# PLOS Journals Scraper (`parseforge/plos-journals-scraper`) Actor Extract scholarly article data from PLOS ONE, one of the largest open-access journals. This scraper automates collection of titles, authors, abstracts, metadata, and more, providing structured research data for academics, scientists, and analysts who rely on large-scale, peer-reviewed content. - **URL**: https://apify.com/parseforge/plos-journals-scraper.md - **Developed by:** [ParseForge](https://apify.com/parseforge) (community) - **Categories:** Automation, Other - **Stats:** 3 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ![ParseForge Banner](https://github.com/ParseForge/apify-assets/blob/ad35ccc13ddd068b9d6cba33f323962e39aed5b2/banner.jpg?raw=true) ## ๐Ÿ“š PLOS Journals Scraper > ๐Ÿš€ **Extract scholarly article data from PLOS journals in minutes.** Filter by search query, subject area, author, or date range. No coding, no subscriptions required. > ๐Ÿ•’ **Last updated:** 2026-04-23 ยท **๐Ÿ“Š 36 fields** ยท **๐Ÿ”„ Runs on Apify cloud or locally** ยท **๐Ÿ“ Export: JSON, CSV, Excel** The PLOS Journals Scraper collects detailed metadata from PLOS ONE and other PLOS journals, the world's largest multidisciplinary open-access publishing platform. Each article record includes **36 structured fields** covering titles, abstracts, full author lists, DOIs, impact metrics, peer review status, publication timelines, and funding information. Whether you need **10 articles** for a quick literature check or **50,000+ records** for a systematic review, this tool handles it automatically. Built for researchers conducting literature reviews, academics monitoring research trends, science librarians building citation databases, and data teams creating training datasets. The scraper supports keyword search, subject area filtering, author-specific queries, and date range selection. It extracts complete metadata from each article listing, handles pagination, and delivers analysis-ready data in JSON, CSV, or Excel format. | **Target Audience** | **Use Cases** | |---|---| | Academic Researchers | Systematic reviews, citation analysis | | Science Librarians | Collection building, usage reporting | | Pharmaceutical Companies | Drug research monitoring | | Data Scientists | NLP corpus creation, metadata analysis | | Funding Agencies | Research output assessment | | Science Journalists | Trend discovery, story research | --- ### ๐Ÿ“‹ What the PLOS Journals Scraper does - ๐Ÿ“ **Extracts article titles and abstracts** for literature review, keyword analysis, and topic classification - ๐Ÿ‘ฅ **Collects complete author information** including names, affiliations, and contribution details for collaboration mapping - ๐Ÿ”— **Captures DOIs and permanent article links** for citation management and direct access to publications - ๐Ÿ“… **Tracks publication timelines** with received, accepted, and publication dates for editorial process analysis - ๐Ÿ“Š **Gathers impact metrics** including view counts, citation counts, and save metrics for measuring article reach - ๐Ÿ“– **Pulls full metadata** with subject areas, keywords, funding information, copyright details, and supporting materials The scraper processes PLOS search results page by page, extracting every available metadata field from each article. Subject area filtering covers **150+ research categories** from Biology and Medicine to Neuroscience and Psychology. Date range filtering lets you target specific publication windows. > ๐Ÿ’ก **Why it matters:** PLOS ONE alone publishes over 100,000 articles per year. Manually searching, reviewing, and organizing this volume of research is impractical. This scraper delivers clean, structured metadata for any filtered subset in minutes. --- ### ๐ŸŽฌ Full Demo _๐Ÿšง Coming soon..._ --- ### โš™๏ธ Input
FieldTypeRequiredDescription
maxItemsintegerNoMaximum articles to collect. Free users: limited to 10. Paid users: up to 1,000,000.
startUrlstringNoDirect PLOS search URL. Mutually exclusive with search filters below.
searchQuerystringNoSearch term (e.g., "genetics", "immunotherapy"). Only works with search filters.
subjectAreastringNoFilter by subject (150+ options including Biology, Medicine, Neuroscience, Psychology).
authorstringNoFilter by author name to find papers by specific researchers.
publicationDateFromstringNoStart date in YYYY-MM-DD format.
publicationDateTostringNoEnd date in YYYY-MM-DD format.
**Example 1: Search by topic and subject area** ```json { "searchQuery": "genetics", "subjectArea": "Genetics", "maxItems": 25, "publicationDateFrom": "2023-01-01", "publicationDateTo": "2024-12-31" } ```` **Example 2: Find articles by a specific author** ```json { "searchQuery": "immunotherapy", "author": "Wei Zhang", "maxItems": 50 } ``` > โš ๏ธ **Good to Know:** Use either startUrl or the search filters (searchQuery, subjectArea, author, dates), not both. The subject area list includes 150+ options. Free users are automatically limited to 10 items per run. *** ### ๐Ÿ“Š Output #### ๐Ÿงพ Schema | Emoji | Field | Type | Description | |---|---|---|---| | ๐Ÿ“ | title | string | Article title | | ๐Ÿ“– | abstract | string | Full article abstract | | ๐Ÿ”— | doi | string | Digital Object Identifier | | ๐ŸŒ | url | string | Article URL | | ๐Ÿ‘ฅ | authors | array | List of author objects with names and affiliations | | ๐Ÿ“… | publicationDate | string | Date published | | ๐Ÿ“… | receivedDate | string | Date manuscript was received | | ๐Ÿ“… | acceptedDate | string | Date manuscript was accepted | | ๐Ÿ“‚ | subjectAreas | array | Research subject categories | | ๐Ÿ”‘ | keywords | array | Article keywords | | ๐Ÿ“Š | viewCount | number | Total article views | | ๐Ÿ“Š | citationCount | number | Total citations received | | ๐Ÿ’พ | saveCount | number | Times saved by users | | ๐Ÿ“ฐ | journal | string | Journal name (PLOS ONE, PLOS Biology, etc.) | | โš–๏ธ | copyright | string | Copyright information | | ๐Ÿ’ฐ | funding | string | Funding statement | | ๐Ÿ“‹ | competingInterests | string | Competing interests declaration | | ๐Ÿ“„ | articleType | string | Research article, review, etc. | | ๐Ÿท๏ธ | peerReviewStatus | string | Peer review status | | ๐Ÿ‘ค | correspondingAuthor | string | Primary contact author | | ๐Ÿ”— | pdfUrl | string | PDF download link | | ๐Ÿ“Ž | supportingInformation | array | Links to supporting materials | | ๐Ÿ“Š | altmetricScore | number | Altmetric attention score | | ๐Ÿ”ข | volume | string | Journal volume number | | ๐Ÿ”ข | issue | string | Journal issue number | | ๐Ÿ“„ | pages | string | Page range | | ๐Ÿซ | editorName | string | Handling editor name | | ๐Ÿซ | editorAffiliation | string | Editor affiliation | | โš–๏ธ | license | string | License type | | ๐Ÿ“… | correctionDate | string | Correction date if applicable | | ๐Ÿ”— | relatedArticles | array | Related article links | | ๐Ÿ–ผ๏ธ | figureUrls | array | Article figure image URLs | | ๐Ÿ“Š | dataAvailability | string | Data availability statement | | ๐Ÿ“… | scrapedAt | string | Data collection timestamp | | ๐Ÿ”„ | retracted | boolean | Whether article was retracted | | โŒ | error | string | Error message if extraction failed | #### ๐Ÿ“ฆ Sample records
๐Ÿ“„ Record 1 - Genetics Research Article ```json { "title": "Genome-wide association study reveals novel loci for crop yield", "abstract": "Background: Understanding the genetic architecture of crop yield requires...", "doi": "10.1371/journal.pone.0123456", "url": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0123456", "authors": [ {"name": "Wei Zhang", "affiliation": "Beijing University"}, {"name": "Sarah Miller", "affiliation": "UC Davis"} ], "publicationDate": "2024-03-15", "receivedDate": "2023-11-20", "acceptedDate": "2024-02-28", "subjectAreas": ["Genetics", "Agriculture"], "viewCount": 3450, "citationCount": 12, "journal": "PLOS ONE", "license": "CC BY 4.0" } ```
๐Ÿ“„ Record 2 - Neuroscience Study ```json { "title": "Neural correlates of decision-making under uncertainty", "abstract": "This study investigated brain activation patterns during...", "doi": "10.1371/journal.pone.0789012", "url": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0789012", "authors": [ {"name": "Emily Chen", "affiliation": "Stanford University"} ], "publicationDate": "2024-06-10", "subjectAreas": ["Neuroscience", "Psychology"], "viewCount": 8900, "citationCount": 25, "journal": "PLOS ONE", "funding": "NIH Grant R01-MH123456" } ```
๐Ÿ“„ Record 3 - Public Health Article ```json { "title": "Impact of air quality interventions on respiratory health outcomes", "abstract": "Objective: To evaluate the effectiveness of municipal air quality...", "doi": "10.1371/journal.pone.0345678", "url": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0345678", "authors": [ {"name": "David Park", "affiliation": "Seoul National University"}, {"name": "Anna Kowalski", "affiliation": "WHO"} ], "publicationDate": "2024-01-22", "subjectAreas": ["Medicine and health sciences", "Public and Global Health"], "viewCount": 15200, "citationCount": 41, "journal": "PLOS ONE", "dataAvailability": "All data files are available from the Dryad repository" } ```
*** ### โœจ Why choose this Actor | Feature | Details | |---|---| | ๐Ÿ“Š 36 data fields | Titles, authors, abstracts, metrics, funding, review status, and more | | ๐Ÿ”ฌ 150+ subject areas | Filter by Biology, Medicine, Neuroscience, Psychology, and many more | | ๐Ÿ“… Date range filtering | Target specific publication windows | | ๐Ÿ‘ค Author search | Find all papers by a specific researcher | | ๐Ÿ“Š Impact metrics | View counts, citation counts, and save metrics | | ๐Ÿ“ Multiple export formats | JSON, CSV, Excel for any workflow | | ๐Ÿ”„ Automatic pagination | Handles multi-page results without manual intervention | > ๐Ÿ“ˆ **Typical performance:** Collects **200+ articles per minute.** A dataset of 5,000 articles takes roughly 25 minutes. *** ### ๐Ÿ“ˆ How it compares to alternatives | Feature | This Actor | Manual PLOS Search | Generic Scrapers | |---|---|---|---| | 36 structured fields per article | โœ… | โŒ | Partial | | 150+ subject area filters | โœ… | โœ… (manual) | โŒ | | Impact metrics (views, citations) | โœ… | โœ… (one at a time) | โŒ | | Author and date filtering | โœ… | โœ… (manual) | Partial | | Export to CSV/JSON/Excel | โœ… | โŒ | Partial | | Scales to 100K+ articles | โœ… | โŒ | โŒ | | Scheduled runs | โœ… | โŒ | Partial | Purpose-built for PLOS journals, with every metadata field mapped and 150+ subject area filters supported. *** ### ๐Ÿš€ How to use 1. **Create a free Apify account** - [Sign up here](https://console.apify.com/sign-up?fpr=vmoqkp) (includes free credits) 2. **Open the PLOS Journals Scraper** - Navigate to the Actor page and click "Start" 3. **Set your search criteria** - Enter a keyword, select a subject area, or filter by author and date range 4. **Choose your limit** - Set maxItems (free users: up to 10) 5. **Run and download** - Click "Start", wait for completion, then export as JSON, CSV, or Excel > โฑ๏ธ **First results appear in under 20 seconds.** A typical run of 100 articles completes in about 1 minute. *** ### ๐Ÿ’ผ Business use cases
**Academic Research** - Build systematic review datasets - Track citation networks across PLOS journals - Monitor publication trends by subject area - Identify high-impact articles in your field **Pharmaceutical & Biotech** - Monitor drug-related research publications - Track clinical study findings - Identify potential research collaborators - Build literature databases for regulatory submissions
**Library & Information Science** - Analyze journal publishing patterns - Track open access adoption metrics - Build recommendation systems for researchers - Monitor editorial timelines and review processes **Data Science & AI** - Create biomedical text corpora for NLP - Build knowledge graphs from article metadata - Train topic classification models - Analyze authorship and collaboration patterns
*** *** ### ๐ŸŒŸ Beyond business use cases Data like this powers more than commercial workflows. The same structured records support research, education, civic projects, and personal initiatives.
#### ๐ŸŽ“ Research and academia - Empirical datasets for papers, thesis work, and coursework - Longitudinal studies tracking changes across snapshots - Reproducible research with cited, versioned data pulls - Classroom exercises on data analysis and ethical scraping #### ๐ŸŽจ Personal and creative - Side projects, portfolio demos, and indie app launches - Data visualizations, dashboards, and infographics - Content research for bloggers, YouTubers, and podcasters - Hobbyist collections and personal trackers
#### ๐Ÿค Non-profit and civic - Transparency reporting and accountability projects - Advocacy campaigns backed by public-interest data - Community-run databases for local issues - Investigative journalism on public records #### ๐Ÿงช Experimentation - Prototype AI and machine-learning pipelines with real data - Validate product-market hypotheses before engineering spend - Train small domain-specific models on niche corpora - Test dashboard concepts with live input
### ๐Ÿค– Ask an AI assistant about this scraper Open a ready-to-send prompt about this ParseForge actor in the AI of your choice: - ๐Ÿ’ฌ [**ChatGPT**](https://chat.openai.com/?q=How%20do%20I%20use%20the%20PLOS%20Journals%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) - ๐Ÿง  [**Claude**](https://claude.ai/new?q=How%20do%20I%20use%20the%20PLOS%20Journals%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) - ๐Ÿ” [**Perplexity**](https://perplexity.ai/search?q=How%20do%20I%20use%20the%20PLOS%20Journals%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) - ๐Ÿ…’ [**Copilot**](https://copilot.microsoft.com/?q=How%20do%20I%20use%20the%20PLOS%20Journals%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) ### โ“ Frequently Asked Questions
๐Ÿ’ณ Do I need a paid Apify plan to run this actor? No. You can start right now on the free Apify plan, which includes **$5 in free monthly credit**. That is enough to run this actor several times and explore the output before committing to anything. Paid plans unlock higher limits, more concurrent runs, and larger datasets. [Create a free Apify account here](https://console.apify.com/sign-up?fpr=vmoqkp) to get started.
๐Ÿšจ What happens if my run fails or returns no results? Failed runs are not charged. If the source site changes, proxies get rate-limited, or a specific input matches nothing, re-run the actor or open our [contact form](https://tally.so/r/BzdKgA) and we will investigate. You can also check the run log in the Apify console to see why the run stopped.
๐Ÿ“ How many items can I scrape per run? Free users are limited to **10 items per run** so you can preview the output and confirm the actor works for your use case. Paid users can raise maxItems up to **1,000,000** per run. [Upgrade here](https://console.apify.com/sign-up?fpr=vmoqkp) if you need full scale.
๐Ÿ•’ How fresh is the data? Every run fetches live data at the moment of execution. There is no cache or delay: the records you get reflect what the source returned at that moment. Schedule the actor to maintain a rolling snapshot of the data you need.
๐Ÿง‘โ€๐Ÿ’ป Can I call this actor from my own code? Yes. Apify exposes every actor as a REST endpoint and ships first-class SDKs for [Node.js](https://docs.apify.com/sdk/js) and [Python](https://docs.apify.com/sdk/python). You can start a run, read the dataset, and handle webhooks from your own app in a few lines. All you need is your Apify API token.
๐Ÿ“ค How do I export the data? Every Apify dataset can be downloaded in one click from the console as CSV, JSON, JSONL, Excel, HTML, XML, or RSS. You can also pull results programmatically via the [Apify API](https://docs.apify.com/api/v2) or stream them into BigQuery, S3, and other destinations through built-in integrations.
๐Ÿ“… Can I schedule the actor to run automatically? Yes. Use the Apify scheduler to run the actor on any cadence, from hourly to monthly. Results are saved to your dataset and can be delivered to webhooks, email, Slack, cloud storage, or automation tools such as Zapier and Make. ***
### ๐Ÿ”Œ Automating PLOS Journals Scraper **Node.js** ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_API_TOKEN' }); const run = await client.actor("parseforge/plos-journals-scraper").call({ searchQuery: "genetics", subjectArea: "Genetics", maxItems: 100 }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items); ``` **Python** ```python from apify_client import ApifyClient client = ApifyClient("YOUR_API_TOKEN") run = client.actor("parseforge/plos-journals-scraper").call(run_input={ "searchQuery": "genetics", "subjectArea": "Genetics", "maxItems": 100 }) items = list(client.dataset(run["defaultDatasetId"]).iterate_items()) print(items) ``` - ๐Ÿ“– [Apify API Reference](https://docs.apify.com/api/v2) - ๐Ÿ [Python API Client docs](https://docs.apify.com/api/client/python) - ๐ŸŸข [Node.js API Client docs](https://docs.apify.com/api/client/js) **Schedules:** Set up weekly or monthly runs with [Apify Schedules](https://docs.apify.com/platform/schedules) to automatically track new publications in your research area and build historical datasets. ### ๐Ÿ”Œ Integrate with any app - ๐Ÿ”— **Make (Integromat)** - Connect PLOS article data to 1,000+ apps with visual workflows - ๐Ÿ”— **Zapier** - Trigger actions when new articles matching your criteria are published - ๐Ÿ”— **Slack** - Get notifications in Slack when new PLOS articles appear in your field - ๐Ÿ”— **Airbyte** - Sync article metadata to your data warehouse - ๐Ÿ”— **GitHub** - Automate literature monitoring pipelines with GitHub Actions - ๐Ÿ”— **Google Drive** - Export article data directly to Google Sheets *** ### ๐Ÿ”— Recommended Actors | Actor | Description | |---|---| | ๐Ÿ“š [PubMed Citation Scraper](https://apify.com/parseforge/pubmed-citation-scraper) | Extract citation data and metadata from PubMed biomedical literature | | ๐Ÿ”ฌ [OpenAlex Scraper](https://apify.com/parseforge/openalex-scraper) | Query 250M+ scholarly records from the OpenAlex open catalog | | ๐Ÿ“ฐ [medRxiv Scraper](https://apify.com/parseforge/medrxiv-scraper) | Collect health sciences preprint data from medRxiv | | ๐Ÿงฌ [bioRxiv Scraper](https://apify.com/parseforge/biorxiv-scraper) | Scrape biology preprint articles from bioRxiv | | ๐Ÿ“– [Crossref Scraper](https://apify.com/parseforge/crossref-scraper) | Collect DOI metadata and citation information from Crossref | > ๐Ÿ’ก **Pro Tip:** Combine the PLOS Journals Scraper with the PubMed Citation Scraper to build a multi-source literature database covering both open access and indexed biomedical research. *** **๐Ÿ†˜ Need Help?** [**Open our contact form**](https://tally.so/r/BzdKgA) and we will get back to you within 24 hours. For bug reports, feature requests, or integration help, we are here to assist. *** > **Disclaimer:** This Actor is provided as-is, without warranty. It is not affiliated with or endorsed by PLOS (Public Library of Science). Use it responsibly and in compliance with applicable terms of service. The authors are not responsible for how the collected data is used. Always verify data accuracy for critical applications. # Actor input Schema ## `maxItems` (type: `integer`): Maximum number of articles to collect (optional). Leave empty for unlimited (paid users only). Free users must specify this parameter and are limited to 50 items. ## `startUrl` (type: `string`): Direct PLOS search URL to scrape (mutually exclusive with search filters). Must include filterJournals=PLoSONE parameter. Example: https://journals.plos.org/ploscompbiol/search?filterJournals=PLoSONE\&q=genetics\&page=1 ## `searchQuery` (type: `string`): Search query string. Only works when using search filters (not with startUrl). Example: 'genetics' ## `subjectArea` (type: `string`): Filter by subject area. Only works when using search filters (not with startUrl). ## `author` (type: `string`): Filter by author name. Only works when using search filters (not with startUrl). Example: 'Wei Zhang' ## `publicationDateFrom` (type: `string`): Start date for publication date range (YYYY-MM-DD format). Only works when using search filters (not with startUrl). ## `publicationDateTo` (type: `string`): End date for publication date range (YYYY-MM-DD format). Only works when using search filters (not with startUrl). ## Actor input object example ```json { "maxItems": 10, "startUrl": "https://journals.plos.org/ploscompbiol/search?filterJournals=PLoSONE&q=genetics&page=1", "searchQuery": "genetics" } ``` # Actor output Schema ## `dataset` (type: `string`): Complete dataset of scraped PLOS articles with all metadata including titles, authors, abstracts, PDF links, metrics, subject areas, and more ## `datasetOverview` (type: `string`): Overview view of scraped articles with key fields displayed in a table format # 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 = { "maxItems": 10, "searchQuery": "genetics" }; // Run the Actor and wait for it to finish const run = await client.actor("parseforge/plos-journals-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "maxItems": 10, "searchQuery": "genetics", } # Run the Actor and wait for it to finish run = client.actor("parseforge/plos-journals-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "maxItems": 10, "searchQuery": "genetics" }' | apify call parseforge/plos-journals-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=parseforge/plos-journals-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PLOS Journals Scraper", "description": "Extract scholarly article data from PLOS ONE, one of the largest open-access journals. This scraper automates collection of titles, authors, abstracts, metadata, and more, providing structured research data for academics, scientists, and analysts who rely on large-scale, peer-reviewed content.", "version": "1.0", "x-build-id": "Os4vJ7PbrYIPTrObT" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/parseforge~plos-journals-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-parseforge-plos-journals-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/parseforge~plos-journals-scraper/runs": { "post": { "operationId": "runs-sync-parseforge-plos-journals-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/parseforge~plos-journals-scraper/run-sync": { "post": { "operationId": "run-sync-parseforge-plos-journals-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 1000000, "type": "integer", "description": "Maximum number of articles to collect (optional). Leave empty for unlimited (paid users only). Free users must specify this parameter and are limited to 50 items." }, "startUrl": { "title": "Start URL", "type": "string", "description": "Direct PLOS search URL to scrape (mutually exclusive with search filters). Must include filterJournals=PLoSONE parameter. Example: https://journals.plos.org/ploscompbiol/search?filterJournals=PLoSONE&q=genetics&page=1" }, "searchQuery": { "title": "Search Query", "type": "string", "description": "Search query string. Only works when using search filters (not with startUrl). Example: 'genetics'" }, "subjectArea": { "title": "Subject Area", "enum": [ "Biology and life sciences", "Medicine and health sciences", "Social sciences", "Psychology", "Research and analysis methods", "Neuroscience", "Medical conditions", "Anatomy", "Developmental psychology", "Pervasive developmental disorders", "Neurology", "Autism spectrum disorder", "Physical sciences", "People and places", "Cognitive science", "Cognitive psychology", "Population groupings", "Developmental neuroscience", "Neurodevelopmental disorders", "Organisms", "Biochemistry", "Physiology", "Genetics", "Age groups", "Autism", "Eukaryota", "Behavior", "Families", "Children", "Zoology", "Cell biology", "Mental health and psychiatry", "Animals", "Mathematics", "Perception", "Computer and information sciences", "Sociology", "Engineering and technology", "Epidemiology", "Developmental biology", "Diagnostic medicine", "Emotions", "Sensory perception", "Imaging techniques", "Mathematical and statistical techniques", "Chemistry", "Proteins", "Gene expression", "Cellular types", "Statistics", "Medical risk factors", "Statistical methods", "Animal cells", "Clinical medicine", "Learning and memory", "Animal studies", "Experimental organism systems", "Molecular biology", "Health care", "Genomics", "Vertebrates", "Cognition", "Molecular biology techniques", "Invertebrates", "Model organisms", "Neuropsychiatric disorders", "Head", "Neuroimaging", "Radiology and imaging", "Diagnostic radiology", "Animal behavior", "Pathology and laboratory medicine", "Animal models", "Arthropoda", "Brain", "Research design", "Amniotes", "Physics", "Geographical locations", "Cardiology", "Microbiology", "Mammals", "Cognitive neuroscience", "Nucleic acids", "Computational biology", "Brain mapping", "Entomology", "Insects", "Chemical compounds", "Vision", "Public and occupational health", "Infectious diseases", "Earth sciences", "Pathogens", "Memory", "Cardiovascular diseases", "Cardiovascular medicine", "Medical microbiology", "Ecology and environmental sciences", "Magnetic resonance imaging" ], "type": "string", "description": "Filter by subject area. Only works when using search filters (not with startUrl)." }, "author": { "title": "Author", "type": "string", "description": "Filter by author name. Only works when using search filters (not with startUrl). Example: 'Wei Zhang'" }, "publicationDateFrom": { "title": "Publication Date From", "pattern": "^(\\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[12]\\d|3[01])$", "type": "string", "description": "Start date for publication date range (YYYY-MM-DD format). Only works when using search filters (not with startUrl)." }, "publicationDateTo": { "title": "Publication Date To", "pattern": "^(\\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[12]\\d|3[01])$", "type": "string", "description": "End date for publication date range (YYYY-MM-DD format). Only works when using search filters (not with startUrl)." } } }, "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 } } } } } } } } } } ```