# bioRxiv Preprints Scraper (`parseforge/biorxiv-preprints-scraper`) Actor

Scrape bioRxiv and medRxiv preprint metadata by date range, subject category, or DOI. Returns title, authors, abstract, category, and version history in a flat schema for CSV, JSON, Excel, or XML export.

- **URL**: https://apify.com/parseforge/biorxiv-preprints-scraper.md
- **Developed by:** [ParseForge](https://apify.com/parseforge) (community)
- **Categories:** Education, Automation, Integrations
- **Stats:** 2 total users, 1 monthly users, 89.7% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $7.50 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#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

[![ParseForge](https://raw.githubusercontent.com/ParseForge/apify-assets/main/banner.jpg)](https://apify.com/parseforge?fpr=vmoqkp)

### bioRxiv Preprints Scraper

**Scrape bioRxiv and medRxiv preprints by date range, category, or DOI, up to a million per run.** Every preprint comes with its title, authors, abstract, DOI, category, and version history. No API key required. Export to CSV, JSON, Excel, or XML.

bioRxiv and medRxiv host the latest life sciences and medical research before peer review, but browsing the web interface or using the official API means manual work or rate limits. This scraper reads the public preprint listings directly, filtering by subject category, date range, or a specific DOI, and returns each match in one consistent schema. It is built for researchers, data scientists, and librarians who need structured preprint metadata for literature reviews, trend analysis, or institutional dashboards.

| Who uses it | What they scrape bioRxiv for |
|---|---|
| Bioinformatics researchers | Build a corpus of recent preprints in a specific subject category for text mining or meta-analysis. |
| Academic librarians | Track new submissions from an institution's researchers to populate a preprint repository or dashboard. |
| Pharma competitive intelligence analysts | Monitor emerging research in a therapeutic area before it appears in journals. |
| Science journalists | Find the latest unreviewed studies on a hot topic by filtering the most recent preprints. |

### What it does

This Actor collects bioRxiv and medRxiv preprint records by date range, subject category, or specific DOI, and returns each one as a flat row with metadata including title, authors, abstract, DOI, category, and version history.

- 📅 **Date range search:** Collect all preprints posted between two dates, from a single day to several years.
- 🏷️ **Subject category filter:** Narrow results to a specific bioRxiv subject category with a case-insensitive substring match.
- 🔗 **DOI lookup:** Fetch the complete metadata for a single preprint by its DOI, overriding any date or category filters.
- 🔄 **Dual server support:** Query bioRxiv for life sciences or medRxiv for clinical and health sciences preprints from the same Actor.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

### What you can do with bioRxiv data

**📈 Monitor research trends in a field.**

A computational biologist scrapes all bioRxiv preprints in the 'bioinformatics' category from the last quarter to analyze keyword frequency and identify emerging methods.

**📚 Build an institutional preprint feed.**

A university librarian runs the scraper weekly with a date range covering the past seven days, then filters the output for authors affiliated with their institution to update a public dashboard.

**🔬 Track a specific preprint's version history.**

A researcher fetches a preprint by DOI to retrieve its full metadata, including all version dates and links, for a systematic review that requires version tracking.

**💊 Conduct competitive intelligence on a drug target.**

A pharma analyst scrapes medRxiv preprints mentioning a specific condition over the past month to identify new preclinical or clinical findings before they reach journals.

### Why choose this scraper

| | What you get |
|---|---|
| **No API key or registration** | Reads the public preprint listings directly, no OAuth or app setup required. |
| **Fixed flat schema** | Every preprint returns the same fields, ready for CSV, JSON, Excel, or XML export. |
| **Up to 1,000,000 preprints per run** | Collect large datasets for bibliometric studies or training language models. |
| **Category substring matching** | Filter by any part of a bioRxiv subject category name, not an exact dropdown value. |

### How it compares

This Actor focuses on flexible date-range and category filtering across both bioRxiv and medRxiv, with a simple flat schema for direct dataset export. Here is how it compares to other bioRxiv scrapers on Apify.

| Feature | ParseForge | bioRxiv Preprints Scraper - Biology Research Data | bioRxiv Scraper · Preprints, Authors, Categories & Versions | bioRxiv & medRxiv Preprint Scraper |
|---|---|---|---|---|
| Scrapes both bioRxiv and medRxiv | Yes | Not listed | Yes | Yes |
| Date range filtering | Yes | Yes | Not listed | Yes |
| Subject category filter | Yes, substring match | Not listed | Not listed | Not listed |
| DOI lookup | Yes | Not listed | Not listed | Yes |
| Version history in output | Yes | Yes | Yes | Not listed |
| JATS XML links | Not listed | Yes | Not listed | Not listed |
| Pay-per-event pricing | Not listed | Not listed | Yes | Not listed |

### Configure the run

Drive the Actor from a date range, a subject category, or a specific DOI, alone or in combination, and filters run as each preprint is read so only matches reach your dataset. The Input tab lists every parameter.

A first run with the defaults:

```json
{
 "maxItems": 10
}
```

A larger pull:

```json
{
 "maxItems": 200
}
```

### Pricing

Pay-per-result: **$0.0085 per result** collected. You pay only for the results written to your dataset.

| Results collected | Approximate cost |
|---|---|
| 100 results | $0.85 |
| 1,000 results | $8.50 |
| 10,000 results | $85.00 |

New Apify accounts start with $5 in free credit.

### Free users

Free-plan runs return up to 10 results as a preview. [Upgrade your Apify plan](https://console.apify.com/sign-up?fpr=vmoqkp) to collect up to 1,000,000 results per run.

### Run it

1. [Create a free Apify account with $5 in credit](https://console.apify.com/sign-up?fpr=vmoqkp).
2. Open the [bioRxiv Preprints Scraper](https://apify.com/parseforge/biorxiv-preprints-scraper?fpr=vmoqkp).
3. Set your inputs and any filters, then click **Start**.
4. Export the results as CSV, Excel, JSON, or XML from the **Dataset** tab.

Run it programmatically through the [Apify API](https://docs.apify.com/api/v2) (`run-sync-get-dataset-items`) or the [ApifyClient](https://docs.apify.com/api/client/js) for JavaScript and Python.

### Use with AI agents (MCP)

Give an AI agent live access to bioRxiv through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

```bash
claude mcp add --transport http apify "https://mcp.apify.com/?tools=parseforge/biorxiv-preprints-scraper"
```

Then prompt it in plain language to run the scraper and read back the results.

### Troubleshooting

**Why am I getting no results?**

Check that your date range contains preprints. bioRxiv and medRxiv have sparse postings on weekends and holidays. Also verify that your category substring matches an existing bioRxiv subject category. Try broadening the date range or removing the category filter to test.

**The run finished but collected fewer preprints than my maxItems setting.**

This is expected when the date range or category filter matches fewer preprints than your maximum. The Actor collects all available matches up to the limit. Try widening the date range or removing the category filter to collect more.

**I entered a DOI but the Actor returned an error or empty result.**

Ensure the DOI is complete and correctly formatted, for example 10.1101/2024.01.01.123456. The DOI must exist on the selected server. If the preprint is on medRxiv, make sure you have selected the medRxiv server.

**The category filter is not returning the preprints I expected.**

The filter uses a case-insensitive substring match. For example, 'genomics' will match 'Genomics' and 'Functional Genomics'. If you are unsure of the exact category name, try a shorter substring or check the bioRxiv website for the full list of subject categories.

**The run is taking a long time or timing out.**

Large date ranges with high maxItems settings can take time. Reduce the maxItems value or narrow the date range. If the issue persists, the bioRxiv or medRxiv servers may be under heavy load. Try running again later.

### FAQ

| Question | Answer |
|---|---|
| Do I need a bioRxiv or medRxiv API key to use this scraper? | No. This Actor reads the public preprint listings directly from the bioRxiv and medRxiv websites. You do not need to register an application, obtain an API key, or handle OAuth tokens. |
| What is the difference between bioRxiv and medRxiv? | bioRxiv hosts preprints in the life sciences, including biology, neuroscience, and bioinformatics. medRxiv hosts preprints in clinical and health sciences. This Actor supports both servers, and you select which one to query in the input settings. |
| Can I scrape preprints by a specific author? | The Actor does not have a dedicated author filter. However, the full author list is included in every output row, so you can filter the dataset after the run using your own tools or a spreadsheet. |
| How do I filter by subject category? | Enter a substring in the Category field. The Actor performs a case-insensitive match against the full category name. For example, entering 'neuro' will match 'Neuroscience' and 'Neurobiology'. |
| What happens if I provide both a DOI and a date range? | The DOI field overrides the date range and category filters. When a DOI is provided, the Actor fetches only that single preprint and ignores the other parameters. |
| How many preprints can I collect in one run? | You can set the maximum up to 1,000,000 preprints per run. The actual number collected depends on how many preprints match your date range and category filters. |
| Does this scraper get the full PDF of the preprint? | No. The Actor collects metadata only: title, authors, abstract, DOI, category, version history, and links. It does not download the full-text PDF. |
| Can I export the results to a specific format? | Yes. The dataset can be exported in CSV, JSON, Excel, or XML formats directly from the Apify platform after the run completes. |
| Is this scraper suitable for systematic reviews? | Yes. The consistent flat schema and version history tracking make it useful for systematic reviews that require reproducible preprint collection and version documentation. |
| What date format should I use? | Use the YYYY-MM-DD format for both the Date from and Date to fields. For example, 2024-01-01 for January 1, 2024. |

### Related actors

Browse the full [ParseForge collection](https://apify.com/parseforge?fpr=vmoqkp) for more scrapers.

🆘 **Need help?** Email parseforge@protonmail.com with your run ID, your input, and what you expected.

⚠️ **Disclaimer.** This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by Cold Spring Harbor Laboratory. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.

# Actor input Schema

## `server` (type: `string`):

Preprint server to query.

## `dateFrom` (type: `string`):

Start date in YYYY-MM-DD format.

## `dateTo` (type: `string`):

End date in YYYY-MM-DD format.

## `doi` (type: `string`):

Specific DOI to fetch. Overrides date range.

## `category` (type: `string`):

Filter results to a bioRxiv subject category, case-insensitive substring match.

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

Maximum number of preprints to collect per run.

## Actor input object example

```json
{
  "server": "biorxiv",
  "maxItems": 10
}
```

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

// Prepare Actor input
const input = {
    "maxItems": 10
};

// Run the Actor and wait for it to finish
const run = await client.actor("parseforge/biorxiv-preprints-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 = { "maxItems": 10 }

# Run the Actor and wait for it to finish
run = client.actor("parseforge/biorxiv-preprints-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 '{
  "maxItems": 10
}' |
apify call parseforge/biorxiv-preprints-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,parseforge/biorxiv-preprints-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/81OCLbe6yXTAlHcTV/builds/NLku4rXIJy98DC4No/openapi.json