This lecture describes how to build research workflows, including a demonstrate using DataJoint Elements to build data pipelines.

This lesson provides an introduction to the Symposium on Science Management at the Canadian Association for Neuroscience 2019 Meeting.

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 covers "Knowledge Translation", the activities involved in moving research from the laboratory, the research journal, and the academic conference into the hands of people and organizations who can put it to practical use.

In this lesson, you will hear about the various methods developed and employed in managing performance. 

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

This lesson discusses FAIR principles and methods currently in development for assessing FAIRness.

In this lesson, you will learn in more detail about neuromorphic computing, that is, non-standard computational architectures that mimic some aspect of the way the brain works. 

This video provides a very quick introduction to some of the neuromorphic sensing devices, and how they offer unique, low-power applications.

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.

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 contains the first part of the lecture Data Science and Reproducibility. You will learn about the development of data science and what the term currently encompasses, as well as how neuroscience and data science intersect. 

The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.

This lecture provides an introduction to reproducibility issues within the fields of neuroimaging and fMRI, as well as an overview of tools and resources being developed to alleviate the problem.

This lecture provides a historical perspective on reproducibility in science, as well as the current limitations of neuroimaging studies to date. This lecture also lays out a case for the use of meta-analyses, outlining available resources to conduct such analyses. 

Serving as good refresher, this lesson explains the maths and logic concepts that are important for programmers to understand, including sets, propositional logic, conditional statements, and more.

This compilation is courtesy of freeCodeCamp.

This lesson provides a useful refresher which will facilitate the use of Matlab, Octave, and various matrix-manipulation and machine-learning software.

This lesson was created by RootMath.

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.