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13,117 results for “Maps”
Swift/BAT-GUANO upper limits map for S240505av
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Figure 7 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 7: Research output for the Southeast Asian region by thematic area presented as decadal totals.
Figure 4 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 4: Halophila sp. 2 collected in 1997 at a mangrove area of Teluk Sepinong, Sandakan, Sabah, Malaysia. Photo credit: © Japar Sidik.
Figure 2 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 2: Marine provinces and ecoregions of Southeast Asia, based on Spalding et al. (2007). Provinces are made out of ecoregions with the following codes: 20108 Northern Bay of Bengal; 20109 Andaman and Nicobar Islands; 20110 Andaman Sea Coral Coast; 20111 Western Sumatra; 20112 Gulf of Tonkin; 20114 South China Sea Oceanic Islands; 20115 Gulf of Thailand; 20116 Southern Viet Nam; 20117 Sunda Shelf/Java Sea; 20118 Malacca Strait; 20119 Southern Java; 20120 Cocos-Keeling/Christmas Island; 20126 Palawan/North Borneo; 20128 Sulawesi Sea/Makassar Strait; 20129 Halmahera; 20130 Papua; 20131 Banda Sea; 20132 Lesser Sunda; 20133 Northeast Sulawesi; 20139 Arafura Sea.
Figure 3 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 3: Halophila major from Tanjung Adang Laut, Sungai Pulai estuary, Johor (refer also to Nguyen et al. 2014 for morphological and genetic identification of the species). Photo credit: © Muta Harah.
Figure 8 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 8: Roadmap for addressing conservation challenges facing seagrass in the Southeast Asian region. Challenges can have multiple solutions and these solutions can contribute to a final aim. The challenges are listed at the bottom of the figure in red ovals and the final aims are at the top of the figure in blue boxes. Intermediate solutions are presented in the middle in green boxes. Dotted lines represent linkages betweeen the challenges and the intermediate solutions, with each dotted line colour coded specific to a challenge. Intermediate solutions that contribute to a final aim are joined by dashed and solid lines and colour coded.
Figure 1 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 1: Total population by country in Southeast Asia. Population figures derived from www.worldatlas.com.
Figure 6 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 6: Research output per decade by country/territory. Numbers based on searches on Web of Science and updated from Ooi et al. (2011a).
Data publication for "Different earthquake nucleation conditions revealed by stress drop and b-value mapping in the northern Chilean subduction zone"
<p><strong>Abstract</strong>: b-value catalog data publication supplement for "Different earthquake nucleation conditions revealed by stress drop and b-value mapping in the northern Chilean subduction zone" (Folesky, (SciRep,2024), <a href="https://doi.org/10.1038/s41598-024-63015-w">https://doi.org/10.1038/s41598-024-63015-w</a>). b-vales were computed based on the IPOC seismic catalog for northern Chile (Sippl et al., 2023). b-values are computed only for events that have a known stress drop value, as assigned by Folesky et al., 2024. b-values are computed for the 200-1000 nearest neighbors of each event within a maximum distance of 50km using the maximum likelihood approach as implemented by T.Goebel (Geobel et al., 2017). Each seismotectonic class (Sippl et al., 2023) is processed separately. The standard deviation of the fit is obtained by 100 bootstrap iterations for each event while discarding randomly selected 10% of the neighbors.</p> <p><strong>File descriptions</strong>: table columns <br>ID, cls, Lon, Lat, Depth, Magntiude, a, b, Std<br>------------------<br>explanation<br>ID : origin time<br>cls : event class<br>Lon : longitude <br>Lat : latitude<br>Depth : depth in km<br>Magnitude : magnitude (MA)<br>a : a- value <br>b : b- value<br>Std : standard deviation from bootstrapping</p> <p><strong>References</strong>:</p> <p>Folesky, Jonas, (SciRep,2024), <a href="https://doi.org/10.1038/s41598-024-63015-w">https://doi.org/10.1038/s41598-024-63015-w</a></p> <p>Sippl, C., Schurr, B., Münchmeyer, J., Barrientos, S., Oncken, O. (2023): Catalogue of Earthquake Hypocenters for Northern Chile from 2007-2021 using IPOC (plus auxiliary) seismic stations. <a title="Follow link" href="https://doi.org/10.5880/GFZ.4.1.2023.004" target="_blank" rel="nofollow noopener">https://doi.org/10.5880/GFZ.4.1.2023.004</a></p> <p>Folesky, J., Pennington, CN., Kummerow J., Hofman LR. (JGR: Solid Earth, 2024) <a href="https://doi.org/10.1029/2023JB027549">https://doi.org/10.1029/2023JB027549</a></p> <p>Goebel, T. H., Kwiatek, G., Becker, T. W., Brodsky, E. E. & Dresen, G. What allows seismic events to grow big?: Insights from b-value and fault roughness analysis in laboratory stick-slip experiments. Geology 45, 815–818 (2017).</p>
Swift/BAT-GUANO upper limits map for S240515m
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Swift/BAT-GUANO upper limits map for S240513ei
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Swift/BAT-GUANO upper limits map for S240514x
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OMAP-19 Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Tonsil with IBEX
<p>Representative dataset acquired using the Iterative Bleaching Extends multi-pleXity (IBEX) imaging method described in:</p> <ol> <li>“IBEX: A versatile multi-plex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues“, A. Radtke et al., <em>Proc. Natl. Acad. Sci. USA,</em> 2020, <a href="https://doi.org/10.1073/pnas.2018488117">doi.org/10.1073/pnas.2018488117</a>.</li> <li>"IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues", A. J. Radtke, et al., <em>Nat Protoc</em>, 2022, <a href="https://doi.org/10.1038/s41596-021-00644-9">doi.org/10.1038/s41596-021-00644-9</a>.</li> </ol> <p>This dataset accompanies the <a href="https://www.nature.com/articles/s41592-023-01846-7">Organ Mapping Antibody Panel (OMAP) effort</a> led by the <a href="https://commonfund.nih.gov/HuBMAP">Human BioMolecular Atlas Program</a> detailed <a href="https://humanatlas.io/omap">here</a>.</p> <p>OMAP-19 was designed for IBEX imaging of human FFPE tonsil samples sectioned onto glass slides. The panel consists of 6 cycles of 20 primary antibodies, 5 secondary antibodies, and the nuclear label Hoechst for image alignment and nuclear segmentation. This OMAP provides a spatial context for 10 anatomical structures and at least 16 cell types present in the <a href="https://cdn.humanatlas.io/hra-releases/v2.0/docs/asct-b/asct-b-vh-palatine-tonsil.copy.html">ASCT+B tonsil table</a>. OMAP-19 was designed to examine myeloid subsets (CD11b, CD11c, CD14, CD15, HLA-DR) and angiogenesis (VEGF-A and VEGF-C) in human ovarian cancer biopsies. The human tonsil was used as a positive control for antibody validation and panel development. Antigen retrieval was performed using a pressure cooker (Borg Decloaker BD1000, 110°C for 15 minutes) and a tris-based buffer (pH 9.5). Several custom antibodies were created using commercial labeling kits as indicated in the associated RRID entries for these antibodies. More details on the antigen retrieval protocol and step-by-step application of antibodies can be found on the <a href="https://ibeximagingcommunity.github.io/ibex_imaging_knowledge_base/">IBEX Imaging Community knowlege-base</a> (current version) and on <a href="../records/7693279">Zenodo</a> (last official version).</p> <p>Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as a 405 nm laser. All images were captured at an 16-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.379 µm), y (0.379 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software.</p> <p>Image supplied as a .ims Imaris Format file and can be opened with the <strong>free</strong> <a href="https://imaris.oxinst.com/imaris-viewer">Imaris Viewer</a> software or <a href="https://imagej.net/software/fiji/">ImageJ/Fiji</a>. Image channel and antibody meta-data supplied as an xlsx file.</p>
WSA Maps produced with OFT+Pot3D + WSA
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Figure 25. Distribution map for Ulonemia leai and U in Phylogenetics and taxonomy of the Indo-Australian genus Ulonemia sensu Drake (Hemiptera: Tingidae), with the recognition of new genera and species collected from Proteaceae in Australia
Figure 25. Distribution map for Ulonemia leai and U. concava incertae sedis. Locality information taken from the PBI database.
Swift/BAT-GUANO upper limits map for S240525p
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Swift/BAT-GUANO upper limits map for S240527fv
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Mapping Tree Species Drought Sensitivity Under Climate Change
<p>Forests cover approximately 30% of Earth's land surface, absorb more carbon than all other terrestrial ecosystems, and provide trillions of dollars' worth of ecosystem services (Food and Agriculture Organization of the United Nations, 2005). However, climate change-induced droughts pose a significant threat to these vital ecosystems. As climate change intensifies, it is critical for our planning and management that we understand how and where trees will be the most threatened. Previous research has examined the effects of these droughts on forests at a global scale, but these large-scale analyses are not particularly helpful for land managers who often focus on specific regions and only a limited number of species. Our project addresses this gap by assessing species-specific sensitivity to increasingly severe and frequent droughts, considering the variations within their ranges. This localized information is crucial for land managers to develop targeted conservation strategies. By analyzing species-specific data, we demonstrate that the impacts of drier conditions are not uniform across or within species. Our findings suggest that effective management strategies must adopt a multifaceted and area-specific approach. To make our findings easily usable, we developed an interactive dashboard for land managers and the public. Here, users can find species-specific sensitivity maps that highlight the areas of greatest concern within manageable spaces, providing a valuable tool for informed decision-making. Our project contributes to the understanding of the potential future drought impacts on forests and emphasizes the need for targeted conservation efforts to mitigate the consequences of climate change on these essential ecosystems.</p>
Temporal overlays of the co-word analysis network on Ecotourism, Sustainable Tourism and Nature Based Tourism (1986-2022): Map and network for VOSViewer visualization
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Geographical overlays of the co-word analysis network on Ecotourism, Sustainable Tourism and Nature Based Tourism (1986-2022): Map and network for VOSViewer visualization
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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.