Digits_In_Noise_Clonan_Escabi_v1_alpha/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: "Alex"
    lastname: "Clonan"
    affiliation: "University of Connecticut"
    id: "ORCID:0009-0007-1460-6483"
    
  -
    firstname: "Xiu"
    lastname: "Zhai"
    affiliation: "Wentworth Institute of Technology"
    id: "ORCID:0000-0003-0341-7816"
    
  -
    firstname: "Ian"
    lastname: "Stevenson"
    affiliation: "University of Connecticut"
    id: "ORCID:0000-0002-1428-5946"
    
  -
    firstname: "Monty"
    lastname: "Escabi"
    affiliation: "University of Connecticut"
    id: "ORCID:0000-0001-7271-1061"


# A title to describe the published resource.
title: "Low-dimensional interference of mid-level sound statistics predicts human speech recognition in natural environmental noise"

# Additional information about the resource, e.g., a brief abstract.
description: |
  This is a supporting dataset for the manuscript "Low-dimensional interference of mid-level sound statistics predicts human speech recognition in natural environmental noise". The dataset
  itself is comprised of three psychoacoustic experiments that investigate human speech recognition in differing natural enviornments.
 
  In the first experiment, (n=18) participants recognize spoken digit triplets in the presence of 11 natural backgrounds, and acoustically perturbed variants that whiten the
  the modulation content (Phase Randomized, PR) or the spectrum content (Spectrum Equalized, SE) of the sound.
  
  In the second experiment, (n=16) participants recognize spoken digit triplets in the presence of the Jackhammer Sound or the 8 Speaker Babble sound, that have been perturbed by gradually added 
  texture statistics (McDermott 2011). 
  
  In the third experiment, (n=9) participants recognize spoken digit triplets in the presence of 11 natural backgrounds at 7 different, signal-to-noise ratios.
  
  The supported data will be able to replicate the psychoacoustic results presented in the paper, in addition to serving as the input for the logistic regression model used in subsequent 
  analysis. 
  
  The repository contains Audio Files (.wav format) and Behavioral Data (MATLAB .mat format).

# Lit of keywords the resource should be associated with.
# Give as many keywords as possible, to make the resource findable.
keywords:
  - Neuroscience
  - Speech
  - Perception
  - Natural Noise
  - Auditory

# 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 Attribution-NonCommercial-ShareAlike 4.0 International Public License"
  url: "https://creativecommons.org/licenses/by-nc-sa/4.0/"

## Optional Fields

# Funding information for this resource.
# Separate funder name and grant number by comma.
funding:
  - "NIDCD, DC020097"

# 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: "https://doi.org/10.1101/2024.02.13.579526"
    reftype: "isSupplementTo"
    citation: "Alex Clonan, Xiu Zhai, Ian Stevenson, Monty Escabi, Low-dimensional interference of mid-level sound statistics predicts human speech recognition in natural environmental noise"

# 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