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Title:      Audio Cartography - Rendered Maps (Tabular)
Copyright:  2017 University of Oregon
Author:     M. Brittell
License:    Creative Commons Attribution-Share Alike 2.0 Generic (CC-BY-SA 2.0)

This work was funded in part by the National Science Foundation (NSF) Doctoral 
Dissertation Research Improvement (DDRI) Grant #1634086 and the University of 
Oregon (UO) Lewis Family Endowment.

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The tabular data files in this collection represent spatial data patterns in an 
8x8 grid. The data were generated using a custom R script that relies on 
library functionality (audioCartography-prepare-generateTrials.R; R, sp).  

Each row in the table represents a modification to the auditory stream to 
reflect values of the underlying data.  A row consists of an identifier (index) 
that indicates the temporal order in which the modifications are applied, the 
note rate (rate; determined by east-west position, or column, of the grid) of a 
component of the auditory stream, the pitch (frequency; north-south position, 
or row, of the grid), the loudness (value; data value in the grid cell(s)), and 
the time (delay; relative to the start of map playback) at which the 
modification should take place.

Delay values range from 0 to 8 (units of grid cells); this range matches the 
map resolution of an 8x8 grid; during rendering, playback of a single grid 
cell lasted 0.5 seconds.

The data format reflects incremental changes to parameterize audio rendering 
for the concurrent auditory map type.  The filenames consist of the prefix 
"table" followed by the numeric identifier of the data set represented in the 
file.  

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File: audioCartography-maps-tabular.json

Each list of modifications to an audio stream to render the concurrent map type 
is presented in a comma separated values (.csv) file, and the columns contained 
in that file are described in the accompanying Brain Imaging Data Structure 
(BIDS)-style JSON (.json) sidecar file (Gorgolewski et al. 2016).  The JSON 
file describes the full format output from the script that generates trials 
(see audioCartography-prepare-generateTrials.R); the trails used in the 
dissertation study contain only the minimum required columns to support audio 
rendering.

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REFERENCES

Bivand, R.S., E. Pebesma, V. Gomez-Rubio. (2013) Applied spatial data analysis 
	with R, Second edition. Springer, NY. http://www.asdar-book.org/

Gorgolewski, K.J., T. Auer, V.D. Calhoun, R.C. Craddock, S. Das, E.P. Duff, G.
	Flandin, S.S. Ghosh, T. Glatard, Y.O. Halchenko, D.A. Handwerker, M. Hanke, 
	D. Keator, X. Li, Z. Michael, C. Maumet, B.N. Nichols, T.E. Nichols, J. 
	Pellman, J.-B. Poline, A. Rokem, G. Schaefer, V. Sochat, W. Triplett, J.A. 
	Turner, G. Varoquaux, and R.A. Poldrack. (June 2016) The brain imaging data 
	structure, a format for organizing and describing outputs of neuroimaging 
	experiments. Scientific Data, 3. Web site: http://bids.neuroimaging.io/

Pebesma, E.J., R.S. Bivand. (2005) Classes and methods for spatial data in R. 
	R News 5 (2), https://cran.r-project.org/doc/Rnews/Rnews_2005-2.pdf

R Core Team. (2016) R: A language and environment for statistical computing. R 
    Foundation for Statistical Computing, Vienna, Austria. Available: 
    https://www.R-project.org/.

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