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13,117 results for “Maps”
How to Snap the Sneed Prairie Map to Google Earth — BlueStems™
<p>A video tutorial showing how to snap a transparent PNG image to Google Earth.</p><p>The video is available on YouTube here: <a href="https://youtu.be/6BRdA6yj57Q">https://youtu.be/6BRdA6yj57Q</a></p>
Mapping multiscale breeding bird species distributions across the United States and evaluating their conservation applications
<p>Species distribution models are vital to management decisions that require understanding habitat use patterns, particularly for species of conservation concern. However, the production of distribution maps for individual species is often hampered by data scarcity, and existing species maps are rarely spatially validated due to limited occurrence data. Furthermore, community-level maps based on stacked species distribution models lack important community assemblage information (e.g., competitive exclusion) relevant to conservation. Thus, multispecies, guild, or community models are often used in conservation practice instead. To address these limitations, we aimed to generate fine-scale, spatially-continuous, nationwide maps for species represented in the North American Breeding Bird Survey (BBS) between 1992-2019. We generated ensemble models for each species at three spatial resolutions – 0.5, 2.5, and 5 km – across the conterminous United States. We also compared species richness patterns from stacked single-species models with those of 19 functional guilds developed using the same data to assess the similarity between predictions. We successfully modeled 192 bird species at 5-km resolution, 160 species at 2.5-km resolution, and 80 species at 0.5-km resolution. However, the species we could model represent only 28-56% of species found in the conterminous US BBS surveys across resolutions owing to data limitations. We found stacked maps and guild maps generally had high correlations across resolutions (median = 84%), but spatial agreement varied regionally by resolution and was most pronounced between the East and West at the 5-km resolution. The spatial differences between our stacked maps and guild maps illustrate the importance of spatial validation in conservation planning. Overall, our species maps are useful for single-species conservation and can support fine-scale decision-making across the United States, and can also support community-level conservation when used in tandem with guild maps. However, there are still data scarcity issues for many species of conservation concern when using the BBS for single-species models.</p>
Scientific Mapping Example with R package `bibliometrix`
<p>Example of the use of the <strong>R</strong> package <i>bibliometrix </i>for bibliometric analysis</p>
India Landslide Susceptibility Map ILSM (100m resolution)
<p>Landslide susceptibility represents the potential of slope failure for given geoenvironmental conditions. The existing landslide susceptibility maps suffer from several limitations, such as being based on limited data, heuristic methodologies, low spatial resolution, and small areas of interest. In this study, we overcome these limitations by developing a probabilistic framework that combines imbalance handling and ensemble machine learning for landslide susceptibility mapping. We employ a combination of one-side sampling and Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE) to eliminate class imbalance and develop smaller representative data from big data for model training. A blending ensemble approach using hyperparameter tuned Artificial Neural Networks, Random Forests, and Support Vector Machine, is employed to reduce the uncertainty associated with a single model. The methodology provides the landslide susceptibility probability and a landslide susceptibility class. A thorough evaluation of the framework is performed using receiver operating characteristic curves, confusion matrices, and the derivatives of confusion matrices. This framework is used to develop India's first national-scale machine learning based landslide susceptibility map. The landslide database is carefully curated from global and local inventories, and the landslide conditioning factors are selected from a multitude of geophysical and climatological variables. The Indian Landslide Susceptibility Map (ILSM) is developed at a resolution of 0.001o (~100 m) and is classified into five classes: very low, low, medium, high, and very high. We report an accuracy of 95.73%, sensitivity of 97.08%, and matthews correlation coefficient (MCC) of 0.915 on test data, demonstrating the accuracy, robustness, and generalizability of the framework for landslide identification. The model classified 4.75% area in India as very highly susceptible to landslides and detected new landslide susceptible zones in the Eastern Ghats, hitherto unreported in the government landslide records. The ILSM is expected to aid policymaking in disaster risk reduction and developing landslide prediction models. </p>
Heat risk map of Riyadh
<p>The dataset is used to assess the urban heat risk in the city of Riyadh using proxy variables to evaluate the environmental, infrastructural, and social dimensions of the city.</p><p>The environmental component was evaluated using the mean values of land surface temperature (LST), air temperature (T2m), and discomfort index (DI) across the districts of Riyadh. These factors, derived from data like MODIS LST and available WRF simulations, represented the degree of heat exposure in different regions. </p><p>The infrastructural component of heat risk was evaluated by looking at the city's infrastructure, that is the building density per district. Buildings can act as "heat traps," thus higher building density suggests increased heat risk.</p><p>The social component considered demographic factors such as the percentage of the population over 65 old (OP) and under 14 years old (YP), which can indicate sensitivity to extreme heat conditions. </p><p>To map the heat risk, these components were combined into a composite heat risk indicator. For this to be achieved, each parameter was reclassified into three categories (1-less, 2-moderate, and 3-high) using the quantile classification which is a data classification method that distributes a set of values into groups that contain an equal number of values. </p><p> </p><p> LST (°C) DI T2m (°C) <14 y.o. (%) >65 y.o (%) Buildings per sq. m.(BD)</p><p>1-Less risk <47.2 <28 <40.6 <23 <1 <66</p><p>2-Moderate risk 47.2 ≤ LST ≤ 47.9 28≤ DI ≤ 28.2 40.6 ≤ T2m ≤ 40.8 23≤ YP ≤28 1≤ OP ≤ 3 66≤ BD ≤ 109</p><p>3-High risk >47.9 >28.2 >40.8 >28 >3 >109</p><p>LST: Land Surface Temperature; DI: Discomfort Index; T2m: Air temperature at 2m height; YP<14 y.o.: People under 14 years old; OP y.o.: Older people over 65 years old; </p><p>Since the relative importance of each parameter is unknown, we considered that all parameters contributed equally to the composite heat risk index and the arithmetic values were aggregated. The final value for each district was then reclassified into three categories using the quantile classification method resulting in the final three categories of Urban Heat Risk (Less heat risk, Moderate heat risk, High heat risk)</p>
QTL mapping for seedling and adult plant resistance to stripe and leaf rust in two winter wheat populations
<p><span>The two recombinant inbred lines (RIL) populations developed by crossing Almaly × Avocet S (206 RILs) and Almaly × Anza (162 RILs) were used to detect the novel genomic regions associated with adult plant resistance (APR) and seedling or all-stage resistance (ASR) to yellow rust (YR) and leaf rust (LR). Both the populations were evaluated for YR APR in two environments (2018 and 2019) and LR APR in three environments (2018, 2019, and 2020) in the Anza population and two environments (2018 and 2019) in the Avocet population; both the populations were phenotyped for one environment during 2020 for LR and YR ASR and genotyped using high throughput DArTseq technology. A set of 51 QTLs including 22 for YR APR, nine for LR APR, nine for YR ASR, and 11 for LR ASR were identified. Also, a set of 13 stable QTLs including nine QTLs (<em>QYR-APR-2A.1, QYR-APR-2A.2, QYR-APR-4D.2, QYR-APR-1B, QYR-APR-2B.1, QYR-APR-2B.2, QYR-APR-3D, QYR-APR-4D.1, </em>and<em> QYR-APR-4D.2</em>) for YR APR and four QTLs (<em>QLR-APR-4A, QLR-APR-2B, QLR-APR-3B, </em>and<em> </em></span><em>QLR-APR-5A.2</em>) <span>for LR APR were identified. </span><span>In silico analysis revealed that the key putative candidate genes such as <em>Cytochrome P450</em></span><em><span>, Protein kinase-like domain superfamily</span><span>, Zinc-binding ribosomal protein</span><span>, SANT/Myb domain</span><span>, WRKY transcription factor</span><span>, Nucleotide-sugar transporter,</span></em><span> and <em>NAC</em> </span><em><span>domain superfamily</span></em><span> were in the QTL regions and involved in the regulation of host response towards the pathogen infection. </span><span>The stable QTLs identified in this study are useful for developing rust-resistant varieties through marker-assisted selection (MAS).</span></p>
VessMAP - Feature-Mapped Cortex Vasculature Dataset
<h2>VessMAP Dataset</h2><p>The VessMAP Dataset comprises 100 manually labeled confocal microscopy images of mouse brain vasculature. Among these images, 20 randomly selected samples were labeled by two annotators. The dataset is organized using the following directory structure:</p><ul><li>images: 100 blood vessel images (.tiff). Each image has a size of 256x256 pixels with a single grayscale channel of 8-bit color depth.</li><li>annotator1:<ul><li>labels: 100 labels annotated by annotator 1. Each file is a 256x256 .png binary image, with a value of 255 assigned to pixels annotated as blood vessels and a value of 0 assigned to background pixels.</li><li>skeletons: skeletons of the labels annotated by annotator 1. Each file is a 256x256 .png binary image, with a value of 255 assigned to pixels belonging to the skeleton and a value of 0 assigned to background pixels. These skeletons were calculated using the Palàgyi-Kuba algorithm [1].</li><li>measures.json: a .json file with four metrics that can be used to map the dataset to a feature space. The four metrics included in the file are the contrast, blood vessel density, estimation of Gaussian noise level, and skeleton heterogeneity. A detailed explanation of the metrics can be found in [2]. These metrics are useful for testing possible biases when training supervised segmentation algorithms.</li></ul></li><li>annotator2:<ul><li>This directory contains annotations for 20 images that were randomly selected from the 100 images of the whole dataset. The organization is the same as for annotator 1.</li></ul></li></ul><p>1. K. Palágyi and A. Kuba, "A 3D 6-subiteration thinning algorithm for extracting medial lines," Pattern Recognit. Lett. 19, 613–627 (1998).</p><p>2. da Silva, MV., Santos, NdC., Lacoste, B., & Comin, CH. A new sampling methodology for creating rich, heterogeneous, subsets of samples for training image segmentation algorithms. arXiv preprint arXiv:2301.04517, (2023).</p>
Argentina National Map of Crops 2022/2023
<p>Crop type map covering the main agricultural areas of Argentina for growing season 2022/2023. This version includes two different maps for winter and summer crops. Maps were generated using supervised classification methods with samples obtained from on-road surveys and Landsat satellite images along the growing season. Files provided include a Geotiff version of each map with a resolution of 30 m and legend style files. The report includes the methodological details, map legend and accuracy assessments (in Spanish). A web visualizer can be accessed through the following link: <a href="https://deabelle.users.earthengine.app/view/mnc22-23">https://deabelle.users.earthengine.app/view/mnc22-23</a></p>
Map 5 in Revision, Phylogenetic, Biogeographic, And Host Analyses Of The Endemic Western North American Phymatopsallus Group, With The Description Of 9 New Genera And 15 New Species (Insecta: Hemiptera: Miridae: Phylinae)
Map 5. Distribution of Cercocarpopsallus and Knightopsallus spp.
Map 6 in Revision, Phylogenetic, Biogeographic, And Host Analyses Of The Endemic Western North American Phymatopsallus Group, With The Description Of 9 New Genera And 15 New Species (Insecta: Hemiptera: Miridae: Phylinae)
Map 6. Distribution of Phymatopsallus acaciae–P. rinconae.
Map 7 in Revision, Phylogenetic, Biogeographic, And Host Analyses Of The Endemic Western North American Phymatopsallus Group, With The Description Of 9 New Genera And 15 New Species (Insecta: Hemiptera: Miridae: Phylinae)
Map 7. Distribution of Phymatopsallus tuberculatus.
Map 3 in Revision, Phylogenetic, Biogeographic, And Host Analyses Of The Endemic Western North American Phymatopsallus Group, With The Description Of 9 New Genera And 15 New Species (Insecta: Hemiptera: Miridae: Phylinae)
Map 3. Distribution of Ceratopsallus aquilonius–C. plautus.
Map 4 in Revision, Phylogenetic, Biogeographic, And Host Analyses Of The Endemic Western North American Phymatopsallus Group, With The Description Of 9 New Genera And 15 New Species (Insecta: Hemiptera: Miridae: Phylinae)
Map 4. Distribution of Ceratopsallus quercicola–C. vauqueliniae.
Map 2 in Revision, Phylogenetic, Biogeographic, And Host Analyses Of The Endemic Western North American Phymatopsallus Group, With The Description Of 9 New Genera And 15 New Species (Insecta: Hemiptera: Miridae: Phylinae)
Map 2. Distribution of Bisulcopsallus spp.
Map 1 in Revision, Phylogenetic, Biogeographic, And Host Analyses Of The Endemic Western North American Phymatopsallus Group, With The Description Of 9 New Genera And 15 New Species (Insecta: Hemiptera: Miridae: Phylinae)
Map 1. Distribution of Angelopsallus and Arizonapsallus spp.
Map of Extant Ceremonial Court Ordinances issued between 1300-1556 and the Marital Relationships of the Burgudian Dukes
<p>Map of Extant Ceremonial Court Ordinances issued between 1300-1556 and the Marital Relationships of the Burgudian Dukes. ArcGIS (Esri). Hatched polygon indicate the boundaries of the Burgundian territories during the reign of Charles the Bold. Dots indicate extant ceremonial court ordinances issued between 1300-1556. Blue dots indicate Burgundian ceremonial court ordinances issued between 1300-1556. Red lines indicate the marital relationships between the Burgundian dukes and their spouses from their place of birth.</p><p>See: Miara Fraikin and Meike Wiedemann, 'The "Burgundian Model" revisited: Using Digital Approaches to Explore the Reach of Burgundy', in Sanne Maekelberg and Krista De Jonge (eds.), <i>Mapping the Space of the Early Modern Court in Europe. Functionality and Representation, </i>2023, pp.13-34.</p>
Map of Extant Court Ordinances issued between 1300-1556 in Western- and Central Europe.
<p>Map depicting the collected extant court ordinances issued between 1300 and 1556 at the Noble Courts of Western- and Central Europe. ArcGIS (Esri). Blue dots refer to court ordinances that describe ceremonial life at court. Red dots refer to court ordinances that do not describe ceremonial life at court.</p><p>See: Miara Fraikin and Meike Wiedemann, 'The "Burgundian Model" revisited: Using Digital Approaches to Explore the Reach of Burgundy', in Sanne Maekelberg and Krista De Jonge (eds.), <i>Mapping the Space of the Early Modern Court in Europe. Functionality and Representation, </i>2023, pp.13-34.</p>
SlumMapVisionBR: A Dataset for Mapping Slums with Medium-Resolution Satellite Imagery
<p>This dataset contains Landsat images (30m/pixel) and masks with slum locations in Brazil. </p><p>This imagery can be used to train models that help keep track of Goal 11.1 of the United Nations Sustainable Development Goals. </p><p>Details on how the data was collected and labelled are available at: <a href="https://github.com/ml-labs-crt/SlumMapVisionBR">https://github.com/ml-labs-crt/SlumMapVisionBR</a>, along with the code to generate the dataset and benchmark results.</p><p>Description of files:</p><ul><li>images.zip contains the images.</li><li>masks.zip contains the masks with slum locations (1 indicates the area has been labelled as a slum).</li><li>metadata.csv contains information about the 116 locations in this dataset.</li><li>metadata_dictionary.txt includes a description of the fields in metadata.csv.</li></ul><p><strong>Refining documentation:</strong> We welcome additions and edits that make using the existing data or adding new data more straightforward for the community. Please get in touch with us by <a href="https://github.com/ml-labs-crt/SlumMapVisionBR/issues">posting an issue on GitHub</a>.</p>
Mapping and modelling global mobility infrastructure stocks, material flows and their embodied greenhouse gas emissions - Data
<p>Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. </p><p>The data in this repository show the material stocks contained in global mobility infrastructure networks at the country-level and mapped at 5arcmins, as well as country-level estimates of material flows for maintenance, replacement and expansion of those infrastructures, and the associated GHG emissions from materials production. This repository contains all data as shown in figures of the article, including the GeoTIFF files for figure 3, and the supplementary data file containing full country-level results.</p><p><strong>Data</strong><br>This dataset includes the following data:</p><ul><li>Global maps of material stocks in mobility infrastructure networks at 5 arcmins, separate for all roads, all rail-based infrastructure, as well as in total and per capita</li><li>Global country-level material stock estimates for mobility infrastructures</li><li>Global country-level estimates of material flows and associated GHG emissions for materials production</li><li>Material intensity in mass per area of road (kg/m²) per road type</li><li>Material intensity in mass per area of railway track (kg/m²) per railway type</li><li>Material intensity in mass per area (kg/m²) per bridges and tunnels</li></ul><p>Material intensity factors are available for iron and steel, concrete, asphalt, aggregate (sand & gravel), timber, and other.</p><p><strong>Further information</strong><br>This dataset complements the following scientific article:</p><p>Wiedenhofer, Dominik, André Baumgart, Sarah Matej, Doris Virág, Gerald Kalt, Maud Lanau, Danielle Densley Tingley, u. a. "Mapping and Modelling Global Mobility Infrastructure Stocks, Material Flows and Their Embodied Greenhouse Gas Emissions". <i>Journal of Cleaner Production</i>, November 2023, 139742. <a href="https://doi.org/10.1016/j.jclepro.2023.139742">https://doi.org/10.1016/j.jclepro.2023.139742</a>.</p><p>For further information please see the publication. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: <i>MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</i></p><p><strong>Funding</strong><br>This research was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p>
National-scale tree species/genera map for Poland from Sentinel-2 time series
<p>Map of 16 dominant tree species/genera in Poland based on classification of time series of Sentinel-2 imagery. This dataset is associated with the article by Grabska-Szwagrzyk et al. (2024)<em>: <a href="https://essd.copernicus.org/articles/16/2877/2024/">Map of forest tree species for Poland based on Sentinel-2 data.</a></em></p> <p>The map is provided as GeoTiff file. In addition, training and test data is provided in shapefile format. The map can be explored online in a <a href="https://ee-aweaksbarg.projects.earthengine.app/view/speciesmappl">webviewer</a>.</p> <p> </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.