This workshop is organized by the German National Research Data Infrastructure Initiative Neuroscience (NFDI-Neuro). The initiative is community driven and comprises around 50 contributing national partners and collaborators. NFDI-Neuro partners with EBRAINS AISB, the coordinating entity of the EU Human Brain Project and the EBRAINS infrastructure. We will introduce common methods that enable digital reproducible neuroscience.

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

Sessions from the INCF Neuroinformatics Assembly 2022 Day 3. 

VIEW THE PROGRAM

Neuroanatomy provides one of the unifying frameworks for neuroscience and thus it is not surprising that it provides the basis for many neuroinformatics tools and approaches.  Regardless of whether one is working at the subcellular, cellular or gross anatomical level or whether one is modeling circuitry, molecular pathways or function, at some point, this work will include an anatomical reference.

Sessions from the INCF Neuroinformatics Assembly 2022 day 2. 

VIEW THE PROGRAM

This workshop delves into the need for, structure of, tools for, and use of hierarchical event descriptor (HED) annotation to prepare neuroimaging time series data for storing, sharing, and advanced analysis. HED are a controlled vocabulary of terms describing events in a machine-actionable form so that algorithms can use the information without manual recoding.

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.

This is a freely available online course on neuroscience for people with a machine learning background. The aim is to bring together these two fields that have a shared goal in understanding intelligent processes. Rather than pushing for “neuroscience-inspired” ideas in machine learning, the idea is to broaden the conceptions of both fields to incorporate elements of the other in the hope that this will lead to new, creative thinking.

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.

The workshop will include interactive seminars given by selected experts in the field covering all aspects of (FAIR) small animal MRI data acquisition, analysis, and sharing. The seminars will be followed by hands-on training where participants will perform use case scenarios using software established by the organizers. This will include an introduction to the basics of using command line interfaces, Python installation, working with Docker/Singularity containers, Datalad/Git, and BIDS.

This course, consisting of one lecture and two workshops, is presented by the Computational Genomics Lab at the Centre for Addiction and Mental Health and University of Toronto. The lecture deals with single-cell and bulk level transciptomics, while the two hands-on workshops introduce users to transcriptomic data types (e.g., RNAseq) and how to perform analyses in specific use cases (e.g., cellular changes in major depression). 

This workshop is organized by the German National Research Data Infrastructure Initiative Neuroscience (NFDI-Neuro). The initiative is community driven and comprises around 50 contributing national partners and collaborators. NFDI-Neuro partners with EBRAINS AISB, the coordinating entity of the EU Human Brain Project and the EBRAINS infrastructure. We will introduce common methods that enable digital reproducible neuroscience.

Sessions from the INCF Neuroinformatics Assembly 2022 day 1. 

VIEW THE PROGRAM

Most approaches within computational neuroscience simulate systems, brain networks, local circuits, as they are now. In recent years, homeostatic regulation has been characterized and modeled; however, for understanding diseases that have their origin in genetic defects that emerge at later age, it is important to understand how these defects interact with developmental processes that occur earlier and last longer that the typical period considered for homeostatic studies.

Sessions from the INCF Neuroinformatics Assembly 2022 day 2. 

VIEW THE PROGRAM

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.

This course consists of three lessons, each corresponding to a lightning talk given at the first day of INCF's Neuroinformatics Assembly 2023. By following along these brief talks, you will hear about topics such as open source tools for computer vision, tools for the integration of various MRI dataset formats, as well as international data governance. 

The emergence of data-intensive science creates a demand for neuroscience educators worldwide to deliver better neuroinformatics education and training in order to raise a generation of modern neuroscientists with FAIR capabilities, awareness of the value of standards and best practices, knowledge in dealing with big datasets, and the ability to integrate knowledge over multiple scales and methods.

This course contains sessions from the second day of INCF's Neuroinformatics Assembly 2022.

Given the extreme interconnectedness of the human brain, studying any one cerebral area in isolation may lead to spurious results or incomplete, if not problematic, interpretations. This course introduces participants to the various spatial scales of neuroscience and the fundamentals of whole-brain modelling, used to generate a more thorough picture of brain activity.