# NSF Research Award Tracker – Federal Science Funding API (`deadwood_data_solutions/nsf-research-award-tracker`) Actor Structured feed of National Science Foundation awards: institution, PI, program, amount and abstract. For research-development offices, science journalists and competitive-funding intel. - **URL**: https://apify.com/deadwood\_data\_solutions/nsf-research-award-tracker.md - **Developed by:** [K O](https://apify.com/deadwood_data_solutions) (community) - **Categories:** Developer tools, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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 ## NSF Research Award Tracker – Federal Science Funding API An **NSF Awards API** wrapper and science-funding feed that pulls National Science Foundation grants and normalizes each into a clean record: award id, title, awardee institution, principal investigator, program, funds obligated, dates and abstract. Use it to build a **research-funding intelligence tool**, a business-development feed for vendors selling to labs, or a competitive-grants monitor. ### Who uses it University research-development and sponsored-programs offices, vendors selling lab equipment/software into funded projects, science journalists, and research-intelligence products. ### Why this is worth charging for New NSF awards signal where money and hiring are flowing in science. The NSF API paginates with a 1-based offset and returns string amounts. This actor coerces amounts to numbers, builds the PI name and award URL, and delivers a deduplicated, keyword- or institution-scoped funding feed you can monitor on a schedule. ### Output Each dataset item is one normalized record: ```json { "awardId": "2412345", "title": "Study of Quantum Widgets", "awardee": "State University", "awardeeState": "CA", "principalInvestigator": "Alice Nguyen", "fundsObligated": 500000, "estimatedTotal": 1200000, "startDate": "09/01/2024", "expirationDate": "08/31/2027", "program": "Physics", "agency": "NSF", "url": "https://www.nsf.gov/awardsearch/showAward?AWD_ID=2412345", "source": "NSF" } ```` ### Input | Field | Description | |---|---| | `keyword` | Search term across award title/abstract, e.g. 'quantum computing'. | | `awardeeName` | Filter by awardee institution name. | | `awardeeStateCode` | 2-letter state of the awardee institution. | | `startDateFrom` | Only awards starting on or after this date (NSF expects MM/DD/YYYY). | | `rpp` | Results per page (NSF max 25). | | `maxPagesToScan` | Pages per run. | | `onlyNewSinceLastRun` | Recommended for schedules — skips records already returned by a previous run using persisted state, so you're only charged for genuinely new items. | | `maxItems` | Stop after this many normalized records. | ### Pricing (Pay-Per-Event) - **`query`** — $0.02, charged once per run for the API query/poll. - **`award-record`** — $0.01 per normalized record pushed. This is the primary event. - `apify-actor-start` (Apify-managed) — covers baseline compute per run. A keyword or institution monitor returns tens of awards per run for pennies. onlyNewSinceLastRun bills only newly-posted awards. ### Source & reliability Data comes from the official `api.nsf.gov/services/v1/awards` service. Amounts are coerced to numbers. No key or proxies required. Run `npm test` for the offline self-test covering the normalizer, including edge cases and missing-field handling. ### FAQ **Is there an API for NSF awards?** Yes — this actor wraps the official NSF Awards API and returns clean, deduplicated award records via the Apify API, webhook or export. **Can I track one university?** Yes. Set awardeeName (and optionally a keyword) and schedule with onlyNewSinceLastRun for an institution-specific funding feed. *** *SEO keywords: NSF API, research grant API, science funding data, NSF award scraper, grants intelligence feed, federal research funding tracker* # Actor input Schema ## `keyword` (type: `string`): Search term across award title/abstract, e.g. 'quantum computing'. ## `awardeeName` (type: `string`): Filter by awardee institution name. ## `awardeeStateCode` (type: `string`): 2-letter state of the awardee institution. ## `startDateFrom` (type: `string`): Only awards starting on or after this date (NSF expects MM/DD/YYYY). ## `rpp` (type: `integer`): Results per page (NSF max 25). ## `maxPagesToScan` (type: `integer`): Pages per run. ## `maxItems` (type: `integer`): Stop after this many normalized records have been pushed. Leave blank for no limit (bounded by pagination settings). ## `onlyNewSinceLastRun` (type: `boolean`): Recommended for scheduled runs. Uses persisted state to skip records already returned earlier, so a recurring schedule only charges for genuinely new items. ## Actor input object example ```json { "rpp": 25, "maxPagesToScan": 4, "onlyNewSinceLastRun": true } ``` # 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("deadwood_data_solutions/nsf-research-award-tracker").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("deadwood_data_solutions/nsf-research-award-tracker").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 deadwood_data_solutions/nsf-research-award-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=deadwood_data_solutions/nsf-research-award-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "NSF Research Award Tracker – Federal Science Funding API", "description": "Structured feed of National Science Foundation awards: institution, PI, program, amount and abstract. For research-development offices, science journalists and competitive-funding intel.", "version": "1.0", "x-build-id": "N0Y7ajI1wi1ywyTlW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/deadwood_data_solutions~nsf-research-award-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-deadwood_data_solutions-nsf-research-award-tracker", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/deadwood_data_solutions~nsf-research-award-tracker/runs": { "post": { "operationId": "runs-sync-deadwood_data_solutions-nsf-research-award-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/deadwood_data_solutions~nsf-research-award-tracker/run-sync": { "post": { "operationId": "run-sync-deadwood_data_solutions-nsf-research-award-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "keyword": { "title": "Keyword", "type": "string", "description": "Search term across award title/abstract, e.g. 'quantum computing'." }, "awardeeName": { "title": "Awardee institution", "type": "string", "description": "Filter by awardee institution name." }, "awardeeStateCode": { "title": "Awardee state", "type": "string", "description": "2-letter state of the awardee institution." }, "startDateFrom": { "title": "Start date from (MM/DD/YYYY)", "type": "string", "description": "Only awards starting on or after this date (NSF expects MM/DD/YYYY)." }, "rpp": { "title": "Results per page", "minimum": 1, "maximum": 25, "type": "integer", "description": "Results per page (NSF max 25).", "default": 25 }, "maxPagesToScan": { "title": "Max pages to scan", "minimum": 1, "maximum": 100, "type": "integer", "description": "Pages per run.", "default": 4 }, "maxItems": { "title": "Maximum records to return", "minimum": 1, "type": "integer", "description": "Stop after this many normalized records have been pushed. Leave blank for no limit (bounded by pagination settings)." }, "onlyNewSinceLastRun": { "title": "Only return records not seen in a previous run", "type": "boolean", "description": "Recommended for scheduled runs. Uses persisted state to skip records already returned earlier, so a recurring schedule only charges for genuinely new items.", "default": true } } }, "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 } } } } } } } } } } ```