The data are open, but we are always interested in discussions or collaborations.
Data collected by @muyuuyum, analyzed by @XiuyeChen and colleagues, collaboration with @EngertLab and @HSompolinsky, @harvardbrainsci and @HHMIJanelia
#zebrafish #openscience
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Pardon my ignorance, but how does this data compress so efficiently? Seems like it's 26700% compression?
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I think he's counting the 15TB of raw data and only exports the segmented data (per neuron), but I have to check. In any case, they use Dropbox to share the data, which makes the whole zip impossible to download.
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Exactly, the compression comes from cell segmentation. The data are now on FigShare, https://figshare.com/s/16927195662ea9ff29c7 …, with DOI 10.25378/janelia.7272617, I hope that downloads better. Let us know
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High-quality data sharing like this is a game changer!
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Cool data and analysis! Would be interesting to interactively explore the dynamics. Check out interactome we developed for
#celeganshttps://github.com/shlizee/C-elegans-Neural-Interactome … -
Also connect neural activity with physical body model?
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Well, at least now I know why Tesla auto-drive keeps demanding to "go upriver and spawn."
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@MInkorrekt das klingt nach 1 spannendem ThemaThanks. Twitter will use this to make your timeline better. UndoUndo
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Nah, not enough neurons :-)
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Gotta do something similar for primate data!
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@fulhack something fishy hereThanks. Twitter will use this to make your timeline better. UndoUndo
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@normanrz sth for webknossos? :)Thanks. Twitter will use this to make your timeline better. UndoUndo
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Do you consider to analyse some interesting behaviours like instinctions and its relational concepts. Running away from a hunter or hiding in sand for example and implement it to the Human made systems such as AI?
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This is interesting
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Let the data mining and model training begin!
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Nice! When can I use this to drive a fish in the
@unity3d game engine?Thanks. Twitter will use this to make your timeline better. UndoUndo
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