This talk enumerates the challenges regarding data accessibility and reusability inherent in the current scientific publication system, and discusses novel approaches to these challenges, such as the EBRAINS Live Papers platform. 

This brief video gives an introduction to the eighth session of INCF's Neuroinformatics Assembly 2023, focusing on FAIR data and the role of academic journals. 

This talk gives an overview of the perspectives and FAIR-aligned policies of the academic journal Public Library of Science, better known as PLOS. This journal is a nonprofit, open access publisher empowering researchers to accelerate progress in science. 

This talk highlights a set of platform technologies, software, and data collections that close and shorten the feedback cycle in research. 

This lightning talk describes an automated pipline for positron emission tomography (PET) data. 

This session introduces the PET-to-BIDS (PET2BIDS) library, a toolkit designed to simplify the conversion and preparation of PET imaging datasets into BIDS-compliant formats. It supports multiple data types and formats (e.g., DICOM, ECAT7+, nifti, JSON), integrates seamlessly with Excel-based metadata, and provides automated routines for metadata updates, blood data conversion, and JSON synchronization. PET2BIDS improves human readability by mapping complex reconstruction names into standardized, descriptive labels and offers extensive documentation, examples, and video tutorials to make adoption easier for researchers.

This session introduces the PET-to-BIDS (PET2BIDS) library, a toolkit designed to simplify the conversion and preparation of PET imaging datasets into BIDS-compliant formats. It supports multiple data types and formats (e.g., DICOM, ECAT7+, nifti, JSON), integrates seamlessly with Excel-based metadata, and provides automated routines for metadata updates, blood data conversion, and JSON synchronization. PET2BIDS improves human readability by mapping complex reconstruction names into standardized, descriptive labels and offers extensive documentation, examples, and video tutorials to make adoption easier for researchers.

This session dives into practical PET tooling on BIDS data—showing how to run motion correction, register PET↔MRI, extract time–activity curves, and generate standardized PET-BIDS derivatives with clear QC reports. It introduces modular BIDS Apps (head-motion correction, TAC extraction), a full pipeline (PETPrep), and a PET/MRI defacer, with guidance on parameters, outputs, provenance, and why Dockerized containers are the reliable way to run them at scale.

This session introduces two PET quantification tools—bloodstream for processing arterial blood data and kinfitr for kinetic modeling and quantification—built to work with BIDS/BIDS-derivatives and containers. Bloodstream fuses autosampler and manual measurements (whole blood, plasma, parent fraction) using interpolation or fitted models (incl. hierarchical GAMs) to produce a clean arterial input function (AIF) and whole-blood curves with rich QC reports ready. TAC data (e.g., from PETPrep) and blood (e.g., from bloodstream) can be ingested using kinfitr to run reproducible, GUI-driven analyses: define combined ROIs, calculate weighting factors, estimate blood–tissue delay, choose and chain models (e.g., 2TCM → 1TCM with parameter inheritance), and export parameters/diagnostics. Both are available as Docker apps; workflows emphasize configuration files, reports, and standard outputs to support transparency and reuse.

This lecture covers positron emission tomography (PET) imaging and the Brain Imaging Data Structure (BIDS), and how they work together within the PET-BIDS standard to make neuroscience more open and FAIR.

This module covers many of the types of non-invasive neurotech and neuroimaging devices including electroencephalography (EEG), electromyography (EMG), electroneurography (ENG), magnetoencephalography (MEG), and more. 

This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.

This tutorial demonstrates how to perform cell-type deconvolution in order to estimate how proportions of cell-types in the brain change in response to various conditions. While these techniques may be useful in addressing a wide range of scientific questions, this tutorial will focus on the cellular changes associated with major depression (MDD). 

This lesson explains the fundamental principles of neuronal communication, such as neuronal spiking, membrane potentials, and cellular excitability, and how these electrophysiological features of the brain may be modelled and simulated digitally. 

This is an in-depth guide on EEG signals and their interaction within brain microcircuits. Participants are also shown techniques and software for simulating, analyzing, and visualizing these signals.

This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health. 

This lecture covers the emergence of cognitive science after the Second World War as an interdisciplinary field for studying the mind, with influences from anthropology, cybernetics, and artificial intelligence.

In this lesson, you will learn about the connectome, the collective system of neural pathways in an organism, with a closer look at the neurons, synapses, and connections of particular species. 

This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein. 

This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved.