# International Plant Names Index (IPNI) Scraper (`jungle_synthesizer/ipni-international-plant-names-index-scraper`) Actor Bulk-extract plant nomenclature records from IPNI (Kew/Harvard/ANBG) — scientific name, authors, protologue publication, BHL scan links, and full author provenance — via the public no-auth REST API. - **URL**: https://apify.com/jungle\_synthesizer/ipni-international-plant-names-index-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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. 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 ## International Plant Names Index (IPNI) Scraper Bulk-extract plant nomenclature records from **IPNI** (the International Plant Names Index — a collaboration between Kew Gardens, Harvard University Herbaria, and the Australian National Botanic Gardens). IPNI is the authoritative global registry of published vascular plant names, covering over 1.7 million nomenclatural acts with full author provenance, protologue citations, and Biodiversity Heritage Library (BHL) scan links. ### What you get Each record includes: | Field | Description | |-------|-------------| | `ipni_id` | IPNI record ID (e.g. `296689-1`) | | `fq_id` | Fully-qualified LSID (e.g. `urn:lsid:ipni.org:names:296689-1`) | | `name` | Scientific name | | `authors` | Author string (abbreviated) | | `author_team` | Full author team with IPNI IDs and full name+dates (JSON) | | `rank` | Nomenclatural rank (spec., var., f., etc.) | | `family` / `genus` / `species` / `infraspecies` | Taxonomic decomposition | | `publication` | Protologue publication abbreviation | | `publication_year` | Year published | | `reference` | Full literature citation | | `collation` | Volume/page reference | | `has_bhl_links` | Whether BHL scans exist | | `bhl_page_link` | BHL deep-link to the protologue page | | `tl2_author` / `tl2_number` | Taxonomic Literature 2 provenance | | `suppressed` | Whether the name is suppressed | | `in_powo` | Whether accepted in Plants of the World Online | | `ipni_url` | Full IPNI URL | Optional **enrichment mode** (`enrich: true`) fetches the per-record detail endpoint to add `original_remarks` and `type_locations`. ### Input ```json { "queries": ["Quercus robur", "Rosa canina"], "recordType": "", "enrich": false, "maxItems": 500 } ```` | Parameter | Type | Description | |-----------|------|-------------| | `queries` | string\[] | **Required.** One or more search terms. Each is paginated independently (50 results/page). Accepts genus names, species binomials, author abbreviations, or free text. | | `recordType` | string | Optional filter: `citation`, `name`, `author`, or `publication`. Leave blank for all types. | | `enrich` | boolean | Fetch the per-record `/n/` detail endpoint for `original_remarks`, `type_locations`, and enriched BHL links. Slower but more complete. Default: `false`. | | `maxItems` | integer | Maximum total records to return across all queries. Default: `15`. | ### Use cases - **Biodiversity informatics pipelines** — resolve plant name strings against the authoritative nomenclatural index and attach full author provenance. - **Herbarium digitisation** — link specimen label names to IPNI records for authoritative citation and LSID anchoring. - **AI training datasets** — build botanical-nomenclature corpora with publication year, author team, and BHL scan provenance for LLM training. - **Flora and checklist projects** — bulk-retrieve name records for a genus or family with protologue citations and suppression flags. - **Citation graph research** — extract the author-team + TL2 provenance layer that POWO/GBIF/USDA do not carry. ### Notes - The IPNI API is public with no authentication required. - No proxy is used — direct requests at polite concurrency (400 ms between pages). - Pagination is automatic: each query sweeps all available pages up to `maxItems`. - `enrich: true` adds a second HTTP request per record — increase `maxItems` conservatively. - IPNI covers vascular plants and some bryophytes; it is distinct from POWO (accepted taxonomy + distribution) and GBIF (occurrence data). # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `queries` (type: `array`): List of search queries to run against IPNI (e.g. 'Quercus robur', 'Rosa', 'Aucuba'). Each query is paginated independently. ## `recordType` (type: `string`): Filter by record type: 'citation', 'name', 'author', or 'publication'. Leave blank for all types. ## `enrich` (type: `boolean`): Fetch the per-record detail endpoint to capture full author team, BHL links, type locations, and original remarks. Slower but more complete. ## `maxItems` (type: `integer`): Maximum number of records to scrape across all queries ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "queries": [ "Quercus robur" ], "recordType": "", "enrich": false, "maxItems": 15 } ``` # Actor output Schema ## `results` (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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "queries": [ "Quercus robur" ], "enrich": false, "maxItems": 15 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/ipni-international-plant-names-index-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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "queries": ["Quercus robur"], "enrich": False, "maxItems": 15, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/ipni-international-plant-names-index-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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "queries": [ "Quercus robur" ], "enrich": false, "maxItems": 15 }' | apify call jungle_synthesizer/ipni-international-plant-names-index-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/ipni-international-plant-names-index-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "International Plant Names Index (IPNI) Scraper", "description": "Bulk-extract plant nomenclature records from IPNI (Kew/Harvard/ANBG) — scientific name, authors, protologue publication, BHL scan links, and full author provenance — via the public no-auth REST API.", "version": "0.1", "x-build-id": "v1Yv0VBYHccn7tWhC" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~ipni-international-plant-names-index-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-ipni-international-plant-names-index-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/jungle_synthesizer~ipni-international-plant-names-index-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-ipni-international-plant-names-index-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/jungle_synthesizer~ipni-international-plant-names-index-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-ipni-international-plant-names-index-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", "required": [ "queries" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "queries": { "title": "Search Queries", "type": "array", "description": "List of search queries to run against IPNI (e.g. 'Quercus robur', 'Rosa', 'Aucuba'). Each query is paginated independently.", "default": [ "Quercus robur" ], "items": { "type": "string" } }, "recordType": { "title": "Record Type Filter", "enum": [ "", "citation", "name", "author", "publication" ], "type": "string", "description": "Filter by record type: 'citation', 'name', 'author', or 'publication'. Leave blank for all types.", "default": "" }, "enrich": { "title": "Enrich Records", "type": "boolean", "description": "Fetch the per-record detail endpoint to capture full author team, BHL links, type locations, and original remarks. Slower but more complete.", "default": false }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of records to scrape across all queries", "default": 15 } } }, "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 } } } } } } } } } } ```