# Unified Preprint Search (`logical_vivacity/unified-preprint-search`) Actor One Apify Actor, five sources: PubMed, arXiv, bioRxiv, medRxiv, chemRxiv. - **URL**: https://apify.com/logical\_vivacity/unified-preprint-search.md - **Developed by:** [Logical Vivacity](https://apify.com/logical_vivacity) (community) - **Categories:** Developer tools, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 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 ## Unified Preprint & Journal Search One Apify Actor, five sources: **PubMed, arXiv, bioRxiv, medRxiv, chemRxiv**. Run a single keyword query, get a normalized, deduped dataset with one paper per row and (optionally) citation counts. ### What it does - Accepts a Boolean keyword query (AND between groups, OR within groups). - Queries each selected source. - Normalizes results into a unified record schema. - Deduplicates across sources by DOI. - Optionally enriches each paper with a Semantic Scholar citation count. - Streams every paper to the dataset as it's processed (no in-memory accumulation). ### Inputs | Field | Type | Default | Description | |---|---|---|---| | `keywords` | array of arrays of strings (or flat array) | — (required) | Nested groups: outer = AND, inner = OR. A flat list is treated as AND of single-term groups. | | `sources` | array of enum | all 5 | Subset of `pubmed`, `arxiv`, `biorxiv`, `medrxiv`, `chemrxiv`. | | `startDate` | ISO date string | — | Drop records with `date` before this. | | `endDate` | ISO date string | — | Drop records with `date` after this. | | `maxResultsPerSource` | integer | `200` | Hard cap per source. | | `enrichWithCitations` | boolean | `false` | Look up citation counts (slow, rate-limited). | #### Example query ```json { "keywords": [ ["COVID-19", "SARS-CoV-2"], ["deep learning", "machine learning"], ["medical imaging"] ], "sources": ["pubmed", "arxiv", "biorxiv"], "maxResultsPerSource": 100, "enrichWithCitations": false } ```` This means: *(COVID-19 OR SARS-CoV-2) AND (deep learning OR machine learning) AND (medical imaging)*. ### Output sample Each dataset item: ```json { "source": "arxiv", "title": "Deep learning for COVID-19 chest X-ray classification", "authors": ["Jane Doe", "John Smith"], "abstract": "We propose a CNN architecture ...", "doi": "10.1234/example.2024.0001", "date": "2024-03-12", "journal": "arXiv:2403.01234", "url": "https://arxiv.org/abs/2403.01234", "citations_count": 17, "raw": { "...": "any unmapped fields preserved here" } } ``` ### Limitations - **Rate limits.** PubMed (NCBI E-utilities), arXiv and Semantic Scholar all rate-limit anonymous traffic. To raise the citation-enrichment limit, paste a free Semantic Scholar API key into the `semanticScholarApiKey` input field. - **X-rxiv server dumps.** bioRxiv, medRxiv and chemRxiv require a local JSONL dump. The first run on a fresh container will trigger a download of these dumps (medRxiv ~30 min, bioRxiv ~3 hr, chemRxiv ~15 min). Plan memory and timeout accordingly, or pre-bake dumps into the Docker image for production use. - **Date filtering** is applied client-side; not all sources return well-formed dates. - **Dedup** is by DOI only — papers without a DOI may appear once per source. # Actor input Schema ## `keywords` (type: `array`): Either a flat list of strings (treated as AND between terms) OR a list of lists for advanced Boolean queries. The OUTER list is logical AND and each INNER list is logical OR (synonyms). Example: \[\["COVID-19", "SARS-CoV-2"], \["deep learning", "machine learning"], \["medical imaging"]] means (COVID-19 OR SARS-CoV-2) AND (deep learning OR machine learning) AND (medical imaging). ## `sources` (type: `array`): Which sources to query. Defaults to all five. ## `startDate` (type: `string`): Optional ISO date (YYYY-MM-DD). Records with publication date before this are dropped. ## `endDate` (type: `string`): Optional ISO date (YYYY-MM-DD). Records with publication date after this are dropped. ## `maxResultsPerSource` (type: `integer`): Hard cap on records pushed per source. ## `enrichWithCitations` (type: `boolean`): If true, look up Semantic Scholar citation counts per DOI. Slow and rate-limited without an API key. Provide one in the field below to raise the limit. ## `semanticScholarApiKey` (type: `string`): Optional. Free key from https://www.semanticscholar.org/product/api raises the citation enrichment rate limit substantially. Only used when 'Enrich with citation counts' is on. ## Actor input object example ```json { "keywords": [ [ "COVID-19", "SARS-CoV-2" ], [ "deep learning", "machine learning" ], [ "medical imaging" ] ], "sources": [ "pubmed", "arxiv", "biorxiv", "medrxiv", "chemrxiv" ], "maxResultsPerSource": 200, "enrichWithCitations": false } ``` # 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": [ [ "COVID-19", "SARS-CoV-2" ], [ "deep learning", "machine learning" ], [ "medical imaging" ] ] }; // Run the Actor and wait for it to finish const run = await client.actor("logical_vivacity/unified-preprint-search").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": [ [ "COVID-19", "SARS-CoV-2", ], [ "deep learning", "machine learning", ], ["medical imaging"], ] } # Run the Actor and wait for it to finish run = client.actor("logical_vivacity/unified-preprint-search").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": [ [ "COVID-19", "SARS-CoV-2" ], [ "deep learning", "machine learning" ], [ "medical imaging" ] ] }' | apify call logical_vivacity/unified-preprint-search --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=logical_vivacity/unified-preprint-search", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Unified Preprint Search", "description": "One Apify Actor, five sources: PubMed, arXiv, bioRxiv, medRxiv, chemRxiv.", "version": "0.1", "x-build-id": "tYk3Hn765VHi8sAWl" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/logical_vivacity~unified-preprint-search/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-logical_vivacity-unified-preprint-search", "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/logical_vivacity~unified-preprint-search/runs": { "post": { "operationId": "runs-sync-logical_vivacity-unified-preprint-search", "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/logical_vivacity~unified-preprint-search/run-sync": { "post": { "operationId": "run-sync-logical_vivacity-unified-preprint-search", "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": "Keyword query", "type": "array", "description": "Either a flat list of strings (treated as AND between terms) OR a list of lists for advanced Boolean queries. The OUTER list is logical AND and each INNER list is logical OR (synonyms). Example: [[\"COVID-19\", \"SARS-CoV-2\"], [\"deep learning\", \"machine learning\"], [\"medical imaging\"]] means (COVID-19 OR SARS-CoV-2) AND (deep learning OR machine learning) AND (medical imaging)." }, "sources": { "title": "Sources", "uniqueItems": true, "type": "array", "description": "Which sources to query. Defaults to all five.", "items": { "type": "string", "enum": [ "pubmed", "arxiv", "biorxiv", "medrxiv", "chemrxiv" ] }, "default": [ "pubmed", "arxiv", "biorxiv", "medrxiv", "chemrxiv" ] }, "startDate": { "title": "Start date", "type": "string", "description": "Optional ISO date (YYYY-MM-DD). Records with publication date before this are dropped." }, "endDate": { "title": "End date", "type": "string", "description": "Optional ISO date (YYYY-MM-DD). Records with publication date after this are dropped." }, "maxResultsPerSource": { "title": "Max results per source", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Hard cap on records pushed per source.", "default": 200 }, "enrichWithCitations": { "title": "Enrich with citation counts", "type": "boolean", "description": "If true, look up Semantic Scholar citation counts per DOI. Slow and rate-limited without an API key. Provide one in the field below to raise the limit.", "default": false }, "semanticScholarApiKey": { "title": "Semantic Scholar API key (optional)", "type": "string", "description": "Optional. Free key from https://www.semanticscholar.org/product/api raises the citation enrichment rate limit substantially. Only used when 'Enrich with citation counts' is on." } } }, "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 } } } } } } } } } } ```