<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Lynne Williams on The Carpentries</title><link>https://deploy-preview-705--carpentries-website.netlify.app/blog/author/lynne-williams/</link><description>Recent content in Lynne Williams on The Carpentries</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 20 Nov 2024 10:51:28 -0500</lastBuildDate><atom:link href="https://deploy-preview-705--carpentries-website.netlify.app/blog/author/lynne-williams/index.xml" rel="self" type="application/rss+xml"/><item><title>Things I Wish Someone Had Told Me About Scientific Computing</title><link>https://deploy-preview-705--carpentries-website.netlify.app/blog/2013/11/what-ive-learned/</link><pubDate>Tue, 26 Nov 2013 00:00:00 +0000</pubDate><guid>https://deploy-preview-705--carpentries-website.netlify.app/blog/2013/11/what-ive-learned/</guid><description>&lt;p>&lt;b>This post originally appeared on the &lt;a href="https://software-carpentry.org/">Software Carpentry website.&lt;/a>&lt;/b>&lt;/p>
&lt;p>When I set out on my path into neuroscience, the idea of scientific computing was not even something that came up in passing. However, with little warning, I started spending my days at the computer writing code to analyse brain data.&lt;/p>
&lt;p>Cleaning neuroimaging data is a multi-step process and I wanted to bring out the most from the data. To do that, I thought, would require using the best bits from a slew of neuroimaging preprocessing tools. Some were better at removing extraneous noise, while others were better at image morphing. But entering the commands one by one was tedious and error-prone and the formats for the different functions were not always compatible. There had to be a better way. So, writing some kind of code became my only option.&lt;/p></description></item></channel></rss>