Data repository

A collection of resources for high-resolution connectomics

Browse the repository online here!!!

How to mount the data repository

The data repository can be found here or mounted as descibed below.

There is no password or username necessary for read-only access.

In Windows:

Simply press “Map network drive” from the Windows Explorer and enter the link provided above.

In Linux:

sudo mount -t davfs SOME_MOUNTPOINT (make sure you have the related packages)

Brief explanantion of content

The content is located in three main folders:

  • onlineMaterial, online material as described in the paper
  • datasets - The dataset ek_0563 (raw data, classification and segmentation)
  • supplement, all supplements to the paper
  • SegEM_challenge, dense skeleton data (and corresponding raw data) for two regions from S1 L4 and the volume annotated training regions

Detailed explanation of content:

Supplemental Material

Supplemental Data (/supplement)

  1. Gallery of retina volume training data (Data S1.pdf).
  2. All trained CNNs used in main figures (Data
  3. All dense skeleton data (Data
  4. Gallery of all skeleton-to-segmentation object overlays (retina test; at node threshold 1; for segmentation with parameters also shown in Fig. 3). Shown volumes are of size 4.6 * 4.6 * 9.6 μm 3 . (Data S4.pdf)
  5. Gallery of all mergers detected in retina test volume (Data S5.pdf)
  6. Whole cell skeleton reconstructions from the mouse retina inner plexiform layer as used in Figure 4 (Data
  7. Whole cell skeleton reconstructions from mouse barrel cortex layer 4 as seen in Figure 5 (Data
  8. SegEM code (Supplemental Code, Data, see for continuously updated versions, compare to workflow in Figure 1)
  9. Movies of top scoring (with respect to rand and warp error) test stack classifications as used for ISBI metric calculation (Data

Online Material (/onlineMaterial)

  1. All volume training data, retina dataset (
  2. CNN classifiers trained on retina dataset (
  3. All volume training data, cortex dataset (
  4. CNN classifiers trained on cortex dataset (

Datasets (/datasets)

  1. Raw EM-data dataset from mouse retina (/ek0563/raw)
  2. Classification of the dataset (/ek0563/class)
  3. Segmentation of the dataset (/ek0563/seg)

SegEM challenge (/SegEM_challenge)

  1. Densely skeltonized training and test regions as .nml (/skeletonData/cortex_training.nml and cortex_test.nml)
  2. Raw data (of densly annotated regions including border for classifier pixel field of view, /skeletonData/cortex_training_raw.mat and cortex_test_raw.mat)
  3. Densely volume annotated regions for classifier training & testing (/volumeTrainingData)