Brief introduction to Research Resource Identifiers (RRIDs), persistent and unique identifiers for referencing a research resource.
Research Resource Identifiers (RRIDs) are ID numbers assigned to help researchers cite key resources (antibodies, model organisms and software projects) in the biomedical literature to improve transparency of research methods.
The Brain Imaging Data Structure (BIDS) is a standard prescribing a formal way to name and organize MRI data and metadata in a file system that simplifies communication and collaboration between users and enables easier data validation and software development through using consistent paths and naming for data files.
Neurodata Without Borders (NWB) is a data standard for neurophysiology that provides neuroscientists with a common standard to share, archive, use, and build common analysis tools for neurophysiology data.
The Neuroimaging Data Model (NIDM) is a collection of specification documents that define extensions the W3C PROV standard for the domain of human brain mapping. NIDM uses provenance information as means to link components from different stages of the scientific research process from dataset descriptors and computational workflow, to derived data and publication.
Neuroscience Information Exchange (NIX) Format data model allows storing fully annotated scientific datasets, i.e. the data together with rich metadata and their relations in a consistent, comprehensive format. Its aim is to achieve standardization by providing a common data structure and APIs for a multitude of data types and use cases, focused on but not limited to neuroscience. In contrast to most other approaches, the NIX approach is to achieve this flexibility with a minimum set of data model elements.
Computational models provide a framework for integrating data across spatial scales and for exploring hypotheses about the biological mechanisms underlying neuronal and network dynamics. However, as models increase in complexity, additional barriers emerge to the creation, exchange, and re-use of models. Successful projects have created standards for describing complex models in neuroscience and provide open source tools to address these issues. This lecture provides an overview of these projects and make a case for expanded use of resources in support of reproducibility and validation of models against experimental data.
Introduction to the Brain Imaging Data Structure (BIDS): a standard for organizing human neuroimaging datasets. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.
NWB: An ecosystem for neurophysiology data standardization
NWB: An ecosystem for neurophysiology data standardization
This lecture covers structured data, databases, federating neuroscience-relevant databases, ontologies.
Since their introduction in 2016, the FAIR data principles have gained increasing recognition and adoption in global neuroscience. FAIR defines a set of high-level principles and practices for making digital objects, including data, software, and workflows, Findable, Accessible, Interoperable, and Reusable. But FAIR is not a specification; it leaves many of the specifics up to individual scientific disciplines to define. INCF has been leading the way in promoting, defining, and implementing FAIR data practices for neuroscience. We have been bringing together researchers, infrastructure providers, industry, and publishers through our programs and networks. In this session, we will hear some perspectives on FAIR neuroscience from some of these stakeholders who have been working to develop and use FAIR tools for neuroscience. We will engage in a discussion on questions such as: how is neuroscience doing with respect to FAIR? What have been the successes? What is currently very difficult? Where does neuroscience need to go?
This lecture covers FAIR atlases, from their background, their construction, and how they can be created in line with the FAIR principles.
This talk highlights a set of platform technologies, software, and data collections that close and shorten the feedback cycle in research.
An agent for reproducible neuroimaging
The Human Connectome Project aims to provide an unparalleled compilation of neural data, an interface to graphically navigate this data and the opportunity to achieve never before realized conclusions about the living human brain.
Tutorial on how to simulate brain tumor brains with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls. Authors: Hannelore Aerts, Michael Schirner, Ben Jeurissen, DIrk Van Roost, Eric Achten, Petra Ritter, Daniele Marinazzo
The tutorial comprises a didactic video and jupyter notebooks (reproducing publication: Falcon et al. 2016 eNeuro). Contributors: Daniele Marinazzo, Petra Ritter, Paul Triebkorn, Ana Solodkin
This presentation by Dr. Michael Schirner population models and phase plane is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc... TVB is a full brain simulation platform.
This tutorial by Paul Triebkorn on how to simulate using TVB is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc. TVB is a full brain simulation platform.
This presentation by Dionysios Perdikis is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging. brain simulation. personalised brain models, TVB use cases, etc. TVB is a full brain simulation platform.