# Figshare Research Data Scraper - Datasets, Papers & DOIs (`ninhothedev/figshare-scraper`) Actor

$0.5/1K 🔥 Figshare scraper! Research datasets, figures & papers with DOI, authors & downloads. No key. JSON, CSV, Excel or API in seconds. Power open science & literature review ⚡

- **URL**: https://apify.com/ninhothedev/figshare-scraper.md
- **Developed by:** [ninhothedev](https://apify.com/ninhothedev) (community)
- **Categories:** AI, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.50 / 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Figshare Research Data Scraper

Scrape **research datasets, papers, figures and software from Figshare** — no API key, no login, no account setup. Search millions of open research outputs by keyword or pull specific articles by ID, and get clean structured JSON with **DOI, authors, ORCIDs, categories, tags, license, file counts, download URLs and citations**.

Figshare hosts open research from thousands of institutions and publishers (Taylor & Francis, Frontiers, Karger, SAGE, and hundreds of university repositories). This actor turns that into a spreadsheet, a database, or a feed for your own tooling.

### Features

- **No API key required** — runs on the public Figshare API v2 out of the box
- **Two modes** — keyword `search` across all of Figshare, or direct `articles` lookup by ID
- **Optional detail enrichment** — full abstract, author list, ORCIDs, license, files and citation
- **DOI + citation ready** — export straight into a reference manager or literature review
- **File-level metadata** — file count, total bytes, and direct download URLs
- **Datacenter-proxy friendly** — works without residential proxies, so runs stay cheap
- **Permissive schema** — every field nullable, nothing breaks on unusual records

### Use cases

- **Research data discovery** — find every open dataset published on a topic
- **Open science** — monitor new deposits from a field, institution or publisher
- **Literature review** — build a DOI + citation table for a systematic review
- **Datasets for ML** — locate and download open training or benchmark data

### Input

| Field | Type | Default | Description |
|---|---|---|---|
| `mode` | select | `search` | `search` = keyword queries, `articles` = fetch specific IDs |
| `queries` | array | `["climate model", "protein structure"]` | Keyword queries for search mode |
| `articleIds` | array | – | Numeric Figshare article IDs for articles mode |
| `enrichDetail` | boolean | `true` | Fetch full detail per article (abstract, authors, files, license) |
| `maxItems` | integer | `100` | Hard cap on articles pushed (max 1000) |

```json
{
  "mode": "search",
  "queries": ["climate model", "single cell RNA-seq"],
  "enrichDetail": true,
  "maxItems": 200
}
```

### Output

One dataset item per article:

```json
{
  "article_id": 33103346,
  "title": "Supplementary file 1: Association of serum uric acid with vascular calcification",
  "doi": "10.3389/fendo.2026.1893598.s001",
  "description": "Background Vascular calcification (VC) is common among patients with T2DM...",
  "authors": ["Xinyu Li", "Taoyuan He", "Guosheng Li"],
  "orcids": ["0000-0002-1825-0097"],
  "categories": ["Cell Metabolism"],
  "tags": ["retrospective cohort study", "risk stratification", "serum uric acid"],
  "license": "CC BY 4.0",
  "version": 1,
  "item_type": "dataset",
  "published_date": "2026-07-28T12:32:26Z",
  "file_count": 1,
  "total_size_bytes": 1588077,
  "download_urls": ["https://ndownloader.figshare.com/files/67060721"],
  "citation": "Li, Xinyu; He, Taoyuan (2026). Supplementary file 1. Frontiers. Dataset.",
  "url": "https://frontiersin.figshare.com/articles/dataset/.../33103346",
  "thumb": "https://s3-eu-west-1.amazonaws.com/.../thumb.png",
  "source": "figshare",
  "scraped_at": "2026-07-28T12:41:03Z"
}
```

All fields are nullable. Items published in institutional repositories may use a handle instead of a DOI, in which case `doi` is `null`.

### Pricing

Roughly **$0.5 per 1,000 articles** on the Apify platform. Turning off `enrichDetail` roughly halves the request count for large keyword sweeps.

### Related actors

- [Zenodo Scraper](https://apify.com/ninhothedev/zenodo-scraper) — open research records from CERN's Zenodo
- [OpenAlex Scraper](https://apify.com/ninhothedev/openalex-scraper) — the open catalog of scholarly works
- [Crossref Scraper](https://apify.com/ninhothedev/crossref-scraper) — DOI metadata for published literature
- [DataCite Scraper](https://apify.com/ninhothedev/datacite-scraper) — DOI metadata for research data

### Notes

This actor reads only publicly available metadata exposed by the official Figshare API. It does not download file contents — `download_urls` are direct links you can fetch yourself, subject to each item's license.

# Actor input Schema

## `mode` (type: `string`):

Choose how to collect articles. 'search' runs keyword queries against the Figshare search API and returns matching research outputs. 'articles' skips searching and fetches full detail for the specific Figshare article IDs you provide.

## `queries` (type: `array`):

Keyword queries to search Figshare for (used in 'search' mode). Each query is paginated until maxItems is reached. Examples: 'climate model', 'protein structure', 'single cell RNA-seq'.

## `articleIds` (type: `array`):

Numeric Figshare article IDs to fetch directly (used in 'articles' mode). Find the ID at the end of any figshare.com article URL, e.g. https://figshare.com/articles/dataset/.../1234567.

## `enrichDetail` (type: `boolean`):

Fetch the full detail record for every search hit to add description, authors, ORCIDs, categories, tags, license, files and citation. Adds one extra request per article (rate-limited with a small delay). Turn off for a much faster, lighter run with basic fields only.

## `maxItems` (type: `integer`):

Hard cap on how many articles to push to the dataset across all queries or IDs. Keeps runs predictable and cheap.

## Actor input object example

```json
{
  "mode": "search",
  "queries": [
    "climate model",
    "protein structure"
  ],
  "articleIds": [
    "1234567"
  ],
  "enrichDetail": true,
  "maxItems": 100
}
```

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "queries": [
        "climate model",
        "protein structure"
    ],
    "articleIds": [
        "1234567"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("ninhothedev/figshare-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "queries": [
        "climate model",
        "protein structure",
    ],
    "articleIds": ["1234567"],
}

# Run the Actor and wait for it to finish
run = client.actor("ninhothedev/figshare-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "queries": [
    "climate model",
    "protein structure"
  ],
  "articleIds": [
    "1234567"
  ]
}' |
apify call ninhothedev/figshare-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,ninhothedev/figshare-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/NpbRrmHJr6St8hHyO/builds/bCaPDlVrT6gWxc0qd/openapi.json