---

name: "biorxiv-database" description: "Query bioRxiv/medRxiv preprints via REST API. Search by DOI, category, or date range; retrieve metadata (title, abstract, authors, category, DOI, version history) and PDFs. No auth. For peer-reviewed biomedical use pubmed-database; broader scholarly search use openalex-database." license: "CC0-1.0"

bioRxiv / medRxiv Preprint Database

Overview

bioRxiv (biology) and medRxiv (health sciences) are free preprint servers hosting 200,000+ and 50,000+ manuscripts, respectively, before or alongside peer review. The unified REST API provides programmatic access to preprint metadata (title, abstract, authors, category, DOI, version history) without authentication. Preprints are available as PDF and can be retrieved by DOI, date range, or category.

When to Use

• Finding the most current research in fast-moving fields before peer review (e.g., infectious disease during outbreaks)
• Monitoring weekly preprint submissions in a specific discipline category (e.g., bioinformatics, genomics, neuroscience)
• Retrieving metadata and abstracts for a set of bioRxiv DOIs for literature screening
• Building a corpus of preprints to track the preprint-to-publication pipeline
• Checking whether a specific preprint has been updated or published in a peer-reviewed journal
• For peer-reviewed biomedical literature use pubmed-database; for all disciplines use openalex-database

Prerequisites

• Python packages: requests, pandas
• Data requirements: bioRxiv/medRxiv DOIs, date ranges, or category names
• Environment: internet connection; no API key or authentication required
• Rate limits: no stated hard limit; use reasonable delays for bulk queries

pip install requests pandas

Quick Start

import requests

BASE = "https://api.biorxiv.org"

# Retrieve recent bioinformatics preprints
r = requests.get(f"{BASE}/details/biorxiv/2024-01-01/2024-01-07/0",
                 params={"category": "bioinformatics"})
r.raise_for_status()
data = r.json()
print(f"Total preprints: {int(data['messages'][0]['total'])}")  # API returns total as a string
for article in data["collection"][:3]:
    print(f"\n{article['title'][:80]}")
    print(f"  Authors : {article['authors'][:60]}")
    print(f"  DOI     : {article['doi']}")
    print(f"  Category: {article['category']}")

Core API

Query 1: Date-Range Preprint Listing

Retrieve all preprints posted within a date range, optionally filtered by category.

import requests, pandas as pd

BASE = "https://api.biorxiv.org"

def get_preprints(server, date_from, date_to, cursor=0, category=None):
    """
    server: 'biorxiv' or 'medrxiv'
    date_from, date_to: 'YYYY-MM-DD' strings
    cursor: page offset (increments of 100)
    """
    url = f"{BASE}/details/{server}/{date_from}/{date_to}/{cursor}"
    r = requests.get(url)
    r.raise_for_status()
    return r.json()

data = get_preprints("biorxiv", "2024-01-01", "2024-01-03")
total = int(data["messages"][0]["total"])  # API returns total as a string — cast for arithmetic
print(f"bioRxiv preprints Jan 1-3, 2024: {total}")

rows = []
for article in data["collection"][:10]:
    rows.append({
        "doi": article["doi"],
        "title": article["title"],
        "authors": article["authors"][:80],
        "category": article["category"],
        "date": article["date"],
        "version": article["version"],
    })
df = pd.DataFrame(rows)
print(df[["title", "category", "date"]].head())

# Paginate through all results for a date range
def get_all_preprints(server, date_from, date_to, max_results=500):
    all_articles = []
    cursor = 0
    while len(all_articles) < max_results:
        data = get_preprints(server, date_from, date_to, cursor)
        collection = data["collection"]
        if not collection:
            break
        all_articles.extend(collection)
        total = int(data["messages"][0]["total"])  # cast: API returns total as string
        cursor += 100
        if cursor >= total:
            break
    return all_articles[:max_results]

articles = get_all_preprints("biorxiv", "2024-01-01", "2024-01-07")
print(f"Retrieved {len(articles)} preprints from first week of 2024")

Query 2: Preprint Detail by DOI

Retrieve full metadata and version history for a specific preprint by DOI.

import requests

BASE = "https://api.biorxiv.org"

# Retrieve specific preprint by DOI
doi = "10.1101/2024.01.01.000001"  # Replace with real DOI

def get_by_doi(server, doi):
    r = requests.get(f"{BASE}/details/{server}/{doi}")
    r.raise_for_status()
    return r.json()

# Generic example using bioRxiv DOI pattern
r = requests.get(f"{BASE}/details/biorxiv/10.1101/2024.05.28.596311")
if r.ok:
    data = r.json()
    articles = data.get("collection", [])
    if articles:
        art = articles[-1]  # Latest version
        print(f"Title   : {art['title']}")
        print(f"Authors : {art['authors'][:100]}")
        print(f"Category: {art['category']}")
        print(f"Date    : {art['date']}")
        print(f"Version : {art['version']}")
        print(f"DOI     : {art['doi']}")
        print(f"Abstract (first 300): {art['abstract'][:300]}")

Query 3: Published Preprint Lookup

Check if a preprint has been published in a peer-reviewed journal.

import requests

BASE = "https://api.biorxiv.org"

def check_published(server, doi):
    """Check if a preprint DOI has a corresponding published article."""
    r = requests.get(f"{BASE}/publisher/{server}/{doi}")
    r.raise_for_status()
    data = r.json()
    return data.get("collection", [])

# Check one known preprint
doi = "10.1101/2024.05.28.596311"
published = check_published("biorxiv", doi)
if published:
    pub = published[0]
    print(f"Published in: {pub.get('published_journal')}")
    print(f"Published DOI: {pub.get('published_doi')}")
else:
    print(f"Preprint {doi} has not been published yet (or not tracked)")

Query 4: Category-Based Monitoring

Monitor preprints by specific research category.

import requests, pandas as pd
from datetime import date, timedelta

BASE = "https://api.biorxiv.org"

# bioRxiv categories include: bioinformatics, genomics, neuroscience,
# immunology, cell-biology, biochemistry, microbiology, etc.

def weekly_category_digest(category, days_back=7):
    """Get preprints from last N days for a specific category."""
    today = date.today()
    date_from = (today - timedelta(days=days_back)).strftime("%Y-%m-%d")
    date_to = today.strftime("%Y-%m-%d")

    all_articles = []
    cursor = 0
    while True:
        r = requests.get(f"{BASE}/details/biorxiv/{date_from}/{date_to}/{cursor}")
        data = r.json()
        batch = [a for a in data["collection"] if category.lower() in a["category"].lower()]
        all_articles.extend(batch)
        if len(data["collection"]) < 100:
            break
        cursor += 100

    return pd.DataFrame(all_articles)[["doi", "title", "authors", "date"]] if all_articles else pd.DataFrame()

df = weekly_category_digest("genomics", days_back=3)
print(f"Recent genomics preprints: {len(df)}")
print(df[["title", "date"]].head())

Query 5: medRxiv Clinical/Health Research

Query medRxiv for health and clinical science preprints.

import requests, pandas as pd

BASE = "https://api.biorxiv.org"

# medRxiv categories: infectious diseases, epidemiology, oncology,
# cardiology, neurology, psychiatry, public and global health, etc.

r = requests.get(f"{BASE}/details/medrxiv/2024-01-01/2024-01-07/0")
r.raise_for_status()
data = r.json()
total = int(data["messages"][0]["total"])  # cast: API returns total as string
print(f"medRxiv preprints Jan 1-7, 2024: {total}")

# Group by category
from collections import Counter
category_counts = Counter(a["category"] for a in data["collection"])
print("\nTop categories:")
for cat, count in category_counts.most_common(5):
    print(f"  {cat}: {count}")

Query 6: Bulk DOI Resolution and Abstract Extraction

Retrieve abstracts for a list of bioRxiv DOIs.

import requests, time, pandas as pd

BASE = "https://api.biorxiv.org"

dois = [
    "10.1101/2024.05.28.596311",
    "10.1101/2023.11.28.569048",
    "10.1101/2023.03.07.531523",
]

rows = []
for doi in dois:
    r = requests.get(f"{BASE}/details/biorxiv/{doi}")
    if r.ok:
        collection = r.json().get("collection", [])
        if collection:
            art = collection[-1]  # Latest version
            rows.append({
                "doi": doi,
                "title": art.get("title"),
                "category": art.get("category"),
                "date": art.get("date"),
                "abstract": art.get("abstract", "")[:300],
            })
    time.sleep(0.2)

df = pd.DataFrame(rows)
if not df.empty:
    df.to_csv("preprint_abstracts.csv", index=False)
    print(df[["doi", "title", "category"]].to_string(index=False))
else:
    print("No valid preprints found for provided DOIs")

Key Concepts

API Endpoint Structure

The bioRxiv API follows the pattern: https://api.biorxiv.org/details/{server}/{interval}/{cursor}

• server: biorxiv or medrxiv
• interval: either a DOI (for single record) or date_from/date_to (for date range)
• cursor: pagination offset (0, 100, 200…)

Version Tracking

Preprints can be updated; each update creates a new version (v1, v2, v3…). The API returns all versions chronologically; the last item in collection is always the most recent.

Common Workflows

Workflow 1: Weekly Preprint Digest Pipeline

Goal: Automatically collect last week's preprints in target categories and export for review.

import requests, time, pandas as pd
from datetime import date, timedelta

BASE = "https://api.biorxiv.org"

TARGET_CATEGORIES = ["bioinformatics", "genomics", "systems biology"]
DAYS_BACK = 7

today = date.today()
date_from = (today - timedelta(days=DAYS_BACK)).strftime("%Y-%m-%d")
date_to = today.strftime("%Y-%m-%d")

print(f"Fetching bioRxiv preprints from {date_from} to {date_to}")

all_articles = []
cursor = 0
while True:
    r = requests.get(f"{BASE}/details/biorxiv/{date_from}/{date_to}/{cursor}")
    r.raise_for_status()
    data = r.json()
    batch = data["collection"]
    if not batch:
        break
    all_articles.extend(batch)
    total = int(data["messages"][0]["total"])  # cast: API returns total as string
    cursor += 100
    if cursor >= total:
        break
    time.sleep(0.1)

# Filter by target categories
filtered = [a for a in all_articles
            if any(cat in a.get("category", "").lower() for cat in TARGET_CATEGORIES)]

df = pd.DataFrame(filtered)[["doi", "title", "authors", "category", "date"]]
df = df.drop_duplicates(subset="doi")  # Remove duplicate versions

output_file = f"biorxiv_digest_{date_to}.csv"
df.to_csv(output_file, index=False)
print(f"\nSaved {len(df)} preprints across {len(TARGET_CATEGORIES)} categories → {output_file}")
print(df[["title", "category", "date"]].head(5).to_string(index=False))

Workflow 2: Preprint-to-Publication Tracker

Goal: For a list of preprint DOIs, check which have been published and retrieve publication details.

import requests, time, pandas as pd

BASE = "https://api.biorxiv.org"

preprint_dois = [
    "10.1101/2024.05.28.596311",
    "10.1101/2023.11.28.569048",
]

results = []
for doi in preprint_dois:
    # Get preprint metadata
    r_meta = requests.get(f"{BASE}/details/biorxiv/{doi}")
    meta = {}
    if r_meta.ok and r_meta.json().get("collection"):
        art = r_meta.json()["collection"][-1]
        meta = {"title": art["title"], "category": art["category"],
                "preprint_date": art["date"]}

    # Check publication status
    r_pub = requests.get(f"{BASE}/publisher/biorxiv/{doi}")
    published = {}
    if r_pub.ok and r_pub.json().get("collection"):
        pub = r_pub.json()["collection"][0]
        published = {"journ