biorxiv-database

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: {data['messages'][0]['total']}")
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 = data["messages"][0]["total"]
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 = data["messages"][0]["total"]
        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/10.1101/2021.01.01.425318")
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'][:]}")

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 = "10.1101/2021.01.01.425318"
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: ")
(df[[, ]].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 = data["messages"][0]["total"]
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 = [
    "10.1101/2023.01.01.000001",
    "10.1101/2023.02.01.000002",
    "10.1101/2023.03.01.000003",
]

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 = data["messages"][0]["total"]
    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", , ]]
df = df.drop_duplicates(subset=)  

output_file = 
df.to_csv(output_file, index=)
()
(df[[, , ]].head().to_string(index=))

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 = [
    "10.1101/2021.01.01.425318",
    "10.1101/2020.04.01.020370",
]

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 = {"journal": pub.get("published_journal"),
                     "pub_doi": pub.get("published_doi")}

    results.append({"preprint_doi": doi, **meta, **published})
    time.sleep(0.25)

df = pd.DataFrame(results)
print(df.to_string(index=False))
df.to_csv("preprint_publication_status.csv", index=False)

Key Parameters

Parameter	Module	Default	Range / Options	Effect
server	URL path	required	"biorxiv", "medrxiv"	Select preprint server
date_from	URL path	required	"YYYY-MM-DD"	Start of date range
date_to	URL path	required	"YYYY-MM-DD"	End of date range
cursor	URL path	0	0, 100, 200…	Pagination offset (100 per page)
category	Filter	—	e.g., "bioinformatics"	Category name substring match (post-filter)
version	—	all versions	—	API returns all versions; use [-1] for latest

Best Practices

1. Always take the last element for latest version: The collection array is sorted oldest-to-newest version. Use collection[-1] to get the most current version of a preprint.

2. Post-filter by category: The API does not natively filter by category; retrieve all preprints for a date range and filter client-side using if category in article["category"].lower().

3. Respect server resources: Add time.sleep(0.2) between individual DOI lookups; avoid bulk hammering the API.

4. Cross-check with PubMed: The publisher endpoint reveals when a preprint is published; use pubmed-database to retrieve the full peer-reviewed article metadata.

5. Handle missing abstracts: Some preprints have empty abstract fields. Always guard with art.get("abstract", "") or "No abstract available".

Common Recipes

Recipe: Download Preprint PDF

When to use: Retrieve full-text PDF for a bioRxiv preprint.

import requests

doi = "10.1101/2021.01.01.425318"
# bioRxiv PDF URL pattern
pdf_url = f"https://www.biorxiv.org/content/{doi}.full.pdf"

r = requests.get(pdf_url, headers={"User-Agent": "Mozilla/5.0"})
if r.ok:
    with open(f"{doi.replace('/', '_')}.pdf", "wb") as f:
        f.write(r.content)
    print(f"Downloaded PDF ({len(r.content)//1024} KB)")
else:
    print(f"PDF not available: HTTP {r.status_code}")

Recipe: Count Preprints by Category

When to use: Analyze the distribution of preprints across bioRxiv categories in a time window.

import requests, pandas as pd
from collections import Counter

r = requests.get("https://api.biorxiv.org/details/biorxiv/2024-01-01/2024-01-07/0")
data = r.json()
total = data["messages"][0]["total"]

# Fetch all pages
all_articles = data["collection"]
for cursor in range(100, min(total, 1000), 100):
    r2 = requests.get(f"https://api.biorxiv.org/details/biorxiv/2024-01-01/2024-01-07/{cursor}")
    all_articles.extend(r2.json()["collection"])

counts = Counter(a["category"] for a in all_articles)
df = pd.DataFrame(counts.most_common(), columns=["category", "count"])
print(df.head(10).to_string(index=False))

Recipe: Check if Preprint Has Been Published

When to use: Quick single-preprint publication check.

import requests

doi = "10.1101/2021.01.01.425318"
r = requests.get(f"https://api.biorxiv.org/publisher/biorxiv/{doi}")
collection = r.json().get("collection", [])
if collection:
    print(f"Published: {collection[0]['published_journal']} | DOI: {collection[0]['published_doi']}")
else:
    print("Not published or not tracked")

Troubleshooting

Problem	Cause	Solution
collection is empty	DOI not found or date range has no results	Verify DOI format (starts with 10.1101/); check date range
Duplicate preprints in results	Multiple versions returned	Deduplicate by DOI: df.drop_duplicates(subset='doi', keep='last')
Missing abstract field	Some preprints don't have structured abstracts	Guard with art.get("abstract", "") or "N/A"
total count vs retrieved mismatch	New preprints added during pagination	Accept approximate totals; preprints are added continuously
PDF download blocked	Anti-bot protection	Add a User-Agent header; respect robots.txt; use for research only
Slow pagination for large date ranges	Large number of preprints	Use narrower date windows (3-7 days) for busy periods

Related Skills

• pubmed-database — Peer-reviewed biomedical literature for verifying published versions of preprints
• openalex-database — Broader scholarly index including bioRxiv content after indexing lag
• literature-review — Guide for incorporating preprints into systematic reviews
• scientific-brainstorming — Using preprint alerts as input for hypothesis generation

References

• bioRxiv API documentation — Official API endpoint and response format
• bioRxiv about page — Server overview, submission guidelines
• medRxiv about page — Health science preprint server details
• Preprint guidelines review (Fraser et al. 2021) — Best practices for citing and using preprints in research