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All the data and code below are made available under the GPL3 license, unless specified otherwise under the corresponding link. Please cite the relevant publication(s) if you use any of the data or code in a manuscript.
Dataspace (dspace) is a data-science platform for scientists. Dspace is based on the assumption that a substantial part of data tasks can be broken down to a few primitives. These primitives are made available to combine in a lego like setting within a GUI. All actions performed are automatically logged and expressed as programming code. Code based work flows and GUI based work flows can merge seamlessly, enabling analyses without coding. At the same time you can easily use dspace with your existing code only when and where needed. dspace can be easily controlled through code and expanded by adding new actions. Data and expansions of dspace can be easily shared.
System requirements: Matlab 2019b or later with the following toolboxes installed:
Citation: Kollmorgen, S., Hahnloser, R.H.R. & Mante, V. Nearest neighbours reveal fast and slow components of motor learning. Nature 577, 526–530 (2020). https://doi.org/10.1038/s41586-019-1892-x
Video Tutorials by Sepp Kollmorgen:
The Matlab code below implements the nearest-neighbour-based analyses in Kollmorgen et al (repertoire dating).
Code for nearest-neighbor analyses (repertoire dating)
Citation: Kollmorgen, S., Hahnloser, R.H.R. & Mante, V. Nearest neighbours reveal fast and slow components of motor learning. Nature 577, 526–530 (2020). https://doi.org/10.1038/s41586-019-1892-x
This dataset includes neural recordings from FEF and pre-arcuate cortex in macaque monkeys and Matlab code to perform Targeted Dimensionality Reduction on the recordings.
Neural recordings (ZIP, 59 MB)
Code for Targeted dimensionality reduction (TDR) (ZIP, 864 KB)
Citation: Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Nature 503, 78–84 (2013).