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26,784 results for “M.”
NN from Colombia collected by M. Montoya, E. Paqui y colaboradores #7100
<p><strong>File Name</strong>: <span>TOLI-24916-PER-02-F4-39.jpg</span></p> <p><strong>CÓDIGO FOTO</strong>: <span>TOLI-24916-PER-02-F4-39-</span></p> <p><strong>Fotografía</strong>: <span>SI</span></p> <p><strong>Nº TOLI</strong>: <span>TOLI-24916</span></p> <p><strong>PARCELA</strong>: <span>PER-02</span></p> <p><strong>CÓDIGO</strong>: <span>F4-39</span></p> <p><strong>Nº COLECTA</strong>: <span>7100</span></p> <p><strong>NUEVOS COLECTORES</strong>: <span>Marcela Montoya</span></p> <p><strong>COLECTORES</strong>: <span>M. Montoya, E. Paqui y colaboradores</span></p> <p><strong>Nº MUESTRAS MONTADAS</strong>: <span>1</span></p> <p><strong>Homologación</strong>: <span>No homologado</span></p> <p><strong>Nueva fecha del evento </strong>: <span>22/12/2018.</span></p> <p><strong>Fecha del evento</strong>: <span>22/08/2018.</span></p> <p><strong>Proyecto </strong>: <span>Recursos Botánicos Disponibles en Línea (BRAVO) para la flora Colombiana</span></p> <p><strong>Hábitat</strong>: <span>Bosque húmedo tropical (bh-T)</span></p> <p><strong>Comentario del evento</strong>: <span>Bosque de tierra firme, dosel abierto, de 25-30 m, emergentes de 35 m, estrato medio de 15 m, sotobosque denso con alta regeneración natural, presencia de palmas como Lepidocaryum tenue, Oenocarpus bataua, Geonoma sp., capa de hojarazca de 15 cm, abundante materia orgánica. Pendientes pronunciadas. Poca intervención antrópica.</span></p> <p><strong>Continente</strong>: <span>SA</span></p> <p><strong>Pais</strong>: <span>Colombia</span></p> <p><strong>Estado/Provincia</strong>: <span>Amazonas</span></p> <p><strong>Municipio</strong>: <span>Puerto Santander</span></p> <p><strong>Localidad</strong>: <span>Resguardo indígena Nonuya de Villazul.</span></p> <p><strong>Elevación minima en metros</strong>: <span>250</span></p> <p><strong>Elevación maxima en metros</strong>: <span>400</span></p> <p><strong>Latitud</strong>: <span>-0.654</span></p> <p><strong>Longitud original</strong>: <span>-72.072</span></p> <p><strong>datum geodésico</strong>: <span>WGS 84</span></p> <p><strong>Latitud decimal</strong>: <span>-0.654</span></p> <p><strong>Longitud decimal</strong>: <span>-72.072</span></p> <p><strong>Nombre cientifico</strong>: <span>NN</span></p> <p><strong>Reino</strong>: <span>Plantae</span></p> <p><strong>Filo</strong>: <span>Magnoliophyta</span></p> <p><strong>Clase</strong>: <span>Equisetopsida</span></p> <p><strong>Orden</strong>: <span>Rosales</span></p> <p><strong>Familia nueva</strong>: <span>Moraceae</span></p> <p><strong>Género nuevo</strong>: <span>NN</span></p> <p><strong></strong>: <span>Moraceae</span></p> <p><strong>genero herbario</strong>: <span>NN</span></p> <p><strong>Especie de herbario para TNRS</strong>: <span>NN </span></p> <p><strong>Especie corregida herbario y desde TNRS</strong>: <span>Indet indet</span></p> <p><strong>Familia corregida desde TNRS</strong>: <span>Moraceae</span></p> <p><strong></strong>: <span>3949</span></p>
NN from Colombia collected by M. Montoya, E. Paqui y colaboradores #7150
<p><strong>File Name</strong>: <span>TOLI-24907-PER-02-B3-16.jpg</span></p> <p><strong>CÓDIGO FOTO</strong>: <span>TOLI-24907-PER-02-B3-16-</span></p> <p><strong>Fotografía</strong>: <span>SI</span></p> <p><strong>Nº TOLI</strong>: <span>TOLI-24907</span></p> <p><strong>PARCELA</strong>: <span>PER-02</span></p> <p><strong>CÓDIGO</strong>: <span>B3-16</span></p> <p><strong>Nº COLECTA</strong>: <span>7150</span></p> <p><strong>NUEVOS COLECTORES</strong>: <span>Marcela Montoya</span></p> <p><strong>COLECTORES</strong>: <span>M. Montoya, E. Paqui y colaboradores</span></p> <p><strong>Nº MUESTRAS MONTADAS</strong>: <span>1</span></p> <p><strong>Homologación</strong>: <span>No homologado</span></p> <p><strong>Nueva fecha del evento </strong>: <span>22/12/2018.</span></p> <p><strong>Fecha del evento</strong>: <span>22/08/2018.</span></p> <p><strong>Proyecto </strong>: <span>Recursos Botánicos Disponibles en Línea (BRAVO) para la flora Colombiana</span></p> <p><strong>Hábitat</strong>: <span>Bosque húmedo tropical (bh-T)</span></p> <p><strong>Comentario del evento</strong>: <span>Bosque de tierra firme, dosel abierto, de 25-30 m, emergentes de 35 m, estrato medio de 15 m, sotobosque denso con alta regeneración natural, presencia de palmas como Lepidocaryum tenue, Oenocarpus bataua, Geonoma sp., capa de hojarazca de 15 cm, abundante materia orgánica. Pendientes pronunciadas. Poca intervención antrópica.</span></p> <p><strong>Continente</strong>: <span>SA</span></p> <p><strong>Pais</strong>: <span>Colombia</span></p> <p><strong>Estado/Provincia</strong>: <span>Amazonas</span></p> <p><strong>Municipio</strong>: <span>Puerto Santander</span></p> <p><strong>Localidad</strong>: <span>Resguardo indígena Nonuya de Villazul.</span></p> <p><strong>Elevación minima en metros</strong>: <span>250</span></p> <p><strong>Elevación maxima en metros</strong>: <span>400</span></p> <p><strong>Latitud</strong>: <span>-0.654</span></p> <p><strong>Longitud original</strong>: <span>-72.072</span></p> <p><strong>datum geodésico</strong>: <span>WGS 84</span></p> <p><strong>Latitud decimal</strong>: <span>-0.654</span></p> <p><strong>Longitud decimal</strong>: <span>-72.072</span></p> <p><strong>Nombre cientifico</strong>: <span>NN</span></p> <p><strong>Reino</strong>: <span>Plantae</span></p> <p><strong>Filo</strong>: <span>Magnoliophyta</span></p> <p><strong>Clase</strong>: <span>Equisetopsida</span></p> <p><strong>Orden</strong>: <span>Myrtales</span></p> <p><strong>Familia nueva</strong>: <span>Vochysiaceae</span></p> <p><strong>Género nuevo</strong>: <span>NN</span></p> <p><strong></strong>: <span>Vochysiaceae</span></p> <p><strong>genero herbario</strong>: <span>NN</span></p> <p><strong>Especie de herbario para TNRS</strong>: <span>NN </span></p> <p><strong>Especie corregida herbario y desde TNRS</strong>: <span>Indet indet</span></p> <p><strong>Familia corregida desde TNRS</strong>: <span>Vochysiaceae</span></p> <p><strong></strong>: <span>3940</span></p>
NN from Colombia collected by M. Montoya, E. Paqui y colaboradores #7146
<p><strong>File Name</strong>: <span>TOLI-24906-PER-02-B3-31.jpg</span></p> <p><strong>CÓDIGO FOTO</strong>: <span>TOLI-24906-PER-02-B3-31-</span></p> <p><strong>Fotografía</strong>: <span>SI</span></p> <p><strong>Nº TOLI</strong>: <span>TOLI-24906</span></p> <p><strong>PARCELA</strong>: <span>PER-02</span></p> <p><strong>CÓDIGO</strong>: <span>B3-31</span></p> <p><strong>Nº COLECTA</strong>: <span>7146</span></p> <p><strong>NUEVOS COLECTORES</strong>: <span>Marcela Montoya</span></p> <p><strong>COLECTORES</strong>: <span>M. Montoya, E. Paqui y colaboradores</span></p> <p><strong>Nº MUESTRAS MONTADAS</strong>: <span>1</span></p> <p><strong>Homologación</strong>: <span>No homologado</span></p> <p><strong>Nueva fecha del evento </strong>: <span>22/12/2018.</span></p> <p><strong>Fecha del evento</strong>: <span>22/08/2018.</span></p> <p><strong>Proyecto </strong>: <span>Recursos Botánicos Disponibles en Línea (BRAVO) para la flora Colombiana</span></p> <p><strong>Hábitat</strong>: <span>Bosque húmedo tropical (bh-T)</span></p> <p><strong>Comentario del evento</strong>: <span>Bosque de tierra firme, dosel abierto, de 25-30 m, emergentes de 35 m, estrato medio de 15 m, sotobosque denso con alta regeneración natural, presencia de palmas como Lepidocaryum tenue, Oenocarpus bataua, Geonoma sp., capa de hojarazca de 15 cm, abundante materia orgánica. Pendientes pronunciadas. Poca intervención antrópica.</span></p> <p><strong>Continente</strong>: <span>SA</span></p> <p><strong>Pais</strong>: <span>Colombia</span></p> <p><strong>Estado/Provincia</strong>: <span>Amazonas</span></p> <p><strong>Municipio</strong>: <span>Puerto Santander</span></p> <p><strong>Localidad</strong>: <span>Resguardo indígena Nonuya de Villazul.</span></p> <p><strong>Elevación minima en metros</strong>: <span>250</span></p> <p><strong>Elevación maxima en metros</strong>: <span>400</span></p> <p><strong>Latitud</strong>: <span>-0.654</span></p> <p><strong>Longitud original</strong>: <span>-72.072</span></p> <p><strong>datum geodésico</strong>: <span>WGS 84</span></p> <p><strong>Latitud decimal</strong>: <span>-0.654</span></p> <p><strong>Longitud decimal</strong>: <span>-72.072</span></p> <p><strong>Familia antigua</strong>: <span>Sapotaceae</span></p> <p><strong>Especie antigua</strong>: <span>NN</span></p> <p><strong>Nombre cientifico</strong>: <span>NN</span></p> <p><strong>Reino</strong>: <span>Plantae</span></p> <p><strong>Filo</strong>: <span>Magnoliophyta</span></p> <p><strong>Clase</strong>: <span>Equisetopsida</span></p> <p><strong>Orden</strong>: <span>Ericales</span></p> <p><strong>Familia nueva</strong>: <span>Sapotaceae</span></p> <p><strong>Género nuevo</strong>: <span>NN</span></p> <p><strong></strong>: <span>Sapotaceae</span></p> <p><strong>genero herbario</strong>: <span>NN</span></p> <p><strong>Especie de herbario para TNRS</strong>: <span>NN </span></p> <p><strong>Especie corregida herbario y desde TNRS</strong>: <span>Indet indet</span></p> <p><strong>Familia corregida desde TNRS</strong>: <span>Sapotaceae</span></p> <p><strong></strong>: <span>3939</span></p>
Hevea pauciflora (Spruce ex Benth.) Müll. Arg. from Colombia collected by M. Montoya, E. Paqui y colaboradores #7105
<p><strong>File Name</strong>: <span>TOLI-24917-PER-02-B3-35.jpg</span></p> <p><strong>CÓDIGO FOTO</strong>: <span>TOLI-24917-PER-02-B3-35-</span></p> <p><strong>Fotografía</strong>: <span>SI</span></p> <p><strong>Nº TOLI</strong>: <span>TOLI-24917</span></p> <p><strong>PARCELA</strong>: <span>PER-02</span></p> <p><strong>CÓDIGO</strong>: <span>B3-35</span></p> <p><strong>Nº COLECTA</strong>: <span>7105</span></p> <p><strong>NUEVOS COLECTORES</strong>: <span>Marcela Montoya</span></p> <p><strong>COLECTORES</strong>: <span>M. Montoya, E. Paqui y colaboradores</span></p> <p><strong>Nº MUESTRAS MONTADAS</strong>: <span>1</span></p> <p><strong>Homologación</strong>: <span>Homologado</span></p> <p><strong>Nueva fecha del evento </strong>: <span>22/12/2018.</span></p> <p><strong>Fecha del evento</strong>: <span>22/08/2018.</span></p> <p><strong>Proyecto </strong>: <span>Recursos Botánicos Disponibles en Línea (BRAVO) para la flora Colombiana</span></p> <p><strong>Hábitat</strong>: <span>Bosque húmedo tropical (bh-T)</span></p> <p><strong>Comentario del evento</strong>: <span>Bosque de tierra firme, dosel abierto, de 25-30 m, emergentes de 35 m, estrato medio de 15 m, sotobosque denso con alta regeneración natural, presencia de palmas como Lepidocaryum tenue, Oenocarpus bataua, Geonoma sp., capa de hojarazca de 15 cm, abundante materia orgánica. Pendientes pronunciadas. Poca intervención antrópica.</span></p> <p><strong>Continente</strong>: <span>SA</span></p> <p><strong>Pais</strong>: <span>Colombia</span></p> <p><strong>Estado/Provincia</strong>: <span>Amazonas</span></p> <p><strong>Municipio</strong>: <span>Puerto Santander</span></p> <p><strong>Localidad</strong>: <span>Resguardo indígena Nonuya de Villazul.</span></p> <p><strong>Elevación minima en metros</strong>: <span>250</span></p> <p><strong>Elevación maxima en metros</strong>: <span>400</span></p> <p><strong>Latitud</strong>: <span>-0.654</span></p> <p><strong>Longitud original</strong>: <span>-72.072</span></p> <p><strong>datum geodésico</strong>: <span>WGS 84</span></p> <p><strong>Latitud decimal</strong>: <span>-0.654</span></p> <p><strong>Longitud decimal</strong>: <span>-72.072</span></p> <p><strong>Identificado por</strong>: <span>Federico Fabriani</span></p> <p><strong>Fecha de identificación</strong>: <span>23/01/2019.</span></p> <p><strong>Familia antigua</strong>: <span>Euphorbiaceae</span></p> <p><strong>Especie antigua</strong>: <span>Hevea sp.</span></p> <p><strong>Nombre cientifico</strong>: <span>Hevea pauciflora (Spruce ex Benth.) Müll. Arg.</span></p> <p><strong>Reino</strong>: <span>Plantae</span></p> <p><strong>Filo</strong>: <span>Magnoliophyta</span></p> <p><strong>Clase</strong>: <span>Equisetopsida</span></p> <p><strong>Orden</strong>: <span>Malpighiales</span></p> <p><strong>Familia nueva</strong>: <span>Euphorbiaceae</span></p> <p><strong>Género nuevo</strong>: <span>Hevea </span></p> <p><strong>especie nueva</strong>: <span>pauciflora </span></p> <p><strong>Autoría del nombre científico</strong>: <span>(Spruce ex Benth.) Müll. Arg.</span></p> <p><strong></strong>: <span>Euphorbiaceae</span></p> <p><strong>genero herbario</strong>: <span>Hevea</span></p> <p><strong>especie herbario</strong>: <span>pauciflora</span></p> <p><strong>Especie de herbario para TNRS</strong>: <span>Hevea pauciflora</span></p> <p><strong>Especie corregida herbario y desde TNRS</strong>: <span>Hevea pauciflora</span></p> <p><strong>Familia corregida desde TNRS</strong>: <span>Euphorbiaceae</span></p> <p><strong></strong>: <span>3950</span></p>
Philodendron sp. from Colombia collected by W. López, J. Rodriguez, M. Salas #10627
<p><strong>File Name</strong>: <span>TOLI-24897-AMA-01-D12-77.jpg</span></p> <p><strong>CÓDIGO FOTO</strong>: <span>TOLI-24897-AMA-01-D12-77-</span></p> <p><strong>Fotografía</strong>: <span>SI</span></p> <p><strong>Nº TOLI</strong>: <span>TOLI-24897</span></p> <p><strong>PARCELA</strong>: <span>AMA-01</span></p> <p><strong>CÓDIGO</strong>: <span>D12-77</span></p> <p><strong>Nº COLECTA</strong>: <span>10627</span></p> <p><strong>NUEVOS COLECTORES</strong>: <span>Wilmar López Oviedo</span></p> <p><strong>COLECTORES</strong>: <span>W. López, J. Rodriguez, M. Salas</span></p> <p><strong>Nº MUESTRAS MONTADAS</strong>: <span>1</span></p> <p><strong>Homologación</strong>: <span>No homologado</span></p> <p><strong>Nueva fecha del evento </strong>: <span>27/11/2018.</span></p> <p><strong>Fecha del evento</strong>: <span>01/05/2012.</span></p> <p><strong>Proyecto </strong>: <span>Recursos Botánicos Disponibles en Línea (BRAVO) para la flora Colombiana</span></p> <p><strong>Hábitat</strong>: <span>Bosque muy húmedo tropical (bmh-T)</span></p> <p><strong>Comentario del evento</strong>: <span>Bosque de tierra firme</span></p> <p><strong>Continente</strong>: <span>SA</span></p> <p><strong>Pais</strong>: <span>Colombia</span></p> <p><strong>Estado/Provincia</strong>: <span>Chocó</span></p> <p><strong>Municipio</strong>: <span>Nuquí</span></p> <p><strong>Centro poblado / Cabecera municipal</strong>: <span>Arusí</span></p> <p><strong>Localidad</strong>: <span>Reserva Natural El Amargal</span></p> <p><strong>Elevación minima en metros</strong>: <span>50</span></p> <p><strong>Elevación maxima en metros</strong>: <span>300</span></p> <p><strong>Latitud</strong>: <span>5.578</span></p> <p><strong>Longitud original</strong>: <span>-77.500</span></p> <p><strong>datum geodésico</strong>: <span>WGS 84</span></p> <p><strong>Latitud decimal</strong>: <span>5.578</span></p> <p><strong>Longitud decimal</strong>: <span>-77.500</span></p> <p><strong>Nombre cientifico</strong>: <span>Philodendron sp.</span></p> <p><strong>Reino</strong>: <span>Plantae</span></p> <p><strong>Filo</strong>: <span>Magnoliophyta</span></p> <p><strong>Clase</strong>: <span>Equisetopsida</span></p> <p><strong>Orden</strong>: <span>Alismatales</span></p> <p><strong>Familia nueva</strong>: <span>Araceae</span></p> <p><strong>especie nueva</strong>: <span>sp.</span></p> <p><strong>genero herbario</strong>: <span>NN</span></p> <p><strong>Especie de herbario para TNRS</strong>: <span>NN </span></p> <p><strong>Especie corregida herbario y desde TNRS</strong>: <span>Indet indet</span></p> <p><strong>Familia corregida desde TNRS</strong>: <span>Araceae</span></p> <p><strong></strong>: <span>3930</span></p>
Virola sp.1 from Colombia collected by M. Montoya, E. Paqui y colaboradores #7151
<p><strong>File Name</strong>: <span>TOLI-24884-PER-02-A3-29.jpg</span></p> <p><strong>CÓDIGO FOTO</strong>: <span>TOLI-24884-PER-02-A3-29-</span></p> <p><strong>Fotografía</strong>: <span>SI</span></p> <p><strong>Nº TOLI</strong>: <span>TOLI-24884</span></p> <p><strong>PARCELA</strong>: <span>PER-02</span></p> <p><strong>CÓDIGO</strong>: <span>A3-29</span></p> <p><strong>Nº COLECTA</strong>: <span>7151</span></p> <p><strong>NUEVOS COLECTORES</strong>: <span>Marcela Montoya</span></p> <p><strong>COLECTORES</strong>: <span>M. Montoya, E. Paqui y colaboradores</span></p> <p><strong>Nº MUESTRAS MONTADAS</strong>: <span>1</span></p> <p><strong>Homologación</strong>: <span>Homologado</span></p> <p><strong>Nueva fecha del evento </strong>: <span>22/12/2018.</span></p> <p><strong>Fecha del evento</strong>: <span>22/08/2018.</span></p> <p><strong>Proyecto </strong>: <span>Recursos Botánicos Disponibles en Línea (BRAVO) para la flora Colombiana</span></p> <p><strong>Hábitat</strong>: <span>Bosque húmedo tropical (bh-T)</span></p> <p><strong>Comentario del evento</strong>: <span>Bosque de tierra firme, dosel abierto, de 25-30 m, emergentes de 35 m, estrato medio de 15 m, sotobosque denso con alta regeneración natural, presencia de palmas como Lepidocaryum tenue, Oenocarpus bataua, Geonoma sp., capa de hojarazca de 15 cm, abundante materia orgánica. Pendientes pronunciadas. Poca intervención antrópica.</span></p> <p><strong>Continente</strong>: <span>SA</span></p> <p><strong>Pais</strong>: <span>Colombia</span></p> <p><strong>Estado/Provincia</strong>: <span>Amazonas</span></p> <p><strong>Municipio</strong>: <span>Puerto Santander</span></p> <p><strong>Localidad</strong>: <span>Resguardo indígena Nonuya de Villazul.</span></p> <p><strong>Elevación minima en metros</strong>: <span>250</span></p> <p><strong>Elevación maxima en metros</strong>: <span>400</span></p> <p><strong>Latitud</strong>: <span>-0.654</span></p> <p><strong>Longitud original</strong>: <span>-72.072</span></p> <p><strong>datum geodésico</strong>: <span>WGS 84</span></p> <p><strong>Latitud decimal</strong>: <span>-0.654</span></p> <p><strong>Longitud decimal</strong>: <span>-72.072</span></p> <p><strong>Identificado por</strong>: <span>Jaime Cabezas</span></p> <p><strong>Fecha de identificación</strong>: <span>24/04/2019.</span></p> <p><strong>Familia antigua</strong>: <span>Myristicaceae</span></p> <p><strong>Especie antigua</strong>: <span>NN</span></p> <p><strong>Nombre cientifico</strong>: <span>Virola sp.1</span></p> <p><strong>Reino</strong>: <span>Plantae</span></p> <p><strong>Filo</strong>: <span>Magnoliophyta</span></p> <p><strong>Clase</strong>: <span>Equisetopsida</span></p> <p><strong>Orden</strong>: <span>Magnoliales</span></p> <p><strong>Familia nueva</strong>: <span>Myristicaceae</span></p> <p><strong>Género nuevo</strong>: <span>Virola </span></p> <p><strong>especie nueva</strong>: <span>sp.</span></p> <p><strong></strong>: <span>Myristicaceae</span></p> <p><strong>genero herbario</strong>: <span>Virola</span></p> <p><strong>especie herbario</strong>: <span>sp.</span></p> <p><strong>Especie de herbario para TNRS</strong>: <span>Virola sp.</span></p> <p><strong>Especie corregida herbario y desde TNRS</strong>: <span>Virola indet</span></p> <p><strong>Familia corregida desde TNRS</strong>: <span>Myristicaceae</span></p> <p><strong></strong>: <span>3917</span></p>
Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution
<p>Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution based on a compilation of data sets (Biome6000k, Geo-Wiki, LandPKS, mangroves soil database, and from various literature sources; total of about 65,000 training points). We used a comparable thematic legend used to produce the Dynamic Land Cover 100m: Version 2. Copernicus Global Land Operations product (Buchhorn et al. 2019), which is based on the UN FAO Land Cover Classification System (LCCS), so that users can compare actual (https://lcviewer.vito.be/) vs potential (this data set) land cover. Two classes not available in the LCCS were added: "subtropical/tropical mangrove vegetation" and "sub-polar or polar barren-lichen-moss, grassland". The map was created using relief and climate variables representing conditions the climate for the last 20+ years and predicted at 250 m globally using an Ensemble Machine Learning approach as implemented in the mlr package for R. Processing steps are described in detail <a href="https://github.com/Envirometrix/PNVmaps"><strong>here</strong></a>. Maps with "_sd_" contain estimated model errors per class. Antarctica is not included.</p> <p>Produced for the needs of the <a href="https://naturemap.earth/"><strong>NatureMap</strong></a> which is project run by the <strong>International Institute for Applied Systems Analysis</strong> (IIASA), the <strong>International Institute for Sustainability</strong> (IIS), the <strong>UN Environment Programme World Conservation Monitoring Centre</strong> (UNEP-WCMC), and the <strong>UN Sustainable Development Solutions Network</strong> (SDSN). NatureMap is funded by Norway’s International Climate Initiative (NICFI).</p> <p>Maps will also be made available via: <a href="https://OpenLandMap.org">OpenLandMap.org</a>. These are initial predictions for testing purposes only. A publication explaining all processing steps is pending.</p> <p>If you discover a bug, artifact or inconsistency in the predictions, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://github.com/Envirometrix/PNVmaps/issues">https://github.com/Envirometrix/PNVmaps/issues</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>pnv = theme: potential natural vegetation,</li> <li>potential.landcover = variable: potential land cover type (e.g. "open forest, evergreen needleleaf"),</li> <li>probav.lc100 = classification model: ProbaV-based land cover mapping legend (LCCS),</li> <li>c = factor,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: period 2000-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Raw stable water isotope measurements in water vapour at 13.5 m a.s.l., made in the austral summer of 2016/2017 during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>This dataset includes the raw data of stable water vapour isotope (δ18O, δ2H, deuterium excess) and water vapour mixing ratio measurements taken in the Southern Ocean during the Antarctic Circumnavigation Expedition (ACE) from November 2016 to April 2017 using a Picarro laser spectrometer L2130. This data has to be calibrated using the calibration runs and applying an appropriate calibration procedure following the IAEA guide lines.</p> <p>Two similar datasets also measuring stable water isotopes (SWIs) in water vapour were collected during the same expedition and can be distinguished from this dataset due to the height of the measurement collection (8 m a.s.l rather than 13.5 m a.s.l). The three ACE datasets of SWIs in water vapour are described and compared in Thurnherr et al. (2020).</p> <p>The calibrated dataset can be found with DOI: 10.5281/zenodo.3250790.</p> <p><strong>Dataset contents</strong></p> <ul> <li>year/month/day/HIDS2054-${year}${month}${day}-${time:Hhmmss}Z-DataLog_User.dat, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Change log</strong></p> <p><strong>v1.1</strong> - Added 13.5 m a.s.l to title of dataset to distinguish it from other similar datasets collected during the expedition. Added calibrated dataset DOI and information about other SWI datasets from ACE, to abstract. Added reference to Thurnherr et al. (2020) to README.txt. Updated citation of dataset in README.txt.</p> <p><strong>v1.0</strong> - Initial release of raw stable water isotope measurements in water vapour dataset.</p> <p><strong>Dataset license</strong></p> <p>This stable water isotope measurements in water vapour dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Raw stable water isotope measurements in water vapour at 8 m a.s.l. on the starboard side of the ship, made in the austral summer of 2016/2017 during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>This dataset includes the raw data of stable water vapour isotope (δ18O, δ2H, deuterium excess) and water vapour mixing ratio measurements at a height of approximately 8 meters above sea level taken in the Southern Ocean during the Antarctic Circumnavigation Expedition (ACE) from November 2016 to April 2017 using a Picarro laser spectrometer L2130-i mounted on the starboard side of the ship. L2130-i was operated only during two months in February and March 2017 from Hobart (Australia) to Cape Town (South Africa). This data has to be calibrated using the calibration runs and applying an appropriate calibration procedure following the IAEA guide lines.</p> <p>Two similar datasets also measuring stable water isotopes (SWIs) in water vapour were collected during the same expedition and can be distinguished from this dataset due to the height of the measurement collection (at 8 m a.s.l and 13.5 m a.s.l). The three ACE datasets of SWIs in water vapour are described and compared in Thurnherr et al. (2020).</p> <p><strong>Dataset contents</strong></p> <ul> <li>leg*/HIDS2018-yyyymmdd-hhmmssZ-DataLog_User.dat, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This raw stable water isotope measurements dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full description can be found at https://creativecommons.org/licenses/by/4.0</p>
The ELF WERA stations measurements used in the paper "TGF 181010 study using extremely low frequency electromagnetic waves" by V. Marchenko, J. Mlynarczyk, M. Ostrowski, A. Kulak, O. Senchenko, J. Kubisz, A. Michalec, J. Salvador, N. Diaz
<p>The data analyzed in the paper are provided as text files containing measurements of two stations of the WERA project:</p> <p>File "TGF181010_Hylaty_201810101300.txt" - from the Hylaty station in Poland, </p> <p>File "TGF181010_Patagonia_201810101300.txt" - from the Patagonia station in Argentina.</p> <p>In each file the first column provides time, as measured in seconds since October 10, 2018, 13:00 UT. The second and third columns provide signals from NS and EW magnetic antennas. The duration of each data set is 5 min. The time accuracy for the Hylaty station is below 1 ms and for the Patagonia station up to several ms.</p>
Calibrated data of stable water isotope measurements in water vapour at 8 m a.s.l. on the port side of the ship, made in the austral summer of 2016/2017 around the Southern Ocean during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>This data set includes the calibrated data of stable water vapour isotope (δ18O, δ2H, deuterium excess) and water vapour mixing ratio measurements at approximately 8 m a.s.l. on the port of the ship, taken around the Southern Ocean during the Antarctic Circumnavigation Expedition (ACE) from November 2016 to April 2017 using a Picarro laser spectrometers L2120. The data provide continuous timelines of atmospheric water vapour properties in the marine boundary layer for studies of the atmospheric water cycle.</p> <p>The raw data from which this calibrated dataset originates has been published separately (Kozachek, 2020; DOI 10.5281/zenodo.3667535).</p> <p>Two other calibrated stable water isotope measurement datasets were also collected during ACE. They differ by the location of measurement on the ship: 8 m a.s.l on the starboard side (DOI: 10.5281/zenodo.3739335) and 13.5 m a.s.l (DOI: 10.5281/zenodo.3250790).</p> <p><strong>Dataset contents</strong></p> <p>Measurements on portside:</p> <ul> <li>ACE_watervapour_isotopes_SWI8-ps_1h.csv, data file, comma-separated values</li> <li>ACE_watervapour_isotopes_SWI8-ps_5min.csv, data file, comma-separated values</li> <li>cal_runs_SWI8-ps.csv, metadata, comma-separated values</li> <li>cal_flag_times_SWI8-ps.csv, metadata, comma-separated values</li> <li>data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p>NaN values denote missing values which occur because of e.g., maintenance, instrument calibration, large cavity variations.</p> <p><strong>Dataset license</strong></p> <p>This calibrated stable water isotope measurements in water vapour is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Fig. 16. Megatrigon nivalis and M in Taxonomic revision of the Afrotropical genus Megatrigon Johnson, 1898 (Diptera: Syrphidae)
Fig. 16. Megatrigon nivalis and M. argenteus species group (subgroup 2), distribution.
AFM movie of DHR10-mica18 on m-mica (001) in 100 mM KCl solution
<p>AFM movie of DHR10-mica18 on m-mica (001) in 100 mM KCl solution. The experimental AFM dataset was collected by Shuai Zhang and converted to Python-compatible format by Maxim Ziatdinov.</p>
iSDAsoil: soil total Carbon for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil total carbon in permilles (g/kg) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.c_tot_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total Carbon mean value,</li> <li>sol_log.c_tot_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total Carbon model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.c_tot R-square: 0.794 Fitted values sd: 0.571 RMSE: 0.291 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -2.70312 -0.16714 -0.00549 0.15691 3.01116 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.025841 0.032713 0.790 0.429570 regr.ranger 0.902240 0.008462 106.619 < 2e-16 *** regr.xgboost 0.066535 0.008145 8.169 3.18e-16 *** regr.cubist 0.145730 0.006927 21.039 < 2e-16 *** regr.nnet -0.048957 0.013466 -3.636 0.000278 *** regr.cvglmnet -0.075212 0.005556 -13.537 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.291 on 50140 degrees of freedom Multiple R-squared: 0.7938, Adjusted R-squared: 0.7938 F-statistic: 3.861e+04 on 5 and 50140 DF, p-value: < 2.2e-16 </code></pre> <p>To back-transform values (y) to g/kg use the following formula:</p> <pre><code>g/kg = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
iSDAsoil: soil extractable Aluminium for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil extractable Aluminium (Al) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, <a href="https://landpotential.org/data-portal/">LandPKS</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.al_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Aluminium mean value,</li> <li>sol_log.al_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Aluminium model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.al_mehlich3 R-square: 0.881 Fitted values sd: 0.872 RMSE: 0.321 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -5.7042 -0.1036 0.0059 0.1189 3.3777 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.675492 2.771906 -0.244 0.807 regr.ranger 0.879567 0.005464 160.969 <2e-16 *** regr.xgboost 0.071537 0.005813 12.306 <2e-16 *** regr.cubist 0.150157 0.004553 32.979 <2e-16 *** regr.nnet 0.087603 0.431261 0.203 0.839 regr.cvglmnet -0.084440 0.003182 -26.534 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3208 on 63551 degrees of freedom Multiple R-squared: 0.8808, Adjusted R-squared: 0.8808 F-statistic: 9.391e+04 on 5 and 63551 DF, p-value: < 2.2e-16</code></pre> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
iSDAsoil: soil extractable Potassium for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil extractable Potassium (K) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.k_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Potassium mean value,</li> <li>sol_log.k_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Potassium model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.k_mehlich3 R-square: 0.773 Fitted values sd: 0.938 RMSE: 0.509 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -4.3088 -0.2648 -0.0037 0.2639 6.8136 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 10.907726 6.134422 1.778 0.0754 . regr.ranger 1.004487 0.003878 259.026 <2e-16 *** regr.xgboost -0.004081 0.004739 -0.861 0.3892 regr.cubist 0.084556 0.004346 19.454 <2e-16 *** regr.nnet -2.205286 1.228586 -1.795 0.0727 . regr.cvglmnet -0.064510 0.003933 -16.401 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.5092 on 139122 degrees of freedom Multiple R-squared: 0.7725, Adjusted R-squared: 0.7725 F-statistic: 9.451e+04 on 5 and 139122 DF, p-value: < 2.2e-16 </code></pre> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
iSDAsoil: soil effective Cation Exchange Capacity (eCEC) for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil effective Cation Exchange Capacity (eCEC) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.ecec.f_tot_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil eCEC mean value,</li> <li>sol_log.ecec.f_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil eCEC model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.ecec.f R-square: 0.754 Fitted values sd: 0.729 RMSE: 0.417 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -3.2877 -0.1888 0.0097 0.2023 3.1494 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.807991 1.806781 1.554 0.1202 regr.ranger 1.046105 0.004845 215.911 < 2e-16 *** regr.xgboost -0.016558 0.005912 -2.801 0.0051 ** regr.cubist 0.031843 0.005063 6.289 3.21e-10 *** regr.nnet -1.142820 0.713071 -1.603 0.1090 regr.cvglmnet -0.027630 0.005607 -4.928 8.34e-07 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.4166 on 66380 degrees of freedom Multiple R-squared: 0.7538, Adjusted R-squared: 0.7538 F-statistic: 4.065e+04 on 5 and 66380 DF, p-value: < 2.2e-16 </code></pre> <p>To back-transform values (y) to cmol(+)/kg use the following formula:</p> <pre><code>cmol(+)/kg = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
iSDAsoil: soil organic carbon for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil organic carbon (C) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.oc_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil organic Carbon mean value,</li> <li>sol_log.oc_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil organic Carbon model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.oc R-square: 0.791 Fitted values sd: 0.716 RMSE: 0.369 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -3.1517 -0.1900 -0.0060 0.1793 4.2621 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.821657 0.794000 2.294 0.0218 * regr.ranger 1.047507 0.005146 203.571 <2e-16 *** regr.xgboost -0.005943 0.005340 -1.113 0.2657 regr.cubist 0.052084 0.004884 10.664 <2e-16 *** regr.nnet -0.867384 0.359213 -2.415 0.0158 * regr.cvglmnet -0.050157 0.003863 -12.984 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3687 on 122457 degrees of freedom Multiple R-squared: 0.7906, Adjusted R-squared: 0.7906 F-statistic: 9.248e+04 on 5 and 122457 DF, p-value: < 2.2e-16 </code></pre> <p>To back-transform values (y) to g/kg use the following formula:</p> <pre><code>g/kg = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
iSDAsoil: soil extractable Zinc for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil extractable Zinc (Zn) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.zn_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Zinc mean value,</li> <li>sol_log.zn_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Zinc model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.zn_mehlich3 R-square: 0.711 Fitted values sd: 0.588 RMSE: 0.375 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -2.1382 -0.2038 -0.0274 0.1632 3.6353 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.836394 1.555108 1.181 0.23766 regr.ranger 0.823144 0.013982 58.871 < 2e-16 *** regr.xgboost 0.037861 0.013644 2.775 0.00552 ** regr.cubist 0.169653 0.010091 16.813 < 2e-16 *** regr.nnet -1.511745 1.244933 -1.214 0.22463 regr.cvglmnet 0.009770 0.008046 1.214 0.22467 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3747 on 39344 degrees of freedom Multiple R-squared: 0.7109, Adjusted R-squared: 0.7109 F-statistic: 1.935e+04 on 5 and 39344 DF, p-value: < 2.2e-16</code></pre> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
iSDAsoil: soil texture class (USDA system) for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil texture classes derived from sand, silt and clay fractions at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_texture.class_c_30m_*..*cm_2001..2017_v0.13_wgs84.tif = soil texture class,</li> </ul> <p>Classes:</p> <pre><code>Code,Name,Value,Color Cl,clay,1,#d5c36b SiCl,silty clay,2,#b96947 SaCl,sandy clay,3,#9d3706 ClLo,clay loam,4,#ae868f SiClLo,silty clay loam,5,#f86714 SaClLo,sandy clay loam,6,#46d143 Lo,loam,7,#368f20 SiLo,silt loam,8,#3e5a14 SaLo,sandy loam,9,#ffd557 Si,silt,10,#fff72e LoSa,loamy sand,11,#ff5a9d Sa,sand,12,#ff005b NODATA,,255,#ffffff </code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.