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419 results for “map of site”
Map of ecological sites and ecological states for the USDA Jornada Experimental Range
This data package includes an ArcMap geodatabase: a polygon feature class, associated attribute table and metadata. The spatial data, JERStateMap_v1.gdb.zip, represents the ecological sites and states on the Jornada Experimental Range. The attribute table for the spatial data, JERStateMap.csv, and a summary of the spatial metadata, JERStateMapMetadata.pdf, are also included.
A map selection of wigeon stopover sites (core areas) based on wetland expert knowledge
<p>Stopover areas (core areas only) along the migration route of wigeons tracked with GPS transmitters were selected when they exhibited forests on more than 50% of their total surface or had less than 50% cover by water and/or wetland on the ESA’s global land cover map. We created a sample of 5,630 regions of interest (3,403 for training and 2,227 for validation), delineated with polygons assigned to land classes listed in the Table 1. We used archives of Google Earth, ESRI, and BING satellites for the photointerpretation of the land classes as described in Table 1. The classification was performed with a Sentinel-2 MultiSpectral Instrument, Level-2A image collection in Google Earth Engine (GEE) through the R-package Rgee to create a batch process applying the GEE Random forest classifier to each selected core home range. The cloudless (maximum 3%) images were selected within the period from 01/06/2021 to 30/09/2021. The optimal number of trees was estimated at 100 for an out of bag error of 14%. The overall accuracy on the validation sample was 82 %. </p>
LTER-Italy terrestrial sites map
<p>Map of Italy where the Italian Long-Term Ecological Research (LTER-Italy) freshwater sites are evidenced. The colours of the dots correspond to the terrestrial ecosystem (brown) typologies. The main features of the sites can be found on DEIMS-SDR, the LTER-Europe repository for research sites and datasets (<a href="https://deims.org/">https://deims.org</a>).</p>
LTER-Italy marine sites map
<p>Map of Italy where the Italian Long-Term Ecological Research (LTER-Italy) freshwater sites are evidenced. The colours of the dots correspond to the marine ecosystem (dark blue) typologies. The main features of the sites can be found on DEIMS-SDR, the LTER-Europe repository for research sites and datasets (<a href="https://deims.org/">https://deims.org</a>).</p>
LTER-Italy freshwater sites map
<p>Map of Italy where the Italian Long-Term Ecological Research (LTER-Italy) freshwater sites are evidenced. The colours of the dots correspond to the freshwater ecosystem (light blue) typologies. The main features of the sites can be found on DEIMS-SDR, the LTER-Europe repository for research sites and datasets (<a href="https://deims.org">https://deims.org</a>).</p>
LTER-Italy sites map
<p>Map of Italy where the Italian Long-Term Ecological Research (LTER-Italy) sites are evidenced. The colours of the dots correspond to the main ecosystem typologies: Dark blue = marine, light blue = freshwater, green = transitional water, brown = terrestrial. The main features of the sites can be found on DEIMS-SDR, the LTER-Europe repository for research sites and datasets (<a href="https://deims.org">https://deims.org</a>).</p>
LTER-Italy marine and transitional sites map
<p>Map of Italy where the Italian Long-Term Ecological Research (LTER-Italy) sites are evidenced. The colours of the dots correspond to the main ecosystem typologies: Dark blue = marine, green = transitional water. The main features of the sites can be found on DEIMS-SDR, the LTER-Europe repository for research sites and datasets (<a href="https://deims.org">https://deims.org</a>).</p>
Complementary data for Iqbal et al. (2023): Geological Mapping and Chronology of Lunar Landing Sites: Apollo 14
<p>Complementary data for Iqbal et al. (2023): Geological Mapping and Chronology of Lunar Landing Sites: Apollo 14</p> <p>Data contains the Geotiff of our geologic map that can be used in any geoinformation system (GIS), and map plate for interpreting the map.</p> <p><strong>If you use these data, please cite BOTH the Icarus publication and the Zenodo dataset</strong></p> <p>Iqbal, W., Hiesinger, H., Borisov, D., van der Bogert, C. H., & Head III, J. W. (2023). Geological mapping and chronology of lunar landing sites: Apollo 14. <em>Icarus</em>, <em>406</em>, 115732. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.icarus.2023.115732" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.icarus.2023.115732</a></p> <p>Iqbal, W., Hiesinger, H., Borisov, D., van der Bogert, C. H., & Head III, J. W. (2023). Complementary data for Iqbal et al. (2023): Geological Mapping and Chronology of Lunar Landing Sites: Apollo 14 [Data set]. In Icarus (Bd. 406, S. 115732). Zenodo. <a href="https://doi.org/10.5281/zenodo.8124259" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8124259</a></p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further questions contact <a href="mailto:lwueller@uni-muenster.de" rel="noopener noreferrer nofollow">iqbalw@uni-muenster.de</a></p> <p>Wajiha Iqbal, Institut für Planetologie, Universität Münster, Germany.</p>
Mapping the Atlantic Ocean i.e. the Gulf of Maine to identify suitable cultivation sites for kelp species
<p>Input source:</p> <ul> <li>Temperature data</li> <li>Depth data</li> <li>Wave data</li> <li>Nutrients data</li> <li>Current data</li> <li>Marine use data</li> </ul> <p><strong>All from other available sources outside the project</strong></p> <p> </p> <p>DATA SET GENERATED:</p> <ul> <li>Environmental data</li> <li>Training/validation data</li> <li>The socioeconomic datasets</li> </ul> <ul> <li>Map of suitable sites</li> <li>Model using GIS</li> </ul>
Baseline map of 137Cs inventories in reference soil sites at the continental scales of South America
<p>This dataset contains the baseline map of <sup>137</sup>Cs inventories in reference soil sites (Bq m<sup>-2</sup>, decay-corrected to 2020) estimated by Partial Least Square Regression (PLSR) with a spatial resolution of 2 km at the continental scale of South America, as well as the prediction uncertainties of the baseline map (coefficient of variation, %).<br> Details information regarding this dataset can be found in the original publication:<br> Mapping the spatial distribution of global <sup>137</sup>Cs fallout in soils of South America as a baseline for Earth Science studies, Earth-Science Reviews, Volume 214, 2021, 103542, ISSN 0012-8252, https://doi.org/10.1016/j.earscirev.2021.103542.</p>
Malwa site survey : archaeological site distribution maps
<p>Malwa site survey : archaeological site distribution maps. (1) mosaic based on Survey of India maps; (2) (3) study area; (4) Vidisha Raisen area.</p>
Map of the archaeological sites mentionned in the paper "Abstraction in Archaeological Stratigraphy: a Pyrenean Lineage of Innovation (late 19th–early 21th century)"
<p>Projection: WGS 84. QGIS 3.14.16</p> <p>Sources:</p> <ul> <li>DEM: GEBCO (<a href="https://doi.org/10.5285/A29C5465-B138-234D-E053-6C86ABC040B9">https://doi.org/10.5285/A29C5465-B138-234D-E053-6C86ABC040B9</a>)</li> <li>Borders:<em> Límites municipales, provinciales y autonómicosRecintos municipales y líneas límite (municipales, provinciales y autonómicos)</em>. BDLJE CC-BY 4.0.</li> </ul>
Quantitative monitoring and mapping of the damage in Syria archeological site Afamia (Apamea).
<p>This data is associated with the paper</p> <p><strong>Remote sensing and map quantitative data in an armed conflict zone.</strong></p> <p><strong>Case study: Quantitative monitoring and mapping of the damage in Syria archeological site Afamia (Apamea).</strong></p> <p>This data is arranged in CSV file.</p> <p>TheCSV file (Data-Afamia-Syria-MOBAIED 2023) contains 143,426 rows and 9 columns that represent the 143,426 entities (points) which can be projected under an adapted GIS.</p> <p>The ID for each entity is specified at the column (A), the spatial coordinates are determined under WGS 84 / UTM zone 37N at the columns (B, C, D, E). </p> <p>The category of each entity is described for each of the years of observation at the columns (F, G, H, I) using the following code:</p> <p>(0) Outside of the archeological site Apamea</p> <p>(1) inside of the archeological site Apamea </p> <p>(2) Fields</p> <p>(3) Other</p> <p>(4) Intact Site</p> <p>(5) Intact Fields</p> <p>(7) Previous Clandestine Excavations</p> <p>(8) Recent Clandestine Excavations</p> <p>(9) Traces of former excavations</p>
Map of ecological sites and ecological states for pastures 1, 4, 14, and 15 on the Chihuahuan Desert Rangeland Research Center, New Mexico
This data package includes an ArcMap geodatabase for the Chihuahuan Desert Rangeland Research Center (CDRRC) pastures 1, 4, 14, and 15: one polygon feature class, one point feature class, associated attribute tables and metadata. The spatial data, CDRRC1_4_14_15_StateMap_v1.gdb.zip, represents the ecological sites and states on Pastures 1, 4, 14 and 15 on the Chihuahuan Desert Rangeland Research Center, and includes field traverse data. CDRRC1_4_14_15_StateMapMetadata.pdf and TraversePointsMetadata.pdf contain the geospatial metadata provided by ArcMap. CDRRC1_4_14_15_StateMap_v1.csv is the attribute table associated with the state map’s polygon feature class, and TraversePoints.xlsx is the attribute table associated with the traverse points feature class and includes a sheet containing detailed attribute metadata.
A method for generating coherent spatially explicit maps of seasonal palaeoclimates from site-based reconstructions
<p>Reconstruction of climate anomalies in southern Europe for the Last Glacial Maximum (LGM, ca 21,000 years ago), made by combining pollen based reconstructions (from Bartlein et al. 2011) and averaged outputs of LGM simulations from the 3rd round of the Palaeoclimate Model Intercomparison Project (PMIP, Braconnot et al. 2011), under a variational data assimilation technique. Reconstructions made using this technique are designed to be used for data-model comparison, specifically against the results of PMIP4. The dataset consists of 6 variables: moisture index (the ratio precipitation and equilibrium evapotranspiration), mean annual precipitation (mm), mean annual temperature (degrees C), mean temperature of the coldest month (degrees C), mean temperature of the warmest month (degrees C), growing degree days above 5 degrees C (day degrees C). The standard deviation of these variables is also given.</p>
FIGURES 21 – 22. Pneuminion distribution maps. — 21. All Pneuminion collecting sites. — 22. P in A revision of the South African endemic water beetle genus Pneuminion Perkins (Coleoptera: Hydraenidae)
FIGURES 21 – 22. Pneuminion distribution maps. — 21. All Pneuminion collecting sites. — 22. P. endroedyi.
DoubleChEC program to identify transcription factor binding sites from mapped ChEC-seq data
<p>ChIP-seq (chromatin immunoprecipitation followed by sequencing) is commonly used to identify genome-wide protein-DNA interactions. However, ChIP-seq often gives a low yield, which is not ideal for quantitative outcomes. An alternative method to ChIP-seq is ChEC-seq (Chromatin endogenous cleavage with high-throughput sequencing). In this method, the endogenous TF (transcription factor) of interest is fused with MNase (micrococcal nuclease) that non-specifically cleaves DNA near binding sites. Compared to the <a href="https://www.nature.com/articles/ncomms9733" rel="nofollow">original ChEC-seq method</a>, the <a href="https://sites.northwestern.edu/bricknerlab/" rel="nofollow">modified version</a> requires far less amplification. Since <a href="https://github.com/macs3-project/MACS/tree/master#introduction">MACS3</a> failed to identify peaks in data generated from the modified ChEC-seq method, a new peak finder has been developed specifically for it.</p> <p>There are three functions in the <em><code>peak_finder/</code></em>. <code>callpeaks()</code> is used to identify peaks from BAM files. <code>goanalysis()</code> is used to make GO (Gene Ontology) term plots from peaks. <code>bedtomeme()</code> is a wrapper function to perform <a href="https://meme-suite.org/meme/tools/meme" rel="nofollow">MEME analysis</a> in R <strong>after <a href="https://meme-suite.org/meme/doc/download.html" rel="nofollow">MEME Suite</a> is installed locally</strong>.</p>
Figure 1. Map showing the sampling site. A, B in A new species of Leipsuropus Stebbing, 1899 (Amphipoda: Podoceridae) from Japan
Figure 1. Map showing the sampling site. A, B, map showing the sampling site in Japan and the Kumano Sea. Maps were generated by using GMT6 (A, Wessel et al., 2019) or based on GSI Maps (B, https://maps.gsi.go.jp).
Fig.ç1.M aps and photograph showing the sampling locality for Echinoderes ohtsukai sp. nov. A, Map of eastern Asia; B, enlargement of the rectangle in A; C, enlargement of the area indicated by the black circle in B; D, photograph of the sampling locality; white arrow indicates the Kamogawa River and dotted circle indicates the sampling site. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç1.M aps and photograph showing the sampling locality for Echinoderes ohtsukai sp. nov. A, Map of eastern Asia; B, enlargement of the rectangle in A; C, enlargement of the area indicated by the black circle in B; D, photograph of the sampling locality; white arrow indicates the Kamogawa River and dotted circle indicates the sampling site.
РИС. 3. Место нахождениЯ Amuranodonta kijaensis в Хабаровском крае: А. Карта-схема краЯ. В. Приустьевый участок р. Амур. С, D. ТопографическаЯ карта и спутниковый снимок с. Чныррах с укаЗанием места сбора. МасШтабные линейки: 200 км (А), 16 км (B), 4 км (C) и 200 м (D). FIG. 3. Locality of Amuranodonta kijaensis in the Khabarovsk Territory: A. Scheme map of the region. B. Amur River mouth area. C, D. Topographic map and satellite image of Chnyrrakh village indicating the collection site. Scale bars: 200 km (А), 16 km (B), 4 km (C), and 200 m (D). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 3. Место нахождениЯ Amuranodonta kijaensis в Хабаровском крае: А. Карта-схема краЯ. В. Приустьевый участок р. Амур. С, D. ТопографическаЯ карта и спутниковый снимок с. Чныррах с укаЗанием места сбора. МасШтабные линейки: 200 км (А), 16 км (B), 4 км (C) и 200 м (D). FIG. 3. Locality of Amuranodonta kijaensis in the Khabarovsk Territory: A. Scheme map of the region. B. Amur River mouth area. C, D. Topographic map and satellite image of Chnyrrakh village indicating the collection site. Scale bars: 200 km (А), 16 km (B), 4 km (C), and 200 m (D).
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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.