# Research Papers Scraper: Citations, Authors & Experts (`scrapemint/research-papers-scraper`) Actor Search 250M+ academic papers across every field: titles, abstracts, citation counts, journals, open-access PDF links. Find the top experts on any topic, look up researchers by name with ORCID, h-index and affiliations, or enrich DOI lists. No API key, no login. - **URL**: https://apify.com/scrapemint/research-papers-scraper.md - **Developed by:** [Ken M](https://apify.com/scrapemint) (community) - **Categories:** Education, Business - **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/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 ## Research Papers Scraper: Citations, Authors & Experts Search over 250 million academic papers across every field of research - no API key, no login, no library subscription. Built on [OpenAlex](https://openalex.org), the open catalog of the world's research, which this actor gratefully credits as its data source. ### Four things it does **1. Search papers by topic.** One row per paper: title, abstract, publication date, journal, authors with first-author institutions, citation count, field-weighted citation impact, and a direct PDF link when a free version exists. Sort by best match, most cited, or newest; filter by year range, minimum citations, and open access. **2. Find the top experts on any topic.** Give it "crispr gene editing" and get the most-published researchers on that topic, ranked, each with ORCID, h-index, total citations and current institutions. An instant expert lead list for consulting, speaking, peer review, due diligence or journalism. **3. Look up researchers by name.** ORCID, h-index, paper and citation counts, institutional affiliations - with multiple matches returned for ambiguous names. **4. Enrich DOI lists.** Paste DOIs and get the full record for each, including citation counts and open-access links. ### Example input ```json { "queries": ["large language models"], "maxPerQuery": 15, "sortBy": "citations" } ```` ### Who uses this - **R\&D and competitive intelligence teams**: track what is being published in your field and by whom. - **Recruiters, event organizers and expert networks**: the expert-finder mode is a ready-made lead list with credentials attached. - **Journalists and analysts**: find the right person to quote and verify their standing. - **Libraries, EdTech and research tools**: enrich reference lists at a fraction of commercial database prices. - **Grant writers and academics**: literature scans sorted by citations, with abstracts, in structured JSON. ### Pricing A small fee per row. Searches that match nothing, unknown DOIs and unmatched names are free note rows, and the first 2 rows of every run are free. ### Notes - Data source: OpenAlex (CC0). Coverage is excellent for journal articles across all disciplines; abstracts are present for most recent papers but not all older ones. - Author name search matches names, not topics - use the expert finder for topic-based discovery. - Citation counts reflect OpenAlex's index and can differ slightly from Google Scholar (which counts more gray literature). # Actor input Schema ## `queries` (type: `array`): Topics or keywords to search papers for, one per line ("large language models", "mRNA vaccine stability"). Each returns matching papers with abstracts and citation counts. ## `maxPerQuery` (type: `integer`): How many papers to return for each search. ## `sortBy` (type: `string`): Order of paper results: best match, most cited first, or newest first. ## `yearFrom` (type: `integer`): Only papers published in or after this year. 0 = no limit. ## `yearTo` (type: `integer`): Only papers published in or before this year. 0 = no limit. ## `minCitations` (type: `integer`): Only papers cited at least this many times. 0 = no limit. ## `openAccessOnly` (type: `boolean`): Only papers with a free-to-read version (most rows then include a direct PDF link). ## `expertQueries` (type: `array`): Topics to find the leading researchers for, one per line ("crispr gene editing"). Returns the most-published authors on that topic with ORCID, h-index and current institutions - an expert lead list. ## `maxExpertsPerQuery` (type: `integer`): How many top authors to return per expert topic. ## `authorNames` (type: `array`): Researcher names to look up, one per line ("Jennifer Doudna"). Returns their profile: ORCID, h-index, paper and citation counts, institutions. ## `maxAuthorsPerName` (type: `integer`): Names can be ambiguous; return up to this many best matches per name. ## `dois` (type: `array`): DOIs to enrich, one per line ("10.1038/s41586-021-03819-2" or full doi.org URLs). Each returns the full paper record. ## `maxRows` (type: `integer`): Stop after this many rows in total. ## Actor input object example ```json { "queries": [ "large language models" ], "maxPerQuery": 15, "sortBy": "relevance", "yearFrom": 0, "yearTo": 0, "minCitations": 0, "openAccessOnly": false, "maxExpertsPerQuery": 10, "maxAuthorsPerName": 3, "maxRows": 1000 } ``` # 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 = { "queries": [ "large language models" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapemint/research-papers-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 = { "queries": ["large language models"] } # Run the Actor and wait for it to finish run = client.actor("scrapemint/research-papers-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 '{ "queries": [ "large language models" ] }' | apify call scrapemint/research-papers-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapemint/research-papers-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Research Papers Scraper: Citations, Authors & Experts", "description": "Search 250M+ academic papers across every field: titles, abstracts, citation counts, journals, open-access PDF links. Find the top experts on any topic, look up researchers by name with ORCID, h-index and affiliations, or enrich DOI lists. No API key, no login.", "version": "0.1", "x-build-id": "gtzq7fHhY19tpDRpe" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapemint~research-papers-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapemint-research-papers-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/scrapemint~research-papers-scraper/runs": { "post": { "operationId": "runs-sync-scrapemint-research-papers-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/scrapemint~research-papers-scraper/run-sync": { "post": { "operationId": "run-sync-scrapemint-research-papers-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", "properties": { "queries": { "title": "Paper searches", "type": "array", "description": "Topics or keywords to search papers for, one per line (\"large language models\", \"mRNA vaccine stability\"). Each returns matching papers with abstracts and citation counts.", "items": { "type": "string" } }, "maxPerQuery": { "title": "Papers per search", "minimum": 1, "maximum": 2000, "type": "integer", "description": "How many papers to return for each search.", "default": 15 }, "sortBy": { "title": "Sort papers by", "enum": [ "relevance", "citations", "newest" ], "type": "string", "description": "Order of paper results: best match, most cited first, or newest first.", "default": "relevance" }, "yearFrom": { "title": "Published from year", "minimum": 0, "maximum": 2100, "type": "integer", "description": "Only papers published in or after this year. 0 = no limit.", "default": 0 }, "yearTo": { "title": "Published up to year", "minimum": 0, "maximum": 2100, "type": "integer", "description": "Only papers published in or before this year. 0 = no limit.", "default": 0 }, "minCitations": { "title": "Minimum citations", "minimum": 0, "maximum": 1000000, "type": "integer", "description": "Only papers cited at least this many times. 0 = no limit.", "default": 0 }, "openAccessOnly": { "title": "Open access only", "type": "boolean", "description": "Only papers with a free-to-read version (most rows then include a direct PDF link).", "default": false }, "expertQueries": { "title": "Find top experts on", "type": "array", "description": "Topics to find the leading researchers for, one per line (\"crispr gene editing\"). Returns the most-published authors on that topic with ORCID, h-index and current institutions - an expert lead list.", "items": { "type": "string" } }, "maxExpertsPerQuery": { "title": "Experts per topic", "minimum": 1, "maximum": 100, "type": "integer", "description": "How many top authors to return per expert topic.", "default": 10 }, "authorNames": { "title": "Author lookups", "type": "array", "description": "Researcher names to look up, one per line (\"Jennifer Doudna\"). Returns their profile: ORCID, h-index, paper and citation counts, institutions.", "items": { "type": "string" } }, "maxAuthorsPerName": { "title": "Matches per author name", "minimum": 1, "maximum": 25, "type": "integer", "description": "Names can be ambiguous; return up to this many best matches per name.", "default": 3 }, "dois": { "title": "DOI lookups", "type": "array", "description": "DOIs to enrich, one per line (\"10.1038/s41586-021-03819-2\" or full doi.org URLs). Each returns the full paper record.", "items": { "type": "string" } }, "maxRows": { "title": "Max rows", "minimum": 1, "maximum": 20000, "type": "integer", "description": "Stop after this many rows in total.", "default": 1000 } } }, "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 } } } } } } } } } } ```