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13,117 results for “Maps”
Geomorphic maps of candidate landing sites in northern Amazonis Planitia
<p>This folder contains four files:</p> <ul> <li>three geopackages (.gpkg files), each containing the geomorphic maps of candidate landing sites AP-1, AP-8, and AP-9 located in northern Amazonis Planitia,</li> <li>one table containing the coordinates of candidate landing sites.</li> </ul>
FIGURE 1. Distribution maps. A in A review of Scaphochlamys (Zingiberaceae) from Borneo, with description of eleven new species
FIGURE 1. Distribution maps. A. ■ Anomala Group, excluding S. anomala and S. gracilipes. ● Calcicola Group. Graveolens Group. ▲ Polyphylla Group. B.*Limiana Group. ♦ Petiolata Group.
Self-organizing map of electron-impact mass-spectra
<p>This text file is a self-organising map (256 x 256 grid units) of mass spectra.</p> <p>The mass spectra are stored as a continuous list. In the file, consecutive sets of 256 rows in the file are one row of the map (each row has 256 grid units). After every 256th row in the file, a new map row begins (256 rows in total).</p> <p>The mass spectra are electron-impact mass-spectra, as they are typically recorded in gas chromatography. The resolution is unit mass, the spectral range is limited to 29 - 200 m/z, and the spectral intensities are normalized.</p> <p>The mass spectra were collected from the Mass Bank of North America (<em>MassBank of North America</em>. <a href="https://mona.fiehnlab.ucdavis.edu/">https://mona.fiehnlab.ucdavis.edu/</a> (accessed 2018-07-05)) and are used under a CC-BY 4.0 license.</p>
Map 1 in Distribution and habitat preferences of Galápagos ants (Hymenoptera: Formicidae)
Map 1. Galápagos archipelago with terrestrial ecological zones. The largest island Isabela is composed of six volcanoes.
FIGURE 16. Distribution maps. A in Revision of the genus Monotes (Dipterocarpaceae) in D.R. Congo, with implications for Angola and its distinction from Marquesia
FIGURE 16. Distribution maps. A. Monotes duvigneaudii var. duvigneaudii: Θ; var. concolor: z; B. M. rubriglans subsp. upembensis var. griseocoriaceus: ·; C. M. rubriglans subsp. upembensis var. upembensis:; D. M. doryphorus: ; E. M. magnificus var. gigantophyllus: z; M. hirtii: ·; F. M. dasyanthus var. heterotrichus:.
Stellar reddening map from DESI imaging and spectroscopy
<p>Data for reproducing figures in "Stellar reddening map from DESI imaging and spectroscopy" by Rongpu Zhou et al. 2024.</p>
FIGURE 1. Distribution map. A in A synopsis of Begonia (Begoniaceae) of Southeastern Sulawesi including four new species
FIGURE 1. Distribution map. A. Overview of Sulawesi. B. Distributions of B. aptera, B. johntania, B. mekonggensis, and B. ozotothrix.C. Distributions of B. balgooyi, B.flacca, B. matarombeoensis, and B. watuwilensis. D. Distributions of B.incudiformicarpa, B. iskandariana, and B. tumburanoensis. Specimen location information was georeferenced using the GeoNames geographical database at http://www. geonames.org/.
FIGURE 3. Heat map showing recovery efficiency for 333 in Delimitation of the new tribe Parartocarpeae (Moraceae) is supported by a 333- gene phylogeny and resolves tribal level Moraceae taxonomy
FIGURE 3. Heat map showing recovery efficiency for 333 genes. Each column is a gene, and each row is one sample. The intensity of color in each cell is determined by the length of sequence recovered divided by the length of the reference gene (maximum of 1.0).
Data for "Bridging reading and mapping: The role of reading annotations in facilitatingfeedback while concept mapping"
<p>This data includes:</p> <ul> <li>Questionnaire for Problem formulation</li> <li>UTAUT questionnaire evaluating Concept&Go</li> <li>SmartPLS model files for the UTAUT evaluation</li> </ul>
Multi-omic Mapping of Human Pancreatic Islet Endoplasmic Reticulum and Cytokine Stress Responses Provide Type 2 Diabetes Genetic Insights
<p>The following data accompanying the manuscript can be found here:</p> <ul> <li>Expression (bulk RNA-seq & scRNA-seq) matrices</li> <li>Chromatin accessibility (bulk ATAC-seq) matrices</li> <li>TDF files for IGV browser visualization of cis-regulatory elements</li> </ul>
EO4Diversity: Experimental Dataset - Invasive Alien Species Modelling & Mapping
<p>To support the EC and ESA joint Flagship Action of Biodiversity and vulnerable ecosystems to advance Earth System Science and its responses to the global challenges, the Earth Observation for Biodiversity Modeling (EO4Diversity) project, developed three Biodiversity Pilots (BD Pilots) that focus on core issues in biodiversity policy priorities:</p> <ul> <li>BD pilot1: Ecosystem productivity and health</li> <li>BD pilot2: Ecosystem accounting, monitoring and reporting towards policy obligations</li> <li>BD pilot3: Ecosystem resilience to invasive species</li> </ul> <p>Each of these pilots develops an integrated solution that brings together the scientific strengths of high-resolution multi-sensor EO data products (such as remote sensing enabled EBVs and products - RS-EBVS) on the one hand, and the state-of-the-art ecological models, on the other.</p> <p>This Zenodo repository contains the <strong>BD Pilot 3 Invasive Alien Species Modelling & Mapping</strong> products and product description.</p>
Characterization of N variations in different organs of winter wheat and mapping NUE using UAV-based remote sensing
Open the record for dataset details and reuse information.
Swift/BAT-GUANO upper limits map for S240908bs
Open the record for dataset details and reuse information.
Swift/BAT-GUANO upper limits map for S240907cg
Open the record for dataset details and reuse information.
FIG. 1. Map depicting the 26 in Impacts of Wastewater Effluent on Temperate Stream Fish Assemblage Structure
FIG. 1. Map depicting the 26 sites sampled in the East (n ¼ 10, gray circles) and West (n ¼ 16, black circles) Forks of the Niangua River during the study. The three circles with X's represent the sites where temperature loggers were deployed, and the WWTP outfall is located immediately south of W1 (right). Map inset (upper left) depicts the location of Missouri in the U.S., and inset (lower left) shows the location of the upper Niangua River drainage in Missouri.
NeurOST-SSH-SST Maps for Ocean Data Challenge 2023a_SSH_mapping_OSE
<p>Global maps of sea surface height (SSH) and surface geostrophic currents generated using NeurOST, a deep learning for mapping SSH from nadir satellite altimetry and sea surface temperature, generated for the observing system experiment outlined in the Ocean Data Challenge '2023a_SSH_mapping_OSE'.</p> <p>Ocean Data Challenge link: https://github.com/ocean-data-challenges/2023a_SSH_mapping_OSE/tree/main </p> <p>These maps were made using L3 SSH + L4 SST.</p> <p>NeurOST citations:</p> <ul> <li>Martin, S. A., Manucharyan, G. E., and Klein, P. (2024). Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics. Geophysical Research Letters, 51, e2024GL110059. https://doi.org/10.1029/2024GL110059</li> <li>Martin, S. A., Manucharyan, G. E., and Klein, P. (2023). Synthesizing Sea Surface Temperature and Satellite Altimetry Observations Using Deep Learning Improves the Accuracy and Resolution of Gridded Sea Surface Height Anomalies. Journal of Advances in Modeling Earth Systems, 15, e2022MS003589. https://doi.org/10.1029/2022MS003589</li> </ul>
Mapping and Quantifying Mexico's Primary Forests: An Operational Approach for Forest Resources Assessment
<h2>Supplementary material of the paper</h2> <h3>Authors:</h3> <ul> <li>Luis Felipe Castelblanco Rivera, master student in Earth Science at Geosciences Center, UNAM.</li> <li>Mario Antonio Guevara Santamaría, associate professor at Geosciences Center, UNAM campus Juriquilla</li> </ul> <h3>More information:</h3> <ul> <li>lcastelblancor@geociencias.unam.mx / lfcastelblancor@unal.edu.co</li> <li>mguevara@geociencias.unam.mx / mguevara@comunidad.unam.mx</li> </ul> <p>Supplementary material containing the vector layers with the information on the exclusion areas and the forest resulting from the multicriteria analysis for the identification of primary forests in Mexico for the years 2015 and 2020.</p> <h3>Summary:</h3> <p>The file is organized as follows:</p> <p>> Primary forest MX</p> <p> > 2015</p> <p> > Forest areas</p> <p> - Forest_1_2 (Bosque_1_2.shp)</p> <p> - Forest_1_3_1_Infrastructure (Bosque_1_3_1_Infraestructura.shp)</p> <p> - Forest_1_3_2_Deforestation (Bosque_1_3_2_Deforestacion.shp)</p> <p> - Forest_1_4_45degrees (Bosque_1_4_45grados.shp)</p> <p> - Forest_1_4_3000masl (Bosque_1_4_3000msnm.shp)</p> <p> - Forest_1_5_NPA (Bosque_1_5_ANP.shp)</p> <p> - Forest_1_6_patch_size_2015 (Bosque_1_6_tamaño_parche_2015.shp)</p> <p> - Primary_forest_2015 (Bosque_primario_2015.shp)</p> <p> > Exclusion areas</p> <p> - Deforestation2015_1km (Deforestacion2015_1km.shp)</p> <p> - AgriculturalBorder_SII_1km (FronteraAgricola_SII_1km.shp)</p> <p> - Infrastructure_1km (Infraestructura_1km.shp)</p> <p> - redvial2015_1km (redvial2015_1km.shp)</p> <p> > 2020</p> <p> > Forest areas</p> <p> - Forest_1_2 (Bosque_1_2.shp)</p> <p> - Forest_1_3_1_Infrastructure (Bosque_1_3_1_Infraestructura.shp)</p> <p> - Forest_1_3_2_Deforestation (Bosque_1_3_2_Deforestacion.shp)</p> <p> - Forest_1_4_45degrees (Bosque_1_4_45grados.shp)</p> <p> - Forest_1_4_3000msnm (Bosque_1_4_3000msnm.shp)</p> <p> - Forest_1_5_NPA (Bosque_1_5_ANP.shp)</p> <p> - Forest_1_6_patch_size_2015 (Bosque_1_6_tamaño_parche_2015.shp)</p> <p> - Primary_forest_2020 (Bosque_primario_2020.shp)</p> <p> > Exclusion areas</p> <p> - Deforestation2020_1km (Deforestacion2020_1km.shp)</p> <p> - Agricultural_Front_SIII_1km (FronteraAgricola_SIII_1km.shp)</p> <p> - Infrastructure_1km (Infraestructura_1km.shp)</p> <p> - road_network2020_1km (redvial2020_1km.shp)</p> <p>*All shape files are accompanied by their respective auxiliary files.</p> <p>Supplementary material 1-4.docx: Word file containing the supplementary materials named in the article, in the following order:</p> <ul> <li>Supplementary material 1. Decision tree.</li> <li>Supplementary material 2. Data sets.</li> <li>Supplementary material 3. Primary forest area by ecoregion and forest type for 2015 and 2020</li> <li>Supplementary material 4. Statistics on concordance and ecological integrity values in primary and other forests in 2020.</li> </ul> <h2>Material suplementario del artículo </h2> <h3>Autores:</h3> <p>- Luis Felipe Castelblanco Rivera, master student in Earth Science at Geosciences Center, UNAM.</p> <p>- Mario Antonio Guevara Santamaría, associate professor at Geosciences Center, UNAM campus Juriquilla </p> <h3>Más información:</h3> <p>- lcastelblancor@geociencias.unam.mx / lfcastelblancor@unal.edu.co</p> <p>- mguevara@geociencias.unam.mx / mguevara@comunidad.unam.mx</p> <p><strong>Bosque primario MX.7z: </strong>Material suplementario que contiene las capas vectoriales con las información de las áreas de exclusión y el bosque resultante del análisis multicriterio para la identificación de bosques primarios en México para los años 2015 y 2020. </p> <h3>Resumen:</h3> <p>El archivo se encuentra organizado de la siguiente forma:</p> <p>> Bosque primario MX</p> <p> > 2015</p> <p> > Áreas de bosque</p> <p> - Bosque_1_2.shp</p> <p> - Bosque_1_3_1_Infraestructura.shp</p> <p> - Bosque_1_3_2_Deforestacion.shp</p> <p> - Bosque_1_4_45grados.shp</p> <p> - Bosque_1_4_3000msnm.shp</p> <p> - Bosque_1_5_ANP.shp</p> <p> - Bosque_1_6_tamaño_parche_2015.shp</p> <p> - Bosque_primario_2015.shp</p> <p> > Áreas de exclusión</p> <p> - Deforestacion2015_1km.shp</p> <p> - FronteraAgricola_SII_1km.shp</p> <p> - Infraestructura_1km.shp</p> <p> - redvial2015_1km.shp</p> <p> > 2020</p> <p> > Áreas de bosque</p> <p> - Bosque_1_2.shp</p> <p> - Bosque_1_3_1_Infraestructura.shp</p> <p> - Bosque_1_3_2_Deforestacion.shp</p> <p> - Bosque_1_4_45grados.shp</p> <p> - Bosque_1_4_3000msnm.shp</p> <p> - Bosque_1_5_ANP.shp</p> <p> - Bosque_1_6_tamaño_parche_2015.shp</p> <p> - Bosque_primario_2020.shp</p> <p> > Áreas de exclusión</p> <p> - Deforestacion2020_1km.shp</p> <p> - FronteraAgricola_SIII_1km.shp</p> <p> - Infraestructura_1km.shp</p> <p> - redvial2020_1km.shp</p> <p>*Todos los archivos shape estan acompomañados de sus respectivos archivos auxiliares.</p> <p>Supplementary material 1-4.docx: Archivo Word que contiene los materiales suplementarios nombrados en el articulo, en el siguiente orden:</p> <ul> <li>Material complementario 1. Árbol de decisiones.</li> <li>Material complementario 2. Conjuntos de datos.</li> <li>Material complementario 3. Área de bosque primario por ecorregión y tipo de bosque para 2015 y 2020</li> <li>Material complementario 4. Estadísticas sobre valores de concordancia e integridad ecológica en bosques primarios y otros bosques en 2020.</li> </ul>
Simulation and empirical data for "Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations"
<p>Simulation data and empirical data used to generate figures from "Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations". Can be used with code provided on the associated github to regenerate the figures. </p>
Map 1 in The Fergusson squathopper, Messena sinuata Atkinson (Hemiptera: Eurybrachidae) and its egg parasitoids from Southern India
Map 1. Distribution of Messena sinuata from southern India. GKVK: Gandhi Krishi Vigyana Kendra; UAS: University of Agricultural Sciences; ZSI: Zoological Survey of India.
Gridded maps of global population scaled to match the 2023 Wittgenstein Center (WIC) Population projections
<p>The gridded population data used for calculating exposed populations is based on the population projections from the original SSPs (KC and Lutz, 2017) which were subsequently gridded (Jones and O’Neill, 2016). These gridded projections were aggregated to 0.5 ° spatial resolution and then scaled to match the latest available projections for population in line with the updated SSPs, v3.0 (KC <em>et al.</em>, 2024). The scaling is done on a country basis for all countries included in the latest SSP projections. Countries, which are not included in these projections, remain unchanged. The scaling process is done on a country-level basis using the following step:<br> </p> <ol> <li>The total population for the original gridded data is calculated using the ISIMIP fractional country raster (Perrette, 2023), excluding border cells containing contributions from more than one country.</li> <li>The population of the fractional border cells is subtracted from the total population of the SSP population projections and the required scalar to match the population from the gridded data to the SSP population projections is calculated.</li> <li>This scalar is applied to all non-fractional cells. </li> </ol> <p>While it is possible to calculate the scalar for each country including the proportion of the population in the fractional border cells, this would require the scalar to also be applied to that proportion of the population in the border cells to match the overall population number for each country. Applying different scalars to the population proportions for each country in the same cell would, however, change the ratios of the population in the fractional border cells and subsequently lead to skewed results when reapplying the fractional country raster to the scaled data for the aggregation to country level. For small countries, where more population lives in fractional border cells than in non-border cells, and for countries that only consist of border cells with contributions from more than one country, all cells were used in the scaling process. <br><br>It should be noted that some smaller countries cannot be scaled properly and that the latest SSP population projections do not contain values for all countries. Since there has been no release of updated gridded population projections yet, the gridded population data created using this approach still provide the closest match to the latest SSP population projections currently available.</p>
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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.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.