# Crossref Scholarly Works Scraper (`compute-edge/crossref-works-scraper`) Actor Extract scholarly works metadata from Crossref — DOIs, titles, authors, journals, publication dates, and citation counts. Filter by query, date range, and work type. No API key required. - **URL**: https://apify.com/compute-edge/crossref-works-scraper.md - **Developed by:** [Compute Edge](https://apify.com/compute-edge) (community) - **Categories:** Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 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 ## Crossref Scholarly Works Scraper **Extract scholarly works metadata from Crossref — DOIs, titles, authors, journals, publication dates, and citation counts.** Query 135+ million scholarly articles, books, proceedings, and datasets via the Crossref REST API. Perfect for academic research, bibliometric analysis, literature reviews, and citation network studies. ### What This Actor Does This Actor provides a complete interface to the Crossref REST API, the world's largest scholarly work database. It supports four flexible search and filtering options: 1. **Free-Text Search** — Search by keyword across titles, abstracts, and metadata (e.g., "machine learning", "COVID-19", "renewable energy") 2. **Publication Date Filtering** — Restrict results to works published within a date range 3. **Work Type Filtering** — Target specific work types (e.g., journal articles, books, proceedings, datasets) 4. **Pagination & Bulk Extraction** — Automatically fetch up to 5,000 records per run using cursor-based pagination #### Key Features - **135+ million works** — Access the complete Crossref dataset - **Rich metadata** — DOI, title, authors, journal/container, publication date, citation counts, references - **Flexible filtering** — Combine free-text search with date range and work type filters - **High-speed pagination** — Cursor-based API ensures fast, stable bulk extracts - **No authentication required** — Public API, free to use - **Error handling** — Graceful fallback for missing or incomplete metadata - **Batch processing** — Efficient extraction for large datasets #### Popular Use Cases | Use Case | Query Example | Work Type | Output | |----------|---------------|-----------|--------| | **Literature Review** | "climate change mitigation" | journal-article | Top 500 recent articles on climate solutions | | **Citation Network Analysis** | "neural networks" | journal-article + proceedings | Papers by citation count for network mapping | | **Trend Tracking** | "AI safety" | all types | New works published in last 30 days | | **Researcher Database** | None (recent works) | all types | Latest 1,000 scholarly works across all fields | | **Book Discovery** | "sustainable development" | book | Recent books on sustainability | | **Conference Proceedings** | "machine learning" | proceedings | Peer-reviewed conference papers | ### Getting Started #### Step 1: Run the Actor 1. Choose your input parameters (see below) 2. Click **Start** 3. Results appear in the **Dataset** tab 4. Export as JSON or CSV via Apify UI #### Step 2: Simple Example — Search Recent Works To fetch 50 recent works (no search query): - **Query:** *(leave blank)* - **Filter From Date:** *(leave blank)* - **Work Type:** *(leave blank)* - **Max Results:** `50` Results include title, authors, journal, publication date, and DOI for each work. ### How to scrape Crossref scholarly works #### Tutorial 1: Search for Papers on Machine Learning **Goal:** Find the top 100 recent journal articles on machine learning. **Input configuration:** - **Query:** `machine learning` - **Work Type:** `journal-article` - **Filter From Date:** *(leave blank for all time)* - **Max Results:** `100` **Expected output:** ```json [ { "doi": "10.1038/nature12373", "title": "Deep Neural Networks Capture Context-Dependent Neural Activity in the Primate Visual System", "type": "journal-article", "publisher": "Nature Publishing Group", "journal": "Nature", "publishedDate": "2024-03-15", "authorsCount": 5, "firstAuthor": "Antolik Mark", "citationCount": 1240, "referenceCount": 45, "issn": "0028-0836", "url": "https://doi.org/10.1038/nature12373" }, ... ] ```` **Use case:** Build a curated bibliography of the most-cited machine learning papers for a literature review or research project. *** #### Tutorial 2: Track Recent Works in a Specific Domain **Goal:** Monitor all scholarly works on renewable energy published in the last 90 days. **Input configuration:** - **Query:** `renewable energy` - **Filter From Date:** `2026-03-21` *(90 days before today)* - **Work Type:** *(leave blank for all types)* - **Max Results:** `500` **Expected output:** ```json [ { "doi": "10.1016/j.renene.2026.03.001", "title": "Advances in Perovskite Solar Cell Efficiency and Stability", "type": "journal-article", "publisher": "Elsevier", "journal": "Renewable Energy", "publishedDate": "2026-03-20", "authorsCount": 8, "firstAuthor": "Liu Chen", "citationCount": 0, "referenceCount": 67, "issn": "0960-1481", "url": "https://doi.org/10.1016/j.renene.2026.03.001" }, ... ] ``` **Use case:** Stay current with emerging research in your domain. Track high-impact journals and new author collaborations. Feed into a data pipeline for weekly research digest emails. *** #### Tutorial 3: Citation Network Analysis **Goal:** Extract 200 highly-cited papers on artificial intelligence to map research influence. **Input configuration:** - **Query:** `artificial intelligence` - **Work Type:** `journal-article` - **Filter From Date:** *(leave blank)* - **Max Results:** `200` **Expected output (sorted by citation count):** ```json [ { "doi": "10.1145/3495243.3560528", "title": "Attention Is All You Need", "type": "journal-article", "publisher": "ACM", "journal": "Transactions on Machine Learning Research", "publishedDate": "2017-12-06", "authorsCount": 8, "firstAuthor": "Vaswani Ashish", "citationCount": 88450, "referenceCount": 72, "issn": "", "url": "https://doi.org/10.1145/3495243.3560528" }, ... ] ``` **Use case:** Build a citation network graph showing how papers reference each other. Identify foundational works and research clusters. Track influence trajectories of key researchers. *** ### Input Parameters #### All Modes | Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | **query** | string | *(blank)* | No | Free-text search query (e.g., "machine learning", "COVID-19"). Leave blank to fetch recent works. Case-insensitive. | | **filterFromDate** | string (YYYY-MM-DD) | *(blank)* | No | Only include works published on or after this date (e.g., "2024-01-01"). Leave blank for all dates. | | **workType** | string | *(blank)* | No | Filter by work type. Common values: `journal-article`, `book`, `proceedings`, `report`, `dataset`. Leave blank for all types. | | **maxResults** | integer | 50 | No | Maximum works to fetch (1–5,000). Default is 50. | #### Common Work Types - `journal-article` — Peer-reviewed journal articles - `proceedings-article` or `proceedings` — Conference proceedings - `book` — Complete books - `book-chapter` — Chapters within books - `report` — Technical reports, white papers - `dataset` — Data publications - `dissertation` — Theses and dissertations - `component` — Article components (figures, tables, appendices) **Full list:** Visit https://github.com/CrossRef/rest-api-doc#work-types *** ### Output Schema Each record contains: | Field | Type | Example | Description | |-------|------|---------|-------------| | **doi** | string | `10.1038/nature12373` | Digital Object Identifier — unique identifier for the work | | **title** | string | `Deep Neural Networks Capture...` | Title of the work | | **type** | string | `journal-article` | Work type (journal-article, book, proceedings, etc.) | | **publisher** | string | `Nature Publishing Group` | Publisher name | | **journal** | string | `Nature` | Journal or container name (empty for books) | | **publishedDate** | string | `2024-03-15` | Publication date (YYYY-MM-DD, YYYY-MM, or YYYY format) | | **authorsCount** | integer | 5 | Number of authors | | **firstAuthor** | string | `Antolik Mark` | First author's full name (Given Family) | | **citationCount** | integer | 1240 | Number of works that cite this work (from-referenced-by-count) | | **referenceCount** | integer | 45 | Number of works referenced by this work | | **issn** | string | `0028-0836` | International Standard Serial Number (for journals) | | **url** | string | `https://doi.org/10.1038/nature12373` | Persistent URL to the work via DOI | *** ### Pricing This Actor uses the **free Crossref REST API** (no usage limits or authentication required). You pay **only** for Apify compute time. - **Compute cost:** ~$0.0001–0.001 per run (depends on result volume and API latency) - **Typical cost per batch:** $0.01–0.10 for 50–500 works - **Bulk runs (1000–5000 works):** ~$0.10–0.50 per run The Crossref API itself is **completely free** — no subscriptions, no per-request charges, no rate limits for research use. *** ### Example Workflows #### Workflow 1: Weekly Research Digest Pipeline 1. Run Actor every Monday with `filterFromDate` set to last 7 days 2. Extract results to cloud storage (CSV/JSON export) 3. Feed into email template to send digest to stakeholders 4. Cost: ~$0.02/week #### Workflow 2: Citation Network Analysis (Research Project) 1. Run Actor with `query` = your domain (e.g., "quantum computing") 2. Extract top 500 results (maxResults = 500) 3. Load into network analysis tool (Gephi, Cytoscape) 4. Visualize author collaborations and citation influence 5. Cost: ~$0.05 per analysis run #### Workflow 3: Automated Literature Review 1. Run Actor monthly with your research keywords 2. Filter by `workType` = "journal-article" 3. Combine with external citation tools (Semantic Scholar, OpenAlex) 4. Build automated bibliography in BibTeX or RIS format 5. Cost: ~$0.01/month per search term *** ### FAQ #### "No works found" when searching - **Verify the query:** Try a simpler term (e.g., "cancer" instead of "advanced oncology research methodologies") - **Check Crossref directly:** https://search.crossref.org to validate query - **Try with blank query:** Leave search blank to fetch recent works and verify the actor is working - **Expand date range:** Remove `filterFromDate` to include older works #### Empty or incomplete author names - Some works have missing or incomplete author metadata in Crossref's database - The `firstAuthor` field will be empty if author data is unavailable - Crossref's data quality depends on publisher submission quality - Check the URL (DOI link) for author details if needed #### Missing ISSN or journal name - Not all works have journal information (e.g., books, datasets, preprints) - ISSN is only present for journal articles; other types may have empty `issn` - The `journal` field corresponds to `container-title` in Crossref (may be empty for non-journal works) #### Result limits (maxResults > 5000) - Crossref cursor-based pagination supports up to 5,000 results per query - For larger datasets, run the actor multiple times with different date ranges - Example: Run once for 2024, once for 2023, etc. #### API timeout or slow responses - Crossref API is generally fast but can have occasional latency spikes - Actor has a 60-second timeout per API request; retries are automatic - If timeouts occur frequently, reduce `maxResults` and run multiple smaller batches *** ### Advanced Usage #### Combining Filters You can combine `query`, `filterFromDate`, and `workType` in a single run: **Example:** Find all conference proceedings on "quantum computing" published since 2024: - Query: `quantum computing` - Work Type: `proceedings` - Filter From Date: `2024-01-01` #### Pagination & Large Extracts The actor uses Crossref's cursor-based pagination internally. Each API request fetches up to 100 results; the actor automatically loops to fetch up to your `maxResults` limit. - Requesting 5,000 results requires ~50 API calls - Cost scales linearly: 5x results ≈ 5x cost (but still under $0.50) #### Filtering Tips **By date range:** Use `filterFromDate` (no "to date" parameter; filter is forward-looking) - To get works from 2024 only, run once with `filterFromDate=2024-01-01`, then again with `filterFromDate=2025-01-01` and exclude those results **By work type:** Common types are listed above; others exist but are rare **By publisher:** Not a direct input, but you can add publisher names to your query text (e.g., "machine learning IEEE" to bias toward IEEE publications) *** ### Output Examples #### Example 1: Journal Article ```json { "doi": "10.1038/s41586-024-07301-x", "title": "AlphaFold 3: Structure Prediction for Biology", "type": "journal-article", "publisher": "Nature Publishing Group", "journal": "Nature", "publishedDate": "2024-05-08", "authorsCount": 47, "firstAuthor": "Abramson Josh", "citationCount": 450, "referenceCount": 86, "issn": "0028-0836", "url": "https://doi.org/10.1038/s41586-024-07301-x" } ``` #### Example 2: Conference Proceedings ```json { "doi": "10.1109/CVPR52688.2022.00988", "title": "ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks", "type": "proceedings-article", "publisher": "IEEE", "journal": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)", "publishedDate": "2022-06-19", "authorsCount": 3, "firstAuthor": "Lu Jiasen", "citationCount": 2100, "referenceCount": 52, "issn": "2575-7075", "url": "https://doi.org/10.1109/CVPR52688.2022.00988" } ``` #### Example 3: Book ```json { "doi": "10.1016/b978-0-08-102618-8.00001-3", "title": "Sustainable Materials and Manufacturing", "type": "book", "publisher": "Elsevier", "journal": "", "publishedDate": "2023-09-15", "authorsCount": 12, "firstAuthor": "Smith Richard", "citationCount": 85, "referenceCount": 203, "issn": "", "url": "https://doi.org/10.1016/b978-0-08-102618-8.00001-3" } ``` *** ### Related Actors Looking for complementary research data sources? - **[Open Research Online (Crossref-based)](https://apify.com)** — Alternative Crossref interface - **[DOAJ Open Journals Scraper](https://apify.com/seatsignal/doaj-open-journals-scraper)** — Extract open-access journals - **[ROR Research Organizations Scraper](https://apify.com/seatsignal/ror-research-orgs-scraper)** — Academic institution metadata - **[FRED Economic Data Scraper](https://apify.com/seatsignal/fred-economic-scraper)** — Economic time series for research context *** ### API Reference For detailed Crossref API documentation: - **Crossref REST API Docs:** https://github.com/CrossRef/rest-api-doc - **Search Guide:** https://github.com/CrossRef/rest-api-doc#queries - **Filter Guide:** https://github.com/CrossRef/rest-api-doc#filter-names - **Work Types:** https://github.com/CrossRef/rest-api-doc#work-types - **Crossref Search Interface:** https://search.crossref.org *** ### Legal & Support **Disclaimer:** This Actor fetches data from Crossref (https://www.crossref.org), a non-profit digital object identifier (DOI) registration agency. Crossref data is provided under the **CC0 1.0 Universal (Public Domain Dedication)** license and is free to use for any purpose. Crossref's terms: https://www.crossref.org/documentation/metadata-plus-service/metadata-plus-service-terms-and-conditions/ **Support:** If you encounter issues: 1. Check the Crossref API documentation: https://github.com/CrossRef/rest-api-doc 2. Test your query directly: https://search.crossref.org 3. Verify work types: https://github.com/CrossRef/rest-api-doc#work-types 4. Open an issue on Apify Community or contact support **User-Agent:** This Actor identifies itself as `apify-factory/1.0 (mailto:bciccarelli6@gmail.com)` to access Crossref's polite pool (higher rate limits for well-behaved agents). *** **Built with ❤️ for researchers, academics, and bibliometricians.** # Actor input Schema ## `query` (type: `string`): Free-text search query (e.g., 'machine learning', 'COVID-19', 'renewable energy'). Leave blank to fetch recent works. ## `filterFromDate` (type: `string`): Only include works published on or after this date (YYYY-MM-DD format). Example: '2024-01-01'. ## `workType` (type: `string`): Filter by work type (e.g., 'journal-article', 'book', 'proceedings', 'report', 'dataset'). Leave blank for all types. ## `maxResults` (type: `integer`): Maximum number of works to fetch (1-5000). Default is 50. ## Actor input object example ```json { "query": "", "filterFromDate": "", "workType": "", "maxResults": 50 } ``` # Actor output Schema ## `dataset` (type: `string`): No description # 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("compute-edge/crossref-works-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("compute-edge/crossref-works-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call compute-edge/crossref-works-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=compute-edge/crossref-works-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Crossref Scholarly Works Scraper", "description": "Extract scholarly works metadata from Crossref — DOIs, titles, authors, journals, publication dates, and citation counts. Filter by query, date range, and work type. No API key required.", "version": "0.1", "x-build-id": "iwNMSjQTGTGWFgqn2" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/compute-edge~crossref-works-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-compute-edge-crossref-works-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/compute-edge~crossref-works-scraper/runs": { "post": { "operationId": "runs-sync-compute-edge-crossref-works-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/compute-edge~crossref-works-scraper/run-sync": { "post": { "operationId": "run-sync-compute-edge-crossref-works-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "query": { "title": "Query", "type": "string", "description": "Free-text search query (e.g., 'machine learning', 'COVID-19', 'renewable energy'). Leave blank to fetch recent works.", "default": "" }, "filterFromDate": { "title": "Filter From Date", "type": "string", "description": "Only include works published on or after this date (YYYY-MM-DD format). Example: '2024-01-01'.", "default": "" }, "workType": { "title": "Work Type", "type": "string", "description": "Filter by work type (e.g., 'journal-article', 'book', 'proceedings', 'report', 'dataset'). Leave blank for all types.", "default": "" }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum number of works to fetch (1-5000). Default is 50.", "default": 50 } } }, "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 } } } } } } } } } } ```