# NSF Research Grants Scraper (`parseforge/nsf-awards-scraper`) Actor Scrape NSF research grant awards with 62 fields per record. Get award titles, abstracts, funding amounts, PI contact info, institution details, program info, and dates. Filter by keyword, agency, state, program, and date range. - **URL**: https://apify.com/parseforge/nsf-awards-scraper.md - **Developed by:** [ParseForge](https://apify.com/parseforge) (community) - **Categories:** Education, Developer tools, Other - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $7.00 / 1,000 results 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 ![ParseForge Banner](https://raw.githubusercontent.com/ParseForge/apify-assets/main/banner.jpg) ## 🔬 NSF Research Grants Scraper Whether you're a university tracking funding opportunities, a researcher monitoring grants in your field, or an analyst studying federal science spending, this tool makes it easy to collect structured award data from the National Science Foundation. > **The NSF Awards Scraper collects research grant records with titles, funding amounts, abstracts, principal investigators, institutions, program details, and dates. Filter by keyword, state, program name, and date range.** ### ✨ What Does It Do - 🎓 **Award details** - collect grant titles, award IDs, abstracts, and funding amounts for NSF-funded research - 👨‍🔬 **Principal investigators** - get PI names, email addresses, and institutional affiliations - 🏛️ **Institution data** - extract awardee names, addresses, cities, states, and ZIP codes - 💰 **Funding amounts** - access estimated totals and obligated funds for each award - 📅 **Date tracking** - get start dates, expiration dates, and transaction types - 🔍 **Powerful filters** - search by keyword, state, NSF program name, and date range ### 🔧 Input - **Max Items** - how many awards to collect (free users get 10 items, paid users up to 1,000,000) - **Keyword** - search awards by topic (e.g., "machine learning", "climate change", "quantum computing") - **Awardee State** - filter by 2-letter state code (e.g., CA, NY, TX) - **Program Name** - filter by NSF program name - **Start Date From / To** - filter awards by start date range (MM/DD/YYYY format) ```json { "maxItems": 100, "keyword": "artificial intelligence", "awardeeState": "CA", "startDateStart": "01/01/2024", "startDateEnd": "12/31/2024" } ```` ### 📊 Output Each award record includes 60+ data fields. Download as JSON, CSV, or Excel. | Field | Description | |-------|-------------| | id | NSF award ID number | | title | Award title | | abstractText | Full abstract describing the research | | agency | Funding agency (NSF) | | awardeeName | Institution receiving the award | | awardeeCity | Institution city | | awardeeStateCode | Institution state code | | estimatedTotalAmt | Total estimated funding amount | | fundsObligatedAmt | Funds currently obligated | | startDate | Award start date | | expDate | Award expiration date | | pdPIName | Principal investigator name | | piEmail | PI email address | | fundProgramName | NSF program funding the award | | primaryProgram | Primary program name | | scrapedAt | Timestamp when the record was collected | ```json { "id": "2401234", "title": "CAREER: Trustworthy Machine Learning for Healthcare Applications", "abstractText": "This project investigates novel approaches to building reliable...", "agency": "NSF", "awardeeName": "University of California-Berkeley", "awardeeCity": "BERKELEY", "awardeeStateCode": "CA", "estimatedTotalAmt": "550000", "fundsObligatedAmt": "110000", "startDate": "06/01/2024", "expDate": "05/31/2029", "pdPIName": "Jane Smith", "piEmail": "jsmith@berkeley.edu", "fundProgramName": "Robust Intelligence", "primaryProgram": "Robust Intelligence", "scrapedAt": "2026-04-09T12:00:00.000Z" } ``` ### 💎 Why Choose the NSF Awards Scraper? | Feature | Our Tool | Manual NSF Search | |---------|----------|--------------------| | Batch collection | Up to 1M awards | Browse one at a time | | Multiple filters | Keyword, state, program, dates | Basic search | | Full abstracts | Complete research descriptions | Click into each award | | PI contact info | Emails and names included | Buried in details | | Structured output | JSON, CSV, Excel | HTML pages only | | Automated scheduling | Daily/weekly monitoring | Manual visits | ### 📋 How to Use 1. **Sign Up** - [Create a free account w/ $5 credit](https://console.apify.com/sign-up?fpr=vmoqkp) 2. **Configure** - enter your keyword, set filters, and choose how many awards you want 3. **Run It** - click "Start" and get structured grant data in seconds No coding, no setup, no manual browsing required. ### 🎯 Business Use Cases - 🎓 **Universities** - track NSF funding in your research areas and identify collaboration opportunities - 🔬 **Researchers** - monitor new awards in your field and discover who is getting funded for similar work - 📊 **Science policy analysts** - study funding trends across programs, states, and time periods - 💼 **Grant writers** - research successful awards to model proposals after funded projects - 🏢 **Technology companies** - identify university research partnerships and emerging technologies - 📰 **Science journalists** - find newsworthy research grants and contact principal investigators ### ❓ FAQ 🔬 **What is the NSF?** The National Science Foundation is a U.S. federal agency that funds fundamental research across all fields of science, engineering, and education. It awards billions of dollars annually. 🔍 **What keywords can I search?** Any research topic. Popular searches include "artificial intelligence", "climate change", "quantum computing", "genomics", "cybersecurity", and "renewable energy". 📋 **How many fields are in each record?** Each award can include 60+ fields covering the award details, PI information, institution data, funding amounts, program details, and dates. ⏱️ **How long does a run take?** About 25 awards per second. A run of 500 awards typically completes in under 30 seconds. 📅 **What date format should I use?** Use MM/DD/YYYY format (e.g., 01/15/2024). ### 🔗 Integrate NSF Awards Scraper with any app - [Make](https://docs.apify.com/platform/integrations/make) - Automate grant monitoring workflows - [Zapier](https://docs.apify.com/platform/integrations/zapier) - Get alerts when new awards match your criteria - [Slack](https://docs.apify.com/platform/integrations/slack) - Get notified in your team channel - [Google Drive](https://docs.apify.com/platform/integrations/drive) - Export award data to spreadsheets - [Webhooks](https://docs.apify.com/platform/integrations/webhooks) - Trigger actions when runs complete ### 💡 Recommended Actors Looking for more research and government data tools? Check out these related actors: | Actor | Description | Link | |-------|-------------|------| | Grants.gov Scraper | Collect federal grant opportunities across all agencies | [Link](https://apify.com/parseforge/grants-gov-scraper) | | GSA eLibrary Scraper | Collect government contract data from GSA | [Link](https://apify.com/parseforge/gsa-elibrary-scraper) | | Hugging Face Model Scraper | Extract ML model data from Hugging Face | [Link](https://apify.com/parseforge/hugging-face-model-scraper) | | FAA Aircraft Registry Scraper | Extract aircraft registration data | [Link](https://apify.com/parseforge/faa-aircraft-registry-scraper) | | USASpending Scraper | Gather federal spending and award data | [Link](https://apify.com/parseforge/usaspending-scraper) | **Pro Tip:** Browse our complete collection of [data collection actors](https://apify.com/parseforge) to find the perfect tool for your needs. ### 🆘 Need Help? - Check the FAQ section above for common questions - Visit the [Apify documentation](https://docs.apify.com) for platform guides - Contact us at [Tally contact form](https://tally.so/r/BzdKgA) ### ⚠️ Disclaimer > This Actor is an independent tool and is not affiliated with, endorsed by, or connected to the National Science Foundation (NSF) or any government agency. It accesses only publicly available data. # Actor input Schema ## `maxItems` (type: `integer`): Free users: Limited to 10 awards. Paid users: Optional, max 1,000,000 ## `keyword` (type: `string`): Search awards by keyword (e.g. 'machine learning', 'climate change', 'quantum computing'). ## `awardeeState` (type: `string`): Filter by state (2-letter code, e.g. CA, NY, TX). ## `fundProgram` (type: `string`): Filter by NSF program name. ## `startDateStart` (type: `string`): Filter awards starting after this date (MM/DD/YYYY). ## `startDateEnd` (type: `string`): Filter awards starting before this date (MM/DD/YYYY). ## Actor input object example ```json { "maxItems": 10, "keyword": "artificial intelligence" } ``` # Actor output Schema ## `overview` (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 = { "maxItems": 10, "keyword": "artificial intelligence" }; // Run the Actor and wait for it to finish const run = await client.actor("parseforge/nsf-awards-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 = { "maxItems": 10, "keyword": "artificial intelligence", } # Run the Actor and wait for it to finish run = client.actor("parseforge/nsf-awards-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 '{ "maxItems": 10, "keyword": "artificial intelligence" }' | apify call parseforge/nsf-awards-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=parseforge/nsf-awards-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "NSF Research Grants Scraper", "description": "Scrape NSF research grant awards with 62 fields per record. Get award titles, abstracts, funding amounts, PI contact info, institution details, program info, and dates. Filter by keyword, agency, state, program, and date range.", "version": "1.0", "x-build-id": "OOusImSMnN3kYRzfN" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/parseforge~nsf-awards-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-parseforge-nsf-awards-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/parseforge~nsf-awards-scraper/runs": { "post": { "operationId": "runs-sync-parseforge-nsf-awards-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/parseforge~nsf-awards-scraper/run-sync": { "post": { "operationId": "run-sync-parseforge-nsf-awards-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 1000000, "type": "integer", "description": "Free users: Limited to 10 awards. Paid users: Optional, max 1,000,000" }, "keyword": { "title": "Keyword", "type": "string", "description": "Search awards by keyword (e.g. 'machine learning', 'climate change', 'quantum computing')." }, "awardeeState": { "title": "Awardee State", "type": "string", "description": "Filter by state (2-letter code, e.g. CA, NY, TX)." }, "fundProgram": { "title": "Program Name", "type": "string", "description": "Filter by NSF program name." }, "startDateStart": { "title": "Start Date From", "type": "string", "description": "Filter awards starting after this date (MM/DD/YYYY)." }, "startDateEnd": { "title": "Start Date To", "type": "string", "description": "Filter awards starting before this date (MM/DD/YYYY)." } } }, "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 } } } } } } } } } } ```