# Sheffield Discovery Parser Spider (`getdataforme/sheffield-discovery-parser-spider`) Actor This Apify Actor efficiently scrapes and extracts detailed course information from the University of Sheffield's discovery platform, including titles, summaries, durations, and start dates.... - **URL**: https://apify.com/getdataforme/sheffield-discovery-parser-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, E-commerce, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 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. 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 ### Description This Apify Actor efficiently scrapes and extracts detailed course information from the University of Sheffield's discovery platform, including titles, summaries, durations, and start dates.... --- ## Sheffield Discovery Parser Spider ### Introduction The Sheffield Discovery Parser Spider is a powerful Apify Actor designed to scrape and extract detailed course information from the University of Sheffield's discovery platform. It efficiently searches for courses based on user-defined keywords and URLs, delivering structured data on programs, durations, and more. This tool is ideal for researchers, educators, and organizations seeking accurate, up-to-date academic data for analysis or integration. ### Features - **Comprehensive Course Extraction**: Parses detailed information including titles, subtitles, summaries, images, durations, and start dates from Sheffield's search results. - **Flexible URL and Keyword Targeting**: Supports multiple starting URLs and customizable search keys to tailor scraping to specific needs. - **High-Performance Scraping**: Built on Apify's robust infrastructure for reliable, fast data collection with minimal errors. - **Structured JSON Output**: Delivers clean, machine-readable data perfect for downstream processing or database integration. - **Error-Resilient Design**: Handles network issues and site changes gracefully, ensuring consistent results. - **Scalable and Customizable**: Easily configurable for batch processing or targeted searches across various academic domains. - **Apify Store Integration**: Seamlessly runs on Apify's cloud platform with built-in monitoring and export options. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|--------|----------|-------------|---------| | Urls | array | Yes | An array of starting URLs to scrape from the Sheffield discovery platform. Must be valid HTTP/HTTPS URLs. | ["https://sheffield.ac.uk"] | | SearchKey | string | No | The keyword or phrase to search for courses. Defaults to a general term if not specified. | "computer science" | ### Example Usage #### Input Configuration ```json { "Urls": ["https://sheffield.ac.uk"], "SearchKey": "computer science" } ```` #### Output Example ```json [ { "page": 0, "search_key": "computer+science", "title": "Advanced Computer Science | MSc", "url": "https://dxp-uk-search.funnelback.squiz.cloud/s/redirect?collection=uos~sp-meta&url=https%3A%2F%2Fsheffield.ac.uk%2Fpostgraduate%2Ftaught%2Fcourses%2F2026%2Fadvanced-computer-science-msc&auth=Bkfk8KMDDU8bZkaULT5usA&profile=_default&rank=1&query=computer+science", "subtitle": "School of Computer Science, Faculty of Engineering", "summary": "With the rapid evolution of technology, understanding both the theoretical foundations and practical applications of computer science is vital. This MSc places you at the cutting edge of developments in this field, giving you the knowledge essential…", "image_url": "https://sheffield.ac.uk/sites/default/files/styles/two_thirds_2x/public/2025-05/Robotics-10.jpg?h=732b3878&itok=SC9rHalH", "duration": "1 year", "course_start": "2026", "actor_id": "NfiSIaineFmH0d1lY", "run_id": "17J8aRJemVbOeywGy" }, { "page": 0, "search_key": "computer+science", "title": "Computer Science | MSc", "url": "https://dxp-uk-search.funnelback.squiz.cloud/s/redirect?collection=uos~sp-meta&url=https%3A%2F%2Fsheffield.ac.uk%2Fpostgraduate%2Ftaught%2Fcourses%2F2026%2Fcomputer-science-msc&auth=PiAby%2FOa%2F2%2BAZ90hAJxC6A&profile=_default&rank=2&query=computer+science", "subtitle": "School of Computer Science, Faculty of Engineering", "summary": "Are you looking to expand your career possibilities in tech? This masters degree is for ambitious graduates with a non-computing background - no coding experience needed. Develop skills in programming and a range of topics including computer science…", "image_url": "https://sheffield.ac.uk/sites/default/files/styles/two_thirds_2x/public/2025-09/Machines%20and%20Intelligence-8.jpg?h=18537e05&itok=P-oS1bYb", "duration": "1 year", "course_start": "2026", "actor_id": "NfiSIaineFmH0d1lY", "run_id": "17J8aRJemVbOeywGy" }, { "page": 0, "search_key": "computer+science", "title": "Computer Science | BSc", "url": "https://dxp-uk-search.funnelback.squiz.cloud/s/redirect?collection=uos~sp-meta&url=https%3A%2F%2Fsheffield.ac.uk%2Fundergraduate%2Fcourses%2F2026%2Fcomputer-science-bsc&auth=uUPSQpouT95ADALbB7XRlg&profile=_default&rank=3&query=computer+science", "subtitle": "School of Computer Science", "summary": "You’ll receive a thorough grounding in both the theory and the practical skills associated with this dynamic subject, encountering application areas such as cybersecurity, deep learning, speech recognition, cryptography and robotics. We’ll equip you with skills needed to succeed in your career.", "image_url": "https://sheffield.ac.uk/sites/default/files/styles/two_thirds_2x/public/2025-05/Machines%20and%20Intelligence-8.jpg?h=18537e05&itok=OEAt_e_k", "duration": "3 years", "course_start": "2026", "actor_id": "NfiSIaineFmH0d1lY", "run_id": "17J8aRJemVbOeywGy" } ] ``` ### Use Cases - **Market Research and Analysis**: Gather data on academic programs to identify trends in education offerings. - **Competitive Intelligence**: Compare course structures and specializations across universities. - **Price Monitoring**: Track tuition-related details indirectly through course durations and starts. - **Content Aggregation**: Build databases of educational resources for online platforms. - **Academic Research**: Collect metadata for studies on curriculum development. - **Business Automation**: Automate data feeds for recruitment agencies or educational consultants. ### Installation and Usage 1. Search for "Sheffield Discovery Parser Spider" in the Apify Store 2. Click "Try for free" or "Run" 3. Configure input parameters 4. Click "Start" to begin extraction 5. Monitor progress in the log 6. Export results in your preferred format (JSON, CSV, Excel) ### Output Format The Actor outputs an array of JSON objects, each representing a scraped course. Key fields include: - `page`: The page number from the search results. - `search_key`: The encoded search query used. - `title`: The course name and qualification. - `url`: Direct link to the course page. - `subtitle`: Department and faculty information. - `summary`: A brief description of the course. - `image_url`: URL to an associated image. - `duration`: Length of the program. - `course_start`: Starting year. - `actor_id` and `run_id`: Metadata for tracking the scrape. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! # Actor input Schema ## `Urls` (type: `array`): The urls for the spider. ## `SearchKey` (type: `string`): The search key for the spider. ## Actor input object example ```json { "Urls": [ "https://sheffield.ac.uk" ], "SearchKey": "computer science" } ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # 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("getdataforme/sheffield-discovery-parser-spider").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("getdataforme/sheffield-discovery-parser-spider").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 getdataforme/sheffield-discovery-parser-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/sheffield-discovery-parser-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Sheffield Discovery Parser Spider", "description": "This Apify Actor efficiently scrapes and extracts detailed course information from the University of Sheffield's discovery platform, including titles, summaries, durations, and start dates....", "version": "0.0", "x-build-id": "fKEyFJ8zcsQcbmoot" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~sheffield-discovery-parser-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-sheffield-discovery-parser-spider", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~sheffield-discovery-parser-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-sheffield-discovery-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~sheffield-discovery-parser-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-sheffield-discovery-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "Urls": { "title": "Urls", "minItems": 1, "type": "array", "description": "The urls for the spider.", "default": [ "https://sheffield.ac.uk" ], "items": { "type": "string" } }, "SearchKey": { "title": "Search Key", "type": "string", "description": "The search key for the spider.", "default": "computer science" } } }, "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 } } } } } } } } } } ```