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Data science - Tools of the trade
Purpose of the collection

This collection looks to introduce neuroscience trainees to many of basic tools and techniques essential for most computationally intensive neuroscience research environments.

This collection has been organized into the following themes:

  1. Conceptual background & refreshers
    1. Review of modelling
    2. Flash math refresher
    3. Models of neural function
    4. Overview of brain-imaging techniques
  2. Programming essentials
    1. Basics required for navigating command-line environments
    2. Python
    3. R
    4. Matlab/Octave
  3. Notebooks
    1. For teaching and learning
    2. As part of an everyday, scientific workflow
    3. As a complement to standard PDF publications
  4. Versioning and containerization
    1. Overview
    2. Reproducibility
    3. Local execution
  5. Data Management, Repositories & Search Engines
    1. The importance and utility of Research Data Management
    2. Sources and reuse of neuroscience data
  6. High-performance computing
    1. Case studies
    2. CLI environments (tools, scheduling, etc.)
    3. GUI environments

Courses in this collection
2

A number of programming languages are ubiquitous in modern neuroscience and are key to the competence, freedom, and creativity necessary in…

3

Notebook systems are proving invaluable to skill acquisition, research documentation, publication, and reproducibility.  This series of…

4

Versioning code, data, and analysis software is crucially important to rigorous and open neuroscience workflows that maximize reproducibility and…

5

The importance of Research Data Management in the conduct of open and reproducible science is better understood and technically supported than…

6

The dimensionality and size of datasets in many fields of neuroscience research require massively parallel computing power.  Fortunately, the…