# Orcid Researcher Leads Scraper (`gocreative.ai/orcid-researcher-leads`) Actor Extract researcher profiles from the free ORCID public registry including names, institutional affiliations, and emails. Ideal for B2B outreach to universities, corporate R\&D labs, and biotech organizations. - **URL**: https://apify.com/gocreative.ai/orcid-researcher-leads.md - **Developed by:** [GoCreative AI](https://apify.com/gocreative.ai) (community) - **Categories:** Lead generation, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $6.00 / 1,000 leads This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 & Academic Leads Scraper ### 📊 Example output (real run) ![Sample output](https://api.apify.com/v2/key-value-stores/1ztQFHwG8acESTazI/records/orcid-researcher-leads-sample) A real record from a live run — clean structured data, exported as CSV / JSON / Excel or via API. Find and export **verified researcher and academic profiles** from the free ORCID public registry — the global standard identifier used by millions of scientists, professors, and PhD-level professionals. Search by topic keywords, institution, or country to build targeted lead lists for scientific software, research tooling, recruiting, conferences, publishing, and academic-services outreach. Every record includes the researcher's ORCID iD, full name, primary institution, and (where the researcher has made it public) their email — pulled straight from the official ORCID API. No API key required. ### Why this Actor | | ORCID Researcher Leads | Manual ORCID website search | Generic academic scraper | |---|---|---|---| | Official ORCID public API | ✅ | ✅ but one profile at a time | ❌ Unverified | | Full name + institution + email | ✅ | ⚠️ Click each profile | ⚠️ Inconsistent | | Search by keyword / institution / country | ✅ | ⚠️ Limited | ❌ | | Stable unique ID per person (ORCID iD) | ✅ | ✅ | ❌ | | Bulk export to JSON / CSV | ✅ | ❌ | ⚠️ | | Ready for CRM / outreach | ✅ | ❌ | ⚠️ | ### Example output Run input: ```json { "keywords": "oncology", "maxResults": 50 } ```` Sample result item (real data from a live run): ```json { "orcidId": "0009-0003-6630-1607", "profileUrl": "https://orcid.org/0009-0003-6630-1607", "fullName": "Oleg Klenov", "givenNames": "Oleg", "familyName": "Klenov", "email": "klenov@ukr.net", "primaryInstitution": "R.E. Kavetsky Institute of Experimental Pathology, Oncology and Radiobiology", "allInstitutions": [ "R.E. Kavetsky Institute of Experimental Pathology, Oncology and Radiobiology", "Taras Shevchenko National University of Kyiv" ], "query": "oncology", "totalFound": 282364 } ``` > Note: `email` is only present when the researcher has chosen to make it public on ORCID. Name, ORCID iD, and profile URL are populated for every record; institution is present for the large majority. ### Output fields | Field | Description | |---|---| | `orcidId` | The researcher's unique ORCID identifier | | `profileUrl` | Direct link to the public ORCID profile | | `fullName` | Full display name | | `givenNames` | First / given name(s) | | `familyName` | Last / family name | | `email` | Public email, when the researcher has shared one | | `primaryInstitution` | Most relevant affiliated institution | | `allInstitutions` | All affiliated institutions on record | | `query` | The search keyword that returned this record | | `totalFound` | Total profiles matching the query on ORCID | ### Input options | Option | Type | Default | Notes | |---|---|---|---| | `keywords` | string | machine learning | Topic / field / name to search | | `institution` | string | — | Narrow to a specific institution | | `countryCode` | string | — | Two-letter country code (e.g. `US`) | | `maxResults` | integer | 100 | Cap on rows (max 500) | ### Common use cases - **Scientific software & research tooling** — reach researchers by field and institution. - **Academic recruiting & talent sourcing** — find PhD-level candidates by expertise. - **Conferences & publishing** — build outreach lists for a given research topic. - **Partnerships & grants** — map who is active in a field and where they work. Pay-per-event pricing on the Apify Store. Clean, structured JSON and CSV output. # Actor input Schema ## `keywords` (type: `string`): Keywords to search in ORCID profiles — research areas, skills, or topics. Uses Lucene syntax. E.g. "CRISPR", "machine learning", "drug discovery". ## `institution` (type: `string`): Filter by affiliated institution or company name. E.g. "MIT", "Pfizer", "Google". ## `countryCode` (type: `string`): ISO 2-letter country code to filter by. E.g. "US", "GB", "DE". ## `maxResults` (type: `integer`): Maximum number of researcher records to return (1-500). ## Actor input object example ```json { "keywords": "machine learning", "institution": "", "countryCode": "", "maxResults": 100 } ``` # Actor output Schema # 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 = { "keywords": "machine learning" }; // Run the Actor and wait for it to finish const run = await client.actor("gocreative.ai/orcid-researcher-leads").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 = { "keywords": "machine learning" } # Run the Actor and wait for it to finish run = client.actor("gocreative.ai/orcid-researcher-leads").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 '{ "keywords": "machine learning" }' | apify call gocreative.ai/orcid-researcher-leads --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=gocreative.ai/orcid-researcher-leads", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Orcid Researcher Leads Scraper", "description": "Extract researcher profiles from the free ORCID public registry including names, institutional affiliations, and emails. Ideal for B2B outreach to universities, corporate R&D labs, and biotech organizations.", "version": "0.1", "x-build-id": "KFjrWc6hvrUbb6C4M" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/gocreative.ai~orcid-researcher-leads/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-gocreative.ai-orcid-researcher-leads", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/gocreative.ai~orcid-researcher-leads/runs": { "post": { "operationId": "runs-sync-gocreative.ai-orcid-researcher-leads", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/gocreative.ai~orcid-researcher-leads/run-sync": { "post": { "operationId": "run-sync-gocreative.ai-orcid-researcher-leads", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "keywords": { "title": "Search Keywords", "type": "string", "description": "Keywords to search in ORCID profiles — research areas, skills, or topics. Uses Lucene syntax. E.g. \"CRISPR\", \"machine learning\", \"drug discovery\".", "default": "machine learning" }, "institution": { "title": "Institution Name (optional)", "type": "string", "description": "Filter by affiliated institution or company name. E.g. \"MIT\", \"Pfizer\", \"Google\".", "default": "" }, "countryCode": { "title": "Country Code (optional)", "type": "string", "description": "ISO 2-letter country code to filter by. E.g. \"US\", \"GB\", \"DE\".", "default": "" }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of researcher records to return (1-500).", "default": 100 } } }, "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 } } } } } } } } } } ```