Static Repositories

Imagery

Electron Microscopy Images

minnie65 and minnie35 imagery
Name Volume Cloudpath Short Description Type (size)
Fine-aligned Image (EM) minnie65 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/em Multi-resolution electron microscopy (EM) imagery from 8,8,40 nm and above Precomputed Image Data (117 TB)
Fine-aligned Image (EM) minnie65 https://storage.googleapis.com/iarpa_microns/minnie/minnie65/em Multi-resolution electron microscopy (EM) imagery from 8,8,40 nm and above Precomputed Image Data (117 TB)
Fine-aligned Image (EM) minnie35 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie35/em Multi-resolution electron microscopy (EM) imagery from 8,8,40 nm and above Precomputed Image Data (55 TB)
Fine-aligned Image (EM) minnie35 https://storage.googleapis.com/iarpa_microns/minnie/minnie35/em Multi-resolution electron microscopy (EM) imagery from 8,8,40 nm and above Precomputed Image Data (55 TB)

This contains the fine aligned Electron Microscopy (EM) image data downsampled to 8,8,40 nm resolution stored in precomputed image format. Lower resolution downsampling is available in this bucket as well, including [16, 16, 40], [32, 32, 40], [64, 64, 40], [128, 128, 80], [256, 256, 160], [512, 512, 320], [1024, 1024, 640], [2048, 2048, 1280]. Folder contains many files, for download use cloud-volume, tensor-store, for bulk download use igneous, AWS CLI or gsutil CLI.

MicroCT

minnie65 and minnie35 MicroCT
Name Cloudpath Short Description Type (size)
MicroCT https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/microCT MicroCT used for inspection and alignment ZIP (13 GB)

Cellular Segmentation

Flat Segmentation

minnie65 and minnie35 flat segmentation v117
Name Volume Cloudpath Short Description Type (size)
Automated Segmentation (v0) minnie65 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/autoseg Mulit-resolution flat / static cellular segmentation voxels and meshes from 8,8,40 nm and above Precomputed Shareded Compressed Segmentation (12 TB)
Proofread Segmentation (v117) minnie65 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/seg Mulit-resolution flat / static cellular segmentation voxels and meshes from 8,8,40 nm and above Precomputed Shareded Compressed Segmentation (12 TB)
Proofread Segmentation (v117) minnie65 https://storage.googleapis.com/iarpa_microns/minnie/minnie65/seg Mulit-resolution flat / static cellular segmentation voxels and meshes from 8,8,40 nm and above Precomputed Shareded Compressed Segmentation (12 TB)
Proofread Segmentation (v343) minnie65 https://storage.googleapis.com/iarpa_microns/minnie/minnie65/seg_m343 Mulit-resolution flat / static cellular segmentation voxels and meshes from 8,8,40 nm and above Precomputed Shareded Compressed Segmentation (12 TB)
Proofread Segmentation (v943) minnie65 https://storage.googleapis.com/iarpa_microns/minnie/minnie65/seg_m943 Mulit-resolution flat / static cellular segmentation voxels and meshes from 8,8,40 nm and above Precomputed Shareded Compressed Segmentation (12 TB)
Proofread Segmentation (v1300) minnie65 https://storage.googleapis.com/iarpa_microns/minnie/minnie65/seg_m1300 Mulit-resolution flat / static cellular segmentation voxels and meshes from 8,8,40 nm and above Precomputed Shareded Compressed Segmentation (12 TB)
Automated Segmentation (v0) minnie35 s3://bossdb-open-data/iarpa_microns/minnie/minnie35/autoseg Mulit-resolution flat / static cellular segmentation voxels and meshes from 8,8,40 nm and above Precomputed Shareded Compressed Segmentation (10 TB)
Proofread Segmentation (v0) minnie35 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie35/seg Mulit-resolution flat / static cellular segmentation voxels and meshes from 8,8,40 nm and above Precomputed Shareded Compressed Segmentation (10 TB)
Proofread Segmentation (v0) minnie35 https://storage.googleapis.com/microns_phase3/minnie/minnie35/seg Mulit-resolution flat / static cellular segmentation voxels and meshes from 8,8,40 nm and above Precomputed Shareded Compressed Segmentation (10 TB)

This contains the fixed state of the cellular segmentation at each version, where each voxel has been assigned an ID which is unique to each cellular object at 8,8,40, along with downsampled versions. Not all objects have been proofread, but a summary of the most focused efforts on cells can be found in the proofreading status metadata. In addition the mesh folder contains meshes of each object available at 3 different levels of downsampling. Folder contains many files, for download use cloud-volume, tensor-store, for bulk download use igneous, AWS CLI or gsutil CLI.

Watershed segmentation and affinities

minnie65 and minnie35 Watershed Segmentation
Name Volume Cloudpath Short Description Type (size)
Watershed Segmentation minnie65 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/ws The supervoxel segmentation Precomputed Shareded Compressed Segmentation (42 TB)
Watershed Segmentation minnie35 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie35/ws The supervoxel segmentation Compressed Sharded Precomputed Segmentation Data (22 TB)

The individual supervoxels predicted by the affinity network before they were agglomerated by the automated segmentation and then modified through proofreading. Folder contains many files, for download use cloud-volume, tensor-store, for bulk download use igneous, AWS CLI or gsutil CLI.

Dynamic segmentation

minnie65 and minnie35 Dynamic Segmentation
Name Volume Cloudpath Short Description Type (size)
Dynamic segmentation (public) minnie65 graphene://https://minnie.microns-daf.com/segmentation/table/minnie65_public Dynamic cellular segmentation on the chunked graph Graphene Segmentation
Dynamic segmentation (production) minnie65 graphene://https://minnie.microns-daf.com/segmentation/table/minnie3_v1 Dynamic cellular segmentation on the chunked graph Graphene Segmentation
Dynamic segmentation (production) minnie35 graphene://middleauth+https://minnie.microns-daf.com/segmentation/table/minnie35_public_v0 Dynamic cellular segmentation on the chunked graph Graphene Segmentation

The cellular segmentation graphene layers, segmentation on the chunkedgraph. Access through cloud-volume or CAVEclient.

Ground truth segmentation

minnie65 and minnie35 Dynamic Segmentation
Name Volume Cloudpath Short Description Type (size)
Ground Truth Segmentation minnie65 https://zenodo.org/record/5760218/files/minnie.tar.gz Manual ground truth HDF5 file

Manual ground truth labels provided for training the automated segmentation pipeline. For details and use, see the Zenodo record associated with the manual lables: https://zenodo.org/records/5760218

Synapse segmentation and graph

Synaptic cleft segmentation

minnie65 synaptic cleft segmentation
Name Volume Cloudpath Short Description Type (size)
PSD Segmentation minie65 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/clefts Voxel segmentation of each synapse (post-synaptic density - PSD) detected Precomputed Compressed Segmentation Data (127 GB)
PSD Segmentation minnie35 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie35/clefts Voxel segmentation of each synapse (post-synaptic density - PSD) detected Precomputed Compressed Segmentation Data (94 GB)

This contains a flattened segmentation of the synaptic clefts where each voxel has been assigned an ID which is unique to each synapse at 8,8,40. Folder contains many files, for download use cloud-volume, for bulk download use igneous or AWS or gsutill CLI.

Synapse graph (versioned)

minnie65 and minnie35 Synapse Graph
Name Volume Cloudpath Short Description Type (size)
Synapse Graph (v117) minnie65 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/synapse_graph/synapses_pni_2.csv Metadata about each synapse detection, including which cellular segmentation(s) are pre/post synaptic CSV (47.5 GB)
Synapse Graph minnie35 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie35/synapse_graph/assigned.csv.gz Metadata about each synapse detection, including which cellular segmentation(s) are pre/post synaptic CSV (14.5 GB)

This CSV contains columns of metadata about the synapse detections. Note that this is as of the segmentation at version 117; pre_pt_root_id and post_pt_root_id are pinned to that version.

Column Descriptions:

  • id: corresponds to the ID from the PSD segmentation volume
  • valid: internal check, uniformly ‘t’
  • pre_pt_position_{x,y,z}: the location in 4,4,40 nm voxels of the pre-synaptic point
  • post_pt_position_{x,y,z}: the location in 4,4,40 nm voxels of the post-synaptic point
  • ctr_pt_position_{x,y,z}: the location of the center of mass of the PSD segmentation in 4,4,40 nm voxels
  • pre_pt_supervoxel_id: the ID of the supervoxel from the watershed segmentation that is under the pre_pt_position
  • post_pt_supervoxel_id: the ID of the supervoxel from the watershed segmentation that is under the post_pt_position
  • pre_pt_root_id: the ID of the segment/root_id under the pre_pt_position from the Proofread Segmentation (v117)
  • post_pt_root_id: the ID of the segment/root_id under the post_pt_position from the Proofread Segmentation (v117)
  • size: the count of voxels in 4,4,40 nm resolution of the PSD segmentation object

Nucleus segmentation and classification

Nucleus segmentation

minnie65 nucleus segmentation
Name Volume Cloudpath Short Description Type (size)
Nucleus Segmentation minnie65 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/nuclei Voxel segmentation and meshes of each cell nucleus detected in image volume Precomputed Compressed Segmentation Data (26.8 GB)
Nucleus Segmentation minnie65 https://storage.googleapis.com/iarpa_microns/minnie/minnie65/nuclei Voxel segmentation and meshes of each cell nucleus detected in image volume Precomputed Compressed Segmentation Data (26.8 GB)

This contains a flattened segmentation of the nucleus segmentation where each voxel has been assigned an ID which is unique to each nucleus at 8,8,40. Folder contains many files, for download use cloud-volume, for bulk download use igneous or AWS CLI.

Nucleus detection and centroids

minnie65 nucleus detection
Name Volume Cloudpath Short Description Type (size)
Nucleus Detection minnie65 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/nucleus_detection/nucleus_detection_v0.csv Metadata about each nucleus detection, including the cellular segment that it overlaps with CSV (10.5 MB)

A table of nuclei detections from a nucleus detection model developed by Shang Mu, Leila Elabbady, Gayathri Mahalingam and Forrest Collman. Only included nucleus detections of volume>25 um^3, below which detections are false positives, though some false positives above that threshold remain.

Column Descriptions:

  • id: corresponds to the ID from the nucleus detection and segmentation
  • valid: internal check, uniformly ‘t’
  • pt_position_{x,y,z}: the location in 4,4,40 nm voxels of the nucleus location
  • pt_supervoxel_id: the ID of the supervoxel from the watershed segmentation that is under the pt_position
  • pt_root_id: the ID of the segment/root_id under the pt_position from the Proofread Segmentation (v117).
  • volume: the volume of the nucleus detection in um^3

Nucleus-based cell typing

minnie65 neuron nucleus classification
Name Volume Cloudpath Short Description Type (size)
Nucleus Neuron Classification minnie65 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/nucleus_neuron_classification/nucleus_neuron_svm.csv An automated annotation of which nuclei are neurons CSV (12.8 MB)

This table contains a prediction about what nucleus detections are neurons and which are likely not neurons. This is based upon a model trained by Leila Elabbady for (Elabbady et al. 2025) on nucleus segmentations in Basil (Phase 2), processed for features such as volume, foldedness, location in cortex, etc, and applied to Minnie65. In Basil the model had a cross validated f1 score of .97 and a recall of .97 for neurons. Manual validation performed on a column of 1316 nuclei in Minnie65 measured a recall of .996 and a precision of .969.

Column Descriptions:

  • id: corresponds to the ID from the nucleus detection and segmentation
  • valid: internal check, uniformly ‘t’
  • pt_position_{x,y,z}: the location in 4,4,40 nm voxels of the nucleus location
  • pt_supervoxel_id: the ID of the supervoxel from the watershed segmentation that is under the pt_position
  • pt_root_id: the ID of the segment/root_id under the pt_position from the Proofread Segmentation (v117).
  • classification_system: uniformly “is_neuron” for all entries.
  • cell_type: ‘neuron’ if the classifier called this a neuron, ‘not-neuron’ if it was not classified as a neuron, this contains both non-neuronal cells as well as false positive detections.

Functional Data

Downloads available from the following links at BossDB: IARPA MICrONS Minnie, and also as Neuro-data without borders (.NWB) format from DANDI: MICrONS Two Photon Functional Imaging

Stimulus presentation (movies)

Functional Data: Stimulus Presentation
Name Cloudpath Short Description Type (size)
Stimulus Presentation https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/stimulus_movies Visual stimulus presented during functional imaging scans AVI (multiple, 9.8 GB each, 186.2 GB total)

The visual stimulus shown to the animal in each scan for 19 scans was recreated by aligning, concatenating, and temporally filtering individual stimulus clips into a single movie, which was sampled by interpolation at scan depth frame times and saved as an avi file. Please see technical documentation for details.

Functional Imaging Scans (tif)

Functional Data: Functional Imaging Scans
Name Cloudpath Short Description Type (size)
Functional Imaging Scans https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/two_photon_functional_scans/ Two-photon functional imaging scans TIF (multiple, 66-95 GB each, 1.3TB total)

The two-photon imaging collected during 19 scans was raster- and motion-corrected, then saved as TIF files. Please see technical documentation for details.

Structural Imaging (z-stack)

Functional Data: Structural Imaging Stack
Name Cloudpath Short Description Type (size)
Structural Imaging Stack https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/two_photon_structural_stacks Two-photon structural volume enclosing imaged area TIFF (multiple, 1.2-9.4 GB each, 10.6 GB total)

Two-photon volume imaging including vasculature label of the tissue enclosing the two-photon imaged area, saved at original and upsampled resolutions as TIF files. Please see technical documentation for details.

DataJoint database

Functional Data: DataJoint
Name Cloudpath Short Description Type (size)
DataJoint Database https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/two_photon_processed_data_and_metadata Functional Data, Meta Data, Experimental Data SQL, Containers (225 GB total)

Scan metadata and processed data including scan and stack metadata, synchronized stimulus movies, synchronized behavioral traces, cell segmentation masks, calcium traces, and inferred spikes, pre-ingested into a containerized MYSQL v5.7 database, schematized using DataJoint. Please see technical documentation for details.

Instructions for setting up the containers available at https://github.com/cajal/microns-nda-access.

Digital twin and derived data

Functional Data: Digital Twin
Name Cloudpath Short Description Type (size)
Digital Twin Properties (netCDF) https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/digital_twin_properties xarray dataset containing the digital twin neural responses and derived characteristics netCDF (30.6 GB)
Digital Twin Properties (csv) https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/units_visual_properties table containing the digital twin derived characteristics CSV (22 MB)

A deep neural network model trained to predict visual responses has been applied to the functional recordings in the MICrONS dataset. This model predicts neural activity in response to the visual stimuli, and also estimates important visual properties of these neurons. This includes fits of their orientation and direction selectivity, as well as an estimate of their receptive field locations.

For details and use, see (Wang et al. 2025) and (Ding et al. 2025).

Variables included in both the .netCDF and .csv data sources are:

  • session: session ID for the recording. The combination of session and scan_idx uniquely identifies the recording
  • scan_idx: scan ID for the recording. The combination of session and scan_idx uniquely identifies the recording scan
  • unit_id: functional unit ID, unique per recording scan
  • pref_ori: preferred orientation in radians (0 - pi), horizontal bar moving upward is 0 and orientation increases clockwise, extracted from model responses to oriented noise stimuli
  • pref_dir: preferred direction in radians (0 - 2pi), horizontal bar moving upward is 0 and orientation increases clockwise, extracted from model responses to oriented noise stimuli
  • gOSI: global orientation selectivity index
  • gDSI: global direction selectivity index
  • readout_weight: readout weight vector
  • readout_location_x: x coordinate of the readout location per unit, in a 128 * 72 downsampled stimulus space, this is an approximation of a neuron’s receptive field center
  • readout_location_y: y coordinate of the readout location per unit, in a 128 * 72 downsampled stimulus space, this is an approximation of a neuron’s receptive field center
  • cc_abs: prediction performance of the model, higher is better
  • cc_max: tuning reliability of functional units, higher is better

The following data structures are available ONLY in the netCDF files:

  • natural_movie: a concatenated collection of 10-sec naturalistic movie clips (netCDF file only)
  • natural_response: model responses to natural_movie, each 10-sec movie clip is fed independently to the model, and the responses are concatenated

The following variables are available ONLY in the .csv file.

  • oracle_scan_set_hash: metadata to lookup the matching the visual stimulus set
  • ori_scan_set_hash: metadata to lookup the matching the visual stimulus set
  • dynamic_model_scan_set_hash: metadata to lookup the matching the digital twin model responses

Proofreading Status

Important

This table is available for posterity, but it is highly recommended you use the up-to-date proofreading status available through CAVE. See CAVE Query: Proofread Cells for the preferred workflow

minnie65 Proofreading Status
Name Volume Cloudpath Short Description Type (size)
Proofreading Status minnie65 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/proofreading_status/proofreading_status_public_release.csv Metadata about which cells have undergone what level of proofreading CSV (56 KB)

The proofreading status of neurons that have been comprehensively proofread as of v117. Axon and dendrite compartment status are marked separately under ‘axon_status’ and ‘dendrite_status’, as proofreading effort was applied differently to the different compartments in some cells. There are three possible status values for each compartment: ‘non’ indicates no comprehensive proofreading. ‘clean’ indicates that all false merges have been removed, but all tips have not necessarily been followed. ‘extended’ indicates that the cell is both clean and all tips have been followed as far as a proofreader was able to. Very small false axon merges (axon fragments approximately 5 microns or less in length) were considered acceptable for clean neurites. Note that this table does not list all edited cells, but only those with comprehensive effort toward the status mentioned here. It is meant to serve as a resource for analysis as to a list of objects that have undergone different levels of quality control by humans.

Column Descriptions:

  • id: a unique identifier for this row
  • valid: internal check, uniformly ‘t’
  • pt_position_{x,y,z}: the location in 4,4,40 nm voxels at a cell body or similar core position for the cell
  • pt_supervoxel_id: the ID of the supervoxel from the watershed segmentation that is under the pt_position
  • pt_root_id: the ID of the segment/root_id under the pt_position from the Proofread Segmentation (v117).
  • valid_id: the root id when the proofreading was last checked. If the current root id in ‘pt_root_id’ is not the same as ‘valid_id’, there is no guarantee that the proofreading status is correct. Should not happen be true for all rows in this release.
  • status_dendrite: the status of proofreading for this cell’s dendrites. (clean, extended, non)
  • status_axon: the status of proofreading for this cell’s axon. (clean, extended, non)

Functional Co-registration

Important

This table is available for posterity, but it is highly recommended you use the up-to-date coregistration table available through CAVE. See Annotation Tables: Functional Coregistration Tables for current best practices.

minnie65 Functional Co-registration
Name Volume Cloudpath Short Description Type (size)
Functional Co-registration minnie65 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_coregistration/func_unit_em_match_release.csv Metadata about which cellular segmentations correspond to which functional ROIs CSV (14KB)

A table of EM nuclear centroids manually matched to corresponding units from the functional scans. Functional imaging performed by Paul Fahey and Jake Reimer of BCM. A functional unit is uniquely identified by its session, scan_idx and unit_id. An EM centroid may been present in more than one imaging field and therefore be associated with more than one functional unit. Coregistration of Two-Photon imaging data and EM data performed by AIBS. Coregistration: Nuno da Costa and Mark Takeno of AIBS generated correspondence points between the datasets and Dan Kapner of AIBS fit the transform. (Github: https://github.com/AllenInstitute/em_coregistration/tree/phase3). Matching: Functional unit to EM cell matching protocol developed by Stelios Papadopoulos of BCM and performed by trained personnel. Briefly, a summary image of the functional imaging field was compared to its corresponding plane from the coregistered EM volume. Both nearby neuronal somas and vessel features were used as fiducials to confirm the accuracy of coregistration locally and to determine the functional unit to EM cell match.

Column Descriptions:

  • id: a unique identifier for this row
  • valid: internal check, uniformly ‘t’
  • pt_position_{x,y,z}: the location in 4,4,40 nm voxels at a cell body or similar core position for the cell
  • pt_supervoxel_id: the ID of the supervoxel from the watershed segmentation that is under the pt_position
  • pt_root_id: the ID of the segment/root_id under the pt_position from the Proofread Segmentation (v117).
  • session: the ID indicating the imaging period for the mouse (combination of session and scan_idx indicate a distinct recording)
  • scan_idx: the index of the scan within the imaging session (combination of session and scan_idx indicate a distinct recording)
  • unit_id: the ID of the functional ROI (unique per scan)

Neuron Skeletons (v661)

Important

These skeletons are available for posterity, but it is highly recommended you use the up-to-date skeletons and meshes available through CAVE. See Download Skeletons for the preferred workflow, or the v1300 section below for a more recent static repository

minni65 SWC Skeletons v661
Name Volume Cloudpath Short Description Type (size)
SWC Skeleton minnie65 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie/minnie65/skeletons/v661/skeletons Graph neuron with nodes, the radius, and compartment .csv (many files)

The swc files contain a simple graphical representation of neurons that has nodes with their 3d points in space, the estimated radius of those points and the compartment of the node (axon, (basal) dendrite, apical dendrite, soma).

minnie65 Meshwork Skeletons v661
Name Volume Cloudpath Short Description Type (size)
Meshwork Skeleton minnie65 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie/minnie65/skeletons/v661/meshworks Graphical representation of neurons as networkx object) .h5 (many files)

Each h5 file here can be ingested into a meshwork object in python with the package MeshParty. This meshwork integrates anatomical data and other user defined annotations such as synapses. The meshworks here contain annotation tables including pre and post synaptic chemical synapses on the given neuron and the ids of the post or presynaptic neurons respectively. Unless the neuron has had a high level of proofreading, the axon has been masked and removed.

minnie65 Skeleton metadata v661
Name Volume Cloudpath Short Description Type (size)
Metadata Json minnie65 https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie/minnie65/skeletons/v661/metadata Features of the skeleton and processing pipeline .json (many files)

The json files contain metadata and various features of the neuron skeleton including the proofreading version of the microns dataset, the various ids of the given body, and information on masked out indices.

The variables included in the Json file are:

  • soma_location: location of the center of the soma for the given neuron in voxels
  • soma_id: identification number of the soma nucleus of the given neuron
  • root_id: identification number (segmentation id) of this specific version of this neuron. If the given neuron undergoes proofreading and some neuron segment is added or removed, it will be given a new root it. The root id recorded here was the root for this neuron in the current version (see ‘version’ value in this meta file)
  • supervoxel_id: identification number of a supervoxel (smallest unit of segmentation) at the center of the soma of the given neuron version MICrONS dataset materialization version from which the meshwork and skeleton was generated from. Different versions have different level of proofreading
  • apical_nodes: indices of excitatory skeleton nodes in this neuron that have been predicted to be apical nodes. Per the system we used, there could be no apicals, one apical, or multiple apicals. One apical is the most common
  • basal_nodes: indices of excitatory skeleton nodes predicted to be basal dendrites
  • true_axon_nodes: Only measured in the highly proofread neurons for which we retained the axons on the skeletons. Indicates the indices of skeleton nodes that represent the axon. Compare to classified_axon_nodes below, and additional metadata on the proofreading status
  • total_skeleton_path_length: total cable length of skeleton (in nm)
  • apical_length: total cable length of apical nodes along skeleton (in nm)
  • basal_length: total cable length of basal nodes along skeleton (in nm)
  • axon_length: total cable length of axon nodes along skeleton (in nm)
  • percent_apical_pathlength: (apical_length/total_skeleton_path_length)*100
  • percent_basal_pathlength: (basal_length/total_skeleton_path_length)*100
  • percent_axon_pathlength: (axon_length/total_skeleton_path_length)*100
  • classified_axon_nodes: indices of skeleton nodes that were predicted to be axons by algorithm, but were not removed during peel-back due to having a downstream segment that was identified as a dendrite. (A measure of potential error in the compartment labeling process)
  • length_remaining_classified_axon: total cable length of skeleton nodes that were predicted to be axons by algorithm, but were not removed due to having a downstream segment that was identified as a dendrite. (A measure of potential error in the compartment labeling process)
  • percent_remaining_classified_axon_pathlength: (total_classified_axon_pathlength/total_skeleton_path_length)*100

The Json metadata files also include the proofreading status indicators for the cells, which determined whether the axon was excluded from skeletonization and compartment labeling. These metrics are as follows:

  • tracing_status_dendrite: indicates the amount and mode of proofreading that has been conducted on the dendrite of the neuron. May be one of: non, clean, extended. See Proofreading Strategies for more information.
  • tracing_status_axon: indicates the amount and mode of proofreading that has been conducted on axon of the neuron. May be one of: non, clean, extended. See Proofreading Strategies for more information.
  • `tracing_pipeline indicates if axon is included or has been masked out/removed from the meshwork and skeletons
  • clean_axon_included: indicates that the neuron has been sufficiently proofread and that the axon has not been removed and has been included in the skeleton and not masked over in the meshwork. There are 373 such neurons in this collection.
  • all_axon_removed: indicates that the neuron has not been sufficiently proofread, meaning the axon is not biologically accurate and has therefor been masked and removed. Asterisk indicates that this process may not have removed or masked out all the axon. See classified_axon_nodes above.

A Use Example for the v661 skeletons follows, and requires the convenience python package skeleton_plot

pip install skeleton_plot

Set paths to the public repository

The skeletons of the meshes are calculated at specific timepoints. This collection of neurons in the dataset was at materialization version 661.

import skeleton_plot as skelplot
import skeleton_plot.skel_io as skel_io
import matplotlib.pyplot as plt

# path to the skeleton .swc files
skel_path = "s3://bossdb-open-data/iarpa_microns/minnie/minnie65/skeletons/v661/skeletons/"

# path to the skeleton and meshwork .h5 files
mesh_path = "s3://bossdb-open-data/iarpa_microns/minnie/minnie65/skeletons/v661/meshworks/"

Example: load cell with known nucleus id and segment id

# Skeleton
nucleus_id = 292685
segment_id = 864691135122603047
skel_filename = f"{segment_id}_{nucleus_id}.swc"

# load the .swc skeleton
sk = skel_io.read_skeleton(skel_path, skel_filename)

# load the meshwork (may take minutes to locate, if using public credentials)
mesh_filename = f"{segment_id}_{nucleus_id}.h5"
mw = skel_io.load_mw(mesh_path, mesh_filename)

Plot skeleton

f, ax = plt.subplots(figsize=(7, 10))
skelplot.plot_tools.plot_skel(
    sk,
    title=nucleus_id,
    line_width=1,
    plot_soma=True,
    invert_y=True,
    pull_compartment_colors=True,
    x="z",
    y="y",
    skel_color_map = { 3: "firebrick",4: "salmon",2: "black",1: "olive" },
)

ax.spines['right'].set_visible(False) 
ax.spines['left'].set_visible(False) 
ax.spines['top'].set_visible(False) 
ax.spines['bottom'].set_visible(False)
# ax.axis('off')

Plot meshwork)

The meshwork object (h5 file) includes additional information about the input and output synapse positions along the skeleton

f, ax = plt.subplots(figsize=(7, 10))
skelplot.plot_tools.plot_mw_skel(
    mw, 
    title=nucleus_id,
    pull_radius = True,
    line_width=1,
    plot_soma=True,
    invert_y=True,
    pull_compartment_colors=True,
    x="z",
    y="y",
    skel_color_map = { 3: "firebrick",4: "salmon",2: "black",1: "olive" },
    plot_presyn = True,
    plot_postsyn = True,)

ax.spines['right'].set_visible(False) 
ax.spines['left'].set_visible(False) 
ax.spines['top'].set_visible(False) 
ax.spines['bottom'].set_visible(False)
# ax.axis('off')

Neuron Skeletons (v1300)

minnie65 SWC Skeletons v1300
Name Volume Cloudpath Short Description Type (size)
SWC Skeleton (proofread) minnie65 https://storage.googleapis.com/microns-static-links/skel/swc/proofread Graph neuron with nodes, the radius, and compartment (axon included) .swc (many files)
SWC Skeleton minnie65 https://storage.googleapis.com/microns-static-links/skel/swc/dendrite Graph neuron with nodes, the radius, and compartment (axon excluded) .swc (many files)

The swc files contain a simple graphical representation of neurons that has nodes with their 3d points in space, the estimated radius of those points and the compartment of the node (axon, (basal) dendrite, apical dendrite, soma). The proofread cells include the axons, while all unproofread cells have had their axons removed from the skeleton reconstruction for clarity.

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References

Ding, Zhuokun, Paul G. Fahey, Stelios Papadopoulos, Eric Y. Wang, Brendan Celii, Christos Papadopoulos, Andersen Chang, et al. 2025. “Functional Connectomics Reveals General Wiring Rule in Mouse Visual Cortex.” Nature 640 (8058): 459–69. https://doi.org/10.1038/s41586-025-08840-3.
Elabbady, Leila, Sharmishtaa Seshamani, Shang Mu, Gayathri Mahalingam, Casey M. Schneider-Mizell, Agnes L. Bodor, J. Alexander Bae, et al. 2025. “Perisomatic Ultrastructure Efficiently Classifies Cells in Mouse Cortex.” Nature 640 (8058): 478–86. https://doi.org/10.1038/s41586-024-07765-7.
Wang, Eric Y., Paul G. Fahey, Zhuokun Ding, Stelios Papadopoulos, Kayla Ponder, Marissa A. Weis, Andersen Chang, et al. 2025. “Foundation Model of Neural Activity Predicts Response to New Stimulus Types.” Nature 640 (8058): 470–77. https://doi.org/10.1038/s41586-025-08829-y.