Dataverse Sync

Harvard Dataverse API Sync

Overview

Sync local files with a Harvard Dataverse dataset using the Native API.

Key API Endpoints

| Action | Endpoint | Method | |--------|----------|--------| | Get dataset metadata | /api/datasets/:persistentId/?persistentId={DOI} | GET | | Add file | /api/datasets/:persistentId/add?persistentId={DOI} | POST | | Replace file | /api/files/{file_id}/replace | POST | | Delete file | /api/files/{file_id} | DELETE |

Authentication

All requests require the X-Dataverse-key header with your API token:

headers = {"X-Dataverse-key": "your-api-token"}

Common Operations

Get Dataset Files

import requests

url = "https://dataverse.harvard.edu/api/datasets/:persistentId/"
params = {"persistentId": "doi:10.7910/DVN/XXXXXX"}
headers = {"X-Dataverse-key": API_TOKEN}

response = requests.get(url, params=params, headers=headers)
data = response.json()
files = data["data"]["latestVersion"]["files"]

for f in files:
    file_id = f["dataFile"]["id"]
    md5 = f["dataFile"].get("md5", "")
    filename = f.get("label", f["dataFile"].get("filename"))
    directory = f.get("directoryLabel", "")

Add File

import json

url = "https://dataverse.harvard.edu/api/datasets/:persistentId/add"
params = {"persistentId": DOI}
headers = {"X-Dataverse-key": API_TOKEN}

json_data = json.dumps({
    "directoryLabel": "path/to/directory",  # Optional
    "categories": [],
})

with open(filepath, "rb") as f:
    files = {
        "file": (filename, f),
        "jsonData": (None, json_data, "application/json"),
    }
    response = requests.post(url, params=params, headers=headers, files=files)

Delete File

url = f"https://dataverse.harvard.edu/api/files/{file_id}"
headers = {"X-Dataverse-key": API_TOKEN}
response = requests.delete(url, headers=headers)

Compare Files Using MD5

import hashlib

def compute_md5(filepath):
    md5_hash = hashlib.md5()
    with open(filepath, "rb") as f:
        for chunk in iter(lambda: f.read(8192), b""):
            md5_hash.update(chunk)
    return md5_hash.hexdigest()

Important Notes

Draft vs Published

• Changes to a RELEASED dataset create a new DRAFT version
• User must manually publish the draft via web UI
• Publishing creates a new version (v1.0 → v2.0), same DOI

AWS WAF Restrictions

Harvard Dataverse uses AWS WAF which may block direct uploads of certain file types (.R, .do files).

Workaround: Upload files inside a ZIP archive. See dataverse-upload-zip skill.

Directory Labels

• Files in Dataverse have a directoryLabel that defines their folder path
• When uploading, set directoryLabel in the JSON metadata
• When uploading ZIPs without a directoryLabel, internal ZIP structure is preserved

File Replacement Strategy

The /api/files/{id}/replace endpoint may return 403 for some file types. Alternative approach:

1. Delete the old file
2. Add the new file with same directoryLabel

Example Sync Script Structure

def sync(dry_run=True):
    # 1. Fetch current Dataverse files
    dataverse_files = get_dataset_files()  # {path: {id, md5, directoryLabel}}

    # 2. Scan local files
    local_files = get_local_files()  # {path: {local_path, md5}}

    # 3. Compare
    for path, local_info in local_files.items():
        if path in dataverse_files:
            if local_info["md5"] != dataverse_files[path]["md5"]:
                # Modified - replace
                replace_file(...)
        else:
            # New - add
            add_file(...)

    # 4. Find deleted
    for path in dataverse_files:
        if path not in local_files:
            # Deleted - optionally remove
            delete_file(...)

References

• Dataverse Native API Guide
• Adding Files via API

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: letitbk
• Source: letitbk/claude-academic-setup
• License: MIT

Install and usage instructions live in the source repository linked above.