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13,117 results for “Maps”
Heat Stress Exposure Maps - Urban Planning Scenario 2026 - 2045: Almada, Portugal
<p><strong>Almada heat stress exposure map: average number of heatwave days per year versus socio economic data - urban planning scenario (2026-2045)</strong></p> <p>Heat stress exposure maps for the city of Almada representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modeled over the reference period 2026-2045 using the present land use / cover situation for the city but combined with urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p>Exposure mapping variable include:<br /> * Total population 2011<br /> * Population density inhabitants per hectare 2011</p>
Heat Stress Maps - Base Scenarios 1986-2005 / 2026 - 2045 / 2081 - 2100: Almada, Portugal
<p>Heat stress maps for the city of Almada representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>* The Urban Heat Island effect at 11pm per year<br /> (1986 - 2005) per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period using the present land use / cover situation for the city.</p> <p>Please note that only the base scenario 1986-2005 has got maps with the 2 heat stress parameters:<br /> * Average number of heat wave days per year<br /> * Urban Heat Island effect at 11pm per year</p>
Heat Stress Maps - Base Scenarios 1986-2005 / 2026 - 2045 / 2081 - 2100: Berlin, Germany
<p>Heat stress maps for Berlin representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>* The Urban Heat Island effect at 11pm per year<br /> (1986 - 2005) per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period using the present land use / cover situation for the city.</p> <p>Please note that only the base scenario 1986-2005 has got maps with the 2 heat stress parameters:<br /> * Average number of heat wave days per year<br /> * Urban Heat Island effect at 11pm per year</p> <p>Scenario: Base scenario (situation LULC today)</p>
Heat Stress Maps - Base Scenarios 1986-2005 / 2026 - 2045 / 2081 - 2100: Antwerp, Belgium
<p>Heat stress maps for Antwerp representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>* The Urban Heat Island effect at 11pm per year<br /> (1986 - 2005) per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period using the present land use / cover situation for the city.</p> <p>Please note that only the base scenario 1986-2005 has got maps with the 2 heat stress parameters:<br /> * Average number of heat wave days per year<br /> * Urban Heat Island effect at 11pm per year</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Antwerp, Belgium
<p>Average number of heatwave days per year versus socio economic data - base scenario (1986-2005)</p> <p>Heat stress exposure maps for the city of Antwerp representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modelled over the reference period 1986-2005 using the present land use / cover situation for the city.</p> <p><strong>Exposure mapping variable include the following: </strong></p> <p>Total population 2014</p> <p>Population density inhabitants per hectare 2014</p> <p>Number of inhabitants aged 0 to 4 years 2014</p> <p>Number of inhabitants aged 0 to 17 years 2014</p> <p>Number of inhabitants aged 18 to 65 years 2014</p> <p>Number of inhabitants aged +65 years 2014</p> <p>Number of schools 2014</p> <p>Number of childcare centers 2014</p> <p>Number of hospitals 2014</p> <p>Number of elderly stay facilities 2014</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany (Map-2 & Map-3)
<p>Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany</p> <p>Map-2 & Map-3 (zip.file) ref. to DOI: 10.5281/zenodo.45015</p>
Bermuda Reef Ecosystem Assessment and Mapping Programme: Baseline Coral Reef and Fish data
<p>BREAM: Bermuda Reef Ecosystem Analysis and Mapping Programme, mapped all coral reefs across the Bermuda platform in 2003. We then surveyed over 200 sites for benthic and fish assemblages using the AGRRA.org v4 methods, with local modifications. See www.bermudaBREAM.org for more information about the baseline monitoring work, or to contact us for additional data and information.</p> <p>Contributors:</p> <p>Research Associates:</p> <p>Jessie Murdoch MSc; Robert Fisher, BFA; Gretchen Goodbody-Gringley, PhD, Matthew Strong BSc; Struan R. Smith PhD</p> <p>Undergraduate Interns</p> <p>Taylor Gorham, Anke Moesinger, Molly Sinnott, D’mitri Williams</p> <p>Graduate Interns</p> <p>Gerardo Toro-Farmer, Mike Colella, Brittany Huntington, Katherine Yates</p> <p>Volunteers</p> <p>Ian Murdoch; Judie Clee; Teddy Gosling; Gil Nolan; Lynn Wolfe</p> <p>RV Endurance Captain: Tim Hasselbring</p> <p> </p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany
<p><strong>Average number of heatwave days per year versus socio economic data - base scenario (1986-2005)</strong></p> <p>Heat stress exposure maps for the city of Berlin representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modelled over the reference period 1986-2005 using the present land use / cover situation for the city.</p> <p><strong>Exposure mapping variable include the following:</strong></p> <p>Total population 2013</p> <p>Population density inhabitants per hectare 2013</p> <p>Number of inhabitants aged 0 to 17 years 2013</p> <p>Number of inhabitants aged 18 to 65 years 2013</p> <p>Number of inhabitants aged +65 years 2013</p> <p>Number of schools 2014</p> <p>Number of childcare centers 2014</p> <p>Number of hospitals 2014</p> <p>Number of elderly stay facilities 2014</p>
Tree map plots of women's share across MSC 2010 classes
<p> Hierarchical maps visualising the share of women authors resp. women's publications in zbMATH across the MSC 2010 classes on level 1 and 2. Each rectangle represents a mathematical class and displays the corresponding name and code within the MSC 2010; its size is proportional to the number of authors resp. publications in that field, and the darker the color, the larger the share of women resp. their publications in that field.</p> <p>Detailed information can be obtained using the hover; a right-click allows to get back from MSC level 2 to level 1. Note that the interactive feature requires the activation of JavaScript.</p>
GridKit extract of ENTSO-E interactive map
<p>This dataset was generated based on a map extract from May 11, 2016. This is an <em>unofficial</em> extract of the ENTSO-E interactive map of the European power system (including to a limited extent North Africa and the Middle East). The dataset has been processed by GridKit to form complete topological connections. This dataset is neither approved nor endorsed by ENTSO-E.</p> <p>This dataset may be inaccurate in several ways, notably:</p> <ul> <li>Geographical coordinates are transfered from the ENTSO-E map, which is known to choose topological clarity over geographical accuracy. Hence coordinates will not correspond exactly to reality.</li> <li>Voltage levels are typically provided as ranges by ENTSO-E, of which the lower bound has been reported in this dataset. Not all lines - especially DC lines - contain voltage information.</li> <li>Line structure conflicts are resolved by picking the first structure in the set</li> <li>Transformers are <em>not present</em> in the original ENTSO-E dataset, there presence has been derived from the different voltages from connected lines.</li> <li>The connection between generators and busses is derived as the geographically nearest station at the lowest voltage level. This information is again not present in the ENTSO-E dataset.</li> </ul> <p>All users are advised to exercise caution in the use of this dataset. No liability is taken for inaccuracies.</p>
Subsidence of Beijing (China) mapped by Copernicus Sentinel-1 time series interferometry
<p><strong>RESULTS DESCRIPTION</strong></p> <p>Recent reports from scientific and mainstream media have indicated that the city of Beijing, together with its surroundings, is subsiding at fast and alarming rate as result of the overexploitation of groundwater. The depletion of groundwater causes underlying soil to compact, creating a phenomenon called subsidence. The Beijing region has been experiencing this phenomenon since 1935, but in last years the rate of sinking has significantly increased.</p> <p>A team of researchers, within ESA sponsored, SEOM InSARap project performed an interferometric analysis of Copernicus Sentinel-1 data which confirms the reported findings also with current data. While the results speak for themselves, we can just once more reiterate on the usefulness of the Copernicus Programme, in this case for deformation monitoring applications.</p> <p><strong>ANALYSIS SUMMARY</strong></p> <ul> <li>Data overview: <ul> <li>Sentinel-1 IW</li> <li>Track 47 descending</li> <li>Observation window December 2014 - June 2016</li> <li>Data download via Scientific Data Hub</li> </ul> </li> <li>Processing overview: <ul> <li>Time series analysis performed with Small Baseline Subset (SBAS) methodology</li> <li>Interferometric combinations of up to 96 days used</li> </ul> </li> </ul> <p><em>More information and context available at insarap.org</em></p> <p><em>Terms and Conditions:</em> All Sentinel-1 results that are available for download are Derived Works of Copernicus data (2014-2016), subject to the "<em>TERMS AND CONDITIONS FOR THE USE AND DISTRIBUTION OF SENTINEL DATA AND SERVICE INFORMATION</em>".</p> <p><em>Acknowledgments: </em> ESA SEOM InSARap project - Sentinel-1 InSAR Performance Study with TOPS Data, contract number 4000110680/14/I-BG-InSARap</p>
Dataset for testing and training map-matching methods
<p>We present a dataset for testing, bench-marking, and offline learning of map-matching algorithms. For the first time, a large enough dataset is available to prove or disprove map-matching hypotheses on a world-wide scale. There are several hundred map-matching algorithms published in literature, each tested only on a limited scale due to difficulties in collecting truly large scale data. Our contribution aims to provide a convenient gold standard to compare various map-matching algorithms between each other. Moreover, as many state-of-the-art map-matching algorithms are based on techniques that require offline learning, our dataset can be readily used as the training set. Because of the global coverage of our dataset, learning does not have to be be biased to the part of the world where the algorithm was tested.</p>
Mapping and analysis of the Central Italy Earthquake (2016) with Sentinel-1 A/B interferometry
<p><strong>Results Description</strong></p> <p><em>Background</em></p> <p>A 6.2M earthquake hit Central Italy, the area of city of Amatrice, on 24 August 2016. The quake epicentre was southeast of Norcia, Italy, in an area near the borders of the Umbria, Lazio, Abruzzo and Marche regions. The quake hypocentre was at a depth of approximately 5 km.</p> <p><em>InSAR</em></p> <p>We computed a set of coseismic Sentinel-1A/B interferograms, over the affected area.</p> <ul> <li>Descending track 95: S1A (2016-08-26) / S1A (2016-08-14)</li> <li>Descending track 22: S1A (2016-08-21) / S1B (2016-08-27)</li> <li>Ascending track 117: S1B (2016-08-21) / S1A (2016-08-27)</li> </ul> <p><em>Earthquake Motion Decomposition</em></p> <p>We also performed the two-dimensional earthquake motion decomposition. As an input for the decomposition, data of tracks 22 descending and 117 ascending were used, since they provide the full coverage of the earthquake.</p> <p><strong>Results and Data Package</strong></p> <p><em>Descending Interferogram, Track 95:</em></p> <ul> <li>Goldstein filtered wrapped interferometric phase, 16x4 ML (KMZ format)</li> <li>Interferometric coherence (KMZ format)</li> <li><em>Descending Interferogram, Track 22:</em></li> <li>Goldstein filtered wrapped interferometric phase, 16x4 ML (KMZ format)</li> <li>Interferometric coherence (KMZ format)</li> <li>Full geo-coded Goldstein filtered wrapped interferogram, 16x4 ML (GeoTiff format)</li> <li>Intereferorgram subset (GeoTiff format) <ul> <li>Specifically for subset: 8x2 ML + goldstein + unwrapping + geocoding, incl oversampling to 10 m + smoothing (5x5 pixel boxcar) + subsampling to ~50m + calibrated at the point at used for 2D decomposition</li> </ul> </li> </ul> <p><em>Ascending Interferogram, Track 117:</em></p> <ul> <li>Goldstein filtered wrapped interferometric phase, 16x4 ML (KMZ format)</li> <li>Interferometric coherence (KMZ format)</li> <li>Full geo-coded Goldstein filtered wrapped interferogram, 16x4 ML (GeoTiff format)</li> <li>Intereferorgram subset (GeoTiff format, 7Mb zip file) <ul> <li>Specifically for subset: 8x2 ML + goldstein + unwrapping + geocoding, incl oversampling to 10 m + smoothing (5x5 pixel boxcar) + subsampling to ~50m + calibrated at the point at used for 2D decomposition</li> </ul> </li> </ul> <p><em>Decomposed Solution</em></p> <ul> <li>Vertical Component (KMZ format)</li> <li>East-West Component (KMZ format)</li> <li>Vertical Component (GeoTiff format)</li> <li>East-West Component (GeoTiff format)</li> </ul> <p><em>Additional Information</em></p> <ul> <li>Filenames are self descriptive and not require additional information</li> <li>KMZ and GeoTiff files that are available for download, are generated for high-resolution investigations in GoogleEarth and post-processing & interpretation. They are not prepared for overviews, and thus are not heavily smoothed nor filtered.</li> </ul> <p><em>Terms and Conditions:</em> All Sentinel-1 results that are available for download are Derived Works of Copernicus data (2014-2016), subject to the "TERMS AND CONDITIONS FOR THE USE AND DISTRIBUTION OF SENTINEL DATA AND SERVICE INFORMATION".</p> <p><em>Acknowledgments:</em> ESA SEOM InSARap project - Sentinel-1 InSAR Performance Study with TOPS Data, contract number 4000110680/14/I-BG-InSARap</p> <p><em>More information and context available at insarap.org .</em></p>
FIGURES 29 – 30. Pneuminion distribution maps. — 29. P. punctatum. — 30. P in A revision of the South African endemic water beetle genus Pneuminion Perkins (Coleoptera: Hydraenidae)
FIGURES 29 – 30. Pneuminion distribution maps. — 29. P. punctatum. — 30. P. natalensis.
Sequence of snow maps produced from Sentinel-2 type of observations (SPOT-5 Take 5) over the Deux Alpes and Alpe d'Huez ski resorts
<p>This is a series of snow cover maps between April 11 and September 8, 2015 over a region that covers the Deux Alpes and Alpe d'Huez ski resorts in France. The snow maps were produced from a SPOT-5 Take 5 images using the "Let-it-snow" processor (v1.0, June 2016: http://tully.ups-tlse.fr/grizonnet/let-it-snow).</p> <p>The SEB folder contains 20 GeoTiff at 10 m résolution in Lambert-93 projection system coded as follows (cf. http://tully.ups-tlse.fr/grizonnet/let-it-snow#products-format):</p> <p> 0: No-snow<br> 100: Snow<br> 205: Cloud including cloud shadow<br> 254: No data</p> <p>The "anim_no05jul_opt" animated gif was produced from these data after performing a simple temporal interpolation given by the following rules:</p> <ul> <li>If a pixel masked by a cloud was marked as snow in the preceding image and in the following image, then it is reclassified as a snow pixel.</li> </ul> <ul> <li>If a pixel masked by a cloud was marked as no-snow in the preceding image and in the following image, then it is reclassified as a no-snow pixel.</li> </ul> <p>The July 05 image was removed from the animation due to a cloud/snow confusion. This type of error should be avoided with Sentinel-2 data thanks to an additional "high cloud" test (see http://tully.ups-tlse.fr/grizonnet/let-it-snow/blob/master/doc/tex/ATBD_CES-Neige.pdf).</p> <p>These data were featured in this blog post : Gascoin, S. "Monitoring the snow cover in ski resorts using Sentinel-2" (19-Sep-2016) http://www.cesbio.ups-tlse.fr/multitemp/?p=8676</p>
Amatrice Earthquake - Sentinel-1 TOPS Ascending - Coherence Map (S1A_20160815-S1A_20160827)
<p>Coherence levels of the ascending S1 TOPSAR interferogram (S1A_20160815-S1A_20160827).</p> <p>A Sentinel-1 TOPS co-seismic interferogram of the Amatrice earthquake in Italy on the 24th of August 2016. Processing was performed with the ESA SNAP toolbox (http://step.esa.int/).</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub.</p> <p>Contains modified Copernicus data (2016)</p> <p> </p>
Amatrice Earthquake - Sentinel-1 TOPS Descending - Coherence Map (S1A_20160821-S1B_20160827)
<p>Coherence levels of the descending S1 TOPSAR interferogram (S1A_20160821-S1B_20160827).</p> <p>A Sentinel-1 TOPS co-seismic interferogram of the Amatrice earthquake in Italy on the 24th of August 2016. Processing was performed with the ESA SNAP toolbox (http://step.esa.int/).</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub.</p> <p>Contains modified Copernicus data (2016)</p>
Raw fMRI data from 12 rats used in the manuscript "Mapping of hemodynamic responses to the sensorymotor stimulation in a rodent model: a BOLD fMRI study" submitted to PlosOne for publication
<p>Raw data are from twelve male adult Wistar rats (Charles River Laboratories, Paris-France) weighing 300 ± 20g.</p> <p>Rats were initially anesthetized (induction) with 3% isoflurane and were maintained under 0,7-0.8% (sedation along with a muscle relaxation) during fMRI session. </p> <p>Each rat was submitted to two fMRI sessions: one with TE of 30 ms and and other with TE of 40 ms.</p> <p>For fMRI acquisition, electrodes were inserted subcutaneously in the palmar surface of the right hindpaw of each rat and electrical stimulation (current pulses with a 1.7 mA amplitude, 10 ms duration and 8 Hz frequency) was applied in a block-design starting with a resting period of 25s as a baseline followed by 25s stimulation, repeated 8 times.</p> <p>Ten 1-mm thick contiguous axial slices, from -6.36 mm to +2.64 mm to Bregma, were acquired with a two-shot gradient echo planar imaging (GE EPI) pulse sequence (2.56 cm2 FOV; 64x64 matrix size; a TR of 1000 ms; a flip angle of 50°) resulting in the pixel size of 0.4 mm.</p> <p>All imaging experiments were performed on a 4.7T Bruker (Biospec 47/40, Bruker, GmbH, Ettlingen,Germany) with a horizontal bore magnet equipped with a 12 cm gradient coil (Bruker BGA12, 400 mT/m) and interfaced to AVANCE III console. Two actively decoupled RF coils were used: a 7.2-cm diameter volume coil for transmission and a 2-cm diameter surface coil (Rapid Biomedical, Rimpar, Germany) positioned on the top of the animal's head for reception.</p>
Figure 1. - World map representing all the locations mentioned in the dataset. Areas of particular interest are represented with the same colour (⬤ Madagascar, ⬤ Western Indian Ocean, ⬤ Papuasia, ⬤ New Caledonia, ⬤ South Pacific). Grey spots gather all the other locations.
Figure 1. - World map representing all the locations mentioned in the dataset. Areas of particular interest are represented with the same colour (⬤ Madagascar, ⬤ Western Indian Ocean, ⬤ Papuasia, ⬤ New Caledonia, ⬤ South Pacific). Grey spots gather all the other locations.
Figure 1. from Extending Marine Species Distribution Maps Using Non-Traditional Sources - Biodiversity Data Journal 3: e4900 (17 April 2015) https://doi.org/10.3897/BDJ.3.e4900
Figure 1. - The IUCN Red List Review Process (IUCN 2014a). Steps refer to the DOCUMENTATION STANDARDS AND CONSISTENCY CHECKS FOR IUCN RED LIST ASSESSMENTS AND SPECIES ACCOUNTS (IUCN 2013).
ScienceDex guides
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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.