Over the last three decades, neuroimaging research has seen large strides in the scale, diversity, and complexity of studies, the open availability of data and methodological resources, the quality of instrumentation and multimodal studies, and the number of researchers and consortia. The awareness of rigor and reproducibility has increased with the advent of funding mandates, and with the work done by national and international brain initiatives.

As models in neuroscience have become increasingly complex, it has become more difficult to share all aspects of models and model analysis, hindering model accessibility and reproducibility. In this session, we will discuss existing resources for promoting FAIR data and models in computational neuroscience, their impact on the field, and remaining barriers.

Since their introduction in 2016, the FAIR data principles have gained increasing recognition and adoption in global neuroscience. FAIR defines a set of high level principles and practices for making digital objects, including data, software and workflows, Findable, Accessible, Interoperable and Reusable. But FAIR is not a specification; it leaves many of the specifics up to individual scientific disciplines to define.

These courses give introductions and overviews of some of the major statistics software packages currently used in neuroscience research.

Difficulties experienced in understanding machine learning techniques often stem from lack of clarity concerning more basic statistical models and fundamental considerations, including the various regression models that can all be subsumed under the General Linear Model.

The dimensionality and size of datasets in many fields of neuroscience research require massively parallel computing power.  Fortunately, the maturity and accessibility of virtualization technologies has made it feasible to run the same analysis environments on platforms ranging from single laptop computers up to high-performance computing networks.

This course outlines how versioning code, data, and analysis software is crucially important to rigorous and open neuroscience workflows that maximize reproducibility and minimize errors.Version control systems, code-capable notebooks, and virtualization containers such as Git, Jupyter, and Docker, respectively, have become essential tools in data science.

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. 

A number of programming languages are ubiquitous in modern neuroscience and are key to the competence, freedom, and creativity necessary in neuroscience research. This course offers lectures on the fundamentals of data science and specific neuroinformatic tools used in the investigation of brain data. Attendees of this course will be learn about the programming languages Python, R, and MATLAB, as well as their associated packages and software environments. 

These lessons give an overview of the principles underpinning the objectives, policies, and practice of Open Science, including several representative policy documents that will be increasingly relevant to neuroscience research.

Ethical conduct of science, good governance of data, and accelerated translation to the clinic are key to high-calibre open neuroscience.  Everyday practitioners of science must be sensitized to a range of ethical considerations in their research, some having especially to do with open data-sharing. The lessons included in this course introduce a number of these topics and end with concrete guidance for participant consent and de-identification of data.

This course is currently under construction but will coming soon.  It will give an overview of the world of scientific publishing, spanning from traditional formats, to open to access, to open, interactive, reproducible, and 'living' publications with modifiable and executable code.

The importance of Research Data Management in the conduct of open and reproducible science is better understood and technically supported than ever, and many of the underlying principles apply as much to everyday activities of a single researcher as to large-scale, multi-center open data sharing.

This course provides introductory and refresher lessons for a range of concepts and methods useful in the field of neuroscience and neuroinformatics. 

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

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

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

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

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

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