# Research Repository Harvester (Dataverse / DSpace / InvenioRDM) (`datamule/repository-platform-extractor`) Actor Point at ANY Dataverse, DSpace 7/8, or InvenioRDM/Zenodo install and harvest every dataset's metadata via one unified API — title, authors, DOI/handle, subjects, date, type, license, files, landing page + raw. One actor spans thousands of institutional repositories. Pay per record. - **URL**: https://apify.com/datamule/repository-platform-extractor.md - **Developed by:** [Datamule](https://apify.com/datamule) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.35 / 1,000 record harvesteds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Research Repository Harvester — Dataverse / DSpace / InvenioRDM Point at **any** Dataverse, DSpace 7/8, or InvenioRDM (Zenodo) install and harvest every dataset's metadata through **one unified API**. Three of the world's most widely-deployed research-data / institutional-repository software platforms each expose a single uniform REST API that every institution's deployment answers identically — so **one actor spans thousands of independent repositories**, and you get the same normalized output no matter which platform or install you point it at. - **Dataverse** — the Harvard/IQSS software behind 150+ registered installs (Harvard, QDR, DaRUS Stuttgart, Borealis Canada, DataverseNL, …). - **DSpace 7/8** — the most-deployed institutional-repository software (theses, ETDs, gray literature) with 2,600+ installs worldwide. - **InvenioRDM / Zenodo** — the CERN/Zenodo platform (Zenodo, Caltech Data, CERN). ### What you get Every dataset / item is normalized into **one flat, consistent row** — the same columns whether it came from Dataverse, DSpace or Invenio — plus a lossless `raw` copy of the entire original object so nothing is ever dropped: | Field | Description | |---|---| | `repoId` | The record's native id (Dataverse global id, DSpace UUID, Invenio record id) | | `platform` | `dataverse` · `dspace` · `invenio` | | `baseUrl` | The install the record came from | | `persistentId` | DOI (Dataverse / Invenio) or Handle (DSpace) | | `title` | Dataset / item title | | `authors` | List of author / creator names | | `description` | Abstract / description | | `subjects` | Subjects + keywords | | `publicationDate` | Publication / issued date | | `type` | Resource type (dataset, thesis, publication, …) | | `license` | Licence / rights | | `fileCount` | Number of files (where the API exposes it) | | `landingPage` | Canonical landing / DOI URL | | `raw` | The complete original platform object (lossless) | Fields that a given platform or record doesn't provide come back as `null` — the schema is fully nullable, so a partial record never breaks a run. ### Input | Field | Required | Description | |---|---|---| | `platform` | ✅ | `dataverse`, `dspace`, or `invenio` | | `baseUrl` | ✅ | Install root — e.g. `https://dataverse.harvard.edu`, `https://dspace.mit.edu`, `https://zenodo.org` | | `query` | | Free-text search (empty = browse everything) | | `maxRecords` | | Max records to return (default: one page) | | `pageSize` | | Records per API page, 1–100 (default 25) | | `bearerToken` | | Optional token for a gated/private install (never required for public search, never logged) | | `extraHeaders` | | Optional extra HTTP headers as a JSON object | #### Examples Zenodo (Invenio): ```json { "platform": "invenio", "baseUrl": "https://zenodo.org", "query": "climate", "maxRecords": 100 } ```` Harvard Dataverse: ```json { "platform": "dataverse", "baseUrl": "https://dataverse.harvard.edu", "query": "climate", "maxRecords": 100 } ``` MIT DSpace (point at the site root — the actor appends `/server/api`): ```json { "platform": "dspace", "baseUrl": "https://dspace.mit.edu", "query": "climate", "maxRecords": 100 } ``` ### How it works For each platform the actor calls its uniform search endpoint, pages through the result set, and maps each record into the unified schema: - **Dataverse** → `GET {base}/api/search?q=…&type=dataset&per_page=N&start=M` (paginated by record offset), with a `{base}/api/info/version` handshake. - **DSpace** → `GET {base}/server/api/discover/search/objects?query=…&page=N&size=M` (HAL/JSON envelope; Dublin-Core `dc.*` metadata flattened), with a `{base}/server/api` handshake that reports `dspaceName` / `dspaceVersion`. - **Invenio** → `GET {base}/api/records?q=…&size=M&page=N`; handles **both** the legacy Zenodo record shape and the modern InvenioRDM shape transparently. It returns catalog **metadata only** (what datasets exist and where to get them), never the underlying data files. A valid install with no matches returns 0 records and exits cleanly; a non-repository host (an HTML anti-bot page, a wrong URL, or a `status:ERROR` envelope) fails fast with a clear error rather than a silent empty run. No authentication is required for public repository search. ### Pricing Pay-per-event: you are charged per repository record returned. No monthly fee — you only pay for the records you harvest. # Actor input Schema ## `platform` (type: `string`): Which repository software the target install runs. dataverse = Harvard/IQSS Dataverse (/api/search). dspace = DSpace 7/8 REST API (/server/api). invenio = InvenioRDM or Zenodo (/api/records). All three expose one uniform API across their entire installed base, so ONE actor spans thousands of independent institutional repositories. ## `baseUrl` (type: `string`): The install root of the repository. Dataverse example: https://dataverse.harvard.edu (or data.qdr.syr.edu, darus.uni-stuttgart.de, borealisdata.ca). DSpace example: https://dspace.mit.edu (the site root — the actor appends /server/api). Invenio example: https://zenodo.org (or data.caltech.edu). A trailing slash is normalized away. ## `query` (type: `string`): Free-text search passed to the platform's search API. Leave empty to browse everything (Dataverse maps empty to q=\*). Example: climate, genomics, "machine learning". ## `maxRecords` (type: `integer`): Maximum number of dataset / item records to return. The actor pages the search API until this cap is reached or the result set is exhausted. Each returned record is one billable event. Default: one page (see Page size). ## `pageSize` (type: `integer`): How many records to request per API page (1-100). Larger pages = fewer round-trips; the total is still capped by Max records. Default 25. ## `bearerToken` (type: `string`): Optional API token for a gated / private institutional install. Sent as Authorization: Bearer \*\*\*. NOT required for public repository search — leave empty. Never logged. ## `extraHeaders` (type: `object`): Optional extra HTTP headers to send with every request, as a JSON object, e.g. {"X-Api-Key": "..."} for a gated deployment. Leave empty for public installs. ## Actor input object example ```json { "platform": "invenio", "baseUrl": "https://zenodo.org", "query": "climate", "maxRecords": 25, "pageSize": 25 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "baseUrl": "https://zenodo.org", "query": "climate", "maxRecords": 25 }; // Run the Actor and wait for it to finish const run = await client.actor("datamule/repository-platform-extractor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "baseUrl": "https://zenodo.org", "query": "climate", "maxRecords": 25, } # Run the Actor and wait for it to finish run = client.actor("datamule/repository-platform-extractor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "baseUrl": "https://zenodo.org", "query": "climate", "maxRecords": 25 }' | apify call datamule/repository-platform-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datamule/repository-platform-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Research Repository Harvester (Dataverse / DSpace / InvenioRDM)", "description": "Point at ANY Dataverse, DSpace 7/8, or InvenioRDM/Zenodo install and harvest every dataset's metadata via one unified API — title, authors, DOI/handle, subjects, date, type, license, files, landing page + raw. One actor spans thousands of institutional repositories. Pay per record.", "version": "0.1", "x-build-id": "p1QWEQtjomvneCUyd" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datamule~repository-platform-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datamule-repository-platform-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datamule~repository-platform-extractor/runs": { "post": { "operationId": "runs-sync-datamule-repository-platform-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datamule~repository-platform-extractor/run-sync": { "post": { "operationId": "run-sync-datamule-repository-platform-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "platform", "baseUrl" ], "properties": { "platform": { "title": "Repository platform", "enum": [ "dataverse", "dspace", "invenio" ], "type": "string", "description": "Which repository software the target install runs. dataverse = Harvard/IQSS Dataverse (/api/search). dspace = DSpace 7/8 REST API (/server/api). invenio = InvenioRDM or Zenodo (/api/records). All three expose one uniform API across their entire installed base, so ONE actor spans thousands of independent institutional repositories.", "default": "invenio" }, "baseUrl": { "title": "Repository base URL", "type": "string", "description": "The install root of the repository. Dataverse example: https://dataverse.harvard.edu (or data.qdr.syr.edu, darus.uni-stuttgart.de, borealisdata.ca). DSpace example: https://dspace.mit.edu (the site root — the actor appends /server/api). Invenio example: https://zenodo.org (or data.caltech.edu). A trailing slash is normalized away." }, "query": { "title": "Search query", "type": "string", "description": "Free-text search passed to the platform's search API. Leave empty to browse everything (Dataverse maps empty to q=*). Example: climate, genomics, \"machine learning\"." }, "maxRecords": { "title": "Max records (total)", "minimum": 1, "type": "integer", "description": "Maximum number of dataset / item records to return. The actor pages the search API until this cap is reached or the result set is exhausted. Each returned record is one billable event. Default: one page (see Page size)." }, "pageSize": { "title": "Page size", "minimum": 1, "maximum": 100, "type": "integer", "description": "How many records to request per API page (1-100). Larger pages = fewer round-trips; the total is still capped by Max records. Default 25.", "default": 25 }, "bearerToken": { "title": "Bearer token (optional)", "type": "string", "description": "Optional API token for a gated / private institutional install. Sent as Authorization: Bearer ***. NOT required for public repository search — leave empty. Never logged." }, "extraHeaders": { "title": "Extra request headers", "type": "object", "description": "Optional extra HTTP headers to send with every request, as a JSON object, e.g. {\"X-Api-Key\": \"...\"} for a gated deployment. Leave empty for public installs." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```