# DLMF NIST Math Functions Scraper (`jungle_synthesizer/dlmf-nist-math-functions-scraper`) Actor Scrapes the NIST Digital Library of Mathematical Functions (DLMF) for structured equation data: MathML, LaTeX source, constraints, and referenced functions — across all 36 chapters and hundreds of sections. - **URL**: https://apify.com/jungle\_synthesizer/dlmf-nist-math-functions-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** AI, Developer tools - **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. 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 ## DLMF NIST Math Functions Scraper Scrapes the [NIST Digital Library of Mathematical Functions](https://dlmf.nist.gov/) (DLMF) — the authoritative reference for special functions in mathematics and physics — to produce a structured, machine-readable corpus of numbered equations with MathML, LaTeX source, and associated metadata. ### What it does The actor performs a three-level hierarchical crawl: 1. **Index** — discovers all 36 DLMF chapters from the homepage 2. **Chapter pages** — discovers all sections within each chapter (typically 10–20 sections) 3. **Section pages** — extracts every numbered equation including MathML, LaTeX TeX source, plain-text rendering, referenced symbols, and the canonical permalink Across all 36 chapters the DLMF contains approximately **5,000–10,000 numbered equations**. A full crawl completes in minutes at the default concurrency. ### Output fields | Field | Description | |-------|-------------| | `chapter` | Chapter number (integer, 1–36) | | `section` | Section identifier, e.g. `1.2` | | `title` | Section title, e.g. `Elementary Algebra` | | `equation_number` | DLMF equation number, e.g. `1.2.1` | | `equation_mathml` | Full MathML XML for the equation | | `equation_tex` | LaTeX source recovered from MathML `alttext` attribute | | `equation_text` | Unicode plain-text rendering of the equation | | `constraints` | Constraint text associated with the equation (if any) | | `referenced_functions` | Pipe-separated list of symbol/function names referenced | | `url` | Canonical DLMF permalink, e.g. `http://dlmf.nist.gov/1.2.E1` | ### Use cases - **Symbolic math / CAS training data** — verified special-function formulas with LaTeX and MathML - **RAG / vector search corpora** — ground-truth equation database for scientific-computing AI agents - **Formula search engines** — structured index of equations by chapter/section with canonical IDs - **Verification datasets** — NIST-authoritative identities for function evaluations ### Input parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `maxItems` | integer | 10 | Maximum number of equations to scrape (0 = unlimited) | | `startChapter` | integer | 1 | First chapter to crawl (1–36) | | `endChapter` | integer | 36 | Last chapter to crawl (1–36, omit for all) | ### Example output record ```json { "chapter": 1, "section": "1.2", "title": "Elementary Algebra", "equation_number": "1.2.1", "equation_tex": "\\genfrac{(}{)}{0.0pt}{}{n}{k}=\\frac{n!}{(n-k)!k!}", "equation_text": "(nk)=n!/(n−k)!⁢k!", "constraints": "", "referenced_functions": "(mn): binomial coefficient | !: factorial (as in n!) | n: nonnegative integer", "url": "http://dlmf.nist.gov/1.2.E1" } ```` ### Notes - The DLMF is a US government publication (NIST). Content is in the public domain. - No proxy required — dlmf.nist.gov is a clean US gov host with no anti-bot measures. - Chapter 1 alone contains ~180 equations across 18 sections. Full 36-chapter run yields ~5,000+ records. - Sections containing only notation tables (no numbered equations) return 0 results — this is expected. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of equations to scrape (0 = unlimited) ## `startChapter` (type: `integer`): Chapter number to start from (1-36). Defaults to 1. ## `endChapter` (type: `integer`): Chapter number to stop at (1-36). Defaults to all chapters. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "startChapter": 1 } ``` # Actor output Schema ## `results` (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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "startChapter": 1 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/dlmf-nist-math-functions-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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "startChapter": 1, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/dlmf-nist-math-functions-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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "startChapter": 1 }' | apify call jungle_synthesizer/dlmf-nist-math-functions-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/dlmf-nist-math-functions-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "DLMF NIST Math Functions Scraper", "description": "Scrapes the NIST Digital Library of Mathematical Functions (DLMF) for structured equation data: MathML, LaTeX source, constraints, and referenced functions — across all 36 chapters and hundreds of sections.\n", "version": "0.1", "x-build-id": "r5SRwG2vGR8Fl7DME" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~dlmf-nist-math-functions-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-dlmf-nist-math-functions-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/jungle_synthesizer~dlmf-nist-math-functions-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-dlmf-nist-math-functions-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/jungle_synthesizer~dlmf-nist-math-functions-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-dlmf-nist-math-functions-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", "required": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of equations to scrape (0 = unlimited)" }, "startChapter": { "title": "Start Chapter", "type": "integer", "description": "Chapter number to start from (1-36). Defaults to 1." }, "endChapter": { "title": "End Chapter", "type": "integer", "description": "Chapter number to stop at (1-36). Defaults to all chapters." } } }, "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 } } } } } } } } } } ```