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 is a tutorial introducing participants to the basics of RNA-sequencing data and how to analyze its features using Seurat.
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.
Brief introduction to Research Resource Identifiers (RRIDs), persistent and unique identifiers for referencing a research resource.
This lesson describes the Neuroscience Gateway , which facilitates access and use of National Science Foundation High Performance Computing resources by neuroscientists.
This lesson provides an overview of the database of Genotypes and Phenotypes (dbGaP), which was developed to archive and distribute the data and results from studies that have investigated the interaction of genotype and phenotype in humans.
This demonstration walks through how to import your data into MATLAB.
This lesson provides instruction regarding the various factors one must consider when preprocessing data, preparing it for statistical exploration and analyses.
This tutorial outlines, step by step, how to perform analysis by group and how to do change-point detection.
This tutorial walks through several common methods for visualizing your data in different ways depending on your data type.
This tutorial illustrates several ways to approach predictive modeling and machine learning with MATLAB.
This brief tutorial goes over how you can easily work with big data as you would with any size of data.
In this tutorial, you will learn how to deploy your models outside of your local MATLAB environment, enabling wider sharing and collaboration.
This tutorial covers the fundamentals of collaborating with Git and GitHub.
This lesson provides an overview of Jupyter notebooks, Jupyter lab, and Binder, as well as their applications within the field of neuroimaging, particularly when it comes to the writing phase of your research.
The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.
This tutorial teaches users how to use Pandas objects to help store and manipulate various datasets in Python.
EyeWire is a game to map the brain. Players are challenged to map branches of a neuron from one side of a cube to the other in a 3D puzzle. Players scroll through the cube and reconstruct neurons with the help of an artificial intelligence algorithm developed at Seung Lab in Princeton University. EyeWire gameplay advances neuroscience by helping researchers discover how neurons connect to process visual information.
This lesson provides an overview of self-supervision as it relates to neural data tasks and the Mine Your Own vieW (MYOW) approach.