# Open Citations Scraper (`parseforge/open-citations-scraper`) Actor Comprehensive OpenCitations scraper for extracting citation and reference data from OpenCitations API. Perfect for researchers, academics, and data scientists who need automated access to citation networks, bibliographic metadata, and citation analysis data. - **URL**: https://apify.com/parseforge/open-citations-scraper.md - **Developed by:** [ParseForge](https://apify.com/parseforge) (community) - **Categories:** Developer tools, Automation, Other - **Stats:** 4 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 OpenCitations Scraper > ๐Ÿš€ **Extract citation networks and bibliographic metadata from OpenCitations in seconds.** Search by DOI, PMID, or OMID. No coding, no API keys required. > ๐Ÿ•’ **Last updated:** 2026-04-23 ยท **๐Ÿ“Š 20 fields** ยท **๐Ÿ” Citations and references modes** ยท **๐Ÿ“„ Optional detailed metadata** OpenCitations is an open scholarly infrastructure providing free access to citation data from millions of academic publications. This scraper collects citation relationships, self-citation flags, and optional bibliographic metadata (authors, titles, venues, publication dates) for any publication identified by DOI, PubMed ID, or OpenCitations Meta ID. Choose between **citations mode** (who cited this work) and **references mode** (what this work cites) to map research influence in either direction. Researchers, bibliometric analysts, and data scientists use this actor to build citation networks, track research impact, identify self-citations, and analyze how knowledge flows between publications. Instead of querying the OpenCitations API manually and parsing responses, you get clean, structured data exported as JSON, CSV, or Excel. With metadata enabled, every record includes the citing and cited entity IDs, creation date, timespan, self-citation flags, plus the full title, authors, publication date, venue, and publisher. | ๐ŸŽฏ Target Audience | ๐Ÿ’ก Use Cases | |---|---| | Bibliometric analysts | Map citation networks and measure impact | | Academic researchers | Track who cites your publications | | University administrators | Evaluate research impact for departments | | Science policy makers | Analyze knowledge flow between institutions | | Data scientists | Build citation graph datasets for analysis | | Librarians | Enrich catalog records with citation data | --- ### ๐Ÿ“‹ What the OpenCitations Scraper does - ๐Ÿ” **DOI-based search** to find citations or references for any published work - ๐Ÿ†” **PMID support** for biomedical publications indexed in PubMed - ๐Ÿ“‹ **OMID support** for OpenCitations internal identifier lookups - ๐Ÿ”„ **Bidirectional search** with citations (incoming) and references (outgoing) modes - ๐Ÿ“Š **Self-citation detection** with flags for author and journal self-citations - ๐Ÿ“ **Optional metadata** including titles, authors, venues, and publication dates The scraper queries the OpenCitations API with your identifier and search type, retrieves all matching citation relationships, and extracts structured data for each record. When metadata is enabled, it also fetches detailed bibliographic information for each citing or cited work. Results include unique citation identifiers (OCI), entity IDs, creation dates, timespans, self-citation flags, and full publication metadata. > ๐Ÿ’ก **Why it matters:** Manually collecting citation data from OpenCitations involves API queries, pagination, and metadata enrichment. This scraper handles everything automatically, delivering structured citation networks ready for analysis, visualization, or integration with other research tools. --- ### ๐ŸŽฌ Full Demo _๐Ÿšง Coming soon..._ --- ### โš™๏ธ Input
FieldTypeRequiredDescription
maxItemsintegerNoMax records to collect. Free: up to 10. Paid: up to 1,000,000
doistringNoDigital Object Identifier (e.g., 10.1016/j.jmb.2005.08.075)
pmidstringNoPubMed ID for biomedical publications
omidstringNoOpenCitations Meta Identifier (e.g., omid:br/06140242082)
searchTypestringNoSearch direction: citations (incoming) or references (outgoing)
includeMetadatabooleanNoFetch detailed metadata (title, authors, date) for each record
**Example 1: Get citations for a DOI** ```json { "doi": "10.1016/j.jmb.2005.08.075", "searchType": "citations", "includeMetadata": true, "maxItems": 50 } ```` **Example 2: Get references from a PubMed article** ```json { "pmid": "16325459", "searchType": "references", "includeMetadata": true, "maxItems": 100 } ``` > โš ๏ธ **Good to Know:** Provide one identifier (DOI, PMID, or OMID), not multiple. Enabling metadata makes the scraper slower but provides full bibliographic details for each citation. The default search type is "citations" (incoming citations). *** ### ๐Ÿ“Š Output #### ๐Ÿงพ Schema | Emoji | Field | Type | Description | |---|---|---|---| | ๐Ÿ“ | oci | string | Unique Open Citation Identifier | | ๐Ÿ‘ค | citing | string | Identifier of the citing entity | | ๐Ÿ‘ค | cited | string | Identifier of the cited entity | | ๐Ÿ“… | creationDate | string | When the citation relationship was recorded | | โฑ๏ธ | timespan | string | Time between publication dates | | ๐Ÿ“Š | journalSelfCitation | boolean | Whether the citation is within the same journal | | ๐Ÿ“Š | authorSelfCitation | boolean | Whether the author cites their own work | | ๐Ÿ“ | title | string | Publication title (with metadata enabled) | | ๐Ÿ‘ฅ | authors | string | Author names (with metadata enabled) | | ๐Ÿ“… | publicationDate | string | Publication date (with metadata enabled) | | ๐Ÿ“– | volume | string | Journal volume | | ๐Ÿ“„ | issue | string | Journal issue | | ๐Ÿ“ | venue | string | Journal or venue name | | ๐Ÿท๏ธ | publicationType | string | Type of publication | | ๐Ÿ“„ | page | string | Page range | | ๐Ÿข | publisher | string | Publisher name | | โœ๏ธ | editor | string | Editor name | | ๐Ÿ†” | workId | string | Internal work identifier | | โฐ | scrapedAt | string | Collection timestamp | | โš ๏ธ | error | string | Error message if processing failed | #### ๐Ÿ“ฆ Sample records
๐Ÿ“„ Citation with full metadata ```json { "oci": "0200100000236102837370200010000073606050563-020010000003619242801370200000200003602006306", "citing": "10.1038/s41586-020-2649-2", "cited": "10.1016/j.jmb.2005.08.075", "creationDate": "2020-08-26", "timespan": "P5478D", "journalSelfCitation": false, "authorSelfCitation": false, "title": "Highly accurate protein structure prediction with AlphaFold", "authors": "Jumper, John; Evans, Richard; Pritzel, Alexander", "publicationDate": "2020-08-26", "volume": "596", "venue": "Nature", "publisher": "Springer Nature", "scrapedAt": "2026-04-16T12:00:00.000Z" } ```
๐Ÿ“„ Self-citation record ```json { "oci": "020010000003619242801-020010000003619242802", "citing": "10.1016/j.jmb.2008.01.032", "cited": "10.1016/j.jmb.2005.08.075", "creationDate": "2008-03-15", "timespan": "P910D", "journalSelfCitation": true, "authorSelfCitation": true, "title": "Improved protein structure prediction using side-chain rotamers", "authors": "Zhang, Yang; Skolnick, Jeffrey", "publicationDate": "2008-03-15", "venue": "Journal of Molecular Biology", "scrapedAt": "2026-04-16T12:00:00.000Z" } ```
๐Ÿ“„ Citation without metadata ```json { "oci": "020010000003619242801370200000200003602006306-020010000003619242801", "citing": "10.1371/journal.pone.0012345", "cited": "10.1016/j.jmb.2005.08.075", "creationDate": "2015-07-20", "timespan": "P3632D", "journalSelfCitation": false, "authorSelfCitation": false, "scrapedAt": "2026-04-16T12:00:00.000Z" } ```
*** ### โœจ Why choose this Actor | Feature | Details | |---|---| | ๐Ÿ” Three identifier types | Search by DOI, PubMed ID, or OpenCitations Meta ID | | ๐Ÿ”„ Bidirectional search | Find incoming citations or outgoing references | | ๐Ÿ“Š Self-citation detection | Flags for author and journal self-citations | | ๐Ÿ“ Optional metadata | Full bibliographic details when enabled | | ๐Ÿ†“ Open data | All OpenCitations data is freely available | | ๐Ÿ“ฆ Flexible export | JSON, CSV, or Excel output | | โšก Automatic pagination | Handles large citation networks automatically | > ๐Ÿ“Š **Map citation networks for any publication with up to 1,000,000 records per run, including self-citation detection and full metadata.** *** ### ๐Ÿ“ˆ How it compares to alternatives | Feature | This Actor | Manual API Queries | Generic Scrapers | |---|---|---|---| | DOI, PMID, and OMID support | โœ… | Manual | โŒ | | Self-citation detection | โœ… | โœ… | โŒ | | Optional metadata enrichment | โœ… | Manual | โŒ | | Bidirectional search | โœ… | Manual | โŒ | | Bulk collection (1M+ records) | โœ… | Manual | โŒ | | Structured JSON/CSV output | โœ… | JSON only | Varies | | Scheduled runs | โœ… | โŒ | โŒ | Get structured citation data at scale without writing API code or managing pagination. *** ### ๐Ÿš€ How to use 1. **Create an Apify account** - [Sign up free with $5 credit](https://console.apify.com/sign-up?fpr=vmoqkp) 2. **Open the OpenCitations Scraper** - Navigate to the actor page on Apify 3. **Enter a DOI, PMID, or OMID** - Provide the identifier for the publication you want to analyze 4. **Choose search type and options** - Select citations or references mode and enable metadata if needed 5. **Click Start** - The actor collects citation relationships and delivers structured data > โฑ๏ธ **A typical run with 50 citations completes in under 1 minute.** *** ### ๐Ÿ’ผ Business use cases
๐Ÿ“Š Bibliometric Analysis
  • Map citation networks for research impact assessment
  • Identify self-citations to calculate adjusted metrics
  • Track citation accumulation over time
  • Compare citation patterns across disciplines
 ๐ŸŽ“ Academic Research
  • Build citation graphs for literature reviews
  • Track who is citing your publications
  • Identify influential papers in your field
  • Analyze reference patterns in competitor research
๐Ÿ›๏ธ Research Administration
  • Evaluate faculty research impact for reviews
  • Track department-level citation metrics
  • Monitor publication influence across programs
  • Build reporting dashboards for stakeholders
 ๐Ÿ“ˆ Data Science
  • Build citation graph datasets for network analysis
  • Train models on citation prediction tasks
  • Analyze knowledge flow between research fields
  • Create visualization datasets for research mapping
*** *** ### 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%20Open%20Citations%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%20Open%20Citations%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%20Open%20Citations%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%20Open%20Citations%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 OpenCitations Scraper Integrate the OpenCitations Scraper into your workflow using the Apify API or client libraries. **Node.js:** ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_API_TOKEN' }); const run = await client.actor("parseforge/open-citations-scraper").call({ doi: "10.1016/j.jmb.2005.08.075", searchType: "citations", includeMetadata: true, 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/open-citations-scraper").call(run_input={ "doi": "10.1016/j.jmb.2005.08.075", "searchType": "citations", "includeMetadata": True, "maxItems": 100 }) items = list(client.dataset(run["defaultDatasetId"]).iterate_items()) print(items) ``` - ๐Ÿ“– [Apify API reference](https://docs.apify.com/api/v2) - ๐Ÿ [Python client docs](https://docs.apify.com/api/client/python) - ๐Ÿ“ฆ [Node.js client docs](https://docs.apify.com/api/client/js) **Schedules:** Set up recurring runs to monitor citation growth for your publications. Configure weekly or monthly schedules from the Apify Console to track new citations automatically. ### ๐Ÿ”Œ Integrate with any app - ๐Ÿ”— **Make (Integromat)** - Connect citation data to Google Sheets, Notion, or any of 1,500+ apps - ๐Ÿ”— **Zapier** - Trigger workflows when new citations are detected - ๐Ÿ”— **Slack** - Get notified when new citations appear for your publications - ๐Ÿ”— **Airbyte** - Stream citation data into your data warehouse - ๐Ÿ”— **GitHub** - Store citation datasets in repositories for version control - ๐Ÿ”— **Google Drive** - Automatically save CSV exports to shared folders *** ### ๐Ÿ”— Recommended Actors | Actor | Description | |---|---| | [Crossref Scraper](https://apify.com/parseforge/crossref-scraper) | Extract DOI metadata for 155M+ research publications | | [PubMed Citation Scraper](https://apify.com/parseforge/pubmed-citation-scraper) | Extract publication metadata from PubMed for biomedical research | | [Open Library Scraper](https://apify.com/parseforge/open-library-scraper) | Search and download book data from the Internet Archive | | [ROR Scraper](https://apify.com/parseforge/ror-scraper) | Collect research organization data from ROR | | [US Census Bureau Scraper](https://apify.com/parseforge/us-census-bureau-scraper) | Extract demographic and economic data from the Census Bureau | > ๐Ÿ’ก **Pro Tip:** Combine the OpenCitations Scraper with the Crossref Scraper to get both citation networks and full publication metadata for each cited work. *** **๐Ÿ†˜ Need Help?** [**Open our contact form**](https://tally.so/r/BzdKgA) and we will get back to you within 24 hours. We are happy to help with custom setups, integrations, or feature requests. *** > **Disclaimer:** This actor is not affiliated with, endorsed by, or connected to OpenCitations. It accesses publicly available data through the OpenCitations API. Use responsibly and in accordance with applicable terms of service. # Actor input Schema ## `maxItems` (type: `integer`): Free users: Limited to 100. Paid users: Optional, max 1,000,000. Leave empty for unlimited (paid users only). ## `doi` (type: `string`): Digital Object Identifier (e.g., 10.1016/j.jmb.2005.08.075). Provide one identifier: DOI, PMID, or OMID. ## `pmid` (type: `string`): PubMed ID (numeric identifier for biomedical publications). Provide one identifier: DOI, PMID, or OMID. ## `omid` (type: `string`): OpenCitations Meta Identifier (e.g., omid:br/06140242082). Provide one identifier: DOI, PMID, or OMID. ## `searchType` (type: `string`): Type of search to perform: citations (incoming citations) or references (outgoing references). ## `includeMetadata` (type: `boolean`): Fetch detailed metadata (title, authors, publication date, etc.) for each citation. This requires additional API calls. ## Actor input object example ```json { "maxItems": 10, "doi": "10.1016/j.jmb.2005.08.075", "searchType": "citations", "includeMetadata": true } ``` # Actor output Schema ## `citations` (type: `string`): Complete dataset of citations/references with all fields including OCI identifiers, citing/cited entities, creation dates, timespans, self-citation flags, and metadata ## `overview` (type: `string`): Overview view of citations 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, "doi": "10.1016/j.jmb.2005.08.075" }; // Run the Actor and wait for it to finish const run = await client.actor("parseforge/open-citations-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, "doi": "10.1016/j.jmb.2005.08.075", } # Run the Actor and wait for it to finish run = client.actor("parseforge/open-citations-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, "doi": "10.1016/j.jmb.2005.08.075" }' | apify call parseforge/open-citations-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=parseforge/open-citations-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Open Citations Scraper", "description": "Comprehensive OpenCitations scraper for extracting citation and reference data from OpenCitations API. Perfect for researchers, academics, and data scientists who need automated access to citation networks, bibliographic metadata, and citation analysis data.", "version": "0.1", "x-build-id": "zUiOHeuVYBWZs1BnE" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/parseforge~open-citations-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-parseforge-open-citations-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/parseforge~open-citations-scraper/runs": { "post": { "operationId": "runs-sync-parseforge-open-citations-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/parseforge~open-citations-scraper/run-sync": { "post": { "operationId": "run-sync-parseforge-open-citations-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Free users: Limited to 100. Paid users: Optional, max 1,000,000. Leave empty for unlimited (paid users only)." }, "doi": { "title": "DOI", "type": "string", "description": "Digital Object Identifier (e.g., 10.1016/j.jmb.2005.08.075). Provide one identifier: DOI, PMID, or OMID." }, "pmid": { "title": "PMID", "type": "string", "description": "PubMed ID (numeric identifier for biomedical publications). Provide one identifier: DOI, PMID, or OMID." }, "omid": { "title": "OMID", "type": "string", "description": "OpenCitations Meta Identifier (e.g., omid:br/06140242082). Provide one identifier: DOI, PMID, or OMID." }, "searchType": { "title": "Search Type", "enum": [ "citations", "references" ], "type": "string", "description": "Type of search to perform: citations (incoming citations) or references (outgoing references).", "default": "citations" }, "includeMetadata": { "title": "Include Metadata", "type": "boolean", "description": "Fetch detailed metadata (title, authors, publication date, etc.) for each citation. This requires additional API calls.", "default": true } } }, "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 } } } } } } } } } } ```