This brief course consists of slides on data science and reproducibility issues from lectures given at Maastricht University. 

Sessions from the INCF Neuroinformatics Assembly 2022 day 2. 

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The Neurodata Without Borders: Neurophysiology project (NWB, https://www.nwb.org/) is an effort to standardize the description and storage of neurophysiology data and metadata. NWB enables data sharing and reuse and reduces the energy-barrier to applying data analytics both within and across labs. Several laboratories, including the Allen Institute for Brain Science, have wholeheartedly adopted NWB.

This course, consisting of one lecture and two workshops, is presented by the Computational Genomics Lab at the Centre for Addiction and Mental Health and University of Toronto. The lecture deals with single-cell and bulk level transciptomics, while the two hands-on workshops introduce users to transcriptomic data types (e.g., RNAseq) and how to perform analyses in specific use cases (e.g., cellular changes in major depression). 

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees.

Probing the organization of interactions within and across neuronal populations is a promising approach to understanding the principles of brain processing. The rapidly advancing technical capabilities to record from hundreds of neurons in parallel open up new possibilities to disentangle the correlative structure within neuronal networks. However, the complexity of these massive data streams calls for novel, tractable analysis tools that exploit the parallel aspect of the data.

This course, consisting of one lecture and two workshops, is presented by the Computational Genomics Lab at the Centre for Addiction and Mental Health and University of Toronto. The lecture deals with single-cell and bulk level transciptomics, while the two hands-on workshops introduce users to transcriptomic data types (e.g., RNAseq) and how to perform analyses in specific use cases (e.g., cellular changes in major depression). 

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. 

EEGLAB is an interactive MATLAB toolbox for processing continuous and event-related EEG, MEG, and other electrophysiological data. In this course, you will learn about features incorporated into EEGLAB, including 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. EEGLAB runs under Linux, Unix, Windows, and Mac OS X.

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. 

This workshop provides basic knowledge on personalized brain network modeling using the open-source simulation platform The Virtual Brain (TVB). Participants will gain theoretical knowledge and apply this knowledge to construct brain models, process multimodal neuroimaging data for reconstructing individual brains, run simulations, and use supporting neuroinformatics tools such as collaboratories, pipelines, workflows, and data repositories.

The field of neuroscience is one of the most interdisciplinary scientific fields. It is constantly expanded and developed further and unites researchers from a vast variety of backgrounds such as chemistry, biology, physics, medicine, or psychology. By examining the principles that influence the development and function of the human nervous system, it advances the understanding of the fundamental mechanisms of human behaviour, emotions, and thoughts, and what happens if they fail.

This course offers lectures on the origin and functional significance of certain electrophysiological signals in the brain, as well as a hands-on tutorial on how to simulate, statistically evaluate, and visualize such signals. Participants will learn the simulation of signals at different spatial scales, including single-cell (neuronal spiking) and global (EEG), and how these may serve as biomarkers in the evaluation of mental health data.

As research methods and experimental technologies become ever more sophisticated, the amount of health-related data per individual which has become accessible is vast, giving rise to a corresponding need for cross-domain data integration, whole-person modelling, and improved precision medicine. This course provides lessons describing state of the art methods and repositories, as well as a tutorial on computational methods for data integration. 

This course consists of a series of lessons which aim to introduce the basic conceptual and experimental approaches in computational neuroscience. 

This course consists of a three-part session from the second day of INCF's Neuroinformatics Assembly 2023. The lessons describe various on-going efforts within the fields of neuroinformatics and clinical neuroscience to adjust to the increasingly vast volumes of brain data being collected and stored.

This course contains videos, lectures, and hands-on tutorials as part of INCF's Neuroinformatics Assembly 2023 workshop on developing robust and reproducible research workflows to foster greater collaborative efforts in neuroscience.

This course consists of two workshops which focus on the need for reproducibility in science, particularly under the umbrella roadmap of FAIR scienctific principles. The tutorials also provide an introduction to some of the most commonly used open-source scientific tools, including Git, GitHub, Google Colab, Binder, Docker, and the programming languages Python and R. 

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.