Data Repository for
Dense Connectomic Reconstruction in Layer 4 of the Somatosensory Cortex

Alessandro Motta1*, Manuel Berning1*, Kevin M. Boergens1*, Benedikt Staffler1*, Marcel Beining1, Sahil Loomba1, Philipp Hennig2, Heiko Wissler1, Moritz Helmstaedter1†. Science (2019). DOI: 10.1126/science.aay3134

Jump to

  1. Overview
  2. EM Volume
  3. Volume Segmentation
  4. Machine Learning Data
  5. Neurite Reconstructions
  6. Synapses and Connectome
  7. Utilities
  8. Code

Research article
Supplementary materials
Electron microscopy volume browser
Data repository

When using any of these data, please cite as
Motta A, Berning M, Boergens KM, Staffler B, Beining M, Loomba S, Hennig Ph, Wissler H, Helmstaedter M (2019). Dense connectomic reconstruction in layer 4 of the somatosensory cortex. Science. DOI: 10.1126/science.aay3134


All of the following datasets are available for download in the data repository at

This repository can also be accessed as network-attached drive from Windows, macOS, and Linux.

Most datasets are provided as HDF5 files. This file format is supported in many programming environments, including MATLAB, Python, R, and Julia. The detailed structure of individual HDF5 files is described below.

Some of the training and validation data for machine learning algorithms are provided in form of NML files. These files can be viewed and edited in the web browser using webKnossos, and can be processed using MATLAB or Python.


Electron Microscopy Volume

The electron microscopy data are available

The electron microscopy volume was split into 216 separate HDF5 files. Each file contains a single dataset, /data, with a subvolume of (1024 voxels)3. The filename encodes the offset of its subvolume. x2y4z3.hdf5, for example, contains the data cube starting at position (X, Y, Z) = (2 × 1024 + 1, 4 × 1024 + 1, 3 × 1024 + 1). The full extent of the image volume is from 104 to 6793 along X, from 104 to 10048 along Y, and from 119 to 3538 along Z, including limits.

Note: The voxel data are stored in Z×Y×X row-major / X×Y×Z column-major order. If you're using Python (with NumPy) or C, for example, access the voxel data as data[z, y, x]. MATLAB, Julia, and R use column-major memory layout, data(x, y, z).

Parts of this electron microscopy volume were previously published in

  1. Yunfeng Hua, Philip Laserstein, Moritz Helmstaedter (2015) Nature Communications
    Large-volume en-bloc staining for electron microscopy-based connectomics
    DOI: 10.1038/ncomms8923
  2. Manuel Berning, Kevin M. Boergens, Moritz Helmstaedter (2015) Neuron
    SegEM: Efficient Image Analysis for High-Resolution Connectomics
    DOI: 10.1016/j.neuron.2015.09.003
  3. Kevin M. Boergens, Manuel Berning, Tom Bocklisch, Dominic Bräunlein, Florian Drawitsch, Johannes Frohnhofen, Tom Herold, Philipp Otto, Norman Rzepka, Thomas Werkmeister, Daniel Werner, Georg Wiese, Heiko Wissler, Moritz Helmstaedter (2017) Nature Methods
    webKnossos: efficient online 3D data annotation for connectomics
    DOI: 10.1038/nmeth.4331
  4. Benedikt Staffler, Manuel Berning, Kevin M. Boergens, Anjali Gour, Patrick van der Smagt, Moritz Helmstaedter (2017) eLife
    SynEM, automated synapse detection for connectomics
    DOI: 10.7554/eLife.26414

Volume Segmentation

The volume segmentation is available

The segmentation volume is stored analogously to the electron microscopy volume. /data contains the unsigned 32-bit segment IDs for a (1024 voxels)3 subvolume. The full extent of the segmentation volume is from 129 to 5574 along X, from 129 to 8509 along Y, and from 129 to 3414 along Z, including limits.

Mapped Volume Segmentation

A volume segmentation in which all segments belonging to a given neurite were mapped to a single segment is available

For further information on viewing of the segmentation volume and on the structure of the HDF5 files, please consult the section above.

The relationship between mapped segment IDs and neurites is stored in the axons.hdf5 and dendrites.hdf5 files.

Blood Vessel Volume Segmentation

A volume segmentation of the blood vessels is available

For further information on the structure of the HDF5 files, please consult the section above.

Training and Evaluation Data for Machine Learning Algorithms

Volume Segmentation (SegEM)

The electron microscopy volume was segmented using SegEM. CNN 20130516T2040408,3 and associated parameters from Table 1 of Berning et al. (2015) Neuron were used. Code, training and evaluation data, and parameters of the trained CNN are available as supplementary material of that publication or at

Neurite Continuity Classification (ConnectEM)

The neurite continuity classifier, ConnectEM, was trained and evaluated on so-called "merger mode tracings". These are skeleton reconstructions in which nodes are spatially mapped onto the corresponding segments. Each skeleton represents the set of segments of a neurite. Interfaces between segments within / across neurites were used as positive / negative classification samples.

Three volumes of (5 µm)3, each, were densely reconstructed this way. The skeleton reconstructions are available as NML files in /connectem/training-and-test-data.

The parameters for automated axon and dendrite reconstructions were separately optimized on a random subset of neurites. NML files with skeleton reconstructions of these neurites are available in /connectem/parameter-grid-search.

Neurite Type Classification (TypeEM)

Each segment was assigned probabilities of being part of an axon, of a dendrite, of an astrocyte, or of a spine head. These neurite type classifiers, called TypeEM, were trained and evaluated on an extended version of the ConnectEM merger mode tracings (see above) in which skeletons were labeled with the type of neurite. These ground truth data are available as NML files in /typeem/training-and-test-data.

Synapse Detection (SynEM)

Synapse-Vesicle-Mitochondrion CNN (SVM CNN)

The training and test data of the synapse-vesicle-mitochondrion CNN consist of seven roughly cubic regions in which voxels belonging to synapses, vesicle clouds, or mitochondria were manually labeled as such. Cubes 1-6 are each 300×300×120 voxels3 in size. Cube 7 is substantially larger and comprises 512×512×256 voxels3.

These data are available in /synem/synapse-vesicle-mitochondrion-cnn/training-and-test-data. Each cube corresponds to an HDF5 files with two datasets:

Note: Details on the format of voxel data are given above.

Interface Classification using SynEM

The interface classifiers used for synapse detection were trained on an extended version of the training set published in Staffler et al. (2017) eLife. The training and test sets were furthermore complemented with annotations of the types of postsynaptic target. This separation of synapses onto spine heads, spine necks, dendritic shafts, or neuronal somata allowed the training of separate classifiers for spine and non-spine synapses.

Each of the HDF5 files in /synem/interface-classification/training-and-test-data is organized as follows:

Note: Details on the format of voxel data are given above.

Test Set for Inhibitory Synapses

The performance of inhibitory synapse detection was evaluated on three inhibitory axons. These axons and their postsynaptic targets were (locally) volumetically reconstructed using merger mode tracings (see above). The reconstructions and classification of postsynaptic targets are provided in /synem/interface-classification/test-data/inhibitory-axons.

Neurite Reconstructions


The axon reconstructions are stored in axons.hdf5. This file is organized as follows:


The dendrite reconstructions are stored in dendrites.hdf5. Here, "dendrites" refers to all postsynaptic targets (including, for example, neuronal somata). The file is organized as follows:

Manual Neuron Reconstructions

The manually generated neuron tracings that were used for quantitative evaluation of the soma-based neuron reconstructions and for analysis of the output synapses of L4 neurons are available as NML files in the manual-neuron-reconstructions directory.

Synapses and Connectome

The connectome shown in figure 3 is available as CSV file. Rows and columns correspond to presynaptic axons and postsynaptic targets, respectively. The first row and column indicate the axon classes and target types, respectively. Each connectome entry contains the number of synapses established between a axon-target pair.

Information on individual synapses is stored in synapses.hdf5.

Each synapse consists of a set of presynaptic segments and of a set of postsynaptic segments. The information pertaining to synapse 244418, for example, is stored in the following two datasets:

The following information was then derived from all of the above:


The segments.hdf5 file contains the following supplementary datasets:


All programming code used in this study is available

Moritz Helmstaedter
Department of Connectomics
Max Planck Institute for Brain Research
Max-von-Laue-Strasse 4
D-60438 Frankfurt am Main