Dataset

This dataset was created by Saurabh Singh

Released under Data files © Original Authors

Contents

This dataset contains data and code associated with the study "Quantifying accuracy and precision from continuous response data in studies of spatial perception and crossmodal recalibration" by Patrick Bruns, Caroline Thun, and Brigitte Röder. example_code.R contains analysis code that can be used to to calculate error-based and regression-based localization performance metrics from single-subject response data with a working example in R. It requires as inputs a numeric vector containing the stimulus location (true value) in each trial and a numeric vector containing the corresponding localization response (perceived value) in each trial. example_data.csv contains the data used in the working example of the analysis code. localization.csv contains extracted localization performance metrics from 188 subjects which were analyzed in the study to assess the agreement between error-based and regression-based measures of accuracy and precision. The subjects had all naively performed an azimuthal sound localization task (see related identifiers for the underlying raw data). recalibration.csv contains extracted localization performance metrics from a subsample of 57 subjects in whom data from a second sound localization test, performed after exposure to audiovisual stimuli in which the visual stimulus was consistently presented 13.5° to the right of the sound source, were available. The file contains baseline performance (pre) and changes in performance after audiovisual exposure relative to baseline (delta) in each of the localization performance metrics. Localization performance metrics were either derived from the single-trial localization errors (error-based approach) or from a linear regression of localization responses on the actual target locations (regression-based approach).The following localization performance metrics were included in the study: bias: overall bias of localization responses to the left (negative values) or to the right (positive values), equivalent to constant error (CE) in error-based approaches and intercept in regression-based approaches absolute constant error (aCE): absolute value of bias (or CE), indicates the amount of bias irrespective of direction mean absolute contant error (maCE): mean of the aCE per target location, reflects over- or underestimation of peripheral target locations variable error (VE): mean of the standard deviations (SD) of the single-trial localization errors at each target location pooled variable error (pVE): SD of the single-trial localization errors pooled across trials from all target locations absolute error (AE): mean of the absolute values of the single-trial localization errors, sensitive to both bias and variability of the localization responses slope: slope of the regression model function, indicates an overestimation (values > 1) or underestimation (values < 1) of peripheral target locations R2: coefficient of determination of the regression model, indicates the goodness of the fit of the localization responses to the regression line

This is a multiannual ground temperature dataset covering sixteen high elevation sites (3493-4377 m a.s.l.) in the Bale Mountains, southern Ethiopian Highlands

The dataset is described in detail in the corresponding data paper by Groos et al. 2021 (https://doi.org/10.5194/essd-2021-268)

The repository contains a readme file ("readme.txt"), a GeoPackage ("Data_Logger_Location.gpkg"), a thermal infrared time-lapse video ("thermal_infrared_time-lapse_video.mp4"), and two sub-folders: "raw_data" and "processed_data"

The GeoPackage provides information on the location and environmental setting of each logger and can be easily opened and displayed in a Geographic Information System. The coordinate reference system is WGS84 / Geographic (EPSG code: 4326).

The thermal infrared time-lapse video visualises the phenomenon of nocturnal cold air ponding in the Bale Mountains (for more information see Fig. C1 in the corresponding data paper).

The folder "raw_data" contains the original logfiles of all GT and TM data loggers (see Table 1) in a tab-delimited text format with the logger ID and download date encoded in the file name. The date format of the GT data loggers is YYYY.MM.DD hh:mm:ss East Africa Time (EAT). The date format of the TM data loggers is DD.MM.YYYY hh:mm:ss EAT.

The folder "processed_data" contains the followings two files:

"Information_Sheet_Data_Gap-Filling.ods": An overview table with relevant information regarding the filling of (longer) data gaps in the ground temperature time series. The gap-filling procedure based on simple linear regression models is described individually for each logger.

"Hourly_Ground_Temperatures.csv": Compilation of hourly ground temperature data from all GT and TM data loggers installed in the Bale Mountains (see Table 1 in the data paper). The dataset covers the period from 1 January 2017 to 31 January 2020, but individual time series may be shorter or contain data gaps (see Fig. 3 in the data paper). We use the international date format (ISO 8601): YYYY-MM-DD hh:mm:ss EAT. The following numerical indices (or a combination of them) in the columns starting with "Flag_*" are used to provide additional information on the post-processing of each hourly measurement of each time series:

0 no data available
1 original data (no post-processing)
2 data interpolated to full hour
3 erroneous data corrected
4 erroneous data removed
5 data gap-filled

The meteorological data from the ten automatic weather stations in the Bale Mountains, which are operated since 2017, are currently post-processed and analysed in the framework of the DFG Research Unit 2358 "The Mountain Exile Hypothesis". The data will be made publicly available at some point in the future. However, individual access to the weather station data may be granted before on request to the coordination board of the research unit (bale@staff.uni-marburg.de).