By Jupyter--Is This the Future of Open Science?

Glyn Moody — Mon, 04 Mar 2019 12:30:00 +0000

Taking the scientific paper to the next level.

In a recent article, I explained why open source is a vital part of open science. As I pointed out, alongside a massive failure on the part of funding bodies to make open source a key aspect of their strategies, there's also a similar lack of open-source engagement with the needs and challenges of open science. There's not much that the Free Software world can do to change the priorities of funders. But, a lot can be done on the other side of things by writing good open-source code that supports and enhances open science.

People working in science potentially can benefit from every piece of free software code—the operating systems and apps, and the tools and libraries—so the better those become, the more useful they are for scientists. But there's one open-source project in particular that already has had a significant impact on how scientists work—Project Jupyter:

Project Jupyter is a set of open-source software projects that form the building blocks for interactive and exploratory computing that is reproducible and multi-language. The main application offered by Jupyter is the Jupyter Notebook, a web-based interactive computing platform that allows users to author documents that combine live code, equations, narrative text, interactive dashboard and other rich media.

Project Jupyter was spun-off from IPython in 2014 by Fernando Pérez. Although it began as an environment for programming Python, its ambitions have grown considerably. Today, dozens of Jupyter kernels exist that allow other languages to be used. Indeed, the project itself speaks of supporting "interactive data science and scientific computing across all programming languages". As well as this broad-based support for programming languages, Jupyter is noteworthy for its power. It enables users to create and share documents that contain live code, equations, visualizations and narrative text. Uses include data cleaning and transformation, numerical simulation, statistical modeling, data visualization and machine learning.

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Open Science, Open Source and R

Andy Wills — Tue, 19 Feb 2019 12:30:00 +0000

by Andy Wills

Free software will save psychology from the Replication Crisis.

"Study reveals that a lot of psychology research really is just 'psycho-babble'".—The Independent.

Psychology changed forever on the August 27, 2015. For the previous four years, the 270 psychologists of the Open Science Collaboration had been quietly re-running 100 published psychology experiments. Now, finally, they were ready to share their findings. The results were shocking. Less than half of the re-run experiments had worked.

When someone tries to re-run an experiment, and it doesn't work, we call this a failure to replicate. Scientists had known about failures to replicate for a while, but it was only quite recently that the extent of the problem became apparent. Now, an almost existential crisis loomed. That crisis even gained a name: the Replication Crisis. Soon, people started asking the same questions about other areas of science. Often, they got similar answers. Only half of results in economics replicated. In pre-clinical cancer studies, it was worse; only 11% replicated.

Open Science

Clearly, something had to be done. One option would have been to conclude that psychology, economics and parts of medicine could not be studied scientifically. Perhaps those parts of the universe were not lawful in any meaningful way? If so, you shouldn't be surprised if two researchers did the same thing and got different results.

Alternatively, perhaps different researchers got different results because they were doing different things. In most cases, it wasn't possible to tell whether you'd run the experiment exactly the same way as the original authors. This was because all you had to go on was the journal article—a short summary of the methods used and results obtained. If you wanted more detail, you could, in theory, request it from the authors. But, we'd already known for a decade that this approach was seriously broken—in about 70% of cases, data requests ended in failure.

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