# Orcid Scraper (`openclawmara/orcid-scraper`) Actor - **URL**: https://apify.com/openclawmara/orcid-scraper.md - **Developed by:** [OpenClaw Mara](https://apify.com/openclawmara) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## ORCID Researcher Profile Scraper Search and extract researcher profiles from [ORCID](https://orcid.org) — the world's largest open researcher ID registry with 18M+ profiles. Get names, bios, publications, employment, education, and funding. **No API key needed.** ### Features - 👤 **18M+ researcher profiles** — the global standard for researcher identification - 📚 **Publications** — titles, types, years, journals, DOIs - 🏢 **Employment history** — organizations, roles, departments, dates - 🎓 **Education** — institutions, degrees, departments - 💰 **Funding** — grants, funders, dates - 🔑 **Keywords & websites** — research interests and personal pages - 🔍 **Search** — by name, affiliation, topic, or any keyword - ⚡ **Public API** — no authentication required ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `searchQueries` | string[] | `[]` | Search by keyword/name/affiliation | | `orcidIds` | string[] | `[]` | Fetch specific ORCID profiles | | `maxResults` | number | `20` | Max profiles per query | | `includeWorks` | boolean | `true` | Include publications | | `maxWorks` | number | `50` | Max publications per profile | | `includeEmployment` | boolean | `true` | Include jobs | | `includeEducation` | boolean | `true` | Include education | | `includeFunding` | boolean | `false` | Include grants | ### Example ```json { "searchQueries": ["deep learning Stanford"], "maxResults": 10, "includeWorks": true, "maxWorks": 20, "includeEmployment": true } ```` ### Output ```json { "orcidId": "0000-0002-1825-0097", "fullName": "Josiah Carberry", "biography": "Professor of Psychoceramics...", "keywords": ["psychoceramics", "cracked pots"], "publicationCount": 42, "publications": [{"title": "...", "year": "2024", "doi": "10.1234/..."}], "employment": [{"organization": "Brown University", "role": "Professor"}], "education": [{"organization": "MIT", "role": "PhD"}] } ``` ### Use Cases - **Academic recruiting** — find researchers by expertise and institution - **Collaboration discovery** — identify potential research partners - **Bibliometric analysis** — study publication patterns across fields - **Grant monitoring** — track funded research and funding trends - **Institutional reporting** — aggregate researcher outputs # Actor input Schema ## `searchQueries` (type: `array`): Search researchers by keyword, affiliation, or name (e.g. 'machine learning MIT', 'CRISPR') ## `orcidIds` (type: `array`): Fetch specific profiles by ORCID ID (e.g. '0000-0002-1825-0097') ## `maxResults` (type: `integer`): Maximum researcher profiles to return per search query ## `includeWorks` (type: `boolean`): Fetch publication list for each researcher ## `maxWorks` (type: `integer`): Maximum publications to include per researcher ## `includeEmployment` (type: `boolean`): Fetch employment history ## `includeEducation` (type: `boolean`): Fetch education history ## `includeFunding` (type: `boolean`): Fetch funding/grants information ## Actor input object example ```json { "maxResults": 20, "includeWorks": true, "maxWorks": 50, "includeEmployment": true, "includeEducation": true, "includeFunding": false } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("openclawmara/orcid-scraper").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/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("openclawmara/orcid-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call openclawmara/orcid-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=openclawmara/orcid-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Orcid Scraper", "version": "1.0", "x-build-id": "ay3ZC7AFo28s9N3ke" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/openclawmara~orcid-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-openclawmara-orcid-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/openclawmara~orcid-scraper/runs": { "post": { "operationId": "runs-sync-openclawmara-orcid-scraper", "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/openclawmara~orcid-scraper/run-sync": { "post": { "operationId": "run-sync-openclawmara-orcid-scraper", "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", "properties": { "searchQueries": { "title": "Search Queries", "type": "array", "description": "Search researchers by keyword, affiliation, or name (e.g. 'machine learning MIT', 'CRISPR')", "items": { "type": "string" } }, "orcidIds": { "title": "ORCID IDs", "type": "array", "description": "Fetch specific profiles by ORCID ID (e.g. '0000-0002-1825-0097')", "items": { "type": "string" } }, "maxResults": { "title": "Max Results per Query", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum researcher profiles to return per search query", "default": 20 }, "includeWorks": { "title": "Include Publications", "type": "boolean", "description": "Fetch publication list for each researcher", "default": true }, "maxWorks": { "title": "Max Publications per Profile", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum publications to include per researcher", "default": 50 }, "includeEmployment": { "title": "Include Employment", "type": "boolean", "description": "Fetch employment history", "default": true }, "includeEducation": { "title": "Include Education", "type": "boolean", "description": "Fetch education history", "default": true }, "includeFunding": { "title": "Include Funding", "type": "boolean", "description": "Fetch funding/grants information", "default": false } } }, "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 } } } } } } } } } } ```