This course corresponds to the second session of INCF's Neuroinformatics Assembly 2023. This series of talks continues a discussion of FAIR principles from the first session, with a greater emphasis on brain data (humans and animals) atlases for data analysis and integation. 

This workshop delves into the need for, structure of, tools for, and use of hierarchical event descriptor (HED) annotation to prepare neuroimaging time series data for storing, sharing, and advanced analysis. HED are a controlled vocabulary of terms describing events in a machine-actionable form so that algorithms can use the information without manual recoding.

In this short course, you will learn about Jupyter Notebooks, an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.

This couse is the opening module for the University of Toronto's Krembil Centre for Neuroinformatics' virtual learning series Solving Problems in Mental Health Using Multi-Scale Computational Neuroscience. Lessons in this course introduce participants to the study of brain disorders, starting from elemental units like genes and neurons, eventually building up to whole-brain modelling and global activity patterns.

Sessions from the INCF Neuroinformatics Assembly 2022 day 1. 

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The importance of Research Data Management in the conduct of open and reproducible science is better understood and technically supported than ever, and many of the underlying principles apply as much to everyday activities of a single researcher as to large-scale, multi-center open data sharing.

This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and F-I curves. The MATLAB code introduces live scripts and functions.

In this course, you will learn about working with calcium-imaging data, including image processing to remove background "blur", identifying cells based on threshold spatial contiguity, time-series filtering, and principal component analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

This module covers fMRI data, including creating and interpreting flatmaps, exploring variability and average responses, and visual eccenticity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.

This workshop provides an opportunity to explore the advanced tools and techniques for data sharing, analysis, visualization, and simulation.

This course contains sessions from the second day of INCF's Neuroinformatics Assembly 2022.

Ethical conduct of science, good governance of data, and accelerated translation to the clinic are key to high-calibre open neuroscience.  Everyday practitioners of science must be sensitized to a range of ethical considerations in their research, some having especially to do with open data-sharing. The lessons included in this course introduce a number of these topics and end with concrete guidance for participant consent and de-identification of data.

This module covers fMRI data, including creating and interpreting flatmaps, exploring variability and average responses, and visual eccenticity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.

This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and F-I curves. The MATLAB code introduces live scripts and functions.

The dimensionality and size of datasets in many fields of neuroscience research require massively parallel computing power.  Fortunately, the maturity and accessibility of virtualization technologies has made it feasible to run the same analysis environments on platforms ranging from single laptop computers up to high-performance computing networks.

This course explores ethical and social issues that have arisen, and continue to arise, from the rapid research development in neuroscience, medicine, and ICT. Lectures focus on key ethical issues contained in the HBP – such as the ethics of robotics, dual use, ICT ethical issues, big data and individual privacy, and the use of animals in research.

Notebook systems are proving invaluable to skill acquisition, research documentation, publication, and reproducibility.  This series of presentations introduces the most popular platform for computational notebooks, Project Jupyter, as well as other resources like Binder and NeuroLibre. 

EEGLAB is an interactive MATLAB toolbox for processing continuous and event-related EEG, MEG, and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data.

Much like neuroinformatics, data science uses techniques from computational science to derive meaningful results from large complex datasets. In this session, we will explore the relationship between neuroinformatics and data science, by emphasizing a range of data science approaches and activities, ranging from the development and application of statistical methods, through the establishment of communities and platforms, and through the implementation of open-source software tools.

The emergence of data-intensive science creates a demand for neuroscience educators worldwide to deliver better neuroinformatics education and training in order to raise a generation of modern neuroscientists with FAIR capabilities, awareness of the value of standards and best practices, knowledge in dealing with big datasets, and the ability to integrate knowledge over multiple scales and methods.