Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/10495/37297
Título : Using MODESTR to download, import and clean species distribution records
Autor : Pelayo Villamil, Patricia
García Roselló, Emilio
Guisande, Cástor
Heine, Juergen
Manjarrés Hernández, Ana
González Vilas, Luis
Vaamonde, Antonio
González Dacosta, Jacinto
Granado Lorencio, Carlos
metadata.dc.subject.*: Almacenamiento de información
Information storage
Calidad de los datos
Data quality
Data cleaning
Geographic records
http://aims.fao.org/aos/agrovoc/c_2fe8a00c
Fecha de publicación : 2014
Editorial : Wiley
British Ecological Society
Citación : Guerrero, M. J., Bedoya, C. L., López, J. D., Daza, J. M., & Isaza, C. (2023). Acoustic animal identification using unsupervised learning. Methods in Ecology and Evolution, 14(6), 1500–1514. https://doi.org/10.1111/2041-210X.14103
Resumen : ABSTRACT: 1. 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. 2. 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. 3. 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.
metadata.dc.identifier.eissn: 2041-210X
metadata.dc.identifier.doi: 10.1111/2041-210X.12209
Aparece en las colecciones: Artículos de Revista en Ciencias Exactas y Naturales

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