Sample Data and Training Modules for Cleaning Biodiversity Information
Issue Date
2018-11-14Author
Cobos, Marlon E.
Jiménez, Laura
Nuñez-Penichet, Claudia
Romero-Alvarez, Daniel
Simões, Marianna
Publisher
GIS Day @ KU Planning Committee
Type
Presentation
Published Version
http://gis.ku.edu/gisday/2018/studentcomp/Poster_Jimenez.pdfMetadata
Show full item recordAbstract
The recent appreciation of rapid global losses of biodiversity has created a growing demand for quick and reliable access to high-quality primary biodiversity data, essential for conservation and evolutionary science, among other applications. As a consequence, biodiversity databases have become increasingly available, allowing large-scale assessment of patterns and processes influencing the evolution of life on Earth. However, data quantity is often compromised by low data quality, and, even though working with plenty of records could be tempting, building ecological models with inaccurate data mislead researchers in driving conclusions about reality. The most common types of errors in biodiversity data are those related to georeferencing, which vary from obvious to barely noticeable, making them challenging to recognize. Fortunately, Geographic Information Systems (GIS) have increasingly allowed identification of georeferencing mistakes. Here, we provide a hands-on exercise for data cleaning that allows easy and prompt detection of inaccurate information. We focus on the use of GIS software for fixing problems related to the geographic coordinates of the data, such as changes in the order or the sign of latitude-longitude values, and records placed outside the study region, or outside continents.
Description
This presentation was given as part of the GIS Day@KU symposium on November 14, 2018. For more information about GIS Day@KU activities, please see http://gis.ku.edu/gisday/2018/
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Citation
Cobos, M.E., Jiménez, L., Nuñez-Penichet, C., Romero-Alvarez, D., Simoes, M. 2018. Sample data and training modules for cleaning biodiversity information. Biodiversity Informatics 13:49–50. https://doi.org/10.17161/bi.v13i0.7600
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