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Ethics and Governance

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.



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. 


Current Methods in Neurotechnology


The lecture series focuses on current trends in modern techniques in neuroscience. Inspiring scientists from the NeurotechEU Alliance will give an overview of the latest advances and developments.


Open Data in Neuroscience: Data Sharing in EBRAINS

Maaike van Swieten, Ida Aasebø, the EBRAINS curation services and HBP-EBRAINS

There is a broad consensus among researchers, publishers, and funding bodies that open sharing of data is needed to address major reproducibility and transparency challenges that currently exist in all scientific disciplines. In addition to potentially increasing the utilization of shared data through re-analysis and integration with other data, data sharing is beneficial for individual researchers through data citation and increased exposure of research.


Neuroimaging Connectomics

Krembil Centre for Neuroinformatics

This course consists of one lesson and one tutorial, focusing on the neural connectivity measures derived from neuroimaging, specifically from methods like functional magnetic resonance imaging (fMRI) and diffusion-weighted imaging (DWI). Additional tools such as tractography and parcellation are discussed in the context of brain connectivity and mental health. The tutorial leads participants through the computation of brain connectomes from fMRI data. 



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. 


Data Science and Neuroinformatics


Much like neuroinformatics, data science uses techniques from computational science to derive meaningful results from large complex datasets. In this session, we will explore the relationship between neuroinformatics and data science, by emphasizing a range of data science approaches and activities, ranging from the development and application of statistical methods, through the establishment of communities and platforms, and through the implementation of open-source software tools.


Machine Learning

Neuromatch Academy

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


Applied Ethics in Machine Learning and Mental Health

Krembil Centre for Neuroinformatics

This course tackles the issue of maintaining ethical research and healthcare practices in the age of increasingly powerful technological tools like machine learning and artificial intelligence. While there is great potential for innovation and improvement in the clinical space thanks to AI development, lecturers in this course advocate for a greater emphasis on human-centric care, calling for algorithm design which takes the full intersectionality of individuals into account.


Module 1: Spikes

Mike X. Cohen

The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.

INCF TrainingSpace

Lifecycle of Human Electroencephalography/Event-Related Potential Data

Czech National Node for Neuroinformatics

This course is intended for those interested in electroencephalography (EEG) and event-related potentials (ERPs) techniques, and those interested in collecting, annotating, standardizing, storing, processing, sharing, and publishing data from electrical activity of the human brain.


Data Management, Repositories, & Search Engines

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.


The Virtual Brain Node #6 Workshop

The Virtual Brain

Get up to speed about the fundamental principles of full brain network modeling using the open-source neuroinformatics platform The Virtual Brain (TVB). This simulation environment enables the biologically realistic modeling of whole-brain network dynamics across different brain scales, using personalized structural connectome-based approach.


Linear Systems

Neuromatch Academy

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

INCF TrainingSpace

Foundations of Neurotechnology

The BCI Guys

This course provides a broad, non-technical overview of the field of neurotechnology. It is intended to provide users with a fundamental understanding of how neurotechnology works.


Module 4: fMRI

Mike X. Cohen

This module covers fMRI data, including creating and interpreting flatmaps, exploring variability and average responses, and visual eccenticity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.


Open Science Framework (OSF)

Center for Open Science

This course is intended to introduce researchers to the Open Science Framework (OSF). OSF is a free, open source web application built by the Center for Open Science, a non-profit dedicated to improving the alignment between scientific values and scientific practices. OSF is part collaboration tool, part version control software, and part data archive.


GLM, Regression Models, and Latent Variables

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.


Introduction to Neurodata Without Borders (NWB) for Python Users I

NWB Core Development Team

The Neurodata Without Borders: Neurophysiology project (NWB, is an effort to standardize the description and storage of neurophysiology data and metadata. NWB enables data sharing and reuse and reduces the energy-barrier to applying data analytics both within and across labs. Several laboratories, including the Allen Institute for Brain Science, have wholeheartedly adopted NWB.

INCF TrainingSpace

Session 3: Streamlining Cross-Platform Data Integration


This course corresponds to the third session of talks given at INCF's Neuroinformatics Assembly 2023. In this session, the talks revolve around the idea of cross-platform data integration, discussing processes and solutions for rapidly developing an integrated workflow across independent systems for the US BRAIN Initiative Cell Census.