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Get Started Free →Deposit and discover research datasets via Harvard Dataverse API
.claude/skills/brycewang-stanford-dataverse-api/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 65% | 0% |
| case-01 | ✓→✓ | = Same ✓ | 28% | 0% |
| case-07 | ✓→✓ | = Same ✓ | 145% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 48% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 62% | 0% |
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/apibash# 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"
bash# 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"
bash# 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" |
json{ "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": 12, "citation": "Smith, J. (2024). Global Temperature Dataset..." } ] } }
pythonimport 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"), }) return results def get_dataset_files(doi: str) -> list: """List files in a dataset.""" resp = requests.get( f"{BASE_URL}/datasets/:persistentId/", params={"persistentId": doi}, ) resp.raise_for_status() data = resp.json().get("data", {}) files = [] version = data.get("latestVersion", {}) for f in version.get("files", []): df = f.get("dataFile", {}) files.append({ "id": df.get("id"), "filename": df.get("filename"), "size": df.get("filesize"), "content_type": df.get("contentType"), "md5": df.get("md5"), }) return files def download_file(file_id: int, output_path: str): """Download a file from Dataverse.""" resp = requests.get( f"{BASE_URL}/access/datafile/{file_id}", stream=True, ) resp.raise_for_status() with open(output_path, "wb") as f: for chunk in resp.iter_content(chunk_size=8192): f.write(chunk) # Example: find social science datasets datasets = search_datasets("income inequality", subject="Social Sciences") for ds in datasets: print(f"[{ds['published']}] {ds['name']} ({ds['files']} files)") print(f" DOI: {ds['doi']}") # Example: list files in a dataset # files = get_dataset_files("doi:10.7910/DVN/EXAMPLE") # for f in files: # print(f" {f['filename']} ({f['size']} bytes)")
| 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 |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 12,901 | 4,084 | -68% | 1 | 1 | 0% | 1,953 | 2,496 | +28% | 0 | 0 | — |
case-07 | pass→pass | 5,504 | 1,786 | -68% | 1 | 1 | 0% | 885 | 2,165 | +145% | 0 | 0 | — |
case-02 | fail→pass | 9,706 | 4,377 | -55% | 1 | 1 | 0% | 1,640 | 2,713 | +65% | 0 | 0 | — |
case-03 | pass→pass | 10,996 | 6,038 | -45% | 1 | 1 | 0% | 1,984 | 2,940 | +48% | 0 | 0 | — |
case-04 | pass→pass | 9,005 | 4,179 | -54% | 1 | 1 | 0% | 1,666 | 2,698 | +62% | 0 | 0 | — |
case-05 | pass→pass | 12,814 | 10,755 | -16% | 1 | 1 | 0% | 2,373 | 3,866 | +63% | 0 | 0 | — |
case-06 | pass→pass | 3,713 | 2,456 | -34% | 1 | 1 | 0% | 603 | 2,332 | +287% | 0 | 0 | — |
case-08 | pass→pass | 6,273 | 2,624 | -58% | 1 | 1 | 0% | 975 | 2,340 | +140% | 0 | 0 | — |
case-09 | pass→pass | 14,720 | 2,832 | -81% | 1 | 1 | 0% | 2,577 | 2,387 | -7% | 0 | 0 | — |
case-10 | pass→pass | 3,258 | 1,870 | -43% | 1 | 1 | 0% | 415 | 2,222 | +435% | 0 | 0 | — |
case-11 | pass→pass | 8,817 | 4,265 | -52% | 1 | 1 | 0% | 1,670 | 2,715 | +63% | 0 | 0 | — |
case-12 | pass→pass | 8,863 | 5,061 | -43% | 1 | 1 | 0% | 1,532 | 2,772 | +81% | 0 | 0 | — |
case-13 | pass→pass | 3,812 | 1,352 | -65% | 1 | 1 | 0% | 415 | 2,103 | +407% | 0 | 0 | — |
case-14 | pass→pass | 2,877 | 1,637 | -43% | 1 | 1 | 0% | 387 | 2,126 | +449% | 0 | 0 | — |
case-15 | pass→pass | 9,506 | 1,655 | -83% | 1 | 1 | 0% | 1,451 | 2,099 | +45% | 0 | 0 | — |
case-16 | pass→pass | 4,377 | 2,723 | -38% | 1 | 1 | 0% | 615 | 2,359 | +284% | 0 | 0 | — |
case-17 | pass→pass | 6,751 | 3,139 | -54% | 1 | 1 | 0% | 1,161 | 2,462 | +112% | 0 | 0 | — |
case-18 | pass→pass | 3,548 | 2,084 | -41% | 1 | 1 | 0% | 563 | 2,261 | +302% | 0 | 0 | — |
case-19 | pass→pass | 4,215 | 1,073 | -75% | 1 | 1 | 0% | 595 | 2,041 | +243% | 0 | 0 | — |
case-20 | pass→pass | 9,822 | 12,902 | +31% | 1 | 1 | 0% | 1,687 | 4,216 | +150% | 0 | 0 | — |
case-21 | pass→pass | 11,828 | 13,742 | +16% | 1 | 1 | 0% | 2,191 | 4,581 | +109% | 0 | 0 | — |
case-22 | pass→pass | 9,405 | 9,639 | +2% | 1 | 1 | 0% | 1,472 | 3,561 | +142% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +5 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.