DOI: 10.5061/dryad.4xgxd25gj
title: Geodatabase of ultramafic soils of the Americas

description: This is a compiled geospatial dataset in ESRI polygon shapefile format of
 ultramafic soils of the Americas showing the location of ultramafic soils
 in Canada, the United States of America, Mexico, Guatemala, Cuba,
 Dominican Republic, Puerto Rico, Costa Rica, Colombia, Argentina, Chile,
 Venezuela, Ecuador, Brazil, Suriname, French Guiana, and Bolivia. The R
 code used to compile the dataset as well as an image of the compiled
 dataset are also included. 
description: The data are derived from ten geospatial datasets. Original
 datasets were subset to include only ultramafic areas, datasets were
 assigned a common projection (WGS84), attribute tables were reconciled to
 a common set of fields, and the datasets were combined.
description: **README: Geodatabase of ultramafic soils of the Americas** **Author**:
 Catherine Hulshof, Virginia Commonwealth University, cmhulshof@vcu.edu
 **Abstract**: This is a compiled geospatial dataset in ESRI polygon
 shapefile format of ultramafic soils of many countries in the Americas
 showing the location of ultramafic soils in Canada, the United States of
 America, Guatemala, Cuba, Dominican Republic, Puerto Rico, Costa Rica,
 Colombia, Argentina, Chile, Venezuela, Ecuador, Brazil, Suriname, French
 Guiana, and Bolivia. The data are derived from nine geospatial datasets.
 Original datasets were subset to include only ultramafic areas, datasets
 were assigned a common projection (WGS84), attribute tables were
 reconciled to a common set of fields, and the datasets were combined.
 **Contents**: The data are in ESRI shapefile format and thus have four
 components with extensions .shp, .shx, .prj, and .dbf. The .shp file
 contains the feature geometries, the .prj file contains the geographic
 coordinate system information (WGS 1984), and the .dbf file contains the
 attribute table. A .jpg file demonstrates the data extent across North,
 Central, and South America. The R code used to compile the dataset as well
 as an image of the compiled dataset are also included.  **Data Fields**:
 GeogUnit: [text] Typically, the country where the ultramafic polygon is
 located; Puerto Rico is also used as a GeogUnit. Formation: [text] Code
 corresponding to geologic formation. As recorded in source dataset. NA
 values signify no data recorded in source dataset. Lithology: [text]
 Description of rock characteristics. As recorded in source dataset. NA
 values signify no data recorded in source dataset. Lat: [numeric] Latitude
 of polygon centroid; decimal degrees. Lon: [numeric] Longitude of polygon
 centroid; decimal degrees. Source: [integer] Numeric code corresponding to
 data sources. See 'Sources' below for detail. Area\_ha:
 [numeric] Area of polygon; hectares. Perim\_m: [numeric] Perimeter of
 polygon; meters. **Sources**: [1] Bonis, S., Bohnenberger, O.H., Dengo, G.
 (1970) Instituto Geográfico Nacional (Guatemala). Mapa geológico de la
 República de Guatemala. and Upie-Maga, & Maga-Bid. (2001). Mapa
 Fisiográfico-Geomorfológico de la República de Guatemala, a escala
 1:250,000 - Memoria Técnica-. Ministerio de Agricultura, Ganadería y
 Alimentación y Programa de Emergencia por Desastres Naturales, Ciudad de
 Guatemala, Guatemala. [2] Bawiec, W.J. (1998) Geology, geochemistry,
 geophysics, mineral occurrences, and mineral resource assessment for the
 commonwealth of Puerto Rico. U.S. Geological Survey, Reston, VA, USA.
 https://doi.org/10.3133/ofr9838, https://mrdata.usgs.gov/geology/pr/ [3]
 Schruben, Paul G. (1997) Geology and resource assessment of Costa Rica at
 1:500,000 scale; a digital representation of maps of the U.S. Geological
 Survey's 1987 Folio I-1865. Digital Data Series DDS-19-R. U.S.
 Geological Survey, Reston VA, USA. https://doi.org/10.3133/ds19,
 https://mrdata.usgs.gov/dds-19/. [4] Mollat, H., Wagner, B. M., Cepek, P.,
 & Weiss, W. (2004). Mapa Geológico de la República Dominicana
 1:250.000. Texto Explicativo. Stuttgart: E. Schweizerbart’sche
 Verlagsbuchhandiung. [5] Gómez, J., Schobbenhaus, C. & Montes,
 N.E., compilers. (2019) Geological Map of South America 2019. Scale
 1:5 000 000. Commission for the Geological Map of the World (CGMW),
 Colombian Geological Survey, and Geological Survey of Brazil. Paris.
 https://doi.org/10.32685/10.143.2019.929​​ [6] Garrity, C.P. and Hackley,
 P.C. (2006) Geologic\_units 1.0. Digital Data. Data Series 199, U.S.
 Geological Survey, Reston, VA, USA. http://pubs.usgs.gov/ds/2006/199. [7]
 Garea, E., Martín, G., Estrada, R. & Gerhartz, J.L. 2003. Mapa
 Geológico de Cuba. Escala 1: 100 000. Centro de Programas y Proyectos
 Priorizados. Ministerio de Ciencia, Tecnología y Medio Ambiente. La
 Habana, Cuba. [8] The National Soil Database [NSDB]. Available online at
 http://sis.agr.gc.ca/cansis/nsdb/index.html [9] U.S. General Soil Map
 (STATSGO2) CONUS. Soil Survey Staff, Natural Resources Conservation
 Service, United States Department of Agriculture. Web Soil Survey.
 Available online at http://websoilsurvey.nrcs.usda.gov. [10] Cartografía
 Geológica de la República Mexicana escala 1:250,000. Continúo Nacional de
 Geología de la República Mexicana escala 1:250,000. Servicio Geológico
 Mexicano. 2017. Available online at
 https://datos.gob.mx/busca/dataset/cartografia-geologica-de-la-republica-mexicana-escala-1-250000 **Usage Notes**: Both source [5] and source [6] contain data on the location of ultramafic soils in Venezuela. They are partly, but not completely overlapping and there are different spatial resolutions; Source [5] has coarser spatial resolution and does not contain several small polygons present in [6]. **Funding Information**: This material is based upon work supported by the National Science Foundation under Grant No. NSF MSB-ECA #1833358 and NSF CAREER #2042453 to CMH. **Acknowledgements**: We are grateful to Jon Walters for help building the geospatial database and documentation. We would also like to thank José Ramón Martínez Batlle for help accessing the geological map of the Dominican Republic. The neotropical portion of this dataset accompanies the article: https://doi.org/10.1007/s12229-022-09278-2