e.g. users outside our bubble: @MichaelKunst5 @mpiNeuro, @dddavi @HHMIJanelia, @ScottishWaddell, @EichlerKathi @albertcardona, @neurojeanne, @AlexVrvoo, @tomtom_auer @bentonlab, @neuroluci, @lillvis @dingy05 @David_L_Stern, @lillvis @dingy05 @David_L_Stern 1.5/10
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The natverse allows you to easily plot neurons and neuroanatomical volumes, measure features and implement interesting algorithms, e.g. flow centrality
@csdashm and NBLAST@martamcosta2 http://natverse.org/gallery/ . We give lots of examples: https://github.com/natverse/nat.examples … 2/10pic.twitter.com/shSyBoRov9
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You should also be able to install the natverse with one line of code from R (http://natverse.org/install/ ) and we have a responsive help group: https://groups.google.com/forum/#!forum/nat-user … 3/10pic.twitter.com/m65RkvtYmP
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Since ~2003,
@gsxej has been developing tools in#rstats to work with neuron data (meanwhile, I was in primary school). Today, thanks to@uni_matrix and@i_am_shri_ you can also interoperate with python 4/10pic.twitter.com/PzT8Dnp4dY
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You can look at a lot, including NeuroMorpho, http://insectbrainDB.org (
@stanley_heinze), FlyCircuit (Ann-Shyn Chiang), MouseLight (@realMouseLight), CATMAID (@tomkazimiers,@clbarnes91,@aschampion), DVID, NeuPrint (@stephenplaza, W. Katz) and NeuroGlancer (@GoogleAI) 5/10pic.twitter.com/1ulCeJWzX5
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One of the things that set the natverse apart is that it geared to consider neurons registered to whole-brain / sub-region standard templates. This is critical in large-scale mapping projects inc connectomics. You must move data between them to compare datasets 6/10pic.twitter.com/aGZFKGAtDc
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@jamesdmanton developed ‘bridging registrations’ to unify the standard templates in fly neuroscience, so you don’t need to waste time and energy re-registering different datasets. There is also support for the latest templates from@BogovicJohn and@herrsaalfeld 7/10pic.twitter.com/gk1ySsEzPt
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This works if you have registered data. An important outcome is that
@gsxej and@martamcosta2 registered hundreds of fly brain images to standard template brains, made available at@virtualflybrain. Here’s@SebaCachero ‘s clones (IS2) matched with Chiang lab neurons (FCWB) 8/10pic.twitter.com/Xr2WWYT09d
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This is super powerful because it allows us to repeatably find the same morphological cell types between different datasets, e.g. linking neurons sparse genetic lines (confocal microscopy) to manual/auto segmentations (flood-filling EM data,
@Chinasaurli and@stardazed0) 9/10pic.twitter.com/CwddDyBLiP
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We want the community to improve the natverse! Just merged a PR from
@RFranconville to read Neuprint ROIs. Future: reading from more online databases, analysis of neurons as 3d meshes and support for other languages, e.g. to play nicely with@HCuntz ‘s MATLAB TREES toolbox 10/10pic.twitter.com/76Rb6oom9D
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