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1,276 results for “distribution map”
FIG. 92. Distribution maps, A. nebojsa species group. A. A. occidentalis. B. A. oviraptor. C. A in Revision Of The Nearctic Species Of The Genus Amiota Loew (Diptera: Drosophilidae)
FIG. 92. Distribution maps, A. nebojsa species group. A. A. occidentalis. B. A. oviraptor. C. A. subnebojsa.
FIG. 94. Distribution maps, A. nagatai species group. A. A. raripennis. Ungrouped species. B. A in Revision Of The Nearctic Species Of The Genus Amiota Loew (Diptera: Drosophilidae)
FIG. 94. Distribution maps, A. nagatai species group. A. A. raripennis. Ungrouped species. B. A. barretti (Johnson). C. A. buccata Wheeler.
FIG. 90. Distribution maps, A. rufescens species group. A. A. tessae. A. subtusradiata species group. B. A. byersi. C. A in Revision Of The Nearctic Species Of The Genus Amiota Loew (Diptera: Drosophilidae)
FIG. 90. Distribution maps, A. rufescens species group. A. A. tessae. A. subtusradiata species group. B. A. byersi. C. A. tibialis.
FIG. 88. Distribution maps, A. avipes species group. A. A in Revision Of The Nearctic Species Of The Genus Amiota Loew (Diptera: Drosophilidae)
FIG. 88. Distribution maps, A. avipes species group. A. A. minor (Malloch). B. A. onyx. C. A. pseudominor.
Fig. 21. Distribution maps. A in A systematic revision of the genus Juga from fresh waters of the Pacific Northwest, USA (Cerithioidea, Semisulcospiridae)
Fig. 21. Distribution maps. A. Juga newberryi (I. Lea, 1860). B. Juga nigrina (I. Lea, 1856). C. Juga occata (Hinds, 1844). Red stars, type localities; black dots, sequenced specimens; gray dots, unsequenced museum material. Abbreviations: CA = California; NV = Nevada; OR = Oregon; WA = Washington.
Fig. 7. Distribution maps. A in A systematic revision of the genus Juga from fresh waters of the Pacific Northwest, USA (Cerithioidea, Semisulcospiridae)
Fig. 7. Distribution maps. A. Juga plicifera (I. Lea, 1838). B. Juga acutifilosa (Stearns, 1890). C. Juga bulbosa (A. Gould, 1847). Red stars, type localities; black dots, sequenced specimens; gray dots, unsequenced museum material. Abbreviations: CA = California; NV = Nevada; OR = Oregon; WA = Washington.
Fig. 9. – Distribution maps. Brachiaria subrostrata A in Revision of some Malagasy forage grasses and their relatives within Brachiaria, Echinochloa, Moorochloa, and Urochloa
Fig. 9. – Distribution maps. Brachiaria subrostrata A. Camus (stars), B. tsiafajavonensis A. Camus (triangles), B. umbellata (Trin.) Clayton (circles), and Echinochloa hubbardii (A. Camus) Voronts. (squares).
Fig. 1. – Distribution maps. Brachiaria antsirabensis A in Revision of some Malagasy forage grasses and their relatives within Brachiaria, Echinochloa, Moorochloa, and Urochloa
Fig. 1. – Distribution maps. Brachiaria antsirabensis A. Camus (stars), B. bemarivensis A. Camus (triangles), B. comorensis (Mez) A. Camus (circles), and B. dimorpha A. Camus (squares). [Map: Sarah Z. Ficinski]
Fig. 8 – Distribution maps. a in Five new species of the genus Griburius Haldeman from Central America (Coleoptera: Chrysomelidae, Cryptocephalinae)
Fig. 8 – Distribution maps. a, Griburius febriculosus; b, G. gracilis; c, G. mokaya; d, G. puncturatus; e, G. textus.
Fig. 4. A in Good Reasons and Guidance for Mapping Planktonic Protist Distributions
Fig. 4. A variogram for the ciliate Pleuronema sp. (inset) abundance. The best fit to the data (points) provided a pure nugget model; i.e. the distribution is random at the measured scale (40 m), with no observed patchiness.
Fig. 3. A in Good Reasons and Guidance for Mapping Planktonic Protist Distributions
Fig. 3. A time series of Cyrtostrombidium sp. (inset) abundance during ~ 1 year at a fix point in a coastal lagoon. The autocorrelation function indicates positive spikes for weeks 2, 3 and 4 suggesting a persistence of Cyrtostrombidium bloom for ~ 1 month. Horizontal dashed lines indicate the ~ 95% confidence interval for the signifi- cance of each autocorrelation value.
Fig. 2 in Good Reasons and Guidance for Mapping Planktonic Protist Distributions
Fig. 2. Geostatistical analysis of Lohmaniella oviformis (inset in a) abundance (cells ml–1) produces: a) the variogram, b) the kriging map, and c) a map of the coefficient of variation (CV). A spherical model (a, line) is fit to the empirical variogram (a, points); the points account for different number of pairs of abundance averaged on a class distance (lag). Only half of the maximum distance was calculated and represented to avoid the edge effect, where there are fewer sampling points (see text). The model (a, line) is used to predict abundance at unsampled points and to assess characteristics of patches. The model is also used to map patches of L. oviformis abundance (b, grey areas) using the kriging interpolator; a patch is operationally defined as abundance in the upper quartile. On the CV map (c), grey areas (with lower abundance and closer to edges) have the highest coefficient of variation of the estimated distribution.
Fig. 1. A in Good Reasons and Guidance for Mapping Planktonic Protist Distributions
Fig. 1. A schematic description of establishing a variogram, modelling a function, and producing maps by kriging. Samples (e.g. to determine ciliate abundance) are collected at points of a sampling grid (a). Variance estimates of ciliate abundances at points separated by a common distance (lag, h) are calculated using the equation (explanations in the text); this is repeated for each lag (three examples of lags are illustrated in a). Each variance estimate is then plotted against its respective lag to produce an empirical variogram (points in b). Then, a model is fit to the variogram data (lines in b), and the model is used to predict abundance at unsampled points and to characterize patches. The parameters of the variogram models are the nugget, the range, and the sill (see text for their interpretation). Three models are the most common: the Gaussian, spherical and exponential (thick, medium, and thin lines, respectively, in b). Models are used to map ciliate abundance by the kriging procedure, with each model producing different predicted distributions (c, d, e): the spherical and exponential produce "fuzzier" images than the Gaussian.
Fig. 6 in Good Reasons and Guidance for Mapping Planktonic Protist Distributions
Fig. 6. Patches of total phytoplankton biomass (ng C ml–1, left) and total ciliate abundance (cells ml–1, right) in the Irminger Sea, North Atlantic. The spatial coincidence indicates a potential prey-predator relationship.
EUPollMap: The European atlas of contemporary pollen distribution maps
<p>This atlas is an ensemble of pollen-presence digital maps for Europe including 194 taxa from the Eurasian Modern<br> Pollen Database (EMPD v2). The pollen presence is estimated using Kriging over a regular 25-km grid and freely shared as multivariate ESRI GeoTIFF file for every taxa together with the related point dataset as ESRI Shapefile. The GeoTIFF maps contain the probabilistic pollen-presence estimation, a discrete map of the pollen presence, and an uncertainty map derived from the Kriging variance.</p> <p>This dataset is released with the following publication:<br> Oriani F., Mariethoz G., Chevalier M., EUPollMap: The European atlas of contemporary pollen distribution maps derived from an integrated Kriging interpolation approach, submitted to Earth System Science Data.</p>
Data from: Mapping coastal redwoods (<em>Sequoia sempervirens</em>) across their natural range: An updateable and field-validated distribution map using Sentinel satellite data and cloud computing
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Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions
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Plant Survey of Current Vegetation: MAP OF SONORAN DESERT PLANT COMMUNITY DISTRIBUTION IN THE CAPLTER STUDY AREA, PHOENIX, ARIZONA
This study represents an effort to map the distribution of plant community types across the Central Arizona - Phoenix Long Term Ecological Research (CAP-LTER) site centered in metropolitan Phoenix using Landsat ETM data. Vegetation classification was carried out using field data collected from within the study area describing woody plant species. A system was devised which represented a compromise between providing floristic information and enabling maximum spectral discrimination between community types. Image classification used reference spectra derived from training sites in the field and was carried out on subsets defined by soil surface texture in order to control for the strong background soil signature inherent to arid regions. While groundtruthing revealed that vegetation on clayey soils was mapped to 91% accuracy, other sections produced maps with less accuracy. The results of this study demonstrate that image classification of desert vegetation using only Landsat ETM data is problematic and may not be practical without other supporting data, such as radar imaging.This project attempts to produce a vegetation distribution map across undeveloped parcels of outlying desert wilderness, as well as remnant mountain parks throughout the city, contained within the Central Arizona Phoenix Long Term Ecological Research (CAPLTER) study area. This effort seeks to create the first successful classification map of Sonoran Desert vegetation derived from satellite imagery. The map would also be the first fine-scale map of plant community types in the Phoenix region. The depiction would allow for a calculation of the land area covered by each vegetation class, and which communities are exposed to development pressures. This map potentially provides a basis from which researchers can measure vegetative biomass distribution across the landscape and attempt to incorporate this component into ecological models of energy flows and biogeochemical cycling in the CAP-LTER site. I
Lyman-alpha tomographic map of the large-scale matter distribution using the eBOSS - Stripe 82 data
<p><strong>Lyman-alpha tomography map using DR16 eBOSS data</strong></p> <p>This data release contains products associated to the Lyman-alpha large-scale tomographic map realized with the 16<sup>th</sup> data release of SDSS-eBOSS. The densest Lyman-alpha forest set of eBOSS in the Stripe 82 field is used : 220 deg<sup>2</sup> wide field with a 37 deg<sup>-2</sup> density. A Lyman-alpha flux contrast map over a volume of 0.94 h<sup>-3</sup>Gpc<sup>3</sup> is obtained. Voids and protoclusters are detected in this portion of the sky.</p> <p>For more details : see https://arxiv.org/abs/2004.01448</p> <p>All the data detailed below can be opened by using the short python script read_data_release.py.</p> <p><strong>Pixels and map data</strong></p> <p>The pixel file pixels_lya_tomography_stripe82.bin was used to create the Lya tomographic map map_lya_tomography_stripe82.bin by using the dachshund algorithm (https://github.com/caseywstark/dachshund).</p> <p>Pixel and map file are in the binary numpy format. Pixels are created from 8999 Lya forests positioned from -43° to +45° in the RA(J2000) direction, from -1.25° to +1.25° in the DEC(J2000) direction and from z=2.1 to z=3.2. The (RA,DEC,z) coordinates are converted to (X,Y,Z) coordinates in Mpc.h<sup>-1</sup>. The pixel file shares the same origin than the map. Each pixel is defined by a set of five floats corresponding to (X, Y, Z, σ, 𝛿) where 𝛿 is the Lyman-alpha flux contrast and σ its associated error based on pipeline noise.</p> <p>The map is also a binary numpy file. It contains a (6354,181,834) Mpc.h<sup>-1</sup> cube of Lyman-alpha flux contrast with a pixel shape (2928,90,417). The origin of the map (0,0,0) corresponds to the coordinates RA=-43°, DEC=-1.25° and z=2.1 and in the cube coordinate to (X,Y,Z)=(0,0,0) Mpc.h<sup>-1</sup>.</p> <p>Note that to obtain this map, the pixel file was first separated to parallelize the tomographic procedure. In comparison to the map detailed in the linked article, this map is rebinned to be less voluminous. Furthermore, a mask is applied to the map where the distance to the nearest line-of-sight of the pixel file is below 20 Mpc.h<sup>-1</sup>. At these locations, map flux contrast is put to 0.</p> <p> </p> <p><strong>Derived catalog data</strong></p> <p>Voids and proto-cluster searches are applied to the tomographic map. The results of this procedure, along with additional catalog cut is given in this data release. Catalogs of voids (catalog_voids_lya_stripe82_*.fits) and proto-clusters (catalog_protoclusters_lya_stripe82_*.fits) are given in the (X,Y,Z) and (RA,DEC,z) coordinates of the map, sharing the same origin. The catalogs are in the FITS format which can be opened with the fitsio python library.</p>
Figure 2 in Mapping the terrestrial reptile distributions in Oman and the United Arab Emirates
Figure 2. The Persian Wonder Gecko Teratoscincus keyserlingii photographed near Jebel Ali, Dubai.
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.
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.