Can I search for specific categories like neuroscience or genomics?

Yes, the skill supports filtering by over 25 specific bioRxiv subject categories including neuroscience, genomics, immunology, and bioengineering.

Does this skill allow for bulk downloading of research papers?

Yes, you can use the search results to programmatically download full-text PDFs in batches using the provided BioRxivSearcher class.

What format is the paper metadata returned in?

All search results are returned in structured JSON format, including titles, authors, abstracts, DOIs, publication dates, and institutional affiliations.

Can I integrate this into a larger data science pipeline?

Absolutely. The skill is designed with a Python API that allows easy integration into pandas DataFrames, machine learning workflows, or custom analysis scripts.

BioRxiv Research Database

Name: BioRxiv Research Database
Author: x-cmd

byx-cmd

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Data Science & ML

Searches and retrieves metadata and full-text PDFs from the bioRxiv preprint server for life sciences research discovery.

About

The BioRxiv Database skill provides a robust Python-based interface for interacting with the bioRxiv preprint repository, enabling researchers and developers to perform complex queries across vast collections of life sciences papers. It facilitates advanced filtering by keywords, authors, subject categories, and date ranges, returning structured JSON metadata that includes abstracts, DOIs, and citation info. Ideal for automated literature reviews, research trend analysis, or programmatic PDF retrieval, this tool streamlines the discovery and collection of cutting-edge biological research before it reaches formal publication.

Key Features

Structured JSON output for metadata and citation management
Date-range filtering and temporal trend analysis
Native Python API for programmatic research workflows
8 GitHub stars
Advanced search by keyword, author, and subject category
Automated PDF downloading for full-text analysis

Use Cases

Tracking the latest preprints from specific research labs or authors
Conducting systematic literature reviews in specific life science domains
Collecting datasets of abstracts and metadata for machine learning models

About

Key Features

Structured JSON output for metadata and citation management
Date-range filtering and temporal trend analysis
Native Python API for programmatic research workflows
8 GitHub stars
Advanced search by keyword, author, and subject category
Automated PDF downloading for full-text analysis

Use Cases

Tracking the latest preprints from specific research labs or authors
Conducting systematic literature reviews in specific life science domains
Collecting datasets of abstracts and metadata for machine learning models