This lesson continues with the second workshop on reproducible science, focusing on additional open source tools for researchers and data scientists, such as the R programming language for data science, as well as associated tools like RStudio and R Markdown. Additionally, users are introduced to Python and iPython notebooks, Google Colab, and are given hands-on tutorials on how to create a Binder environment, as well as various containers in Docker and Singularity.

This talk describes the relevance and power of using brain atlases as part of one's data integration pipeline. 

In this lesson, you will learn how to understand data management plans and why data sharing is important. 

This quick visual walkthrough presents the steps required in uploading data into a brainlife project using the graphical user interface (GUI). 

This short walkthrough documents the steps needed to find a dataset in OpenNeuro, a free and open platform for sharing MRI, MEG, EEG, iEEG, ECoG, ASL, and PET data, and import it directly to a brainlife project. 

This lesson describes and shows four different ways one may upload their data to brainlife.io.

This lecture covers why data sharing and other collaborative practices are important, how these practices are developed, and the challenges involved in their development and implementation.

This lecture discusses the FAIR principles as they apply to electrophysiology data and metadata, the building blocks for community tools and standards, platforms and grassroots initiatives, and the challenges therein.

This lecture contains an overview of electrophysiology data reuse within the EBRAINS ecosystem.

This lecture contains an overview of the Distributed Archives for Neurophysiology Data Integration (DANDI) archive, its ties to FAIR and open-source, integrations with other programs, and upcoming features.

This lesson provides a short overview of the main features of the Canadian Open Neuroscience Platform (CONP) Portal, a web interface that facilitates open science for the neuroscience community by simplifying global access to and sharing of datasets and tools. The Portal internalizes the typical cycle of a research project, beginning with data acquisition, followed by data processing with published tools, and ultimately the publication of results with a link to the original dataset.

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 lesson gives an in-depth introduction of ethics in the field of artificial intelligence, particularly in the context of its impact on humans and public interest. As the healthcare sector becomes increasingly affected by the implementation of ever stronger AI algorithms, this lecture covers key interests which must be protected going forward, including privacy, consent, human autonomy, inclusiveness, and equity. 

This is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.

This is the second of three lectures around current challenges and opportunities facing neuroinformatic infrastructure for handling sensitive data. 

In this lesson you will learn about current efforts towards integrating multimodal human brain data using the open source SCORE HED library schema. 

This lecture aims to help researchers, students, and health care professionals understand the place for neuroinformatics in the patient journey using the exemplar of an epilepsy patient. 

The lesson introduces the Brain Imaging Data Structure (BIDS), the community standard for organizing, curating, and sharing neuroimaging and associated data. The session focuses on understanding the BIDS framework, learning its data structure and validation processes.

This session moves from BIDS basics into analysis workflows, focusing on how to turn raw, BIDS-organized data into derivatives using BIDS Apps and containers for reproducible processing. It compares end-to-end pipelines across fMRI and PET (and notes EEG/MEG), explains typical preprocessing choices, and shows how standardized inputs plus containerized tools (Docker/AppTainer) yield consistent, auditable outputs.

The session explains GDPR rules around data sharing for research in Europe, the distinction between law and ethics, and introduces practical solutions for securely sharing sensitive datasets. Researchers have more flexibility than commonly assumed: scientific research is considered a public interest task, so explicit consent for data sharing isn’t legally required, though transparency and informing participants remain ethically important. The talk also introduces publicneuro.eu, a controlled-access platform that enables sharing neuroimaging datasets with open metadata, DOIs, and customizable access restrictions while ensuring GDPR compliance.