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Using ModestR to download, import and clean species distribution records

Using ModestR to download, import and clean species distribution records
Description 

This article describes how to use the ModestR software to access data from online repositories such as GBIF, and how to perform data cleaning operations on them.

Author(s) 
García-Roselló, E., Guisande, C., Heine, J., Pelayo-Villamil, P., Manjarrés-Hernández, A., González Vilas, L., González-Dacosta, J., Vaamonde, A. & Granado-Lorencio, C.
Publisher 
British Ecological Society, Tuesday, Jul 15, 2014
Abstract 
Data quality is one of the highest priorities for species distribution data warehouses, as well as one of the main concerns of data users. There is the need, however, for computational procedures with the facility to automatically or semi-automatically identify and correct errors and to seamlessly integrate expert knowledge and automated processes. New version modestr 2.0 (http://www.ipez.es/ModestR) makes it easy to download occurrence records from the Global Biodiversity Information Facility (GBIF), to import shape files with species range maps such as those available at the website of the International Union for Conservation of Nature (IUCN), to import KML files, to import CSV files with records of the users, to import ESRI ASCII grid probability files generated by distribution modelling software and show the resulting records on a map. ModestR supports five different methods for cleaning the data: (i) data filtering when downloading records from GBIF, (ii) habitat data filtering, (iii) taxonomic disambiguation filtering, (iv) automatic spatial dispersion and environmental layer filters and (v) custom data filtering.
Bibliographic citation 
García-Roselló, E., Guisande, C., Heine, J., Pelayo-Villamil, P., Manjarrés-Hernández, A., González Vilas, L., González-Dacosta, J., Vaamonde, A. & Granado-Lorencio, C. 2014. Using ModestR to download, import and clean species distribution records. Methods in Ecology and Evolution 2014, 5, 708–713.
Target audience 
Data users
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