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 provides an overview of the FAIR-aligned efforts of MATLAB and MathWorks, from the technological building blocks to the open science coordination involved in facilitating greater transparency and efficiency in neuroscience and neuroinformatics.
This tutorial is part 1 of 2. It aims to provide viewers with an understanding of the fundamentals of R tool. Note: parts 1 and 2 of this tutorial are part of the same YouTube video; part 1 ends at 17:42.
This tutorial is part 2 of 2. It aims to provide viewers with an understanding of the fundamentals of R tool. Note: parts 1 and 2 of this tutorial are the same YouTube video. The portion related to this tutorial begins at 17:43.
In this lesson, users can follow along as a spaghetti script written in MATLAB is turned into understandable and reusable code living happily in a powerful GitHub repository.
This lesson covers Python applications to data analysis, demonstrating why it has become ubiquitous in data science and neuroscience. The lesson was given in the context of the BrainHack School 2020.
This talk describes the NIH-funded SPARC Data Structure, and how this project navigates ontology development while keeping in mind the FAIR science principles.
This lecture covers structured data, databases, federating neuroscience-relevant databases, and ontologies.