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ARS Home » Plains Area » Lubbock, Texas » Cropping Systems Research Laboratory » Plant Stress and Germplasm Development Research » Research » Publications at this Location » Publication #327150

Research Project: Enhancing Plant Resistance to Water-Deficit and Thermal Stresses in Economically Important Crops

Location: Plant Stress and Germplasm Development Research

Title: Development and methods for an open-sourced data visualization tool

Author
item Mahan, James
item Payton, Paxton

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 4/2/2016
Publication Date: 4/2/2016
Citation: Mahan, J.R., Payton, P.R. 2016. Development and methods for an open-sourced data visualization tool [abstract]. Southern Section of the American Society of Plant Biologists, April 2-4,2016, Denton, Texas. 1(64):11.

Interpretive Summary:

Technical Abstract: This paper presents an open source on-demand web tool, which is specifically addressed to scientists and researchers that are non-expert in converting time series data into a time surface visualization. Similar to a GIS environment the time surface shows time on two axes; time of day vs. day of year. The variable(s) of interest is/are plotted as a time surface using color and elevation. This research tool showcases a simple user interface allowing a straightforward execution of data exploration and manipulation in real-time from either locally stored time series data or exported time series data provided by individual users. The time series data stored locally within the program include environmental variables: ambient temperature, dew point, relative humidity, vapor pressure deficit, solar radiation, soil temperature, rain events, and wind speed. Datasets were generated from three different locations (Lubbock, Texas/Citra, Florida/Narrabri, Australia), occupying a time frame starting at midnight January 1, 2003 until 23:45 December 31, 2015. By means of this time surface imaging approach, it is relatively simple to interactively inspect large datasets containing millions of individual observations.