https://github.com/berenslab/morphology-benchmark
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
✓DOI references
Found 4 DOI reference(s) in README -
✓Academic publication links
Links to: springer.com, zenodo.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (9.7%) to scientific vocabulary
Last synced: 10 months ago
·
JSON representation
Repository
Basic Info
- Host: GitHub
- Owner: berenslab
- License: gpl-3.0
- Language: Jupyter Notebook
- Default Branch: master
- Size: 3.74 MB
Statistics
- Stars: 1
- Watchers: 5
- Forks: 1
- Open Issues: 0
- Releases: 0
Created over 6 years ago
· Last pushed about 5 years ago
https://github.com/berenslab/morphology-benchmark/blob/master/
# morphology-benchmark
Code for the paper [_A systematic evaluation of interneuron morphology representations for cell type discrimination_](https://link.springer.com/article/10.1007/s12021-020-09461-z).
All data for the publication figures can be found here
## Reproducing the figures
Make sure the following dependecies are installed:
`numpy [v1.17.3]`, `pandas[v0.24.0]` , `scipy[v1.3.1]`, `sklearn[v0.21.3]` , `matplotlib [v3.0.3]` and `seaborn[v0.8.1]`
Check out the repository via
`git clone https://github.com/berenslab/morphology-benchmark`.
Download all data from [](https://doi.org/10.5281/zenodo.3696638) and unpack the folder `data` to the location of the repository.
Now you can run all notebooks to generate the published figures.
## Reproducing the study
Since this study has been implemented using [DataJoint](https://datajoint.io/), it cannot be readily executed. The available notebooks are meant to showcase the processing. The exact code can be found in the folder `schemata`.
Example code on the computation of density maps, 2D persistence diagrams and morphometric statistics is shown in `ROBUSTNESS ANALYSIS data generation.ipynb`. The computation of all features can be found in the DataJoint tables `schema.density`, `schema.morphometry` and `schema.persistence`.
## Cite the paper ##
```
@article{laturnus2019systematic,
title={A systematic evaluation of interneuron morphology representations for cell type discrimination},
author={Laturnus, Sophie and Kobak, Dmitry and Berens, Philipp},
journal={bioRxiv},
pages={591370},
year={2019},
publisher={Cold Spring Harbor Laboratory}
}
```
## Errata ##
|page|Original text| Correction|
|:---|:------------|:----------|
|p.8 | "...e.g. it grew from 0.14 0.06 to 0.17 0.07, mean95CI across all 21 pairs..."| (Cor)"...e.g. it grew from 0.14 (0.08 - 0.2, mean and 95% CI across all 21 pairs) to 0.17 (0.1 - 0.24)..."|
Owner
- Name: Berens Lab @ University of Tübingen
- Login: berenslab
- Kind: organization
- Email: philipp.berens@uni-tuebingen.de
- Location: Tübingen, Germany
- Website: https://hertie.ai/data-science
- Repositories: 60
- Profile: https://github.com/berenslab
Department of Data Science at the Hertie Institute for AI in Brain Health, University of Tübingen