This lesson continues with the second workshop on reproducible science, focusing on additional open source tools for researchers and data scientists, such as the R programming language for data science, as well as associated tools like RStudio and R Markdown. Additionally, users are introduced to Python and iPython notebooks, Google Colab, and are given hands-on tutorials on how to create a Binder environment, as well as various containers in Docker and Singularity.

This talk gives an overview of the perspectives and FAIR-aligned policies of the academic journal Public Library of Science, better known as PLOS. This journal is a nonprofit, open access publisher empowering researchers to accelerate progress in science. 

This lecture gives a tour of what neuroethics is and how it applies to neuroscience and neurotechnology, while also addressing justice concerns within both fields. 

This lecture covers the biomedical researcher's perspective on FAIR data sharing and the importance of finding better ways to manage large datasets.

This lecture covers the benefits and difficulties involved when re-using open datasets, and how metadata is important to the process.

This video gives a short introduction to the EBRAINS data sharing platform, why it was developed, and how it contributes to open data sharing.

This video introduces the key principles for data organization and explains how you could make your data FAIR for data sharing on EBRAINS.

This video introduces the importance of writing a Data Descriptor to accompany your dataset on EBRAINS. It gives concrete examples on what information to include and highlights how this makes your data more FAIR.

This video demonstrates how to find, access, and download data on EBRAINS.

This talk enumerates the challenges regarding data accessibility and reusability inherent in the current scientific publication system, and discusses novel approaches to these challenges, such as the EBRAINS Live Papers platform. 

This lesson aims to define computational neuroscience in general terms, while providing specific examples of highly successful computational neuroscience projects. 

This lesson covers membrane potential of neurons, and how parameters around this potential have direct consequences on cellular communication at both the individual and population level. 

In this lesson you will learn about neurons' ability to generate signals called action potentials, and biophysics of voltage-gated ion channels.

This lesson discusses voltage-gating kinetics of sodium and potassium channels.

In this lesson, you will learn about the ionic basis of the action potential, including the Hodgkin-Huxley model.

This lesson delves into the specifics of how action potentials propagate through individual neurons.

This lesson discusses long-range inhibitory connections in the brain, with examples from three different systems.

An introduction to data management, manipulation, visualization, and analysis for neuroscience. Students will learn scientific programming in Python, and use this to work with example data from areas such as cognitive-behavioral research, single-cell recording, EEG, and structural and functional MRI. Basic signal processing techniques including filtering are covered. The course includes a Jupyter Notebook and video tutorials.