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Tucker-Brown et al. - Astronify Efficacy Testing - Data

dataset
posted on 08.09.2022, 10:55 authored by Christopher HarrisonChristopher Harrison

This is the dataset for Tucker-Brown et al. (2022), MNRAS, (accepted for publication with minor revision as of 05/09/22). This publication involved producing synthetic light curves (brightness versus time), injecting signals (in the form of dips in brightness) and the converting them into plots, sonifications (audio versions) and a combination of both. This was all done using the tool Python tool astronify, with the goal of performing efficacy testing of the sonification approach.  


Included in this dataset are the plots, sonification files and movie files (54 files in total) of the synthetic data presented to the volunteers during user testing. Also included are: the results of all of the surveys; the code used to analyse the data and make the figures for the publication and a transcript of the survey text.


Four example sonification files are included separately, which are those used in Figure 1 of Tucker-Brown et al., for a more direct link to these particular examples from the manuscript. 


Finally, the two sonification examples presented in Figure 5 (and described in Section 5.4) of the manuscript are included. 


The attached README files contains more information about the files. 


Resource Title: Evaluating the efficacy of sonification for signal detection in univariate, evenly sampled light curves using astronify


Resource DOI: TBC

Funding

Resolving How Black Holes Influence Galaxy Evolution

UK Research and Innovation

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