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13,117 results for “Maps”
Supplementary data to article "From single trees to country-wide maps: Modeling mortality rates in Germany based on the Crown Condition Survey"
<p>This repository provides regression models and annual prediction rasters for tree mortality in Germany. </p> <p><strong>Regression models:</strong><br>Logistic regression models which predict tree mortality for the species (beech = Fagus sylvatica, <br>oak = Quercus petraea and robur, pine = Pinus sylvestris, spruce = Picea abies) and species <br>groups (OB = other broadleaves, OC = other conifers) based on observations of dead trees in the<br>German Crown Condition Survey (Waldzustandserhebung) and a set of environmental predictor <br>variables. The predictors come from the domains of climate (clim), site conditions (site, i.e. <br>topography, soil, land cover, deposition), tree age (age) and some models contain pairwise <br>interaction terms between predictors (inter). All models were fit in R and are represented as <br>objects of the class glm and stored in files of the type rds.</p> <p><strong>Prediction rasters:</strong><br>Spatial predictions of the mortality rate across Germany for each tree species and species group <br>and for each year from 1998 to 2022. The rasters have a spatial resolution of 100 m. Missing values<br>mark areas where the species/group does not occur. The mortality values are given as integers <br>between 0 (no mortality) and 10000 (100% mortality). The coordinate reference system is Lambert <br>Azimuthal Equal Area (LAEA; EPSG:3035). The rasters are provided in the file format GeoTIFF (tif).</p> <p>A detailed description of the data sources and analyses can be found in the following article.</p> <p><strong>Citation:</strong><br><em>Knapp, N., Wellbrock, N., Bielefeldt, J., Dühnelt, P., Hentschel, R., Bolte, A., 2024. </em><br><em>From single trees to country-wide maps: Modeling mortality rates in Germany based on the Crown Condition Survey.</em></p> <p><strong>Contact:</strong><br>nikolai.knapp@thuenen.de</p> <p> </p> <p> </p>
Figure 1. The map illustrates 142 in Taxonomy, distribution, and ecology of crustacean zooplankton in trough waters of Ankara (Turkey)
Figure 1. The map illustrates 142 trough sites sampled in Ankara.
Supplementary materials for "Operando phase mapping in multi-material laser powder bed fusion"
<p>This dataset includes the experimental data for the paper: "Operando phase mapping in multi-material laser powder bed fusion" by S. Sumarli, F. Malamud, S. Van Petegem, S. Gaudez, A. Baganis, M. Busi, E. Polatidis, C. Leinenbach, R. Logé, and M. Strobl. For more information, see the "Open data structure.pdf" file.</p>
dem files for Antarctic maps and data analysis
<div> <pre><br>We use BedMachine Antarctica v2, 1km resolution [https://nsidc.org/data/nsidc-0756/versions/2](https://nsidc.org/data/nsidc-0756/versions/2) (to be updated one day to v3, see below) to:<br> * estimate the surface elevation of observation when not available: [BedMachineAntarctica_v02.nc](./data/dem/BedMachineAntarctica_v02.nc)<br> * generate lower resolution maps of Antarctica for plots: e.g. at 10km, [ANT10km_BedMachineAntarctica_v02.nc](./data/dem/ANT10km_BedMachineAntarctica_v02.nc)<br><br>You can load this data in the [./data/dem](./data/dem) from the<br><br>> [!IMPORTANT]<br>> Do not forget to cite the following articles when using this script<br>><br>> #### Data Citation and Acknowledgment<br>> As a condition of using these data, you must cite the use of this data set. Such a practice gives credit to data set producers and advances principles of transparency and reproducibility.<br>><br>> * Morlighem, M. (2020). MEaSUREs BedMachine Antarctica. (NSIDC-0756, Version 2). [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/E1QL9HFQ7A8M. [describe subset used if applicable]. Date Accessed 11-13-2024.<br>><br>> When using this data product in a publication, please include the following citations in addition to the data product citation provided above:<br>><br>> * Morlighem, M., Rignot, E., Binder, T., Blankenship, D. D., Drews, R., Eagles, G., Eisen, O., Ferraccioli, F., Forsberg, R., Fretwell, P., Goel, V., Greenbaum, J. S., Gudmundsson, H., Guo, J., Helm, V., Hofstede, C., Howat, I., Humbert, A., Jokat, W., Karlsson, N. B., Lee, W., Matsuoka, K., Millan, R., Mouginot, J., Paden, J., Pattyn, F., Roberts, J. L., Rosier, S., Ruppel, A., Seroussi, H., Smith, E. C., Steinhage, D., Sun, B., van den Broeke, M. R., van Ommen, T., van Wessem, M. & Young, D. A.. 2020. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet. Nature Geoscience. 13. DOI: 10.1038/s41561-019-0510-8.<br>><br>> To promote open science principles and reproducibility, we encourage you to make your data citation specific to the subset used in your research. Common examples of information include spatial and temporal range and file types if relevant.<br><br>You can load your favorite dem for Antarctica in [./data/dem](./data/dem):<br>* The dem should be in the Spatial Reference System "Antarctic Polar Stereographic EPSG:3031"<br>* E.g. the last version of BedMachine Antarctica: https://nsidc.org/data/nsidc-0756/</pre> </div>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh outward facing state (OFS) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh outward facing state (OFS) conformation at apo condition, imaged from the cytoplasmic side, in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state closed (IFSclosed) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state closed (IFSclosed) conformation at apo condition, imaged from the cytoplasmic side, in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open (IFSopen) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open (IFSopen) conformation at apo condition, imaged from the cytoplasmic side , in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
Global Airborne Observatory: Example Rapid Water Quality Maps of Hawaiian Island Coastline
<p>Example maps from the publication:</p> <p>Nicholas R. Vaughn, Marcel König, Kelly L. Hondula, Dominica E. Harrison and Gregory P. Asner. 2024. Rapid Water Quality Mapping from Imaging Spectroscopy with a Superpixel Approach to Bio-Optical Inversion. <em>Remote Sensing</em>. (in press)</p> <h2>Data:</h2> <p>Maps of water quality and auxiliary information produced from Global Airborne Observatory (GAO) VSWIR Spectrometer data for five example sites:</p> <table> <tbody> <tr> <td>Site</td> <td>Island</td> <td>Date Collected</td> <td>Time Collected (24hr)</td> <td>Latitude</td> <td>Longitude</td> </tr> <tr> <td>1. Pelekane Bay</td> <td>Hawaiʻi</td> <td>30 October 2023</td> <td>10:02</td> <td>20.0243</td> <td>−155.8256</td> </tr> <tr> <td>2. East Kaho’olawe</td> <td>Kaho’olawe</td> <td>22 January 2024</td> <td>09:15</td> <td>20.6027</td> <td>−156.5638</td> </tr> <tr> <td>3. Hilo Bay</td> <td>Hawaiʻi</td> <td>15 January 2023</td> <td>11:30</td> <td>19.7353</td> <td>−155.0636</td> </tr> <tr> <td>4. Mā’alaea Bay</td> <td>Maui</td> <td>22 January 2024</td> <td>10:19</td> <td>20.7805</td> <td>−156.4842</td> </tr> <tr> <td>5. South Moloka’i</td> <td>Moloka’i</td> <td>7 January 2024</td> <td>11:31</td> <td>21.0866</td> <td>−157.2159</td> </tr> </tbody> </table> <div> <div> <div> <p>There are 41 maps for each example site:</p> <ol> <li>A single 3-band true-color map of the example site for reference (Site<<em>X_Name></em>_3band.tif)</li> <li>A single full-resolution water quality map produced using a bio-optical inversion model on each pixel (Site<<em>X_Name></em>_PixelbyPixel.tif)</li> <li>For each of 13 different superpixel size settings (50px, 100px, 250px, 500px, 600px, 750px, 1000px, 1250px, 1500px, 2000px, 3000px, 4000px, 5000px), a map of the superpixel ID of each pixel (Site<X_Name>_Size<####>_ClusterID.tif)</li> <li>For each of 13 different superpixel size settings, a superpixel-level map of water quality as estimated at the cluster level. (i.e., each pixel in a superpixel will have the same values, Site<X_Name>_Size<####>_Cluster_Preds.tif)</li> <li>For each of 13 different superpixel size settings, a full-resolution water quality map produced by interpolation of the superpixel level maps in 4. above (Site<X_Name>_Size<####>.tif)</li> </ol> <p>All files are in GeoTiff format using 32-bit floating point values. Most are compressed with the LZW algorithm. Maps are not georeferenced. No data value for map types 2-5 is -9999.</p> </div> </div> </div>
Assessment of Vegetation Indices for Mapping Burned Areas Using a Deep Learning Method and a Comprehensive Forest Fire Dataset from Landsat Collection.
<p>This repository contains a dataset focused on the delineation of burned areas (BA) in forests, created from Landsat satellite images covering the period from 1985 to 2021. The study also explores the integration of vegetation spectral indices (VIs) within a Convolutional Neural Network (CNN) detector, utilizing U-Net architecture. Along with the dataset of historical BA in Galicia from 1985, we provide the necessary images and code to facilitate the analysis and application of these methods. This repository aims to serve as a valuable resource for researchers and professionals in the field of forest fire management and remote sensing, highlighting the potential advantages of using VIs for improved burned area detection and analysis.</p> <p>DOI for published article: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.asr.2024.12.001" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.asr.2024.12.001</span></span></a></p>
A probabilistic map of Costa Rican peatlands based on vegetation, ecosystem, and soil inventories
<p>There are the GIS files that accompany a peer-reviewed article.</p> <p>This is the first published effort aimed at developing a peatland map for Costa Rica. A probabilistic approach using vegetation, ecosystem, and soil datasets was used to predict the distribution and extent of peatlands below 700 m in elevation. High-elevation sites found in the Talamanca Mountains were visually identified using satellite imagery; those peatlands are small in size (< 0.05 km<sup>2</sup>). Our analysis produced an estimated low-elevation peatland extent of 1433 km<sup>2</sup> and a high-elevation peatland extent of 23.08 km<sup>2</sup>, yielding an estimated total extent of 1456 km<sup>2</sup> for Costa Rica. This figure is in line with previously published extent estimates for this country (577-2670 km<sup>2</sup>). As for all maps, we stress that the accuracy of this product is ultimately limited by data availability and quality, as well as ground-referencing information. Still, the new map can provide guidance for land management, policymaking, and future science endeavors.</p>
HiRISE and MOC-NA Images Used in "Mapping of Western Valles Marineris Light-toned Layered Deposits and Newly Classified Rim Deposits"
<p>These files contain the images used in the paper "Mapping of Western Valles Marineris Light-toned Layered Deposits and Newly Classified Rim Deposits"</p> <p>All images examined, and all images used in the creation of figures are listed within these documents. They are seperated by region, and type of image (HiRISE, MOC-NA)</p>
SHARAD Tracks Mapped in "Mapping of Western Valles Marineris Light-toned Layered Deposits and Newly Classified Rim Deposits"
<p>These three .CSV files contain the SHARAD tracks, as exported by JMARS.</p> <p>This data contains overlaps in each file, as they are sorted by the location in which they were examined, for example, if the SHARAD track crosses all three plateaus examined in the study, that SHARAD track will be listed in all three files. </p> <p>The Radar_Track_LOCATION_and_DETECTIONS.xlxs file contains information on if a basal detection was located in a particular track, and on what plateau the detection was located. </p>
Figure 1 – Distribution map for L. eketi, L. seyboui, L in Taxonomic notes on Liptena eketi Bethune-Baker, 1926 and related species (Papilionoidea: Lycaenidae: Poritiinae)
Figure 1 – Distribution map for L. eketi, L. seyboui, L. kiellandi
50k Maps of Bhutan
<p>A set of 59 topo maps of Bhutan at a scale of 1:50,000, with an index map to show their locations. The maps were issued from 1961 through 2002 by the Government of Bhutan, and created with the assistance of the Survey of India under the Indo-Bhutan Survey Collaboration Project.</p>
A Prefrontal Cortex Map based on Single Neuron Activity
<p>Intermediate results files (after preprocessing) to recreate the figures from:</p> <p><em><strong>A Prefrontal Cortex Map based on Single Neuron Activity, </strong></em>Pierre Le Merre*, Katharina Heining*<em>, Marina Slashcheva, Felix Jung, Eleni Moysiadou, Nicolas Guyon, Ram Yahya, Hyunsoo Park, Fredrik Wernstal, and </em><em>Marie </em><em>Carlén.</em> Equal contribution.</p> <p>Article currently available at: https://www.biorxiv.org/content/10.1101/2024.11.06.622308v2</p> <p>The raw data can be found at: https://dandiarchive.org/dandiset/001260?pos=1</p> <p>Description: Brain-wide high-density extracellular recordings (Neuropixels) in head-fixed mice during distinct passive listening and auditory behavioral tasks. About half of the recordings are in the prefrontal cortex (PFC) and half from other brain regions (3 cortical and 10 subcortical). In PFC we include 11 subregions: secondary motor area (MOs), anterior cingulate area – dorsal and ventral part (ACAd, ACAv), prelimbic area (PL), infralimbic area (ILA), orbital area – medial, lateral, and ventrolateral part (ORBm, ORBvl, ORBl), agranular insular area – dorsal and ventral part (AId, AIv), and frontal pole (FRP).</p> <p>The code generating these intermediate files from the raw data can be found at: https://github.com/hejDMC/pfcmap</p> <p> </p>
Reconstructed Global Monthly Burned Area Maps from 1901 to 2020
<p>Satellites provide direct observations of fire activities (e.g., burned area, fire radiative power) (Andela et al., 2017; Giglio et al., 2006; Luo et al., 2024), but their temporal coverages are limited because most satellite data were available only after 1980s (Chuvieco et al., 2019). Fire modules in dynamic global vegetation models (DGVMs) are able to simulate long-term burned area and the interactions with vegetation dynamics based on climate conditions and soil properties (Sitch et al., 2015; Sitch et al., 2024), but the spatial resolution at the global scale is usually coarse due to the coarse resolution of the input meteorological forcing data, and most models failed in capturing the global trends of burned area (Andela et al., 2017; Hantson et al., 2020). The processes included and the parameterizations of fire processes are widely different across fire models, resulting in a large range of simulated burned area at both the regional and global scales (Hantson et al., 2020). Considering the limitations of satellite observations and fire models, a spatiotemporally consistent burned area dataset over the 20th century trained from present-day observations, is essential for fire modelling and can serve as publicly available benchmark for fire ecology and carbon cycle studies.</p> <p>This study produced a global monthly 0.5°×0.5° burned area fraction (BAF) dataset from1901 to 2020 using machine learning models based on climate conditions, vegetation states, population density and land use data. We first divided the globe into 14 regions following the Global Fire Emission Dataset (GFED regions) (Giglio et al., 2006; van Der Werf et al., 2017) and conducted all steps described below in each GFED region individually. To better capture extreme fires, we first developed a classification model to distinguish grid cells with extreme and regular fires, using the 90th percentile of all burned area fractions within a region as the threshold to define extreme fires. We then trained separate regression models for grid cells categorized as having extreme or regular fires. The models were trained against the satellite-based burned area product (FireCCI51) during 2003-2020 excluding cropland fires, and then used to reconstruct the burned area from 1901 to 2020. In addition to validation against satellite observations that were not used for model training, we also compared our burned area predictions with charcoal records and other independent global and regional burned area datasets. In addition to the historical reconstructed burned area dataset based on the FireCCI51 mentioned above, we also produced two additional products of historical burned area with the same spatiotemporal resolution as the FireCCI51-based burned area reconstruction: 1) the GFED5-based data version, which is based on machine-learning models trained by the burned area from GFED5 which has much more fires than GFED4 (Chen et al., 2023) instead of FireCCI51, and 2) the FireCCI51-based data with burned area further calibrated using the relationship between statistic-based burned area (Mouillot and Field, 2005) and GDP (Bolt and Van Zanden, 2024) at the regional scale before 2000 (named as FireCCI51-GDP version).</p> <p>This dataset can be used to benchmark historical simulations from fire modules in DGVMs, re-calculate historical fire emissions and estimate legacy effects of vegetation recovery after fires on terrestrial carbon sink. Though the temporal coverage of our product is long enough to support studies related to fire disturbance, carbon dynamics and climate change, more reliable explanatory data for model training and burned area data for validation would help further improve the accuracy of the reconstructed burned area product.</p> <p>Description of variable names in netcdf files:<br>'lat' -- latitude of the center of grid cells<br>'lon' -- longitude of the center of grid cells<br>'ba' -- burned area fraction in 0.5°×0.5°grid cells<br>'ba_type' -- 0=no fire; 1=regular burned area; 2=extreme burned area. Note that regular & extreme BA is defined in each GFED region individually. Extreme BA refers to that burned area within grid cells surpassing the 90th percentile of all grid cells with burned area in each GFED region.</p> <p>This dataset is currently for manuscript submission to the scientifc journal <Earth System Science Data>.</p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open, kinetically locked, (IFSopen-1) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open, kinetically locked, (IFSopen-1) conformation at apo condition, imaged from the cytoplasmic side , in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
Supplementary information for the report "Fracture Mapping on the Reykjanes Peninsula"
<p>Supplementary material for the University of Iceland's Institute of Earth Sciences (Jarðvísindastofnun Háskólans) research report RH-06-24: "Fracture Mapping on the Reykjanes Peninsula". These are fractures mapped with TerraSAR-X data following the methods of Ducrocq et al. (2024), published in Bulletin of Volcanology (<a href="https://doi.org/10.1007/s00445-023-01699-0" target="_blank" rel="noopener">https://doi.org/10.1007/s00445-023-01699-0</a>), for September 2021 to July 2024. </p>
Flood maps of Rainfall Event of November 2023 in Tuscany, Italy
<p>To support the evaluation of the Digital Twin system of the SCORE project, we defined an improved flood mapping procedure based on high-resolution SAR data to obtain flood maps of hystorical events, which will be used as validation reference for the Digital Twin outputs maps. We analyzed a recent significant flood event occurred in the densely populated Florence-Prato-Pistoia plain area (Tuscany, Italy) between 2 and 5 November, 2023. The estimated total losses for the entire region are at 1890 million euros.<br>The available images used for the the flood event analysis were acquired by COSMO-SkyMed Second Generation on 5 November 2023 in dual polarization HH-HV and by COSMO-SkyMed First Generation on 6 November 2023 in HH polarization. The flood mapping procedure, based on clustering and fuzzy logic approach, has been applied to Florence-Prato-Pistoia plain study area in rural areas and, when cross-polarization data are available, in urban areas. The dataset includes two images: the first one (named flood_05.11.2023_compressed.tif) is a map of the flood extent in rural areas of the region of interest and urban areas of Pistoia, Prato, Sesto Fiorentino and Firenze on 5 November; the second one (named flood_06.11.2023_compressed.tif) is a map of the flood extent in rural areas of the region of interest on 6 November.</p> <div> <div> <div> <div> </div> </div> </div> </div> <p> </p>
Nearctic Salpinginae COI phylogenetic analysis and map data
<p>This dataset includes three files associated with the "Revision of the Nearctic Salpinginae (Coleoptera: Salpingidae) manuscript by Johnston and Pollock.</p> <p> </p> <p>The first file is a Nexus file for Mesquite which includes the final alignment of COI DNA barcodes and the results of their analysis under maximum likelihood.</p> <p>The second two files are an R script for the creation of maps from the associated csv file of point data for specimen records of Nearctic salpingines.</p>
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.