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

<p dir="ltr">This is the dataset for Tucker-Brown et al., <i>MNRAS, 516, 5674, (2022)</i>. This is also on arXiv: https://arxiv.org/abs/2209.04465.<br><br>This publication involves 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 Python tool astronify, with the goal of performing efficacy testing of the sonification approach.</p><p dir="ltr"><br></p><p dir="ltr">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.</p><p dir="ltr">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.</p><p dir="ltr">Finally, the two sonification examples presented in Figure 5 (and described in Section 5.4) of the manuscript are included.</p><p dir="ltr">The attached README files contains more information about the files.</p><p dir="ltr">Resource Title: Evaluating the efficacy of sonification for signal detection in univariate, evenly sampled light curves using astronify</p><p dir="ltr">Resource DOI: <a href="https://ui.adsabs.harvard.edu/link_gateway/2022MNRAS.516.5674T/doi:10.1093/mnras/stac2590" target="_blank">10.1093/mnras/stac2590</a></p>