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 goes over Neurobagel, an open-source platform developed for improved dataset sharing and searching. 

In this lesson, you will learn about the BRAIN Initiative Cell Atlas Network (BICAN) and how this project adopts a federated approach to data sharing. 

In this second part of the lecture Data Science and Reproducibility, you will learn how to apply the awareness of the intersection between neuroscience and data science (discussed in part one) to an understanding of the current reproducibility crisis in biomedical science and neuroscience. 

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

This lesson provides a quick tour of some data repositories and how to download and manipulate data from them.

In this lesson, attendees will learn about the data structure standards, specifically the Brain Imaging Data Structure (BIDS), an INCF-endorsed standard for organizing, annotating, and describing data collected during neuroimaging experiments. 

This presentation by the OHBM OpenScienceSIG covers common scenarios where Git can be extremely valuable. The essentials covered include cloning a repository and keeping it up to date, how to create and use your own repository, and how to contribute to other projects via forking and pull requests.

DataLad is a versatile data management and data publication multi-tool. In this session, you can learn the basic concepts and commands for version control and reproducible data analysis. You’ll get to see, create, and install DataLad datasets of many shapes and sizes, master local version workflows and provenance-captured analysis-execution, and you will get ideas for your next data analysis project.

This lecture provides an introduction to the study of eye-tracking in humans. 

From the retina to the superior colliculus, the lateral geniculate nucleus into primary visual cortex and beyond, this lecture gives a tour of the mammalian visual system highlighting the Nobel-prize winning discoveries of Hubel & Wiesel.

From Universal Turing Machines to McCulloch-Pitts and Hopfield associative memory networks, this lecture explains what is meant by computation.

In an overview of the structure of the mammalian neocortex, this lecture explains how the mammalian cortex is organized in a hierarchy, describing the columnar principle and canonical microcircuits.

The retina has 60 different types of neurons. What are their functions? This lecture explores the definition of cell types and their functions in the mammalian retina.

Optical imaging offers a look inside the working brain. This lecture takes a look at orientation and ocular dominance columns in the visual cortex, and shows how they can be viewed with calcium imaging.

Functional imaging has led to the discovery of a plethora of visual cortical regions. This lecture introduces functional imaging techniques and their teachings about the visual cortex.

This lecture explains these ideas and explores the task of characterizing neuronal response properties using information theory.

What is color? This lecture explores how color is "made" in the brain and variations of color perception including trichromacy, color blindness in men, tetrachromatic vision in women, and genetic engineering of color perception.

How does the brain learn? This lecture discusses the roles of development and adult plasticity in shaping functional connectivity.