# Academic Research & Citation Tracker (`second_coming/academic-research-tracker`) Actor Searches academic databases (arXiv, PubMed, Crossref) for research papers matching keywords. Returns structured citation data including title, authors, journal, DOI, abstract, and URL. - **URL**: https://apify.com/second\_coming/academic-research-tracker.md - **Developed by:** [Richard P](https://apify.com/second_coming) (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 usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Academic Research & Citation Tracker An [Apify Actor](https://apify.com/actors) that searches multiple academic databases for research papers matching your keywords and returns structured citation data. ### Features - **Multi-source search** — queries arXiv, PubMed, and Crossref simultaneously - **Structured citation output** — title, authors, journal, DOI, abstract, URL, published date - **Concurrent searching** — parallel queries across sources and keywords - **Date filtering** — optional date range to find only recent publications - **Graceful abort** — handles stop signals cleanly to minimize costs - **Webhook notifications** — optional POST callback when the run completes ### Data Sources | Source | Coverage | |-----------|--------------------------------------------------------------------------| | **arXiv** | Preprints in physics, mathematics, computer science, and related fields | | **PubMed** | Biomedical literature from MEDLINE, life science journals, and online books | | **Crossref** | Scholarly publications across all disciplines (DOI registration agency) | ### Input Schema | Field | Type | Required | Description | |--------------|-----------------|----------|------------------------------------------------| | `keywords` | `array[string]` | ✅ | Research keywords or topics to search for | | `sources` | `array[string]` | ❌ | Sources: `arxiv`, `pubmed`, `crossref` (default: all) | | `maxResults` | `integer` | ❌ | Max results per source per keyword (default: 20) | | `dateFrom` | `string` | ❌ | Filter: YYYY-MM-DD format date | | `webhookUrl` | `string` | ❌ | URL for completion notification | ### Example Input ```json { "keywords": ["machine learning", "transformer architecture"], "sources": ["arxiv", "crossref"], "maxResults": 10, "dateFrom": "2024-01-01" } ```` ### Dataset Output Each result in the dataset has the following structure: | Field | Type | Description | |-----------------|-----------------|--------------------------------------------| | `timestamp` | `string` | ISO 8601 retrieval timestamp | | `source` | `string` | Source database (`arxiv`, `pubmed`, `crossref`) | | `title` | `string` | Full paper title | | `authors` | `array[string]` | List of author names | | `journal` | `string` | Journal or conference name | | `publishedDate` | `string` | Publication date | | `doi` | `string` | Digital Object Identifier | | `url` | `string` | Link to the paper | | `abstract` | `string` | Paper abstract or summary | ### Local Development #### Prerequisites - Python 3.10+ - [Apify CLI](https://docs.apify.com/cli) #### Setup ```bash ## Create virtual environment and install dependencies python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt ## Run locally with test input apify run --purge ``` #### Deploy ```bash apify login apify push ``` ### Use Cases - Literature review for research projects - Keeping up with recent publications in your field - Building a dataset of academic citations - Monitoring research output on specific topics - Automated citation tracking workflows ### License MIT # Actor input Schema ## `keywords` (type: `array`): List of research keywords or topics to search for across academic databases. ## `sources` (type: `array`): Academic databases to search. Leave empty to search all sources. Options: arxiv, pubmed, crossref. ## `maxResults` (type: `integer`): Maximum number of results to return per source per keyword. ## `dateFrom` (type: `string`): Optional. Only return results published on or after this date. ## `webhookUrl` (type: `string`): Optional URL to receive a POST notification when the run completes. ## Actor input object example ```json { "keywords": [ "machine learning", "transformer architecture" ], "sources": [ "arxiv", "pubmed", "crossref" ], "maxResults": 20 } ``` # 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 = { "keywords": [ "machine learning", "transformer architecture" ], "sources": [ "arxiv", "pubmed", "crossref" ], "maxResults": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("second_coming/academic-research-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 = { "keywords": [ "machine learning", "transformer architecture", ], "sources": [ "arxiv", "pubmed", "crossref", ], "maxResults": 20, } # Run the Actor and wait for it to finish run = client.actor("second_coming/academic-research-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 '{ "keywords": [ "machine learning", "transformer architecture" ], "sources": [ "arxiv", "pubmed", "crossref" ], "maxResults": 20 }' | apify call second_coming/academic-research-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=second_coming/academic-research-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Academic Research & Citation Tracker", "description": "Searches academic databases (arXiv, PubMed, Crossref) for research papers matching keywords. Returns structured citation data including title, authors, journal, DOI, abstract, and URL.", "version": "0.0", "x-build-id": "a3qhyuHBOjka1CUQP" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/second_coming~academic-research-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-second_coming-academic-research-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/second_coming~academic-research-tracker/runs": { "post": { "operationId": "runs-sync-second_coming-academic-research-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/second_coming~academic-research-tracker/run-sync": { "post": { "operationId": "run-sync-second_coming-academic-research-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", "required": [ "keywords" ], "properties": { "keywords": { "title": "Search Keywords", "type": "array", "description": "List of research keywords or topics to search for across academic databases.", "items": { "type": "string" } }, "sources": { "title": "Data Sources", "type": "array", "description": "Academic databases to search. Leave empty to search all sources. Options: arxiv, pubmed, crossref.", "default": [ "arxiv", "pubmed", "crossref" ], "items": { "type": "string" } }, "maxResults": { "title": "Max Results", "type": "integer", "description": "Maximum number of results to return per source per keyword.", "default": 20 }, "dateFrom": { "title": "Date From (YYYY-MM-DD)", "type": "string", "description": "Optional. Only return results published on or after this date." }, "webhookUrl": { "title": "Webhook URL", "type": "string", "description": "Optional URL to receive a POST notification when the run completes." } } }, "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 } } } } } } } } } } ```