Leads: Lauren Snyder (TIB) and Markus Stocker (TIB)
Overview
As part of international Love Data Week, the Technische Informationsbibliothek (TIB) hosted a free webinar introducing reborn articles, an open-source approach to producing machine-readable scientific knowledge. The webinar featured two early-career marine biologists who demonstrated the approach using their own scientific publications. They also explained how reborn articles enhanced the impact of their research by making it easier for other researchers to understand and reuse their work. A recording of the webinar is available here.
Why Should We Make Scientific Knowledge Machine-readable?
From groundbreaking medical advances to quantum computing, scientific breakthroughs impact our everyday lives. Despite the tremendous advances made by science, we rely on antiquated methods to formally communicate this knowledge. Since the first scientific article published in 1665, we have managed the switch from physically printed articles to PDFs, but nothing more.
While PDFs are easy to share electronically, they remain unstructured text-based articles that are difficult for machines to read without human assistance. This means researchers are largely left to manually sift through tens, hundreds, or even thousands of articles to find and extract the information they need. If we instead produced scientific knowledge in a format machines could read—rather than burying it in PDFs—we could more effectively leverage digital tools to help us with these organizational activities. This would give researchers more time to focus on the actual science, rather than being encumbered with information quality control.
What is the Role of Reborn Articles?
Reborn articles is a new open-source approach that integrates with common data analysis tools like R and Python, and allows researchers to produce their results in a format that can be easily read by both humans and machines. This means other researchers can reproduce the analyses themselves and even download reborn article data as machine-readable Excel or CSV files. While this may seem trivial, the main alternative for reusing published scientific results largely relies on manually extracting them from PDF articles, which is time-consuming and error-prone.
To learn more about the approach and how to apply it to your own research:
Watch the Webinar Recording here.
Explore the Emerging Digital Library for Reborn Articles here.
