Subscribe to ChinaRxiv
Get automatic updates when new papers matching your interests are translated.
Quick Start
- Search for papers on ChinaRxiv
- Click the Subscribe button next to the search box
- Copy the feed URL (Atom recommended)
- Paste into your feed reader (Zotero, Feedly, Slack, etc.)
Feed URLs
Feeds accept the same search parameters as the website. Here are some examples:
| Type | Example URL |
|---|---|
| All papers | /feed/atom.xml |
| Keyword search | /feed/atom.xml?q=machine+learning |
| Category filter | /feed/atom.xml?category=ai_cs |
| Combined filters | /feed/atom.xml?q=neural&from=2025-01-01 |
Available Parameters
| Parameter | Description | Example |
|---|---|---|
q |
Search keywords (title, abstract, authors) | q=deep+learning |
category |
Category ID | category=ai_cs |
subjects |
Subject filter (can repeat for multiple) | subjects=Physics&subjects=Chemistry |
from |
Papers published after this date | from=2025-01-01 |
to |
Papers published before this date | to=2025-12-31 |
figures |
Only papers with translated figures | figures=1 |
has_pdf |
Only papers with English PDF | has_pdf=1 |
Feed Formats
Atom 1.0 (Recommended)
Modern format with better metadata support. Works with most feed readers.
/feed/atom.xml
RSS 2.0
Classic format with widest compatibility.
/feed/rss.xml
Popular Feed Readers
- Zotero: File → New Feed from URL → Paste feed URL
- Feedly: Add Content → Paste feed URL
- Inoreader: Click + → Feeds → Paste URL
- Slack:
/feed subscribe [feed_url] - NetNewsWire: File → New Web Feed → Paste URL
Feed Behavior
- Feeds return up to 50 papers per request, sorted by publication date (newest first)
- Feeds are cached for 15 minutes to reduce server load
- New papers appear in feeds as soon as translation is complete
- Each paper entry includes title, authors, abstract summary, and link to full text
API Access
For programmatic access, check out our REST API which provides JSON responses with full paper metadata.