A brief overview of the Python programming language, with an emphasis on tools relevant to data scientists. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.
Jake Vogel gives a hands-on, Jupyter-notebook-based tutorial to apply machine learning in Python to brain-imaging data.
The lesson was presented in the context of the BrainHack School 2020.
This book was written with the goal of introducing researchers and students in a variety of research fields to the intersection of data science and neuroimaging. This book reflects our own experience of doing research at the intersection of data science and neuroimaging and it is based on our experience working with students and collaborators who come from a variety of backgrounds and have a variety of reasons for wanting to use data science approaches in their work. The tools and ideas that we chose to write about are all tools and ideas that we have used in some way in our own research. Many of them are tools that we use on a daily basis in our work. This was important to us for a few reasons: the first is that we want to teach people things that we ourselves find useful. Second, it allowed us to write the book with a focus on solving specific analysis tasks. For example, in many of the chapters you will see that we walk you through ideas while implementing them in code, and with data. We believe that this is a good way to learn about data analysis, because it provides a connecting thread from scientific questions through the data and its representation to implementing specific answers to these questions. Finally, we find these ideas compelling and fruitful. That’s why we were drawn to them in the first place. We hope that our enthusiasm about the ideas and tools described in this book will be infectious enough to convince the readers of their value.
This lecture and tutorial focuses on measuring human functional brain networks. The lecture and tutorial were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.
A short reel on who we are, what we're doing and why we're doing it
In this webinar, educators currently implementing collaborative annotation in their classrooms discuss their experiences with collaborative annotation and using Hythothes.is and Canvas App.
Tutorial that provides an overview of how to use the feature of Hypothes.is.
A brief overview of the Hypothesis functionality from an end user's perspective.
This video will teach you the basics of navigating the OSF, a free research management tool, and creating your first projects.
This webinar walks you through the basics of creating an OSF project, structuring it to fit your research needs, adding collaborators, and tying your favorite online tools into your project structure.
This webinar will introduce how to use the Open Science Framework (OSF; https://osf.io) in a Classroom. The OSF is a free, open source web application built to help researchers manage their workflows. The OSF is part collaboration tool, part version control software, and part data archive. The OSF connects to popular tools researchers already use, like Dropbox, Box, Github and Mendeley, to streamline workflows and increase efficiency.
This webinar will introduce the integration of JASP Statistical Software (https://jasp-stats.org/) with the Open Science Framework (OSF; https://osf.io). The OSF is a free, open source web application built to help researchers manage their workflows
JupyterHub is a simple, highly extensible, multi-user system for managing per-user Jupyter Notebook servers, designed for research groups or classes. This lecture covers deploying JupyterHub on a single server, as well as deploying with Docker using GitHub for authentication.
This tutorial talks about how to upload and version your data in OpenNeuro.org