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 lecture covers the benefits and difficulties involved when re-using open datasets, and how metadata is important to the process.

This lecture provides guidance on the ethical considerations the clinical neuroimaging community faces when applying the FAIR principles to their research. 

This lesson provides an overview of the Miniscope project, explaining the motivation behind the how and why of Miniscope development, why Miniscopes may be useful for researchers, and the differences between previous and current versions.

This lesson provides instruction on how to build a Miniscope and stream data, including an overview of the software involved.

An introduction to data management, manipulation, visualization, and analysis for neuroscience. Students will learn scientific programming in Python, and use this to work with example data from areas such as cognitive-behavioral research, single-cell recording, EEG, and structural and functional MRI. Basic signal processing techniques including filtering are covered. The course includes a Jupyter Notebook and video tutorials.

This lesson introduces several open science tools like Docker and Apptainer which can be used to develop portable and reproducible software environments. 

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

This video explains what metadata is, why it is important, and how you can organize your metadata to increase the FAIRness of your data on EBRAINS.

This lesson provides an overview of how to conceptualize, design, implement, and maintain neuroscientific pipelines in via the cloud-based computational reproducibility platform Code Ocean. 

This lesson provides an overview of how to construct computational pipelines for neurophysiological data using DataJoint.

This talk describes approaches to maintaining integrated workflows and data management schema, taking advantage of the many open source, collaborative platforms already existing.

This hands-on tutorial walks you through DataJoint platform, highlighting features and schema which can be used to build robost neuroscientific pipelines. 

This lecture provides a detailed description of how to incorporate HED annotation into your neuroimaging data pipeline. 

This lesson gives an introductory presentation on how data science can help with scientific reproducibility.

This lecture covers how to make modeling workflows FAIR by working through a practical example, dissecting the steps within the workflow, and detailing the tools and resources used at each step.

DataLad is a versatile data management and data publication multi-tool. In this session, you can learn the basic concepts and commands for version control and reproducible data analysis. You’ll get to see, create, and install DataLad datasets of many shapes and sizes, master local version workflows and provenance-captured analysis-execution, and you will get ideas for your next data analysis project.

EyeWire is a game to map the brain. Players are challenged to map branches of a neuron from one side of a cube to the other in a 3D puzzle. Players scroll through the cube and reconstruct neurons with the help of an artificial intelligence algorithm developed at Seung Lab in Princeton University. EyeWire gameplay advances neuroscience by helping researchers discover how neurons connect to process visual information. 

Mozak is a scientific discovery game about neuroscience for citizen scientists and neuroscientists alike. Players to help neuroscientists build models of brain cells and learn more about the brain through their efforts.

This lesson discusses both state-of-the-art detection and prevention schema in working with neurodegenerative diseases.