This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

This lesson is part 1 of 2 of a tutorial on statistical models for neural data.

This lesson is part 2 of 2 of a tutorial on statistical models for neural data.

This lecture covers an Introduction to neuron anatomy and signaling, as well as different types of models, including the Hodgkin-Huxley model.

This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.

This lesson provides a short reel on who we are, what we're doing and why we're doing it.

This video will teach you the basics of navigating the Open Science Framework 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) in a classroom setting.

This lesson provides instruction on how to organize related projects with OSF features such as links, forks, and templates.

This webinar will introduce the integration of JASP Statistical Software with the Open Science Framework (OSF).

This lesson describes the value of version control, as well as how to do so with your own files and data on OSF. 

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

In this tutorial, you will learn the basic features of uploading and versioning your data within OpenNeuro.org.

This tutorial shows how to share your data in OpenNeuro.org.

Following the previous two tutorials on uploading and sharing data with OpenNeuro.org, this tutorial briefly covers how to run various analyses on your datasets.

This lecture provides an introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.

This lesson gives an introductory presentation on how data science can help with scientific reproducibility.

This lesson discusses FAIR principles and methods currently in development for assessing FAIRness.