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.
KnowledgeSpace is a community-based encyclopedia that links brain research concepts to data, models, and literature. It provides users with access to anatomy, gene expression, models, morphology, and physiology data from over 15 different neuroscience data/model repositories, such as Allen Institute for Brain Science and the Human Brain Project.
The Identifiers.org system is a central infrastructure for findable, accessible, interoperable and re-usable (FAIR) data. It provides a range of services to generate, resolve and validate persistent Compact Identifiers to promote the citability of individual data providers and integration with e-infrastructures.
Introduction to the FAIR Principles and examples of applications of the FAIR Principles in neuroscience. This lecture was part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.
The tutorial is intended primarily for beginners, but it will also beneficial to experimentalists who understand electroencephalography and event related techniques, but need additional knowledge in annotation, standardization, long-term storage and publication of data.
Introduction to the first phases of EEG/ERP data lifecycle
EEGLAB is an interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. EEGLAB runs under Linux, Unix, Windows, and Mac OS X.