<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Luke Johnston on The Carpentries</title><link>https://deploy-preview-705--carpentries-website.netlify.app/blog/author/luke-johnston/</link><description>Recent content in Luke Johnston on The Carpentries</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 18 Nov 2024 23:02:38 -0500</lastBuildDate><atom:link href="https://deploy-preview-705--carpentries-website.netlify.app/blog/author/luke-johnston/index.xml" rel="self" type="application/rss+xml"/><item><title>New book: Research Software Engineering with Python</title><link>https://deploy-preview-705--carpentries-website.netlify.app/blog/2021/07/pyrse-book/</link><pubDate>Mon, 19 Jul 2021 00:00:00 +0000</pubDate><guid>https://deploy-preview-705--carpentries-website.netlify.app/blog/2021/07/pyrse-book/</guid><description>&lt;p>&lt;strong>Note: This blog post was updated in April 2024 to link to a new version of this book. The corresponding R version is no longer available.&lt;/strong>&lt;/p>
&lt;p>A big part of the Carpentries success is the two-day workshop format, but that hasn&amp;rsquo;t stopped countless instructors over the years speculating about the great things we could teach if only we had more time. With an entire semester, for instance, you could take researchers through the entire lifecycle of a data analysis project, from the initial setup and code development through to a fully automated data processing pipeline and published software package. A small group of us decided to act on that speculation a few years ago, and set about writing &lt;em>Research Software Engineering with Python&lt;/em>. It effectively represents our attempt at lesson materials for a Software Carpentry workshop that runs for the duration of a university semester course.&lt;/p></description></item></channel></rss>