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On October 25, the INCF SBP committee voted to endorse Neo as a standard, with standard number INCFSN-22-02.
Neo is an object model for handling electrophysiology data in multiple formats. It is suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations.
Mark Alan Musen, professor of Biomedical Informatics and of Biomedical Data Science at Stanford University points out in Nature that without appropriate metadata, shared data cannot be reused and data-sharing mandates will be pointless.
The Neurodata Without Borders (NWB) team recently published a paper in eLife, “The Neurodata Without Borders ecosystem for neurophysiological data science” that describes their 8 year long effort to develop a data standard and a surrounding software ecosystem for neurophysiology data.
INCF is asking for your help to review the Neo object model for electrophysiology data, to assess its value as a community standard. Participating is simple; read the INCF SBP committee review report on F1000 and leave your feedback in the comments!
Infrastructure Committee is trying to identify barriers to data sharing and reuse among neuroscience researchers worldwide, with a brief anonymous survey. The results will be made public, and will be used by INCF to develop strategies and activities for supporting the global neuroscience community.
There is still time to register for our yearly neuroinformatics community meet-up and training event, the INCF Assembly in September - and we have just opened up submissions of late-breaking abstracts! Submit your abstracts by Friday August 19.
Invited special session on "Tensor Representation, Completion, Modeling and Analytics of Complex Data" and would like to invite the INCF Community to submit abstracts for presentations at the upcoming Joint Mathematics Meetings (JMM) in Boston, MA, January 4-7, 2023.
One of INCF’s core activities is the endorsement and promotion of already existing community Standards and Best Practices (SBPs). We have working groups developing newSBPs, and/or developing tools that implement SBPs to make them useful for the rest of the community. You can read more about why and how we endorse Standards and Best Practices, and browse our Standards and Best Practices portfolio.
Having read about the FAIR principles in our last post, you might wonder - what is metadata? Metadata is data that describes other data. It summarizes basic information about data, making it easier to find and work with particular instances of data. To be as useful as possible, metadata needs to be standardized so it can be used and understood by many, and especially by machines.
Rapid technical development means that neuroscience datasets are growing ever bigger and more complex, which makes them harder to store, analyze and share. However, if data are organized, well defined and well described in a standardized way, computational methods can help.