Title

Analysis of large scale data sets from environmental data loggers

Poster Number

09C

Lead Author Major

Kelly Inuzuka

Lead Author Status

Senior

Second Author Major

Erin Thompson

Third Author Major

Ryan I Hill

Format

Poster Presentation

Faculty Mentor Name

Ryan Hill

Faculty Mentor Email

rhill@pacific.edu

Faculty Mentor Department

Biological Sciences

Graduate Student Mentor Name

Erin Thompson

Graduate Student Mentor Email

erinmichaelthompson@gmail.com

Graduate Student Mentor Department

Biology

Abstract/Artist Statement

Advances in data collection technology can provide data with improved resolution for answering long-standing questions and lead to the possibility of answering new questions. In the area of environmental monitoring, massive amounts of data can now be gathered with relatively inexpensive data loggers such as HOBO pendants (temperature and light), or ibuttons (temperature and humidity). The low cost means numerous loggers can be deployed simultaneously to explore fine scale patterns to help understand and model microhabitat use and niches of animals or plants. Deploying large numbers of these loggers present several challenges, such as inconsistent and incomplete data sets as a result of missing loggers, varied output formats from different brands, as well as erroneous readings. An additional major obstacle to analyzing fine scale environmental questions is the scope of the data, since manually assembling and reviewing data from dozens of loggers each with thousands of lines of data is error prone and time prohibitive. These issues necessitate caution in analyzing data from large numbers of loggers, and require computing resources not provided by proprietary logger software. Here we report on our approach in analyzing a large data set of 64 ibutton thermochrons that were deployed for up to one year in different habitats and strata of a tropical wet forest ecosystem in Costa Rica. By writing scripts in the widely available bash language the numerous and variable data files can be relatively quickly assembled into comma separated value files for subsequent examination via statistical applications such as R and MATLAB. Graphical review of data for each trap allowed inconsistencies to be removed before statistical analysis of microhabitat niches were assessed.

Location

DeRosa University Center, Ballroom

Start Date

29-4-2017 10:00 AM

End Date

29-4-2017 12:00 PM

This document is currently not available here.

Share

COinS
 
Apr 29th, 10:00 AM Apr 29th, 12:00 PM

Analysis of large scale data sets from environmental data loggers

DeRosa University Center, Ballroom

Advances in data collection technology can provide data with improved resolution for answering long-standing questions and lead to the possibility of answering new questions. In the area of environmental monitoring, massive amounts of data can now be gathered with relatively inexpensive data loggers such as HOBO pendants (temperature and light), or ibuttons (temperature and humidity). The low cost means numerous loggers can be deployed simultaneously to explore fine scale patterns to help understand and model microhabitat use and niches of animals or plants. Deploying large numbers of these loggers present several challenges, such as inconsistent and incomplete data sets as a result of missing loggers, varied output formats from different brands, as well as erroneous readings. An additional major obstacle to analyzing fine scale environmental questions is the scope of the data, since manually assembling and reviewing data from dozens of loggers each with thousands of lines of data is error prone and time prohibitive. These issues necessitate caution in analyzing data from large numbers of loggers, and require computing resources not provided by proprietary logger software. Here we report on our approach in analyzing a large data set of 64 ibutton thermochrons that were deployed for up to one year in different habitats and strata of a tropical wet forest ecosystem in Costa Rica. By writing scripts in the widely available bash language the numerous and variable data files can be relatively quickly assembled into comma separated value files for subsequent examination via statistical applications such as R and MATLAB. Graphical review of data for each trap allowed inconsistencies to be removed before statistical analysis of microhabitat niches were assessed.