Toward reproducible research

Knowledge sharing (source: https://futurism.com/what-is-open-science-and-why-some-researchers-want-it)

Reproducibility seems to become one of hot issues in the field of neuroscience. Lots of Twitter accounts relevant to open science can be easily found. I support their endeavor to make scientific results more reproducible. Simine Vazire mentioned, "Without data transparency, assessing a study is like buying a used car without being able to look under hood."

Reproducibity crisis in original studies #keynotelecture #ESPAmsterdam2017 #ECP2017 @openscience pic.twitter.com/eRg3AnMZeY

— Shatavisha Dasgupta (@shatavishadg) September 5, 2017

I totally agree. Indeed, I have built my own framework to secure reproducibility in my neuroimaging research. One way was to build a systematic structure of data storage so that any colleague can easily have access to imaging data. The other way was to make a software toolbox which provides robust functionalities for data processing and analyses. Not only did the toolbox enable to reproduce reliable experimental results using a common pipeline, but also it allowed to automatize the whole processes of data analysis.
In the beginning of my recent research project, the progress looked like sluggish. However, it was because I was trying to build a systematic framework to make my analyses more efficient. I have greatly benefited from the well-made framework; for example, I could prominently reduce time for processing a big data using the framework. I have no doubt that reproducibility is necessary for efficient and reliable scientific research.