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101 results for “Range Maps”
Mapping test data with various range sensors
<p>This is a test data for range-IMU SLAM systems recorded with various range sensors:</p> <p>- Ouster OS0-32 & OS0-64</p> <p>- Livox Avia</p> <p>- Intel Realsense L515 & D455</p> <p>- Microsoft Azure Kinect</p> <p>- Stereolabs ZED2i</p> <p> </p> <p>The groundtruth trajectories are estimated by aligning point cloud scans with a 3D environment map created with a survey-grade LiDAR (FARO Focus).</p> <p> </p> <p> </p>
LiDAR-IMU Mapping Test with Various Range Sensors
<p>This is a set of LiDAR-IMU mapping test sequences recorded with various range sensors.</p> <p>- Ouster OS0-32 & OS0-64</p> <p>- Livox Avia</p> <p>- Intel Realsense L515 & D455</p> <p>- Microsoft Azure Kinect</p> <p>- Stereolabs ZED2i</p> <p>The groundtruth IMU trajectories (gt.tar.gz) are estimated by a batch LiDAR-IMU optimization that aligns point cloud scans with an environmental map (map_e232b.las) recorded with a survey-grade LiDAR (FARO Focus).</p> <p> </p>
Distribution. SE Guinea, E Liberia, S Ivory Coast, and W Ghana; populations ofeither this species, the Ivory Coast White-toothed Shrew (C. eburnea), or both species are found in S Sierra Leone and are included in the range map ofthis species but have not been investigated genetically and could represent either species. There are apparently records that represent this species from S Nigeria, although further research is needed to confirm thatthis speciesis truly found there. in Soricidae
Distribution. SE Guinea, E Liberia, S Ivory Coast, and W Ghana; populations ofeither this species, the Ivory Coast White-toothed Shrew (C. eburnea), or both species are found in S Sierra Leone and are included in the range map ofthis species but have not been investigated genetically and could represent either species. There are apparently records that represent this species from S Nigeria, although further research is needed to confirm thatthis speciesis truly found there.
Distribution. Originally distributed throughout the Indo-Malayan Region and S China, including Taiwan, Hainan, and Sri Lanka (only original range shaded in the map). Possible human-mediate introduced range in Maldives, islands of Malaysia, Indonesia, Brunei, Philippines, Japan (Kyushu and Ryukyu Is), Guam, Palau, and New Guinea. Introduced in historical times into East Africa (Egypt, Sudan, Eritrea, Djibouti, Kenya, Rwanda, and Tanzania), Pemba and Zanzibar (Unguja) Is, Madagascar, Comoro Is, Mauritius, Réunion I, and into coastal Arabia (in the vicinity of seaports in Iraq, Kuwait, Bahrain, Saudi Arabia, Yemen, and Oman). in Soricidae
Distribution. Originally distributed throughout the Indo-Malayan Region and S China, including Taiwan, Hainan, and Sri Lanka (only original range shaded in the map). Possible human-mediate introduced range in Maldives, islands of Malaysia, Indonesia, Brunei, Philippines, Japan (Kyushu and Ryukyu Is), Guam, Palau, and New Guinea. Introduced in historical times into East Africa (Egypt, Sudan, Eritrea, Djibouti, Kenya, Rwanda, and Tanzania), Pemba and Zanzibar (Unguja) Is, Madagascar, Comoro Is, Mauritius, Réunion I, and into coastal Arabia (in the vicinity of seaports in Iraq, Kuwait, Bahrain, Saudi Arabia, Yemen, and Oman).
Population abundance data and species range maps
<p><b>Aim </b>–<b> </b>The abundant-center hypothesis (ACH) predicts a negative relationship between species abundance and the distance to geographic range center. Since its formulation, empirical tests of the ACH have involved different settings (e.g. the distance to the ecological niche or to the geographic range center), but studies found contrasting support for this hypothesis. Here, we evaluate whether these discrepancies might stem from differences regarding the context in which the ACH is tested (geographical or environmental), how distances are measured, how species envelopes are delineated, how the relationship is evaluated and which data are used.</p> <p><b>Location</b> – Americas.</p> <p><b>Time Period </b>– 1800-2017.</p> <p><b>Major taxa studied</b> – mammal, bird, fish and tree seedlings.</p> <p><b>Methods</b> – Using published abundance data for 801 species, together with species range maps, we tested the ACH using three distance metrics in both environmental and geographical spaces with range and niche envelopes delineated using two different algorithms, totaling 12 different settings. We then evaluated the distance-abundance relationship using correlation coefficients (traditional approach) and mixed-effect models to reduce the effect of sampling noise on parameter estimates.</p> <p><b>Results</b> – Similar to previous studies, correlation coefficients indicated an absence of effect of distance on abundance for all taxonomic groups and settings. In contrast, mixed-effect models highlighted relationships of various strengths and shapes, with a tendency for more theoretically-supported settings to provide stronger support for the ACH. The relationships were however not consistent across taxonomic groups and settings, and were sometimes even opposite to ACH expectations.</p> <p><b>Main conclusions</b> – We found mixed and inconclusive results regarding the ACH. These results corroborate recent findings, and suggest either that our ability to predict abundances from the location of populations within geographical or environmental spaces is low, or that the data used here have a poor signal-to-noise-ratio. The latter calls for further testing on other datasets using the same range of settings and methodological framework.</p>
Simulation Code for Plotting the Range-Doppler Map
<p>This file contains simulation code for plotting a Range-Doppler map. Key parameters have been left blank and should be populated with your specific data before running the code. Ensure all required fields are correctly set to generate accurate simulation results.</p>
Figure 1. Map showing the 10 in Plant consumption in coastal populations of the lizard Tropidurus torquatus (Reptilia: Squamata: Tropiduridae): how do herbivory rates vary along their geographic range?
Figure 1. Map showing the 10 restingas from where the lizards Tropidurus torquatus were captured along the Brazilian coast in the states of Bahia (1: Trancoso, 2: Prado), Espírito Santo (3: Guriri, 4: Setiba, 5: Praia das Neves) and Rio de Janeiro (6: Grussaí, 7: Jurubatiba, 8: Massambaba, 9: Maricá, 10: Grumari).
Snowmelt runoff onset maps for stratovolcanoes in the Cascade Range
<p>This data archive contains snowmelt runoff onset maps for stratovolcanoes in the Cascade Range. They were created using the open-source <a href="https://github.com/egagli/sar_snowmelt_timing">sar_snowmelt_timing</a> toolbox. Raster values correspond to the day of year [0,365] of predicted snowmelt runoff onset. All products are in UTM Zone 10N (EPSG:32610) projected coordinate system. </p>
Data from: Range-wide spatial mapping reveals convergent character displacement of bird song
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Data from: QTL mapping identifies candidate alleles involved in adaptive introgression and range expansion in a wild sunflower
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Data from: Resource-Area-Dependence Analysis: inferring animal resource needs from home-range and mapping data
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Population abundance data and species range maps
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Data from: Genome-wide association mapping of phenotypic traits subject to a range of intensities of natural selection in Timema cristinae
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Connecting species’ geographical distributions to environmental variables: range maps versus observed points of occurrence
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FIGURE 42. Range map for Hippocampus zosterae. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 42. Range map for Hippocampus zosterae. See Figure 2 caption for further details.
FIGURE 40. Range map for Hippocampus whitei. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 40. Range map for Hippocampus whitei. See Figure 2 caption for further details.
FIGURE 41. Range map for Hippocampus zebra. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 41. Range map for Hippocampus zebra. See Figure 2 caption for further details.
FIGURE 36. Range map for Hippocampus spinosissimus. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 36. Range map for Hippocampus spinosissimus. See Figure 2 caption for further details.
FIGURE 37. Range map for Hippocampus subelongatus. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 37. Range map for Hippocampus subelongatus. See Figure 2 caption for further details.
FIGURE 38. Range map for Hippocampus trimaculatus. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 38. Range map for Hippocampus trimaculatus. See Figure 2 caption for further details.
ScienceDex guides
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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.