In this lesson, you will learn about how team science unfolds in practice, as well as what are the standards and best practices used by teams, and how well these best practices function and support scientific output.
In this lesson, you will learn about approaches to make the field of neuroscience more open and fair, particularly regarding the integration of equality, diversity, and inclusion (EDI) as guiding principles for team collaboration.
This lesson discusses the topic of credit and contribution in open and FAIR neuroscience, looking through the respective lenses of systems, teams, and people.
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 lesson consists of a brief discussion around this sessions previous talks.
This brief talk goes into work being done at The Alan Turing Institute to solve real-world challenges and democratize computer vision methods to support interdisciplinary and international researchers.
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 third and final lecture of this course on neuroinformatics infrastructure for handling sensitive data.
In this lecture, you will learn about virtual research environments (VREs) and their technical limitations, (i.e., a computing platform and the software stack behind it) and the security measures which should be considered during implementation.
This lesson consists of a panel discussion, wrapping up the INCF Neuroinformatics Assembly 2023 workshop Research Workflows for Collaborative Neuroscience.
This lesson introduces several open science tools like Docker and Apptainer which can be used to develop portable and reproducible software environments.
This brief talk outlines the obstacles and opportunities involved in striving for more open and reproducible publishing, highlighting the need for investment in the technical and governance sectors of FAIR data and software.
This talk gives an overview of the complicated nature of sharing of neuroscientific data in an environment of numerous and often conflicting legal systems around the world.
In this short talk you will learn about The Neural System Laboratory, which aims to develop and implement new technologies for analysis of brain architecture, connectivity, and brain-wide gene and molecular level organization.
This talk covers the differences between applying HED annotation to fMRI datasets versus other neuroimaging practices, and also introduces an analysis pipeline using HED tags.
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.
This brief video provides a welcome and short introduction to the outline of the INCF Short Course in Neuroinformatics, held Seattle, Washington in October 2023, in coordination with the West Big Data Hub and the University of Washington.
This opening lecture from INCF's Short Course in Neuroinformatics provides an overview of the field of neuroinformatics itself, as well as laying out an argument for the necessity for developing more sophisticated approaches towards FAIR data management principles in neuroscience.
In this lecture, you will learn about current methods, approaches, and challenges to studying human neuroanatomy, particularly through the lense of neuroimaging data such as fMRI and diffusion tensor imaging (DTI).
This lesson provides an overview of the current status in the field of neuroscientific ontologies, presenting examples of data organization and standards, particularly from neuroimaging and electrophysiology.