Automated_classification_of_stroke_deficit/datacite.yml

# Metadata for DOI registration according to DataCite Metadata Schema 4.1.
# For detailed schema description see https://doi.org/10.5438/0014

## Required fields

# The main researchers involved. Include digital identifier (e.g., ORCID)
# if possible, including the prefix to indicate its type.
authors:
  -
    firstname: "Sebastian"
    lastname: "Kloubert"
    affiliation: "University Hospital Cologne"

  -
    firstname: "Markus"
    lastname: "Aswendt"
    affiliation: "University Hospital Cologne"
    id: "ORCID:0000-0003-1423-0934"

# A title to describe the published resource.
title: "Automated classification of sensorimotor deficit in stroke mice"

# Additional information about the resource, e.g., a brief abstract.
description: "this repository contains the data and evaluation results of the master thesis 'Automated classification of sensorimotor deficit in stroke mice' by Sebastian Kloubert.
The thesis aimed to evaluate whether a combination of different camera angles
and the application of the software package DeepLabCut (DLC) to video tracking
data is appropriate to automatically detect events related to sensorimotor behavior
of the behavior tests Grid Walk and Cylinder and simultaneously
overcome the manual rater-based detection errors. The new test setup promises
a better view of the behavior tests Grid Walk and Cylinder for manual analysis. 
The attempt to use these new camera angles to automate the behavior tests by applying
the software package DeepLabCut failed for both the two-dimensional and the three-dimensional
analysis approach and did not overcome manual rater-based detection errors. 
However, several possible improvements can be outlined, which should allow for quick
automation in future work. The calculation of additional kinematic features promises
to make the occurrence of the events related to sensorimotor behavior of the behavior
tests more precise and perhaps in the future to allow a separation of stroke and healthy mice.


# Lit of keywords the resource should be associated with.
# Give as many keywords as possible, to make the resource findable.
keywords:
  - Neuroscience
  - DeepLabCut
  - DLC
  - Cylinder
  - Grid Walk
  - 3D
  - Random Forest
  - Feature Importance

# License information for this resource. Please provide the license name and/or a link to the license.
# Please add also a corresponding LICENSE file to the repository.
license:
  url: "https://creativecommons.org/licenses/by-nc/4.0/"



# Resource type. Default is Dataset, other possible values are Software, DataPaper, Image, Text.
resourcetype: Dataset

# Do not edit or remove the following line
templateversion: 1.2