The Kavli Foundation provides seed grants to aid labs in standardizing their experimental data using the NWB data format. Funds are used to support the building and sharing of data or software in the NWB:N ecosystem.
Recent seed grant awardees include:
- Joshua Berke, University of California, San Francisco (2018)
- Convert a data set collect from dopamine neurons in the rat ventral tegmental area into the NWB format.
- Tim Buschman, Princeton Neuroscience Institute, Princeton University (2019)
- Convert electrophysiological recordings from non-human primates performing a working memory task into the NWB format.
- Eddie Chang, University of California, San Francisco (2018)
- Convert an ECoG dataset from a human electrophysiological experiment into the NWB format for sharing.
- Anne Churchland, Cold Spring Harbor Laboratory (2017)
- Convert existing data sets from the Churchland lab (imaging and electrophysiology) into NWB format for sharing.
- Andrea Giovannucci, The University of North Carolina at Chapel Hill (2019)
- A graphical visualization tool for NWB calcium imaging pipelines.
- Nuo Li, Baylor College of Medicine (2018)
- Create tutorials for the neuroscience community to demonstrate how to use API functions to convert MATLAB data structures into NWB files, using data taken from premotor cortex during decision-making.
- Daniel O’Connor, Johns Hopkins University (2018)
- Convert dataset of recordings from primary afferent neurons that innervate mouse whiskers in behaving animals into NWB format.
- Ueli Rutishauser, Cerdars-Sinai Medical Center (2018)
- Convert dataset of >1000 human single neuron recordings collected during a recognition memory task into the NWB format.
- Bernardo Sabatini, Harvard University (2018)
- Convert whole-cell electrophysiological data from mouse acute brain slices in response to stimulation and/or after application of peptide and small-molecule neuromodulators into the NWB format.
- Taufik Valiante, University of Toronto (2019)
- Convert whole cell patch-clamp electrophysiological recordings from human cortical neurons into the NWB format.
Please see here for instructions for 2019 seed grant applications.
The Kavli Foundation provided support (2016-2018) to Kristofer Bouchard at the Lawrence Berkeley National Lab for the continued development of the NWB 2.0 data format by creating a modern software architecture and high-performance computing (HPC) enabled data analysis tools utilizing NWB formatted data.
The Berkeley Lab team also included:
The Kavli Foundation is providing support (2019-present) to Oliver Rübel at the Lawrence Berkeley National Lab to support community engagement, outreach and software development. This program is currently supporting Benjamin Dichter as a community liaison for NWB:N.
The National Institutes of Health, through project 1R24MH116922, funded Oliver Rübel (Lawrence Berkeley National Laboratory) and Lydia Ng (Allen Institute for Brain Science) to continue development of the NWB data format and software ecosystem to enable standardization, sharing, and reuse of neurophysiology data and analyses, enhancing discovery and reproducibility. This funding is part of the NIH’s BRAIN Initiative support to develop standards.(September 2018-May 2020).
The National Institutes of Health, through project 1R44MH115731, funded William Schroeder (Kitware, Inc.) to create software tools to browse, process, analyze, and visualize NWB data. This funding is part of the NIH’s BRAIN Initiative Small Business Innovation Research (SBIR) grant program. (July 2017-June 2020).
The National Institutes of Health, through project 1R24MH117295, funded Satrajit Ghosh (Massachusetts Institute of Technology) and Yaroslav Halchenko (Center for Open Neuroscience) to create the DANDI archive for Neurophysiology Data. DANDI will: 1) provide a cloud platform for versioned neurophysiology data storage for the purposes of collaboration, archiving, and preservation. 2) provide easy to use tools for neurophysiology data submission and access in the archive; and 3) facilitate adoption of NWB via standardized applications for data ingestion, visualization and processing. (August 2019-April 2024).