# Research Paper Scraper (`moving_beacon-owner1/my-actor-77`) Actor Gather information such as paper titles, authors, abstracts, categories, PDF links, DOIs, and additional relevant details. This process does not require an API key for access. - **URL**: https://apify.com/moving\_beacon-owner1/my-actor-77.md - **Developed by:** [Jamshaid Arif](https://apify.com/moving_beacon-owner1) (community) - **Categories:** Automation, Developer tools, Integrations - **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 ## 📚 arXiv Research Paper Scraper **Scrape academic papers from [arXiv.org](https://arxiv.org)** — the world's largest open-access preprint repository with 2M+ papers in Physics, Mathematics, Computer Science, Biology, Finance, and Statistics. Extract paper titles, authors, abstracts, categories, PDF links, DOIs, and more. No API key needed. --- ### What does this actor do? This actor searches arXiv.org and returns structured data for academic papers. It uses the official arXiv API, which is free and open — no authentication required. **Use it to:** - Monitor new papers in your research field daily - Build literature review databases for any topic - Track a specific author's publication history - Retrieve full metadata for known papers by their arXiv IDs - Filter papers by date range, category, or keywords in the abstract - Analyze research trends by category and year distribution --- ### How to use #### 1. Choose a scrape mode The actor supports **4 scraping modes**, selected via the `scrape_mode` input: | Mode | When to use | Required input | |------|-------------|---------------| | **Keyword Search** | Find papers on a topic | `search_query` | | **Author Search** | Find all papers by a researcher | `author_name` | | **Category Browse** | Browse latest papers in a field | `category` | | **ID Lookup** | Fetch specific known papers | `paper_ids` | #### 2. Configure your search Fill in the input fields in Apify Console. Only the fields relevant to your chosen mode are required — everything else is optional. #### 3. Run and export Click **Start** and wait for the results. Export your dataset as JSON, CSV, Excel, or connect it to Google Sheets, Slack, or any other integration. --- ### Input parameters #### Core settings | Field | Type | Default | Description | |-------|------|---------|-------------| | `scrape_mode` | Select | `keyword_search` | Which search mode to use | | `search_query` | Text | `machine learning` | Keywords to search for (Keyword Search mode) | | `search_field` | Select | `all` | Which field to search: `all`, `ti` (title), `abs` (abstract), `au` (author) | | `author_name` | Text | — | Researcher name (Author Search mode). Example: `Yoshua Bengio` | | `category` | Text | — | arXiv category code (Category Browse mode, or as a filter in other modes) | | `paper_ids` | Text | — | Comma-separated arXiv IDs (ID Lookup mode). Example: `1706.03762,2005.14165` | #### Filters | Field | Type | Default | Description | |-------|------|---------|-------------| | `date_from` | Text | — | Only papers from this date onward (`YYYY-MM-DD`) | | `date_to` | Text | — | Only papers up to this date (`YYYY-MM-DD`) | | `abstract_contains` | Text | — | Abstract must contain ALL of these comma-separated keywords | | `exclude_keywords` | Text | — | Exclude papers containing ANY of these keywords in title or abstract | | `min_authors` | Number | `0` | Minimum number of authors (0 = no filter) | #### Output settings | Field | Type | Default | Description | |-------|------|---------|-------------| | `max_results` | Number | `50` | Number of papers to scrape (1–2000) | | `sort_by` | Select | `relevance` | Sort order: `relevance`, `submitted_date`, or `last_updated` | | `include_abstract` | Boolean | `true` | Include full abstract text | | `include_pdf_links` | Boolean | `true` | Include direct PDF download URLs | --- ### Input examples #### Example 1: Find recent NLP papers about transformers ```json { "scrape_mode": "keyword_search", "search_query": "transformer attention mechanism", "search_field": "all", "category": "cs.CL", "max_results": 50, "sort_by": "submitted_date", "date_from": "2025-01-01" } ```` #### Example 2: Get all papers by a specific author ```json { "scrape_mode": "author_search", "author_name": "Geoffrey Hinton", "max_results": 100, "sort_by": "submitted_date", "include_abstract": true } ``` #### Example 3: Browse latest Machine Learning papers ```json { "scrape_mode": "category_browse", "category": "cs.LG", "max_results": 30, "sort_by": "submitted_date" } ``` #### Example 4: Fetch 5 famous AI papers by ID ```json { "scrape_mode": "id_lookup", "paper_ids": "1706.03762, 2005.14165, 1512.03385, 1406.2661, 2301.07041" } ``` #### Example 5: Advanced — diffusion models in ML, 2024+, excluding "survey" ```json { "scrape_mode": "keyword_search", "search_query": "diffusion model", "search_field": "ti", "category": "cs.LG", "max_results": 40, "sort_by": "submitted_date", "date_from": "2024-01-01", "abstract_contains": "generative", "exclude_keywords": "survey, review" } ``` *** ### Output format The actor produces two types of records in the dataset: #### Summary record (first item) The first record is a summary of the entire scrape run: ```json { "type": "summary", "total_papers": 50, "unique_authors": 187, "top_categories": [ { "category": "cs.CL", "count": 32 }, { "category": "cs.LG", "count": 18 }, { "category": "cs.AI", "count": 9 } ], "year_distribution": { "2024": 12, "2025": 38 }, "query_used": "all:transformer AND all:attention AND cat:cs.CL", "scrape_mode": "keyword_search" } ``` #### Paper records Each paper is a flat JSON object: ```json { "rank": 1, "arxiv_id": "2301.07041v1", "title": "LLaMA: Open and Efficient Foundation Language Models", "authors": ["Hugo Touvron", "Thibaut Lavril", "Gautier Izacard", "..."], "authors_short": "Hugo Touvron, Thibaut Lavril, Gautier Izacard et al.", "num_authors": 14, "published": "2023-02-27", "updated": "2023-02-27", "primary_category": "cs.CL", "categories": ["cs.CL"], "category_names": ["Computer Science"], "abstract": "We introduce LLaMA, a collection of foundation language models...", "abstract_length": 1247, "arxiv_url": "http://arxiv.org/abs/2301.07041v1", "pdf_url": "http://arxiv.org/pdf/2301.07041v1", "source_url": "https://arxiv.org/e-print/2301.07041v1", "doi": "", "journal_ref": "", "comment": "Submitted to NeurIPS 2023", "links": [ { "title": "pdf", "href": "http://arxiv.org/pdf/2301.07041v1", "type": "application/pdf" } ], "scraped_at": "2026-04-04T12:00:00+00:00" } ``` *** ### arXiv categories quick reference Use these codes in the `category` field: #### Computer Science | Code | Field | |------|-------| | `cs.AI` | Artificial Intelligence | | `cs.CL` | Computation & Language (NLP) | | `cs.CV` | Computer Vision | | `cs.LG` | Machine Learning | | `cs.NE` | Neural & Evolutionary Computing | | `cs.RO` | Robotics | | `cs.SE` | Software Engineering | | `cs.CR` | Cryptography & Security | | `cs.DB` | Databases | | `cs.IR` | Information Retrieval | #### Other popular categories | Code | Field | |------|-------| | `stat.ML` | Statistics — Machine Learning | | `math.OC` | Mathematics — Optimization & Control | | `quant-ph` | Quantum Physics | | `econ.GN` | Economics — General | | `q-bio.NC` | Quantitative Biology — Neurons & Cognition | | `q-fin.ST` | Quantitative Finance — Statistical Finance | The full category list is at [arxiv.org/category\_taxonomy](https://arxiv.org/category_taxonomy). *** ### Advanced query syntax In `search_query`, you can use arXiv's native query syntax for precise searches: | Syntax | Meaning | Example | |--------|---------|---------| | `ti:keyword` | Search in title only | `ti:deep learning` | | `abs:keyword` | Search in abstract only | `abs:reinforcement` | | `au:name` | Search by author | `au:bengio` | | `cat:code` | Search by category | `cat:cs.AI` | | `AND` | Both terms must match | `ti:neural AND ti:network` | | `OR` | Either term matches | `cat:cs.CL OR cat:cs.LG` | | `ANDNOT` | Exclude a term | `ti:transformer ANDNOT ti:survey` | | `"phrase"` | Exact phrase match | `abs:"language model"` | **Example:** Find papers by Bengio about attention in NLP: ``` au:bengio AND abs:attention AND cat:cs.CL ``` *** ### How much does it cost to run? This actor is very lightweight because arXiv's API is free and doesn't require browser rendering. | Papers | Estimated time | Apify platform credits | |--------|---------------|----------------------| | 50 | ~30 seconds | < $0.01 | | 200 | ~2 minutes | ~$0.01 | | 500 | ~5 minutes | ~$0.02 | | 2000 | ~20 minutes | ~$0.05 | The arXiv API requests a 3-second delay between requests, which the actor respects automatically. *** ### Integrations and scheduling #### Schedule daily paper monitoring Set up a scheduled run in Apify Console to scrape new papers in your field every day. Combine with the Google Sheets or Slack integration to get automatic notifications. **Example: Daily cs.AI papers to Slack** 1. Create a scheduled task with `category_browse` mode and `cs.AI` category 2. Sort by `submitted_date`, limit to `max_results: 20` 3. Connect the Slack integration to post results to your `#research` channel #### Export to Google Sheets After each run, the dataset can be automatically synced to a Google Sheet for easy sharing with your research team. *** ### Tips and best practices - **Start with a small `max_results`** (10–20) to preview your query, then scale up. - **Use `category` as a filter** in Keyword Search mode to narrow results to your field. - **Combine `abstract_contains` with broad searches** to find niche papers — for example, search `cat:cs.LG` but require "graph neural" in the abstract. - **Use `exclude_keywords`** to filter out survey/review papers if you only want original research. - **arXiv limits results to 2000** per query — for larger datasets, run multiple searches with different date ranges. - **Sort by `submitted_date`** for monitoring new papers; sort by `relevance` for topic exploration. *** ### Changelog #### v1.0.0 (2026-04-04) - Initial release - 4 scraping modes: keyword search, author search, category browse, ID lookup - Advanced filtering: date range, abstract keywords, exclude keywords, min authors - Summary statistics with top categories and year distribution - Batched dataset output for large result sets # Actor input Schema ## `scrape_mode` (type: `string`): How to search arXiv. ## `search_query` (type: `string`): Keywords to search for (used in 'Keyword Search' mode). Supports arXiv query syntax: 'ti:deep learning', 'abs:transformer', 'all:reinforcement learning'. Plain text also works. ## `search_field` (type: `string`): Which field to search in (used in 'Keyword Search' mode). ## `author_name` (type: `string`): Author name to search for (used in 'Author Search' mode). Example: 'Yoshua Bengio'. ## `category` (type: `string`): arXiv category to browse (used in all modes as a filter). Examples: cs.AI, cs.LG, cs.CL, stat.ML, math.CO, physics, quant-ph, econ, q-bio. ## `date_from` (type: `string`): Only include papers submitted on or after this date (YYYY-MM-DD). Leave empty for no lower bound. ## `date_to` (type: `string`): Only include papers submitted on or before this date (YYYY-MM-DD). Leave empty for no upper bound. ## `paper_ids` (type: `string`): Comma-separated arXiv paper IDs to look up (used in 'ID Lookup' mode). Example: '2301.07041,1706.03762,2005.14165'. ## `max_results` (type: `integer`): Maximum number of papers to scrape (1–2000). arXiv API limit is 2000 per query. ## `sort_by` (type: `string`): How to sort the results. ## `include_abstract` (type: `boolean`): Include the full abstract text in results. ## `include_pdf_links` (type: `boolean`): Include direct PDF download URLs. ## `abstract_contains` (type: `string`): Optional: only include papers whose abstract contains ALL of these comma-separated keywords. Example: 'transformer,attention'. ## `exclude_keywords` (type: `string`): Optional: exclude papers whose title or abstract contains ANY of these comma-separated keywords. ## `min_authors` (type: `integer`): Minimum number of authors a paper must have (0 = no filter). ## `proxy_configuration` (type: `object`): Proxy configuration (rarely needed — arXiv API is open). ## Actor input object example ```json { "scrape_mode": "keyword_search", "search_query": "machine learning", "search_field": "all", "author_name": "", "category": "", "date_from": "", "date_to": "", "paper_ids": "", "max_results": 50, "sort_by": "relevance", "include_abstract": true, "include_pdf_links": true, "abstract_contains": "", "exclude_keywords": "", "min_authors": 0 } ``` # 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 = { "scrape_mode": "keyword_search", "search_query": "machine learning", "max_results": 50 }; // Run the Actor and wait for it to finish const run = await client.actor("moving_beacon-owner1/my-actor-77").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 = { "scrape_mode": "keyword_search", "search_query": "machine learning", "max_results": 50, } # Run the Actor and wait for it to finish run = client.actor("moving_beacon-owner1/my-actor-77").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 '{ "scrape_mode": "keyword_search", "search_query": "machine learning", "max_results": 50 }' | apify call moving_beacon-owner1/my-actor-77 --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=moving_beacon-owner1/my-actor-77", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Research Paper Scraper", "description": "Gather information such as paper titles, authors, abstracts, categories, PDF links, DOIs, and additional relevant details. This process does not require an API key for access.", "version": "0.0", "x-build-id": "A4QyhRpg0ePTmOnQX" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/moving_beacon-owner1~my-actor-77/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-moving_beacon-owner1-my-actor-77", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/moving_beacon-owner1~my-actor-77/runs": { "post": { "operationId": "runs-sync-moving_beacon-owner1-my-actor-77", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/moving_beacon-owner1~my-actor-77/run-sync": { "post": { "operationId": "run-sync-moving_beacon-owner1-my-actor-77", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "scrape_mode", "max_results" ], "properties": { "scrape_mode": { "title": "Scrape Mode", "enum": [ "keyword_search", "author_search", "category_browse", "id_lookup" ], "type": "string", "description": "How to search arXiv.", "default": "keyword_search" }, "search_query": { "title": "Search Query", "type": "string", "description": "Keywords to search for (used in 'Keyword Search' mode). Supports arXiv query syntax: 'ti:deep learning', 'abs:transformer', 'all:reinforcement learning'. Plain text also works.", "default": "machine learning" }, "search_field": { "title": "Search Field", "enum": [ "all", "ti", "abs", "au", "co", "jr", "cat" ], "type": "string", "description": "Which field to search in (used in 'Keyword Search' mode).", "default": "all" }, "author_name": { "title": "Author Name", "type": "string", "description": "Author name to search for (used in 'Author Search' mode). Example: 'Yoshua Bengio'.", "default": "" }, "category": { "title": "arXiv Category", "type": "string", "description": "arXiv category to browse (used in all modes as a filter). Examples: cs.AI, cs.LG, cs.CL, stat.ML, math.CO, physics, quant-ph, econ, q-bio.", "default": "" }, "date_from": { "title": "Date From", "type": "string", "description": "Only include papers submitted on or after this date (YYYY-MM-DD). Leave empty for no lower bound.", "default": "" }, "date_to": { "title": "Date To", "type": "string", "description": "Only include papers submitted on or before this date (YYYY-MM-DD). Leave empty for no upper bound.", "default": "" }, "paper_ids": { "title": "Paper IDs", "type": "string", "description": "Comma-separated arXiv paper IDs to look up (used in 'ID Lookup' mode). Example: '2301.07041,1706.03762,2005.14165'.", "default": "" }, "max_results": { "title": "Max Results", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Maximum number of papers to scrape (1–2000). arXiv API limit is 2000 per query.", "default": 50 }, "sort_by": { "title": "Sort By", "enum": [ "relevance", "submitted_date", "last_updated" ], "type": "string", "description": "How to sort the results.", "default": "relevance" }, "include_abstract": { "title": "Include Abstract", "type": "boolean", "description": "Include the full abstract text in results.", "default": true }, "include_pdf_links": { "title": "Include PDF Links", "type": "boolean", "description": "Include direct PDF download URLs.", "default": true }, "abstract_contains": { "title": "Abstract Must Contain", "type": "string", "description": "Optional: only include papers whose abstract contains ALL of these comma-separated keywords. Example: 'transformer,attention'.", "default": "" }, "exclude_keywords": { "title": "Exclude Keywords", "type": "string", "description": "Optional: exclude papers whose title or abstract contains ANY of these comma-separated keywords.", "default": "" }, "min_authors": { "title": "Min Authors", "minimum": 0, "type": "integer", "description": "Minimum number of authors a paper must have (0 = no filter).", "default": 0 }, "proxy_configuration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy configuration (rarely needed — arXiv API is open)." } } }, "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 } } } } } } } } } } ```