tactile.proprio-evoked_DCN_potentials/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: "Alastair"
    lastname: "Loutit"
    affiliation: "UNSW Sydney"
    id: "ORCID:0000-0002-4102-8814"
  -
    firstname: "Jason"
    lastname: "Potas"
    affiliation: "UNSW Sydney"
    id: "ORCID:0000-0002-1973-8211"
    

# A title to describe the published resource.
title: "Surface potential recordings from rat brainstem dorsal column nuclei in response to tactile and proprioceptive stimuli"

# Additional information about the resource, e.g., a brief abstract.
description: |
  The raw data are electrical signals recorded from the surface of rat brainstem using a 7-electrode surface array.
  Rats were urethane anaesthetised and 4 different tactile and proprioceptive stimuli were applied to each of the four limbs while the electrical brainstem signals were simultaneously recorded. 
  Twenty-eight different types of signal features were quantified from these recordings and stored in matrices that can be used as inputs for machine-learning. 
  The features were quantified from 17 different time windows ranging from 20 ms to 1000 ms in length. 
  In total, there is data from six rats (n = 6). 
  
  For more details and the analysis and discussion of this dataset, see the paper at
  doi: 10.3389/fnsys.2020.00046

# Lit of keywords the resource should be associated with.
# Give as many keywords as possible, to make the resource findable.
keywords:
  - Neuroscience
  - somatosensation
  - electrophysiology
  - brain
  - brainstem
  - neural coding
  - touch
  - proprioception
  - rat
  - in vivo

# 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:
  name: "Creative Commons CC0 1.0 Public Domain Dedication"
  url: "https://creativecommons.org/publicdomain/zero/1.0/"



## Optional Fields

# Funding information for this resource.
# Separate funder name and grant number by comma.
funding:
  - "Bootes Medical Research Foundation"


# Related publications. reftype might be: IsSupplementTo, IsDescribedBy, IsReferencedBy.
# Please provide digital identifier (e.g., DOI) if possible.
# Add a prefix to the ID, separated by a colon, to indicate the source.
# Supported sources are: DOI, arXiv, PMID
# In the citation field, please provide the full reference, including title, authors, journal etc.
references:
  -
    id: "doi: https://doi.org/10.3389/fnsys.2020.00046"
    reftype: "IsDescribedBy"
    citation: "Alastair J Loutit, Jason R Potas (2020). Loutit AJ and Potas JR (2020). Dorsal Column Nuclei Neural Signal Features Permit Robust Machine-Learning of Natural Tactile- and Proprioception-Dominated Stimuli. Front. Syst. Neurosci. 14:46; doi: 10.3389/fnsys.2020.00046"
  -  
    id: "doi: https://doi.org/10.1101/831164"
    reftype: "IsDescribedBy"
    citation: "Alastair J Loutit, Jason R Potas (2020). Novel neural signal features permit robust machine-learning of natural tactile- and proprioception-dominated dorsal column nuclei signals. bioRxiv 831164; doi: https://doi.org/10.1101/831164"


# 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