This lesson gives a primer to project management in a scientific context, with a particular neuroinformatic case study. 

In this lesson, you will hear about the current challenges regarding data management, as well as policies and resources aimed to address them. 

This lesson provides an overview of how to manage relationships in a research context, while highlighting the need for effective communication at various levels.

Overview of Day 2 of this course.

This talk compares various sensors and resolutions for in vivo neural recordings.

This hands-on tutorial explains how to run your own Minion session in the MetaCell cloud using jupityr notebooks.

In this hands-on analysis tutorial, users will mimic a kernel crash and learn the steps to restore inputs in such a case.

This lesson will go through how to extract cells from video that has been cleaned of background noise and motion.

This final hands-on analysis tutorial walks users through the last visualization steps in the cellular data.

This lecture covers infrared LED oblique illumination for studying neuronal circuits in in vitro block-preparations of the spinal cord and brain stem.

This lecture covers the application of diffusion MRI for clinical and preclinical studies.

In this hands-on session, you will learn how to explore and work with DataLad datasets, containers, and structures using Jupyter notebooks. 

This video shows how to use the brainlife.io interface to edit the participants' info file. This file is the ParticipantInfo.json file of the Brain Imaging Data Structure (BIDS).

This quick video presents some of the various visualizers available on brainlife.io

This video demonstrates each required step for preprocessing T1w anatomical data in brainlife.io.

This lecture will provide an overview of neuroimaging techniques and their clinical applications.

BioImage Suite is an integrated image analysis software suite developed at Yale University. BioImage Suite has been extensively used at different labs at Yale since about 2001.

Fibr is an app for quality control of diffusion MRI images from the Healthy Brain Network, a landmark mental health study that is collecting MRI images and other assessment data from 10,000 New York City area children. The purpose of the app is to train a computer algorithm to analyze the Healthy Brain Network dataset. By playing fibr, you are helping to teach the computer which images have sufficiently good quality and which images do not. 

This lecture covers the needs and challenges involved in creating a FAIR ecosystem for neuroimaging research.

This lecture covers the NIDM data format within BIDS to make your datasets more searchable, and how to optimize your dataset searches.