Ror Organization ApiSAFE

🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: ror-organization-api
description: "Identify and link research organizations via the ROR registry API"
metadata:
  openclaw:
    emoji: "🏢"
    category: "literature"
    subcategory: "metadata"
    keywords: ["ROR", "research organizations", "institution registry", "affiliation", "organization identifiers", "ISNI"]
    source: "https://ror.org/"
---

# ROR (Research Organization Registry) API

## Overview

ROR is the community-led registry of open persistent identifiers for research organizations worldwide — 110,000+ entries covering universities, research institutes, government agencies, hospitals, and companies. The API enables affiliation disambiguation, institutional search, and metadata retrieval. Essential for bibliometrics, funder compliance, and research analytics. Free, no authentication required.

## API Endpoints

### Base URL

```
https://api.ror.org/v2
```

### Search Organizations

```bash
# Text search
curl "https://api.ror.org/v2/organizations?query=MIT"

# Affiliation matching (fuzzy match for messy affiliation strings)
curl "https://api.ror.org/v2/organizations?affiliation=Dept+of+CS,+Massachusetts+Inst+of+Technology"

# Filter by country
curl "https://api.ror.org/v2/organizations?query=university&filter=locations.geonames_details.country_code:US"

# Filter by organization type
curl "https://api.ror.org/v2/organizations?query=research&filter=types:facility"
```

### Get Organization by ROR ID

```bash
# Retrieve full record
curl "https://api.ror.org/v2/organizations/https://ror.org/042nb2s44"

# Also works with just the ID portion
curl "https://api.ror.org/v2/organizations/042nb2s44"
```

### Query Parameters

| Parameter | Description | Example |
|-----------|-------------|---------|
| `query` | Text search | `query=Harvard` |
| `affiliation` | Fuzzy affiliation match | `affiliation=MIT Cambridge MA` |
| `filter` | Faceted filtering | `filter=types:education` |
| `page` | Page number (1-based) | `page=2` |

### Organization Types

| Type | Description |
|------|-------------|
| `education` | Universities, colleges |
| `facility` | Research facilities, labs |
| `healthcare` | Hospitals, medical centers |
| `company` | Companies with research activities |
| `government` | Government agencies |
| `nonprofit` | Non-profit research organizations |
| `funder` | Funding agencies |
| `archive` | Archives, libraries |

## Response Structure

```json
{
  "number_of_results": 3,
  "items": [
    {
      "id": "https://ror.org/042nb2s44",
      "names": [
        {"value": "Massachusetts Institute of Technology", "types": ["ror_display"]},
        {"value": "MIT", "types": ["acronym"]}
      ],
      "types": ["education"],
      "locations": [
        {
          "geonames_details": {
            "country_code": "US",
            "country_name": "United States",
            "name": "Cambridge"
          }
        }
      ],
      "external_ids": [
        {"type": "isni", "all": ["0000 0001 2341 2786"]},
        {"type": "grid", "all": ["grid.116068.8"]},
        {"type": "wikidata", "all": ["Q49108"]}
      ],
      "links": [{"type": "website", "value": "https://www.mit.edu/"}],
      "relationships": [
        {"id": "https://ror.org/01a8ajp77", "label": "Lincoln Laboratory", "type": "child"}
      ],
      "status": "active",
      "established": 1861
    }
  ]
}
```

## Python Usage

```python
import requests

BASE_URL = "https://api.ror.org/v2/organizations"


def search_organizations(query: str,
                         country: str = None,
                         org_type: str = None) -> list:
    """Search ROR for research organizations."""
    params = {"query": query}
    filters = []
    if country:
        filters.append(
            f"locations.geonames_details.country_code:{country}")
    if org_type:
        filters.append(f"types:{org_type}")
    if filters:
        params["filter"] = ",".join(filters)

    resp = requests.get(BASE_URL, params=params)
    resp.raise_for_status()
    data = resp.json()

    results = []
    for org in data.get("items", []):
        display_name = next(
            (n["value"] for n in org.get("names", [])
             if "ror_display" in n.get("types", [])),
            org.get("names", [{}])[0].get("value", ""),
        )
        acronyms = [n["value"] for n in org.get("names", [])
                     if "acronym" in n.get("types", [])]
        loc = org.get("locations", [{}])[0].get("geonames_details", {})

        results.append({
            "ror_id": org.get("id"),
            "name": display_name,
            "acronyms": acronyms,
            "types": org.get("types", []),
            "country": loc.get("country_name"),
            "city": loc.get("name"),
            "established": org.get("established"),
        })
    return results


def match_affiliation(affiliation_string: str) -> dict:
    """Disambiguate a messy affiliation string to a ROR record."""
    resp = requests.get(
        BASE_URL,
        params={"affiliation": affiliation_string},
    )
    resp.raise_for_status()
    items = resp.json().get("items", [])
    if items and items[0].get("chosen"):
        return items[0].get("organization", {})
    return items[0] if items else {}


def get_organization(ror_id: str) -> dict:
    """Get full ROR record for an organization."""
    resp = requests.get(f"{BASE_URL}/{ror_id}")
    resp.raise_for_status()
    return resp.json()


# Example: find German research institutes
orgs = search_organizations("Max Planck", country="DE",
                            org_type="facility")
for o in orgs:
    print(f"{o['name']} ({', '.join(o['acronyms'])}) "
          f"— {o['city']}, {o['country']} (est. {o['established']})")

# Example: disambiguate messy affiliations
result = match_affiliation(
    "Dept. of Computer Sci., Stanford Univ., CA, USA")
print(f"Matched: {result.get('id')} — "
      f"{result.get('names', [{}])[0].get('value')}")
```

## Use Cases

1. **Affiliation disambiguation**: Clean messy author 

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.