There are many fascinating and exciting developments in human cognitive and clinical neurosciences. We are constantly drawn to novel and groundbreaking discoveries. There is nothing wrong with this - I would even say that's part of the human nature. This kind of research is not, however, what I want to talk about today. This post is dedicated to people building tools that play a crucial role as a backbone of research - helping novel discoveries happen. They go beyond providing a proof of concept, publishing a paper and pointing to undocumented piece of code that works only in their labs. They provide maintenance, respond to user needs, and constantly update their tools fixing bugs and adding features. Here I will highlight two tools which in my personal (and very biased) opinion play an important role in supporting human neuroscience, and could do with some more appreciation.
|Early years of Captain Neuroimaging|
Anyone dealing with MRI data in Python must know about this library. Nibabel allows you to read and write a variety of different file formats used in neuroimaging (most importantly NIFTI). It hides the obscurity of those standards and provides easy to use objects and methods that let you efficiently access, modify and visualize neuroimaging data. It seems like nothing, but not having to deal with finding the right header format each time you want to read a file can be easily overlooked. I use nibabel all the time and I am very grateful for its existence!
Its a really good example of something that even though is not "novel" or sexy but is absolutely crucial and enables many researchers to get closer to understanding how the human brain works. Despite the fact that nibabel plays an essential role in python neuroimaging ecosystem it does not get enough credit. Nibabel is an open source project lead by +Matthew Brett who is tirelessly keeping it up to date with frequent release cycle.
Papaya is also an open source project, but it is mainly developed by Biomedical Image Analysis Division of the Research Imaging Institute at University of Texas San Antonio lead by Jack Lancaster. Their (sadly) unnamed developers are doing a great job by constantly improving the viewer and providing new features upon user request.
I love those two projects and I have written this post to tip my hat towards people spending their time making this software happen. It enabled me to do research over the years and build tools of my own. Behind my urge to compliment the unappreciated there is a bigger issue. Science is currently so obsessed with novelty and groundbreaking discovery there is no space for appreciating, crediting and most importantly funding those that provide essential support for this science to happen. If we want to have solid reproducible and robust findings we need to improve our tools and focus on maybe less fascinating, but nonetheless important work.