In this talk, you will hear about the challenges and costs of being FAIR in the many scientific fields, as well as opportunities to transform the ecology of the academic crediting system.
This brief talk describes the challenge of global data sharing and governance, as well as efforts of the the Brain Research International Data Governance & Exchange (BRIDGE) to develop ready-made workflows to share data globally.
This talk describes how to use DataLad for your data management and curation techniques when dealing with animal datasets, which often contain several disparate types of data, including MRI, microscopy, histology, electrocorticography, and behavioral measurements.
This brief talk covers an analysis technique for multi-band, multi-echo fMRI data, applying a denoising framework which can be used in an automated pipeline.
This lesson gives a quick introduction to the rest of this course, Research Workflows for Collaborative Neuroscience.
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 lesson consists of a panel discussion, wrapping up the INCF Neuroinformatics Assembly 2023 workshop Research Workflows for Collaborative Neuroscience.
This lecture provides a detailed description of how to incorporate HED annotation into your neuroimaging data pipeline.
This lesson gives an in-depth description of scientific workflows, from study inception and planning to dissemination of results.
This lesson gives an introductory presentation on how data science can help with scientific reproducibility.
This lecture discusses how FAIR practices affect personalized data models, including workflows, challenges, and how to improve these practices.
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 lesson introduces concepts and practices surrounding reference atlases for the mouse and rat brains. Additionally, this lesson provides discussion around examples of data systems employed to organize neuroscience data collections in the context of reference atlases as well as analytical workflows applied to the data.
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 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.