This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.
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.
In this lesson, while learning about the need for increased large-scale collaborative science that is transparent in nature, users also are given a tutorial on using Synapse for facilitating reusable and reproducible research.
This lesson is an overview of transcriptomics, from fundamental concepts of the central dogma and RNA sequencing at the single-cell level, to how genetic expression underlies diversity in cell phenotypes.
This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment.
This lesson corresponds to slides 1-64 in the PDF below.
This lecture covers a lot of post-war developments in the science of the mind, focusing first on the cognitive revolution, and concluding with living machines.
This lesson delves into the the structure of one of the brain's most elemental computational units, the neuron, and how said structure influences computational neural network models.
This lesson goes over the basic mechanisms of neural synapses, the space between neurons where signals may be transmitted.
While the previous lesson in the Neuro4ML course dealt with the mechanisms involved in individual synapses, this lesson discusses how synapses and their neurons' firing patterns may change over time.
Whereas the previous two lessons described the biophysical and signalling properties of individual neurons, this lesson describes properties of those units when part of larger networks.
This lesson discusses a gripping neuroscientific question: why have neurons developed the discrete action potential, or spike, as a principle method of communication?
This lecture focuses on how the immune system can target and attack the nervous system to produce autoimmune responses that may result in diseases such as multiple sclerosis, neuromyelitis, and lupus cerebritis manifested by motor, sensory, and cognitive impairments. Despite the fact that the brain is an immune-privileged site, autoreactive lymphocytes producing proinflammatory cytokines can cause active brain inflammation, leading to myelin and axonal loss.
This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.
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 a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.
This lecture covers the NIDM data format within BIDS to make your datasets more searchable, and how to optimize your dataset searches.
This lecture covers positron emission tomography (PET) imaging and the Brain Imaging Data Structure (BIDS), and how they work together within the PET-BIDS standard to make neuroscience more open and FAIR.
This lecture discusses the FAIR principles as they apply to electrophysiology data and metadata, the building blocks for community tools and standards, platforms and grassroots initiatives, and the challenges therein.
This lecture contains an overview of electrophysiology data reuse within the EBRAINS ecosystem.