INCF Brain Data Science Bootcamp 2026

Organized by INCF, Led by SeRC, In collaboration with EBRAINS Sweden, Hosted at Karolinska Institutet
Dates: 11 - 13 May
Venue: Karolinska Institutet

The INCF Brain Data Science Bootcamp 2026 brings together leading experts and hands-on training across brain atlasing, electrophysiology, data sharing, and computational modeling within the EBRAINS research infrastructure. Designed for neuroscientists at different career stages, the bootcamp offers practical workshops on whole-brain mapping with the QUINT workflow, interactive exploration of multiscale brain atlases using the siibra tool suite and Voluba, reproducible electrophysiology analysis with Neo and Elephant, and impactful data sharing through EBRAINS Data and Knowledge Services. Participants will work directly with real datasets and pre-built Jupyter notebooks, gaining skills in atlas-based analysis, multimodal data integration, workflow reproducibility, and Python-based neuroscience research. From beginner-friendly introductions to advanced, code-driven sessions, the bootcamp provides an immersive environment to strengthen data science competencies and foster interoperable, standards-aligned neuroscience workflows.

SESSIONS

VENUES

Time	Session	Speaker(s)
12.30 - 13.00	Registration
13.00 - 13.30	Welcome and Introduction to FAIR Neuroscience	Mathew Abrams INCF Secretariat
13.30 - 14.15	The EBRAINS Ecosystem for sharing, finding, and using neuroscience data	Trygve Leergaard University of Oslo
14.15 - 14.45	Introduction to EBRAINS data and knowledge services	Sophia Pieschnik & Signy Benediktsdottir University of Oslo
14.45 - 15.00	Break
15.00 - 15.30	Introduction to EBRAINS digital brain atlasing services	Sebastian Bludau Jülich Research Centre
15.30 - 16.00	Introduction to Neo and Elephant	Cristiano Köhler Jülich Research Centre
16.00 - 16.30	Introduction to The Virtual Brain	Leon Stefanovski Charité
16.30 - 17.00	Spotlight Talk: SHAREbrain	TBA
17.00 - 19.00	Reception and Drop-in Consultation sessions for researchers interested in discussing organization, sharing possibilities and resources of relevance for their own data.	EBRAINS Data Curation Team

Time	Track 1	Time	Track 2	Time	Track 3	Time	Track 4
09.00 - 10.00	Basic Python for Neuroscientists (optional)	09.00 - 10.00	Basic Python for Neuroscientists (optional)			09.00 - 12.00	Towards translational and mechanistic whole-brain simulations with The Virtual Brain
10.00 - 12.00	EBRAINS Atlas services I: The QUINT workflow - whole brain section mapping using brain atlases and machine learning	10.00 - 12.00	EBRAINS Modelling & Simulation I: Managing and analyzing electrophysiology data using Neo and Elephant	10.00 - 12.00	EBRAINS Data & Knowledge services I: Impactful data sharing via EBRAINS	09.00 - 12.00
12.00 - 13.00	Lunch	12.00 - 13.00	Lunch	12.00 - 13.00	Lunch	12.00 - 13.00	Lunch
13.00 - 15.00	EBRAINS Atlas services II: Using the Siibra Tool Suite and Voluba	13.00 - 15.00	EBRAINS Modelling & Simulation II: Managing and analyzing electrophysiology data using Neo and Elephant	13.00 - 15.00	EBRAINS Data & Knowledge services II: The SHAREbrain workflow - Automated standardization of functional data	13.00 - 15.00	Towards translational and mechanistic whole-brain simulations with The Virtual Brain
15.00 - 15.30	Break	15.00 - 15.30	Break	15.00 - 15.30	Break	15.00 - 15.30
15.30 - 17.30	EBRAINS Atlas services III: Navigating & visualizing data and anatomical locations (45 min) or Advanced Siibra Python Tutorial	15.30 - 17.30	EBRAINS Modelling & Simulation III: Managing and analyzing electrophysiology data using Neo and Elephant	15.30 - 16.15	EBRAINS Atlas services III: Navigating & visualizing data and anatomical locations (45 min)	15.30 - 17.30	Towards translational and mechanistic whole-brain simulations with The Virtual Brain

Time	Track 4 (cont.)	Time	Track 5
09.00 - 11.30	Towards translational and mechanistic whole-brain simulations with The Virtual Brain	09.00 - 11.30	Intro to Python for Neuroscientists

11.30 - 12.00	Break	11.30 - 12.00	Break
12.00 -13.30	Towards translational and mechanistic whole-brain simulations with The Virtual Brain	12.00 - 13.30	Intro to Python for Neuroscientists

Track 1: EBRAINS Atlas Services

The QUINT workflow: Whole brain section mapping using brain atlases and machine learning (90 min): Maja Puchades, University of Oslo

The QUINT workflow is an analysis solution for 2D rodent brain microscopy data, enabling brain-wide mapping and regional quantification using a reference brain atlas. It combines the use of several software with graphical user interfaces; hence no coding ability are required. The QUINT workflow takes brain section image series as input, and generates counts of labelled objects, area fraction per atlas-region, and coordinates for visualising objects in 3D atlas space.

Requirements: A laptop with an up-to-date web browser (Chrome or Firefox is recommended) is required for the hands-on examples. All examples will be run on pre-built Jupyter notebooks, which will be provided for downloading. Please register for an EBRAINS account in advance.

How to use EBRAINS atlas services with the Siibra Tool Suite and Voluba (120 min): Ahmet Simisek and Sebastian Bludau, Jülich Research Centre

Siibra-explorer is a software tool suite that implements a multilevel atlas of the brain by providing streamlined access to reference templates at different spatial scales, complementary brain parcellations maps, and multimodal regional data from different sources which is linked to brain anatomy at different spatial scales. It addresses interactive exploration via an interactive 3D web viewer (siibra-explorer) and integration into data analysis and simulation workflows with a comprehensive Python library (siibra-python), supporting a broad range of workflows for anatomists, experimentalists and computational neuroscientists with varying experience levels, from beginners to those with a solid background in Python. siibra-python is a Python client to a brain atlas framework that integrates brain parcellations and reference spaces at different spatial scales, and connects them with a broad range of multimodal data features. It aims to facilitate programmatic and reproducible incorporation of brain parcellations and brain region features from different sources into neuroscience workflows.

Voluba is a browser-based tool for interactively aligning volumes of interest to 3D reference templates. Voluba currently supports the BigBrain model, Waxholm rattemplate, and Allen mouse template as reference spaces. Voluba is compatible with siibra-explorer, so you can directly inspect aligned data superimposed with brain region maps and other datasets.

Format: This hands-on session will offer participants an immersive opportunity to explore the advanced tools and techniques for data analysis and visualization. Participants will be introduced to the siibra tool suite with highlights on its features and benefits. Participants will learn to access 3D reference templates and maps, including anatomical, and connectivity atlases. Participants will interactively explore BigBrain cytoarchitectonic maps and cortical layer segmentation and extract region-specific information via the EBRAINS Knowledge Graph.

Moving beyond the graphical interface of siibra-explorer, the session will proceed with siibra-python. Participants will be guided through coding exercises demonstrating how to fetch brain region maps, access the BigBrain dataset, and extract multimodal regional features such as cortical thicknesses, cell and neurotransmitter densities, gene expressions, and connectivity data. In addition, participants will be introduced to Voluba. After completing the training, participants will have a first insight of the features of siibra-explorer, siibra-python, and Voluba to enhance their ability to explore brain atlases and perform advanced neuroimaging analyses.

Target audience: This beginner-friendly course is aimed at researchers interested in exploring human brain atlases using siibra-explorer and Voluba. No programming experience is required. Participants will interact with both tools through intuitive, web-based interfaces. Toward the end of the course, we will review and explain working siibra-python code examples. While no coding is required, basic understanding of the examples will be supported and encouraged.

An optional follow-up session will be offered for participants interested in hands-on coding with siibra-python. This part of the session will focus on creating our own workflow and some more advanced functionalities of siibra-python. For this, a working local Python setup with siibra-python installed (pip install siibra) and basic coding experience in Python are required.

Advanced siibra-python Ahmet Simisek and Sebastian Bludau, Jülich Research Centre
This part of the session will focus on creating our own workflow and some more advanced functionalities of siibra-python. For this, a working local Python setup with siibra-python installed (pip install siibra) and basic coding experience in Python are required.

Navigating and visualizing data and anatomical locations using EBRAINS atlas resources (45 min): Trygve Leergaard and Maja Puchades, University of Oslo

Open access three-dimensional brain atlases provide new opportunities for navigating complex neuroanatomy, defining regions-of-interest, and visualizing different parts of the brain. In this session Maja Puchades and Trygve Leergaard will give some practical examples of how brain atlases and software tools available via EBRAINS can be used for stereotaxic navigation in the mouse and rat brain, to visualize and analyse data, and for making “eye candy” visualizations for illustrations of brain anatomy. Participants are encouraged to bring computers for hands-on exploration of possibilities.

Track 2: Electrophysiology Managing and Analyzing electrophysiology data using Neo and Elephant

Electrophysiology Managing and Analyzing electrophysiology data using Neo and Elephant Cristiano Köhler and Harris Jos, Jülich Research Centre

Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for writing to a subset of these formats plus non-proprietary formats including HDF5. The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data (such as OpenElectrophy, NeuroTools, G-node, Helmholtz, PyNN) by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to representation of data, with no functions for data analysis or visualization

Elephant, the Python library Electrophysiology Analysis Toolkit, provides tools for analysing neuronal activity data, such as spike trains, local field potentials and intracellular data. In addition to providing a platform for sharing analysis codes from different laboratories, Elephant provides a consistent and homogeneous framework for data analysis built on a modular foundation. The underlying data model is the Neo library. This framework easily captures a wide range of neuronal data types and methods, including dozens of file formats and network simulation tools. A common data description, as the Neo library provides, is essential for developing interoperable analysis workflows

Format: This hands-on tutorial delves into challenges in the reproducibility of neuroscience workflows dealing with classical electrophysiological activity data on the cellular scale, such as spiking data or local field potentials, from experiment or simulation. The training will cover the complete cycle from generating structured and consistent data and metadata, accessing the data, pre-processing, setting up analysis workflows, up to the tracking of the provenance of the analysis results. In this context, the e-infrastructure services of EBRAINS offer a mature data, software and compute services ecosystem with community-driven tools developed in the framework of the Human Brain Project. In the first part of the workshop, participants will be trained in the use of tools covering the following topics: reading and manipulating electrophysiology data in Python using Neo, analysis of such data using Elephant, best practices for integrating metadata into your workflow to aid the analysis process, and structuring analysis results for sharing, e.g., using the nix data format. At the end of the workshop, time is dedicated for participants to explore the tools on their own data and particular personal interests.

Requirements: Basic Python knowledge

Target audience: Neuroscientists analyzing electrophysiology data on the cellular scales (spikes, LFP); computational neuroscientists performing simulations on a comparable scale (spiking neural network simulations)

Track 3: EBRAINS Data and Knowledge Services: Impactful data sharing via EBRAINS

EBRAINS Data and Knowledge Services: Impactful data sharing via EBRAINS I (120 min): Sophia Pieschnik and Signý Benediktsdóttir, University of Oslo

The EBRAINS research infrastructure includes a database of neuroscience data, computational models and software. In this workshop the EBRAINS Data Curation Team will introduce the EBRAINS data sharing workflow from initiating curation to publishing data. They will explain the steps involved and give practical guidance on how data and metadata should be prepared, how different computational resources can be utilized, and how data can be found and used. The workshop will include hands-on sessions allowing participants to experience different workflow steps working with example data sets. The hands-on sessions will include modules adapted to the participants’ competence and needs. The Data Curation Team will also be available for drop in consulting of participants data sets.
Requirements: A laptop with an up-to-date web browser (Chrome or Firefox is recommended) is required for the hands-on examples. All examples will be run on pre-built Jupyter notebooks, which will be provided for downloading. Please register for an EBRAINS account in advance.

EBRAINS Data and Knowledge Services II: The SHAREbain workflow—automated standarization of functional data (120 min): Eivind Hennestad and Aree Witoelar, University of Oslo.

Description: TBA

Track 4: Towards translational and mechanistic whole-brain simulations with The Virtual Brai

Towards translational and mechanistic whole-brain simulations with The Virtual Brain (TBA) Leon Stefanovski, Charité – Berlin University Medicine

Description: TBA

Optional tutorial:

Basic Python for Neuroscientists (60 min): This tutorial aims to provide participants with enough knowledge about Python to successfully participate in the Digital Brain Atlases and Electrophysiology tutorials. No prior experience with Python is required. The session will be held before the official start of the tutorials on Day 2, so all participants will be able to join this session in addition to their preferred tutorials.

Track 5: Introduction Python for neuroscientists

Introduction Python for neuroscientists (4 hours): Ahmet Simisek and Sebastian Bludau, Jülich Research Centre

This hands-on workshop introduces Python as a practical tool for neuroscience research. Designed for graduate students and postdoctoral researchers, the session focuses on analyzing real neural and behavioral data using core scientific libraries including NumPy, Pandas, Matplotlib, and SciPy. Participants will learn how to build simple analysis pipelines, perform basic signal processing, visualize results, and structure reproducible workflows in Jupyter notebooks. By the end of the workshop, attendees will be equipped to begin applying Python directly to their own research projects. No prior experience with Python is required.

Requirements: A laptop with an up-to-date web browser (Chrome or Firefox is recommended)

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