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13,117 results for “Maps”
MAP 3 in Taxonomic revision of the genus Parena Motschulsky, 1860 (Coleoptera, Carabidae, Lebiini, Metallicina)
MAP 3. Distributions for species in the Parena (Crossoglossa) laesipennis group.
MAP 1 in Taxonomic revision of the genus Parena Motschulsky, 1860 (Coleoptera, Carabidae, Lebiini, Metallicina)
MAP 1. Distributional range and species richness of genus Parena.
MAP 1 in Occurrence of Geranium mascatense (Geraniaceae) in India and the speculations about its global distribution
MAP 1. Distribution of Geranium mascatense and G. ocellatum in India
MAP 4 in Systematics of the Nososticta salomonis complex (Odonata: Zygoptera: Platycnemididae)
MAP 4.—Distribution of species of the Nososticta salomonis complex on mainland New Guinea.
MAP 1 in Systematics of the Nososticta salomonis complex (Odonata: Zygoptera: Platycnemididae)
MAP 1.—Distribution of species of the Nososticta salomonis complex in the Solomon Islands.
Using the Hybrid U-Net model based on 2 m multisource high resolution for mapping mangroves
<p>Mangrove wetlands, U-Net, multisource high-resolution, remote sensing, mangrove habit</p>
Map of the rate of elevation change of Mont-Blanc glaciers derived from Pléiades stereo-images
<p>This collection contains three map of elevation changes derived form Pléiades images acquired in 2012, 2021 and 2022 over the Mont-Blanc Massif (Alps) with a horizontal grid spacing of 4 m. They were derived from stereo-pairs acquired 19 August 2012, 15 August 2021, 12 October 2022, 8 August 2022 and 5 October 2022. No data are indicated by the -9999 value.</p> <p>We generated 4-m DEMs from five Pléiades stereo-pairs using the semi-global matching algorithm of the Ames Stereo Pipeline (Beyer et al., 2018) and the set of parameters defined in Deschamps-Berger et al. (2020). The DEMs were first coregistered on stable terrain, masking out glacierized areas using a glacier inventory from year 2015 (Paul et al., 2020). Spatially-coherent biases in the elevation difference maps were next corrected using a polynomial fit across-track and a spline fit in the along-track direction (Falaschi et al., 2023). </p> <p>Pléiades stereo-images were obtained thanks to DINAMIS Project, for “Dispositif Institutionnel National d’Approvisionnement Mutualisé en Imagerie Satellitaire”, a French platform that acquires and distributes very high resolution Earth satellite imagery for French and foreign institutional users under specific subscription conditions.</p>
The 6 February 2023 M7.8 & M7.6 earthquake ruptures mapped with InSAR
<p><strong>Data Supporting Text</strong></p> <p>We used C-band SAR (Sentinel-1) data in order to extract information about the ground displacements that occurred due to the M<sub>w</sub>=7.8 earthquake and M<sub>w</sub>=7.6 earthquake in southeast Turkey (Türkiye). We retrieved the satellite data from the Alaska Satellite facility and from the Copernicus Hub of ESA. We used differential SAR (DInSAR) and the sub-pixel correlation methods to retrieve the surface motions in LOS direction and in azimuth (along satellite track) direction. We processed the SAR images using the (ISCE) v2 software and we applied pixel offset tracking through the cross correlation of the amplitude of the primary (before the earthquake) and the secondary (after the earthquake) SLC images. The pixel offset method gave us the capability to map the surface rupture extent of the strike-slip fault segments along the East Anatolian Fault Zone (EAFZ) and the Sürgü fault (SF). Also, we identified the splay fault known as Nurdağı-Pazarcık fault (NPF; Melgar et al. 2023) where the rupture process began and a second splay fault in the area of Adiyaman near the main trace of EAFZ. We mapped the total rupture length of the EAFZ which is ~ 400 km. We suggest that the maximum displacement was ~ 7 m in line of sight (LOS) direction (central area of EAFZ and SF). Also, the maximum displacement of the NPF was found ~ 1 m. It is necessary to clarify that the total displacement field occurred by the combined motion of the two mainshocks, as well as, possibly by some large-magnitude aftershocks.</p> <p><strong>Acknowledgments </strong></p> <p>This research was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the 3rd Call for HFRI PhD Fellowships (Fellowship Number: 05176).</p> <p><strong>References</strong></p> <p>Melgar, D., Taymaz, T., Ganas, A., Crowell, B., Öcalan, T., Kahraman, M., Tsironi, V., Yolsal-Çevikbilen, S., Valkaniotis, S., Irmak, T. S., Eken, T., Erman, C., Özkan, B., Dogan, A. H., & Altuntaş, C. 2023. Sub- and super-shear ruptures during the 2023 Mw 7.8 and Mw 7.6 earthquake doublet in SE Türkiye. Seismica, 2(3). <a href="https://doi.org/10.26443/seismica.v2i3.387">https://doi.org/10.26443/seismica.v2i3.387</a></p>
Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Pancreas with CODEX
<p>Representative dataset using OMAP-13: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Pancreas with CODEX, v1.0. See https://humanatlas.io/omap.</p>
Raw motif mapping bedfile data and model training set class probabilities
<p>Leveraging prior viral genome sequencing data to make predictions on whether an unknown, emergent virus harbors a 'phenotype-of-concern' has been a long-sought goal of genomic epidemiology. A predictive phenotype model built from nucleotide-level information alone is challenging with respect to RNA viruses due to the ultra-high intra-sequence variance of their genomes, even within closely related clades. We developed a degenerate k-mer method to accommodate this high intra-sequence variation of RNA virus genomes for modeling frameworks. By leveraging a taxonomy-guided 'group-shuffle-split' cross validation paradigm on complete coronavirus assemblies from prior to October 2018, we trained multiple regularized logistic regression classifiers at the nucleotide k-mer level. We demonstrate the feasibility of this method by finding models accurately predicting withheld SARS-CoV-2 genome sequences as human pathogens and accurately predicting withheld Swine Acute Diarrhea Syndrome coronavirus (SADS-CoV) genome sequences as non-human pathogens. Feature selection using L1 regularization identified several degenerate nucleotide predictor motifs with high model coefficients for the human pathogen class that were present across widely disparate clades of coronaviruses. However, these motifs differed in which genes they were present in, what specific codons were used to encode them, and what the translated amino acid motif was. This emphasizes the importance of a phenetic view of emerging pathogenic RNA viruses, as opposed to the canonical phylogenetic interpretations most commonly used to track and manage viral zoonoses. Applying our model to more recent Orthocoronavirinae genomes deposited since October 2018 yields a novel contextual view of pathogen potential across bat-related, canine-related, porcine-related, and rodent-related coronaviruses and critical adaptations which may have contributed to the emergence of the pandemic SARS-CoV-2 virus. Finally, we discuss the next steps to achieve robust predictive ensembles and the utility of these models (and their associated predictor motifs) to novel biosurveillance protocols that substantially increase the 'pound-for-pound' information content of field-collected sequencing data and make a strong argument for the necessity of routine collection and sequencing of zoonotic viruses. </p>
MAP 1 in Integrative taxonomic revision of the African taxa of the Racotis Moore, 1887 generic complex (Lepidoptera, Geometridae, Ennominae, Boarmiini)
MAP 1. Distribution of the Afroracotis (Afroracotis) squalida species-group.
MAP 2 in Integrative taxonomic revision of the African taxa of the Racotis Moore, 1887 generic complex (Lepidoptera, Geometridae, Ennominae, Boarmiini)
MAP 2. Distribution of the Afroracotis (Afroracotis) angulosa species-group.
MAP 4 in Integrative taxonomic revision of the African taxa of the Racotis Moore, 1887 generic complex (Lepidoptera, Geometridae, Ennominae, Boarmiini)
MAP 4. Distribution of the Afroracotis (Herbuloracotis) argillacea species-group.
Oracle segmentation maps for fine-grained multitask CLIP
<p>Segmentation maps for attributes of CUB200 dataset using OFA and Unified-IO.</p>
FIGURE 1 in Lycopodiaceae of the Guianas: List of exsiccata and distribution maps
FIGURE 1. Phlegmariurus tenuicaulis—(Guyana, Pipoly et al. 11042, US): growth habit.
MAP 27 in Lycopodiaceae of the Guianas: List of exsiccata and distribution maps
MAP 27. Pseudolycopodiella meridionalis var. mesetarum
MAP 28 in Lycopodiaceae of the Guianas: List of exsiccata and distribution maps
MAP 28. Pseudolycopodiella tatei
MAP 18 in Lycopodiaceae of the Guianas: List of exsiccata and distribution maps
MAP 18. Phlegmariurus reflexus
MAP 19 in Lycopodiaceae of the Guianas: List of exsiccata and distribution maps
MAP 19. Phlegmariurus robustus
MAP 16 in Lycopodiaceae of the Guianas: List of exsiccata and distribution maps
MAP 16. Phlegmariurus myrsinites
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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.