# Academic Corpus Harvester (`trisert/academic-corpus-harvester`) Actor Harvest academic paper metadata from arXiv, enrich with OpenAlex citation/concept data, and optionally extract full-text from open-access PDFs. Outputs clean JSON and RAG-ready Markdown chunks. - **URL**: https://apify.com/trisert/academic-corpus-harvester.md - **Developed by:** [Nicola Destro](https://apify.com/trisert) (community) - **Categories:** Automation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 record scrapeds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Academic Corpus Harvester **Harvest academic paper metadata from arXiv, enrich with OpenAlex citation/concept data, and optionally extract full-text from open-access PDFs.** Outputs clean structured JSON or RAG-ready Markdown chunks. ### Why this Actor? Building AI/ML applications requires high-quality academic data — papers, citations, and concepts. This Actor gives you programmatic access to the arXiv + OpenAlex ecosystem: - **Build RAG datasets** for scientific question answering or literature review - **Create training data** for fine-tuning domain-specific language models - **Track citation impact** across research fields - **Analyze research trends** by concept, category, or time period Unlike general web scrapers, this Actor speaks the native APIs of arXiv and OpenAlex, understands the data schema, and handles pagination, rate limits, and enrichment automatically. ### Input | Field | Type | Default | Description | |---|---|---|---| | `query` | string | *required* | Search terms or arXiv category code (e.g. `transformer attention` or `cs.LG`) | | `dateFrom` | string | — | ISO date lower bound (e.g. `2024-01-01`) | | `dateTo` | string | — | ISO date upper bound (e.g. `2024-12-31`) | | `maxResults` | integer | `100` | Max papers to return (hard cap: 1000) | | `enrichCitations` | boolean | `true` | Look up citation count and concepts from OpenAlex | | `includeFullText` | boolean | `false` | Download and extract text from open-access PDFs (increases compute) | | `outputFormat` | enum | `json` | `json` for structured records, `markdown-chunks` for RAG-ready chunks | | `chunkSize` | integer | `500` | Target words per chunk (only used with `markdown-chunks` output) | ### Output Each paper record includes: ```` id — arXiv ID title — paper title authors — list of author names abstract — paper abstract publishedDate — ISO publication date source — "arxiv" sourceUrl — link to arXiv abstract page pdfUrl — link to PDF (may be null) categories — arXiv category tags citationCount — OpenAlex citation count (null if enrichment disabled) concepts — OpenAlex topic tags (null if enrichment disabled) fullText — extracted PDF text (null if full-text disabled) markdownChunks — chunks for RAG pipelines (null if json output) ```` ### Pricing (Pay-Per-Event) | Event | Fired | Suggested Price | |---|---|---| | `metadata-record` | Once per paper metadata record | $0.001–0.002 | | `citation-enrichment` | Once per paper enriched via OpenAlex | $0.003–0.005 | | `full-text-extracted` | Once per paper with PDF text extracted | $0.01–0.02 | ### Data Sources - **[arXiv](https://arxiv.org)** — Open-access repository of 2M+ scholarly articles. No API key required. - **[OpenAlex](https://openalex.org)** — Open catalog of research works, authors, and concepts. No API key required. ### Use Cases - **RAG over Scientific Literature** — Chunk papers into `markdown-chunks` and feed them into a vector database - **Citation Network Analysis** — Enrich papers with OpenAlex citation counts to identify influential works - **Trend Discovery** — Search by category + date range to track emerging research directions - **Dataset Building** — Build custom corpora for fine-tuning domain-specific LLMs ### Local Development ```bash ## Enter the Nix devShell nix develop ## Install dependencies uv sync ## Run tests uv run pytest ## Test locally with Apify CLI apify run ```` ### Technical Notes - arXiv API returns Atom XML (parsed via lxml) - OpenAlex REST API queried by arXiv ID; gracefully handles misses - PDF extraction uses pypdf with pdfplumber fallback for poor-quality extractions - Chunking uses word-count-based splitting with 10% overlap for RAG readiness # Actor input Schema ## `query` (type: `string`): Search terms or arXiv category code (e.g. 'transformer attention' or 'cs.LG') ## `dateFrom` (type: `string`): ISO date string (e.g. 2024-01-01). Optional — leave empty for no lower bound. ## `dateTo` (type: `string`): ISO date string (e.g. 2024-12-31). Optional — leave empty for no upper bound. ## `maxResults` (type: `integer`): Maximum number of papers to return (hard cap: 1000) ## `enrichCitations` (type: `boolean`): Look up citation count and concepts from OpenAlex ## `includeFullText` (type: `boolean`): Download and extract text from open-access PDFs (increases compute cost) ## `outputFormat` (type: `string`): json for structured records, markdown-chunks for RAG-ready chunks ## `chunkSize` (type: `integer`): Target words per chunk when outputFormat=markdown-chunks ## Actor input object example ```json { "maxResults": 100, "enrichCitations": true, "includeFullText": false, "outputFormat": "json", "chunkSize": 500 } ``` # Actor output Schema ## `results` (type: `string`): JSON or markdown-chunk output of harvested papers. # 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("trisert/academic-corpus-harvester").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("trisert/academic-corpus-harvester").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 trisert/academic-corpus-harvester --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=trisert/academic-corpus-harvester", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Academic Corpus Harvester", "description": "Harvest academic paper metadata from arXiv, enrich with OpenAlex citation/concept data, and optionally extract full-text from open-access PDFs. Outputs clean JSON and RAG-ready Markdown chunks.", "version": "0.1", "x-build-id": "TxxWO49Ggvcs5dVRx" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/trisert~academic-corpus-harvester/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-trisert-academic-corpus-harvester", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/trisert~academic-corpus-harvester/runs": { "post": { "operationId": "runs-sync-trisert-academic-corpus-harvester", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/trisert~academic-corpus-harvester/run-sync": { "post": { "operationId": "run-sync-trisert-academic-corpus-harvester", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "query": { "title": "Search Query", "type": "string", "description": "Search terms or arXiv category code (e.g. 'transformer attention' or 'cs.LG')" }, "dateFrom": { "title": "From Date", "type": "string", "description": "ISO date string (e.g. 2024-01-01). Optional — leave empty for no lower bound." }, "dateTo": { "title": "To Date", "type": "string", "description": "ISO date string (e.g. 2024-12-31). Optional — leave empty for no upper bound." }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of papers to return (hard cap: 1000)", "default": 100 }, "enrichCitations": { "title": "Enrich with Citations", "type": "boolean", "description": "Look up citation count and concepts from OpenAlex", "default": true }, "includeFullText": { "title": "Extract Full Text", "type": "boolean", "description": "Download and extract text from open-access PDFs (increases compute cost)", "default": false }, "outputFormat": { "title": "Output Format", "enum": [ "json", "markdown-chunks" ], "type": "string", "description": "json for structured records, markdown-chunks for RAG-ready chunks", "default": "json" }, "chunkSize": { "title": "Chunk Size (words)", "minimum": 100, "maximum": 2000, "type": "integer", "description": "Target words per chunk when outputFormat=markdown-chunks", "default": 500 } } }, "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 } } } } } } } } } } ```