This lesson gives a quick introduction to the rest of this course, Research Workflows for Collaborative Neuroscience.
This lesson provides an overview of how to conceptualize, design, implement, and maintain neuroscientific pipelines in via the cloud-based computational reproducibility platform Code Ocean.
This lesson provides an overview of how to construct computational pipelines for neurophysiological data using DataJoint.
This talk describes approaches to maintaining integrated workflows and data management schema, taking advantage of the many open source, collaborative platforms already existing.
This hands-on tutorial walks you through DataJoint platform, highlighting features and schema which can be used to build robost neuroscientific pipelines.
This lesson consists of a panel discussion, wrapping up the INCF Neuroinformatics Assembly 2023 workshop Research Workflows for Collaborative Neuroscience.
This lecture provides a detailed description of how to incorporate HED annotation into your neuroimaging data pipeline.
This lesson gives an in-depth description of scientific workflows, from study inception and planning to dissemination of results.
This lesson gives an introductory presentation on how data science can help with scientific reproducibility.
This lecture discusses how FAIR practices affect personalized data models, including workflows, challenges, and how to improve these practices.
This lecture covers how to make modeling workflows FAIR by working through a practical example, dissecting the steps within the workflow, and detailing the tools and resources used at each step.
This lesson introduces concepts and practices surrounding reference atlases for the mouse and rat brains. Additionally, this lesson provides discussion around examples of data systems employed to organize neuroscience data collections in the context of reference atlases as well as analytical workflows applied to the data.
This lesson gives an introduction to the Mathematics chapter of Datalabcc's Foundations in Data Science series.
This lesson serves a primer on elementary algebra.
This lesson provides a primer on linear algebra, aiming to demonstrate how such operations are fundamental to many data science.
In this lesson, users will learn about linear equation systems, as well as follow along some practical use cases.
This talk gives a primer on calculus, emphasizing its role in data science.
This lesson clarifies how calculus relates to optimization in a data science context.
This lesson covers Big O notation, a mathematical notation that describes the limiting behavior of a function as it tends towards a certain value or infinity, proving useful for data scientists who want to evaluate their algorithms' efficiency.