var _gaq = _gaq || []; _gaq.push(['_setAccount', 'UA-21462253-7']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + ''; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })();

College of Science and Mathematics

Enhancing lives through learning, discovery and innovation

Website Update

Cal Poly Helps Make Gravitational Wave Calculations Available to All

The gravitational waves observed recently sent a ripple through space-time and a tsunami through the scientific community. Now, thanks to an open-source software package whose development was led by Cal Poly and UC Berkeley, anyone with the right programming know-how can double-check the scientists’ data analysis.

Scientists at the Laser Interferometer Gravitational-Wave Observatory (LIGO), who discovered the gravitational waves, have released their data and analysis as a Jupyter Notebook. The Jupyter Notebook allows users to integrate code, data and text in one document and share that document interactively with others. That means anyone can verify the original computations.

Project Jupyter logo
Project Jupyter logo

“This is a huge step forward for the transparency and reproducibility of science,” said Brian Granger, a Cal Poly physics professor who co-leads Project Jupyter.

Jupyter allowed the LIGO scientists to include a textual narrative alongside their programming code. Without that, if other scientists wanted to verify the LIGO team’s results, they would have had to figure out how the analysis was done, a difficult task.

“Jupyter allows scientists, hobbyists and anyone who has access to a computer to rerun their data analysis and see how they produced the results that they presented,” said Jonathan Frederic, a software engineer at Cal Poly with Project Jupyter.

Though reproducibility is one of the core aspects of scientific experimentation, it’s often not feasible given the size and complexity of data sets. With Jupyter, that all may change in the future.

Related Content