preprint-tracker

You can track bioRxiv and medRxiv preprints by querying their APIs with specific topic parameters and date ranges. This fetches recent preprints, aggregates metadata, abstracts, and author lists, and compiles them into a digest-ready markdown report for fast literature scanning.

Yes, you can filter bioRxiv and medRxiv preprint digests by accepting parameters for server scope and category filters. This restricts the fetched metadata, abstracts, and author lists to your specified biological or clinical research domains.

You can analyze preprint trends by extracting keywords and identifying prolific authors from the fetched bioRxiv and medRxiv metadata. The process generates visualizations and a readable digest highlighting topics with rising activity for quick literature reviews.

The best way to export bioRxiv and medRxiv preprint metadata is to generate machine-readable TSV or CSV outputs. These structured files accompany the markdown digests and allow seamless downstream pandas-based analysis of authors, abstracts, and trends.

Yes, visualizing bioRxiv preprint activity trends requires Python libraries like matplotlib and pandas. These dependencies support generating the visualizations and structured data outputs needed to analyze rising topics and prolific authors.

An automated preprint tracker synthesizes the ever-moving bioscience and clinical preprint landscape efficiently, overcoming the time-consuming nature of manual browsing. It packages metadata, abstracts, and authors into a digest, identifying rising topics and prolific authors automatically.