# UK University Open Days Scraper (`sheeny_pediment/my-actor-5`) Actor Automatically extract UK university open day events, dates, and registration links based on academic subject or specialization. Perfect for student lead generation. - **URL**: https://apify.com/sheeny\_pediment/my-actor-5.md - **Developed by:** [Ram Vittanala](https://apify.com/sheeny_pediment) (community) - **Categories:** Other, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.10 / 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## UK University Open Days Scraper Automatically extract university open day information and events from opendays.com using a high-performance Cheerio crawler. ### Features - **Ultra-Fast Crawling**: By targeting calendar pages instead of waiting for heavy detailed pages, it bypasses the strict rate-limits and timeouts on `opendays.com` and provides near-instant results. - **Smart Subject Filtering**: If searching for `Aerospace` specifically, the scraper smartly filters the open days to only include the universities that are known to offer Aerospace Engineering. For other inputs, it uses standard keyword matching against the university names and URLs. - **Customizable Dates**: Extract open days for specific months and years. ### Input This actor takes the following inputs: - **specialization** (String): The subject or specialization you want to find open days for (e.g., `Aerospace`). - **year** (String): The year to search for open days (e.g., `2026`). If left blank, it defaults to checking standard future months. - **month** (String): The specific month to search (e.g., `September`). - **audience** (Dropdown): Filter the events by the target audience using the corresponding number: - `0`: Everyone / Any - `8`: School/College Groups (Ages 7-11) - `2`: School/College Groups (Ages 11-14) - `3`: School/College Groups (Ages 14-16) - `4`: School/College Groups (Ages 16-18) (Pre-undergraduate) - `10`: College Groups (Ages 19+) - `5`: Individuals (Undergraduate / General) - `6`: Teachers (CPD) - `7`: Parents - `9`: Families - **maxItems** (Integer): Maximum number of events to scrape (default: `100`). ### Output The actor stores the results in the default dataset. Each item will contain: - `institution`: The name of the university or college hosting the open day. - `url`: The link to the institution's open day page on opendays.com. - `month`: The calendar month of the event. - `year`: The year of the event. # Actor input Schema ## `specialization` (type: `string`): The subject or specialization to search for, e.g. 'Aerospace' or 'Computer Science'. ## `maxItems` (type: `integer`): Maximum number of open days to scrape. ## `year` (type: `string`): The year to search for open days (e.g., 2025). If left blank, it starts from the current year. ## `month` (type: `string`): The month to start searching from (e.g., september). If left blank, it starts from the current month. ## `audience` (type: `string`): Filter by target audience (e.g. Undergraduate individuals, school groups, etc.). ## Actor input object example ```json { "specialization": "Aerospace", "maxItems": 100, "year": "", "month": "", "audience": "0" } ```` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("sheeny_pediment/my-actor-5").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("sheeny_pediment/my-actor-5").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 sheeny_pediment/my-actor-5 --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=sheeny_pediment/my-actor-5", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "UK University Open Days Scraper", "description": "Automatically extract UK university open day events, dates, and registration links based on\n academic subject or specialization. Perfect for student lead generation.", "version": "0.0", "x-build-id": "oj2oMWVZXeKdEmjgc" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/sheeny_pediment~my-actor-5/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-sheeny_pediment-my-actor-5", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/sheeny_pediment~my-actor-5/runs": { "post": { "operationId": "runs-sync-sheeny_pediment-my-actor-5", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/sheeny_pediment~my-actor-5/run-sync": { "post": { "operationId": "run-sync-sheeny_pediment-my-actor-5", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "specialization" ], "properties": { "specialization": { "title": "Subject or Specialization", "type": "string", "description": "The subject or specialization to search for, e.g. 'Aerospace' or 'Computer Science'.", "default": "Aerospace" }, "maxItems": { "title": "Max items to extract", "type": "integer", "description": "Maximum number of open days to scrape.", "default": 100 }, "year": { "title": "Year", "type": "string", "description": "The year to search for open days (e.g., 2025). If left blank, it starts from the current year.", "default": "" }, "month": { "title": "Month", "type": "string", "description": "The month to start searching from (e.g., september). If left blank, it starts from the current month.", "default": "" }, "audience": { "title": "Target Audience / Level", "enum": [ "0", "8", "2", "3", "4", "10", "5", "6", "7", "9" ], "type": "string", "description": "Filter by target audience (e.g. Undergraduate individuals, school groups, etc.).", "default": "0" } } }, "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 } } } } } } } } } } ```