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.

Organizing related projects with Links, Forks, and Templates.

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

1. How keeping track of the different file versions is important for efficient reproducible research practices
2. How version control works on the OSF
3. How researchers can view and download previous versions of files

This lecture focuses on where and how Jupyter notebooks can be used most effectively for education

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.

The practical usage of The Virtual brain in its graphical user interface and via python scripts is introduced. In the graphical user interface, you are guided through its data repository, simulator, phase plane exploration tool, connectivity editor, stimulus generator and the provided analyses. The implemented iPython notebooks of TVB are presented, and since they are public, can be used for further exploration of The Virtual brain.

Brief introduction to Research Resource Identifiers (RRIDs), persistent and unique identifiers for referencing a research resource. 

The Identifiers.org system is a central infrastructure for findable, accessible, interoperable and re-usable (FAIR) data. It provides a range of services to generate, resolve and validate persistent Compact Identifiers to promote the citability of individual data providers and integration with e-infrastructures.

Web annotation provides a powerful new capability to biomedicine. With Hypothes.is, any content on the web can be turned into an interactive forum for on-line discussions or annotated with additional information. Because Hypothes.is is engineered for the web, annotations can be replied to, shared and searched across contexts. Hypothesis supports public, private and group annotation.

Tutorial on collaborating with Git and GitHub. This tutorial was 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.

Next generation science with Jupyter. This lecture was 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.

Introduction to reproducible research. The lecture provides an overview of the core skills and practical solutions required to practice reproducible research. 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.

Introduction to the central concepts of machine learning, and how they can be applied in Python using the Scikit-learn Package. 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.