea-data-apiInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
PANGAEA is the world's leading data repository for earth and environmental sciences, hosting 400K+ datasets with 20B+ data points. It archives research data from oceanography, paleoclimatology, geology, ecology, and atmospheric science. Each dataset has a DOI and is linked to the originating publication. The API provides search, metadata retrieval, and data download. Free, no authentication required.
# Search datasets by keyword
curl "https://www.pangaea.de/advanced/search.php?q=ocean+temperature&count=20&type=json"
# Search with geographic bounding box
curl "https://www.pangaea.de/advanced/search.php?\
q=sediment+core&minlat=-60&maxlat=-30&minlon=-180&maxlon=180&type=json"
# Filter by parameter (measurement type)
curl "https://www.pangaea.de/advanced/search.php?\
q=carbon+dioxide¶m=Atmospheric+CO2&type=json"
# Filter by date range
curl "https://www.pangaea.de/advanced/search.php?\
q=Arctic+ice&mindate=2020-01-01&maxdate=2026-12-31&type=json"
# Full-text search via Elasticsearch
curl -X POST "https://ws.pangaea.de/es/pangaea/panmd/_search" \
-H "Content-Type: application/json" \
-d '{
"query": {
"bool": {
"must": [
{"match": {"citation.title": "ocean temperature"}}
],
"filter": [
{"range": {"citation.year": {"gte": 2020}}}
]
}
},
"size": 20
}'
# Get dataset metadata
curl "https://doi.pangaea.de/10.1594/PANGAEA.123456?format=metainfo_json"
# Download dataset as tab-delimited text
curl "https://doi.pangaea.de/10.1594/PANGAEA.123456?format=textfile"
# Download as CSV
curl "https://doi.pangaea.de/10.1594/PANGAEA.123456?format=csv"
# List records
curl "https://ws.pangaea.de/oai/provider?verb=ListRecords&metadataPrefix=oai_dc"
# Get specific record
curl "https://ws.pangaea.de/oai/provider?verb=GetRecord&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.123456&metadataPrefix=oai_dc"
| Parameter | Description | Example |
|-----------|-------------|---------|
| q | Search query | q=coral+reef+bleaching |
| count | Results per page | count=50 |
| offset | Pagination offset | offset=20 |
| minlat/maxlat | Latitude bounds | -90 to 90 |
| minlon/maxlon | Longitude bounds | -180 to 180 |
| mindate/maxdate | Temporal filter | 2020-01-01 |
| param | Parameter/measurement | Temperature |
| topic | Topic filter | Atmosphere, Biosphere |
| type | Response format | json, xml |
import requests
import pandas as pd
from io import StringIO
SEARCH_URL = "https://www.pangaea.de/advanced/search.php"
ES_URL = "https://ws.pangaea.de/es/pangaea/panmd/_search"
def search_pangaea(query: str, count: int = 20,
bbox: dict = None) -> list:
"""Search PANGAEA for earth science datasets."""
params = {"q": query, "count": count, "type": "json"}
if bbox:
params.update({
"minlat": bbox.get("south", -90),
"maxlat": bbox.get("north", 90),
"minlon": bbox.get("west", -180),
"maxlon": bbox.get("east", 180),
})
resp = requests.get(SEARCH_URL, params=params, timeout=30)
resp.raise_for_status()
data = resp.json()
results = []
for item in data.get("results", []):
results.append({
"doi": item.get("URI", ""),
"title": item.get("citation", ""),
"year": item.get("year"),
"size": item.get("size"),
"parameters": item.get("params", []),
"score": item.get("score"),
})
return results
def download_dataset(doi: str) -> pd.DataFrame:
"""Download a PANGAEA dataset as a pandas DataFrame."""
url = f"https://doi.pangaea.de/{doi}?format=textfile"
resp = requests.get(url, timeout=60)
resp.raise_for_status()
lines = resp.text.split("\n")
header_end = next(
(i for i, line in enumerate(lines) if line.startswith("*/")),
-1,
)
data_text = "\n".join(lines[header_end + 1:])
return pd.read_csv(StringIO(data_text), sep="\t")
def search_by_location(query: str, lat: float, lon: float,
radius_deg: float = 5.0) -> list:
"""Search datasets near a geographic location."""
bbox = {
"south": lat - radius_deg,
"north": lat + radius_deg,
"west": lon - radius_deg,
"east": lon + radius_deg,
}
return search_pangaea(query, bbox=bbox)
# Example: find ocean temperature datasets
datasets = search_pangaea("sea surface temperature", count=5)
for ds in datasets:
print(f"[{ds['year']}] {ds['title'][:80]}...")
print(f" DOI: {ds['doi']} | Size: {ds['size']}")
# Example: download a specific dataset
# df = download_dataset("10.1594/PANGAEA.123456")
# print(df.head())
# Example: find Arctic research data
arctic = search_by_location("permafrost", lat=70, lon=25)
for ds in arctic[:3]:
print(f"{ds['title'][:80]}...")
| Topic | Coverage | |-------|----------| | Oceans | Temperature, salinity, currents, chemistry | | Paleoclimate | Ice cores, sediment cores, tree rings | | Atmosphere | CO2, aerosols, weather observations | | Lithosphere | Geology, tectonics, geochemistry | | Biosphere | Biodiversity, ecology, marine biology | | Cryosphere | Sea ice, glaciers, permafrost |
tools
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文(用于「文献综述工具选型」与「一键安装/运行」)。
development
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.
documentation
Use when the project collects primary data or runs a field, lab, or survey experiment, before the intervention begins — write the pre-analysis plan, size the sample from a power calculation, and register with the AEA RCT Registry. Apply after the design is chosen in aer-identification and before any outcome data are seen.
tools
Guide economists to authoritative data sources with explicit, confirmed data specifications before retrieval; interfaces with Playwright MCP to navigate portals and extract real data, not articles about data.