# NIH Grant Award Scraper — Research Leads (`gocreative.ai/nih-grant-leads`) Actor Scrape NIH-funded research projects from NIH Reporter. Find principal investigators, institutions, award amounts, and research focus areas. Ideal for CROs, lab equipment vendors, and biotech SaaS targeting funded research labs. - **URL**: https://apify.com/gocreative.ai/nih-grant-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, NaN 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.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 ## NIH Grant Award Scraper — Funded Researcher & Institution Leads **Find researchers and institutions that just won NIH funding.** This scraper pulls National Institutes of Health grant awards from the official NIH RePORTER database and turns them into clean, structured B2B leads: project title, principal investigator, institution, department, and location. NIH-funded researchers control real budgets for lab equipment, software, reagents, clinical services, and consulting. This actor surfaces exactly who just received funding — with the principal investigator named — so you can reach decision-makers with money to spend. ### What you get per grant - **Project number** and **project title** - **Principal investigator (PI) name** — the researcher who controls the grant - **Institution / organization** (university, hospital, research center) - **Department** - **City, state, and country** ### Why use this NIH grant scraper - **Real, current funding** — sourced directly from NIH RePORTER, the authoritative US biomedical grant database. - **Named decision-makers** — every record names the PI, so you skip the research step. - **Target by topic** — filter by keyword (e.g. cancer, genomics, machine learning), fiscal year, or activity code. - **Export anywhere** — CSV, JSON, or Excel from Apify, or pull via API into your CRM. ### Use cases - **Life-science & lab-equipment sales** — sell to newly-funded labs. - **Research software & SaaS vendors** — reach PIs with fresh grant budgets. - **CRO / clinical-services prospecting** — find funded clinical and translational projects. - **Academic & grants intelligence** — track funding trends in your field. - **Recruiting** — funded labs hire postdocs and staff. ### Input | Field | Description | |---|---| | `keywords` | List of topic keywords (e.g. ["cancer", "genomics"]) | | `fiscalYears` | List of fiscal years (e.g. [2025, 2026]) | | `activityCodes` | Optional NIH activity codes (e.g. R01, R21) | | `maxResults` | How many awards to return | #### Example input ```json { "keywords": ["machine learning", "cancer"], "fiscalYears": [2025, 2026], "maxResults": 100 } ```` ### Example output ```json { "projectNumber": "1R01CA123456-01", "projectTitle": "Machine Learning for Early Cancer Detection", "piName": "SMITH, JANE A", "institution": "STANFORD UNIVERSITY", "department": "Radiology", "city": "Stanford", "state": "CA", "country": "UNITED STATES" } ``` ### How it works The scraper queries the NIH RePORTER API for awards matching your keywords and fiscal years, then structures each result into a clean lead record. No API key, no signup, no account — just run it and export. ### Pricing Pay-per-result via Apify — you only pay for the leads you get. No subscription. ### FAQ **What's the data source?** Official NIH RePORTER — the US government's database of funded biomedical research. **Do I get the researcher's name?** Yes — the principal investigator (PI) is named on every award. **Can I target a specific field?** Yes — filter by keyword, fiscal year, and NIH activity code. **Can I export to CSV / Excel?** Yes — Apify exports any run as CSV, JSON, or Excel, or pull via API. # Actor input Schema ## `keywords` (type: `array`): Keywords to search across NIH project titles, abstracts, and terms. E.g. \["CRISPR", "machine learning", "oncology"]. ## `fiscalYears` (type: `array`): NIH fiscal years to include. E.g. \[2024, 2025]. ## `activityCodes` (type: `array`): Filter by NIH grant activity codes, e.g. R01, R21, U01, K99. Leave empty to include all. ## `maxResults` (type: `integer`): Maximum number of grant records to return (1–500). ## Actor input object example ```json { "keywords": [ "machine learning", "cancer", "genomics" ], "fiscalYears": [ "2024", "2025" ], "activityCodes": [], "maxResults": 100 } ``` # 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", "cancer", "genomics" ], "fiscalYears": [ "2024", "2025" ] }; // Run the Actor and wait for it to finish const run = await client.actor("gocreative.ai/nih-grant-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", "cancer", "genomics", ], "fiscalYears": [ "2024", "2025", ], } # Run the Actor and wait for it to finish run = client.actor("gocreative.ai/nih-grant-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", "cancer", "genomics" ], "fiscalYears": [ "2024", "2025" ] }' | apify call gocreative.ai/nih-grant-leads --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=gocreative.ai/nih-grant-leads", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "NIH Grant Award Scraper — Research Leads", "description": "Scrape NIH-funded research projects from NIH Reporter. Find principal investigators, institutions, award amounts, and research focus areas. Ideal for CROs, lab equipment vendors, and biotech SaaS targeting funded research labs.", "version": "0.1", "x-build-id": "zDvjsakytZAnGP5PI" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/gocreative.ai~nih-grant-leads/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-gocreative.ai-nih-grant-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~nih-grant-leads/runs": { "post": { "operationId": "runs-sync-gocreative.ai-nih-grant-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~nih-grant-leads/run-sync": { "post": { "operationId": "run-sync-gocreative.ai-nih-grant-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": "array", "description": "Keywords to search across NIH project titles, abstracts, and terms. E.g. [\"CRISPR\", \"machine learning\", \"oncology\"].", "default": [ "machine learning", "cancer", "genomics" ], "items": { "type": "string" } }, "fiscalYears": { "title": "Fiscal Years", "type": "array", "description": "NIH fiscal years to include. E.g. [2024, 2025].", "default": [ 2024, 2025 ], "items": { "type": "string" } }, "activityCodes": { "title": "Activity Codes (optional)", "type": "array", "description": "Filter by NIH grant activity codes, e.g. R01, R21, U01, K99. Leave empty to include all.", "default": [], "items": { "type": "string" } }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of grant 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 } } } } } } } } } } ```