This video will document the process of launching a Jupyter Notebook for group-level analyses directly from brainlife.

This video will demonstrate how to create and launch a pipeline using FreeSurfer on brainlife.io.

As the previous lesson of this course described how researchers acquire neural data, this lesson will discuss how to go about interpreting and analysing the data. 

This lecture covers the description and characterization of an input-output relationship in a information-theoretic context. 

In this tutorial, you will learn the basic features of uploading and versioning your data within OpenNeuro.org.

This tutorial shows how to share your data in OpenNeuro.org.

Following the previous two tutorials on uploading and sharing data with OpenNeuro.org, this tutorial briefly covers how to run various analyses on your datasets.

This lesson provides instruction on how to infer results from incomplete data.

This lesson provides instruction on finding parameter values, computing confidence levels, and other various statistical methods employed in data investigation.

In this lesson, statistical methods and tools are described for estimating parameters in your dataset.

This lesson covers how to measure the correspondece between data and model.

In this lesson, you will learn the concepts behind choosing useful variables, as well as various analyses and tools to do so.

This lesson goes over some of the common problems in statistical modeling.

This lesson continues describing some of the common problems in statistical modelling, particularly when it comes to model validation. 

You don't have to be a wizard to do statistics!

This lesson provides an overview of possible follow-up courses and subjects from the same publisher.

This talk highlights a set of platform technologies, software, and data collections that close and shorten the feedback cycle in research. 

This lesson gives a quick walkthrough the Tidyverse, an "opinionated" collection of R packages designed for data science, including the use of readr, dplyr, tidyr, and ggplot2.

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.

In this session the Medical Informatics Platform (MIP) federated analytics is presented. The current and future analytical tools implemented in the MIP will be detailed along with the constructs, tools, processes, and restrictions that formulate the solution provided. MIP is a platform providing advanced federated analytics for diagnosis and research in clinical neuroscience research. It is targeting clinicians, clinical scientists and clinical data scientists. It is designed to help adopt advanced analytics, explore harmonized medical data of neuroimaging, neurophysiological and medical records as well as research cohort datasets, without transferring original clinical data. It can be perceived as a virtual database that seamlessly presents aggregated data from distributed sources, provides access and analyze imaging and clinical data, securely stored in hospitals, research archives and public databases. It leverages and re-uses decentralized patient data and research cohort datasets, without transferring original data. Integrated statistical analysis tools and machine learning algorithms are exposed over harmonized, federated medical data.