Tutorials & E-Library

A hands-on Jupyter notebook on finding and reusing EBRAINS research products with the fairgraph Python library. It covers what the Knowledge Graph contains and how its nodes and typed links are represented as Python objects; retrieving a node by identifier, by name, or by filtering on its properties, including filters that reach across a link; following links efficiently, and why the starting point of a query determines what it costs; controlled terms and the vocabularies that make metadata comparable; and answering one scientific question across datasets, models and software at once. Later sections, for working through afterwards, cover local JSON-LD snapshots with Collection, writing custom queries, release status, spaces and result ordering, and error handling. The notebook only reads from the Knowledge Graph; nothing is saved. A companion notebook covers creating metadata, for data providers.

A hands-on Jupyter notebook on creating and updating metadata in the EBRAINS Knowledge Graph with the fairgraph Python library. It covers building openMINDS nodes in Python and validating them locally, how fairgraph decides between creating a node and updating an existing one, assembling a complete dataset record, collab spaces and why metadata in a private space can never be curated, the curation workflow and what only EBRAINS curators can do, and bulk upload with Collection. Most data providers describe their datasets through the Metadata Wizard instead; this tutorial is for those with many datasets to register, or who want to generate metadata from a pipeline. All exercises write to the pre-production Knowledge Graph, so nothing reaches the production KG. A companion notebook covers reading metadata, for data consumers.

This tutorial is a parenthesis to explain how the figures are positioned on the screen and how you can organize your workspace more efficiently. One interesting feature of the Brainstorm interface is the ability to open easily multiple views or multiple datasets simultaneously.

This tutorial provides a comprehensive overview of event markers (also referred to as triggers or markers) within MEG recordings, focusing on their identification, manipulation, and management in Brainstorm software. Event markers denote key points in the data, including stimulus presentations and subject responses. Users learn how to visualize, add, edit, and remove events, including simple time-point markers and extended time segments, such as bad data intervals caused by artifacts. Advanced functionalities are also covered, including channel-specific events, event annotations, display customization, keyboard shortcuts, and event group management. The tutorial highlights best practices for marking noisy segments as bad to improve analysis quality and explains how event data are saved separately from raw recordings. Overall, it equips users with practical skills to efficiently handle event data in MEG experiments for enhanced data inspection and processing.

The anatomy of your subject is ready. Before we can start looking at the MEG/EEG recordings, we need to make sure that the sensors (electrodes, magnetometers or gradiometers) are properly aligned with the MRI and the surfaces of the subject.

In this tutorial, we will start with a detailed description of the experiment and the files that were recorded, then we will link the original CTF files to the database in order to get access to the sensors positions, and finally we will explore the various options for aligning these sensors on the head of the subject.

This tutorial provides a comprehensive introduction to reviewing and interacting with MEG data using Brainstorm. Participants will learn how to open, navigate, and interpret time series recordings saved in CTF MEG format. The session focuses on understanding various data channel types, switching between epoched and continuous modes, customizing display settings, and applying visualization tools to optimize data inspection. Emphasis is placed on user interaction techniques—both keyboard and mouse-based—for efficient navigation and analysis, along with guidance on managing montages, selecting channels, adjusting amplitude scales, and applying basic filters. By the end of this tutorial, learners will be well-prepared to explore MEG datasets interactively and prepare them for further preprocessing and analysis.

Presentation by Katrin Amunts (Institute of Neuroscience and Medicine, Structural and functional organisation of the brain (INM-1), Research Centre Jülich, Germany, C. u. O. Vogt-Institute for Brain Research University Hospital Düsseldorf, Germany, EBRAINS AISBL, Brussels, Belgium) and Timo Dickscheid (Institute of Computer Science, Heinrich-Heine-University Düsseldorf, Germany, Helmholtz AI, Research Centre Jülich, Germany)

Brain atlases enable the localization and analysis of data from different sources in a common reference system, making them an essential research tool for understanding the structural and functional organization of the brain. EBRAINS offers a multilevel atlas of the human brain, which captures different principles of brain organization in a comprehensive anatomical framework. It integrates the Julich-Brain probabilistic cytoarchitectonic maps and the BigBrain microscopic 3D model as core elements, and links them with multimodal data features describing microstructure, connectivity and function. The atlas is deeply integrated into the EBRAINS infrastructure, making use of its sustainable data sharing capabilities and cloud resources. This session provides a conceptual introduction to the multilevel human brain atlas, and an overview of the EBRAINS research infrastructure as a sustainable platform for accessing, operating and developing the atlas.

'jaxsnn' (https://github.com/electronicvisions/...) is a novel library based on JAX. It facilitates the simulation and machine learning inspired training of spiking neural networks with a focus on event-driven computation. The library provides flexible data structures and time handling, while preserving Autograd functionality. The approach enables efficient training and compatibility with time-continuous neuromorphic backends such as BrainScaleS-2, thus shrinking the gap between traditional neuromorphic architectures and modern machine learning frameworks.

Siibra-explorer is a web-based atlas viewer for multi-resolution brain atlases. It bridges the gap between human brain mapping and accessible exploration of the Julich Brain Atlas. This tutorial shows how to zoom into a brain region of interest to the level of individual cells.

This course provides a comprehensive introduction to Technology Readiness Levels (TRLs) and their relevance to innovation and exploitation within the context of Horizon 2020 and the Human Brain Project (HBP). Part 1 covers the origins and applications of TRLs, their importance for innovation, and their use in the HBP context, including guidance on the TRL assessment process and its connection to the Innovation Radar. Part 2 focuses on assessing the maturity of various outputs, such as hardware, software, services, datasets, and models, with practical examples to reinforce the concepts in real-world scenarios.

The goal of these introduction tutorials is to guide you through most of the features of the software. All the pages use the same example dataset. The results of one section are most of the time used in the following section, so read these pages in the correct order.

This lecture introduces SpiNNaker 2 and goes into depth on what its new properties are compared to SpiNNaker 1. It walks you through the current state and outlook of SpiNNaker 2.

You can find the slides of the presentation here: https://flagship.kip.uni-heidelberg.de/jss/HBPm?m=displayPresentation&mI=235&mEID=8941