This lightning talk describes the heterogeneity of the MR field regarding types of scanners, data formats, protocols, and software/hardware versions, as well as the challenges and opportunities for unifying these datasets in a common interface, MRdataset.
This lesson describes the current state of brain-computer interface (BCI) standards, including the present obstacles hindering the forward movement of BCI standardization as well as future steps aimed at solving this problem.
This lightning talk gives an outline of the DataLad ecosystem for large-scale collaborations, and how DataLad addresses challenges that may arise in such research cooperations.
In this lightning talk, you will learn about BrainGlobe, an initiative which exists to facilitate the development of interoperable Python-based tools for computational neuroanatomy.
This is the second of three lectures around current challenges and opportunities facing neuroinformatic infrastructure for handling sensitive data.
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 hands-on tutorial walks you through DataJoint platform, highlighting features and schema which can be used to build robost neuroscientific pipelines.
This lesson provides an introduction to the DataLad, a free and open source distributed data management system that keeps track of your data, creates structure, ensures reproducibility, supports collaboration, and integrates with widely used data infrastructure.
This lesson introduces several open science tools like Docker and Apptainer which can be used to develop portable and reproducible software environments.
This lecture provides a detailed description of how to incorporate HED annotation into your neuroimaging data pipeline.
This talk provides an overview of the FAIR-aligned efforts of MATLAB and MathWorks, from the technological building blocks to the open science coordination involved in facilitating greater transparency and efficiency in neuroscience and neuroinformatics.
In this lesson, you will learn how to understand data management plans and why data sharing is important.
This lecture covers a wide range of aspects regarding neuroinformatics and data governance, describing both their historical developments and current trajectories. Particular tools, platforms, and standards to make your research more FAIR are also discussed.
This talk highlights a set of platform technologies, software, and data collections that close and shorten the feedback cycle in research.
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.
This lecture focuses on the structured validation process within computational neuroscience, including the tools, services, and methods involved in simulation and analysis.
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 session provides users with an introduction to tools and resources that facilitate the implementation of FAIR in their research.
This session will include presentations of infrastructure that embrace the FAIR principles developed by members of the INCF Community.
This lecture provides an overview of The Virtual Brain Simulation Platform.