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532 results for “Extent”
Fig. 5 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 5. UPGMA of Paspalum urvillei accessions from USDA germplasm.
Fig. 4 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 4. Principal Coordinate Analysis of Paspalum urvillei accessions from USDA germplasm.
Fig. 3 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 3. Principal Component Analysis of Paspalum urvillei accessions from USDA germplasm.
Fig. 2 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 2. The phenotypic structure of Paspalum urvillei accessions from USDA germplasm.
Fig. 1 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 1. Spatial clustering of Paspalum urvillei accessions from USDA germplasm.
Fig. 6 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 6. Violin plot of Paspalum urvillei accessions from USDA germplasm.
Pacific decadal oscillation influences tropical oxygen minimum zone extent and obscures anthropogenic changes
<p>Model outputs needed to reproduce the figures in the article : "Pacific decadal oscillation influences tropical oxygen minimum zone extent and obscures anthropogenic changes"</p>
Table 2 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
<p><b>Table 2.</b> Summary statistics of evaluated variables of <i>Paspalum urvillei</i> from USDA germplasm accessions.</p><table><tbody><tr><th>Variable</th><th>Main category frequency</th><th>Trait diversity</th><th>PIC</th></tr></tbody><tbody><tr><th>Plant width</th><td>0.439</td><td>0.659</td><td>0.595</td></tr><tr><th>Foliage height</th><td>0.585</td><td>0.603</td><td>0.565</td></tr><tr><th>Plant height</th><td>0.537</td><td>0.627</td><td>0.576</td></tr><tr><th>Leaf length</th><td>0.317</td><td>0.736</td><td>0.688</td></tr><tr><th>Leaf width</th><td>0.561</td><td>0.616</td><td>0.571</td></tr><tr><th>Maturity</th><td>0.537</td><td>0.664</td><td>0.635</td></tr><tr><th>Foliage distribution</th><td>0.854</td><td>0.256</td><td>0.233</td></tr><tr><th>Stem size</th><td>0.951</td><td>0.093</td><td>0.089</td></tr><tr><th>Tillers</th><td>0.951</td><td>0.093</td><td>0.089</td></tr><tr><th>Foliage amount</th><td>0.585</td><td>0.485</td><td>0.368</td></tr><tr><th>Seed production</th><td>0.805</td><td>0.330</td><td>0.300</td></tr><tr><th>Winter survival</th><td>0.268</td><td>0.829</td><td>0.809</td></tr><tr><th>Mean</th><td>0.616</td><td>0.499</td><td>0.460</td></tr></tbody></table>
Table 1 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
<p><b>Table 1.</b> Description of <i>Paspalum urvillei</i> accessions of USDA germplasm.</p><table><tbody><tr><th>Plant ID</th><th>Plant #</th><th>Taxonomy</th><th>Country</th><th>City</th></tr></tbody><tbody><tr><th>PI 164065</th><td>1</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Florianópolis</td></tr><tr><th>PI 202046</th><td>2</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Buenos Aires</td></tr><tr><th>PI 203747</th><td>3</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>São Gabriel</td></tr><tr><th>PI 203749</th><td>4</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Rivera</td></tr><tr><th>PI 203752</th><td>5</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Porto Alegre</td></tr><tr><th>PI 204237</th><td>6</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Rivera</td></tr><tr><th>PI 276255</th><td>7</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Montevideo</td></tr><tr><th>PI 283025</th><td>8</td><td><i>P. urvillei</i></td><td>Uruguay (3),</td><td>Tacuarembo</td></tr><tr><th>PI 304040</th><td>9</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Uruguaiana</td></tr><tr><th>PI 304041</th><td>10</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 304042</th><td>11</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 304043</th><td>12</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 304044</th><td>13</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 304045</th><td>14</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Montenegro</td></tr><tr><th>PI 304047</th><td>15</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Uruguaiana</td></tr><tr><th>PI 304048</th><td>16</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 304050</th><td>17</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Uruguaiana</td></tr><tr><th>PI 310253</th><td>18</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Uruguaiana</td></tr><tr><th>PI 310255</th><td>19</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Tupancireta</td></tr><tr><th>PI 310257</th><td>20</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Pelotas</td></tr><tr><th>PI 310258</th><td>21</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Pelotas</td></tr><tr><th>PI 310263</th><td>22</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Pelotas</td></tr><tr><th>PI 310266</th><td>23</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Julio de Castilho</td></tr><tr><th>PI 364984</th><td>24</td><td><i>P. urvillei</i></td><td>South Africa (4)</td><td>Limpopo</td></tr><tr><th>PI 365131</th><td>25</td><td><i>P. urvillei</i></td><td>Swaziland (6)</td><td>Malkerns</td></tr><tr><th>PI 202296</th><td>26</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Buenos Aires</td></tr><tr><th>PI 300079</th><td>27</td><td><i>P. urvillei</i></td><td>South Africa (4)</td><td>East London</td></tr><tr><th>PI 303957</th><td>28</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Uruguaiana</td></tr><tr><th>PI 304146</th><td>29</td><td><i>P. urvillei</i></td><td>Mexico (5)</td><td>Lagos-Aquascalientes</td></tr><tr><th>PI 404506</th><td>30</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 404885</th><td>31</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Paysandu</td></tr><tr><th>PI 404886</th><td>32</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Tranqueras</td></tr><tr><th>PI 462304</th><td>33</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Cerro Largo</td></tr><tr><th>PI 462305</th><td>34</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Cerro Largo</td></tr><tr><th>PI 462306</th><td>35</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Salto</td></tr><tr><th>PI 509008</th><td>36</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Ivoti</td></tr><tr><th>PI 509013</th><td>37</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Villa Maria</td></tr><tr><th>PI 509014</th><td>38</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Olivia</td></tr><tr><th>PI 509015</th><td>39</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Arroyito</td></tr><tr><th>PI 509016</th><td>40</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Buenos Aires</td></tr><tr><th>PI 509017</th><td>41</td><td><i>P. urvillei</i></td><td>Bolivia (7)</td><td>Villa Montes</td></tr></tbody></table>
Modelled gas hydrate stability zone extents for Svalbard's fjord and near-shore settings
<p>Supplementary data to the published paper 'Modelling of the gas hydrate potential in Svalbard's fjords' (https://doi.org/10.1016/j.jngse.2021.104127). This archive includes geopackage files for each of the targeted fjords and contains (amongst others) bathymetric depth (Z, metres), top and base of the modelled GHSZ (GHSZ_T/GHSZ_B, metres depth), cumulative GHSZ cell count (#, 25 metres per), and the number of CTD data points within the radius (CTD_count and CTD2deep). Included onshore modelling data is to be ignored and considered a modelling artefact.</p>
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017 - Maximum Extent
<p>The maximum extent of supraglacial lakes and channels in West Antarctica and the Antarctic Peninsula in January 2017 was produced by a Dual-NDWI (Normalised Difference Water Index) approach with thresholds. >2000 individual scenes were captured by Sentinel-2 (S2) and Landsat-8 (L8) satellite sensors during the entire month of January 2017. To obtain maximum coverage on the cloudy Antarctic Peninsula, the time period is extended to February 10, 2017 over this region.</p> <p>This dataset consists of the maximum extent of supraglacial hydrological activity during January 2017 and detailed 10,478 supraglacial features (10,223 lakes and 255 channels), with cumulative area 119.4 square km in total on the West Antarctic ice sheet and Antarctic Peninsula. In addition to the final product, the supraglacial hydrological features from both sensors (23,389 polygons for S2 and 17,571 polygons for L8) overlapping the final map are included in supplementary datasets. The supraglacial lake and channel polygons are available as digital GIS, Geographic Information System, shapefiles (.shp) and GeoJSON files as well as Google Earth format (.kmz). The code used to produce the lake and channel dataset for each sensor (S2 and L8) is implemented using Python, and can be accessed on Zenodo (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0</a>) . Landsat-8 and Sentinel-2 imagery are freely available at (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0</a>) and (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0</a>), respectively.</p> <p>The products provide a scientific benchmark to monitor the development of these features in a warming climate, and thus enhancing our capability to predict the calving and collapse of any ice shelves in the future. The results provide a baseline for future monitoring of supraglacial hydrology and can be particularly useful to train supervised machine learning algorithms. The lake and channel dataset will be valuable as training data for pixel-based or object-based approaches to map large-scale features automatically using machine learning. This dataset can also provide an a-priori lake distribution for studies incorporating synthetic-aperture radar, SAR and other sensors and platforms.</p> <p> </p>
Climatic similarity and genomic background shape the extent of parallel adaptation in Timema stick insects
<p>Evolution can repeat itself, resulting in parallel adaptations in independent lineages occupying similar environments. Moreover, parallel evolution sometimes, but not always, uses the same genes. Two main hypotheses have been put forth to explain the probability and extent of parallel evolution. First, parallel evolution is more likely when shared ecologies result in similar patterns of natural selection in different taxa. Second, parallelism is more likely when genomes are similar, because of shared standing variation and similar mutational effects in closely related genomes. Here we combine ecological, genomic, experimental, and phenotypic data with Bayesian modeling and randomization tests to quantify the degree of parallelism and its relationship with ecology and genetics. Our results show that the extent to which genomic regions associated with climate are parallel among species of <em>Timema</em> stick insects is shaped collectively by shared ecology and genomic background. Specifically, the extent of genomic parallelism decays with divergence in climatic conditions (i.e., habitat or ecological similarity) and genomic similarity. Moreover, we find that climate-associated loci are likely subject to selection in a field experiment, overlap with genetic regions associated with cuticular hydrocarbon traits, and are not strongly shaped by introgression between species. Our findings shed light on when evolution is most expected to repeat itself.</p>
Data for: Barnegat Bay (NJ) salt marsh extent 1995 and 2015
<p>We provide salt marsh delineations for Barnegat Bay, New Jersey, by means of object-based image analysis of high-resolution aerial imagery and digital elevation models. We performed trends analyses of salt marsh extent from 1995 to 2015 and estimated drivers of marsh area change. We found that in 1995, 8,830 ± 390 ha were covered with marsh vegetation, while in 2015 only 8,180 ± 380 ha of salt marsh habitat remained.</p>
Snow phenological parameters based on Northern Hemisphere EASE Grid 2.0 Weekly Snow and Sea Ice Extent (NHSCE) from 1972-2019
<p>This dataset include three parts to obtain Figures 1 and 2 in the study of "<strong>Review of snow phenology variation in the Northern Hemisphere and its relationship with climate and vegetation</strong>". The first one is the annual spatial distribution maps of the Snow Cover Onset Date(SCOD), Snow Cover End Date(SCED) and the Snow Cover Days(SCD). The second one is the annual average SCOD, SCED and SCD. The three one is the annual statistics of the SCOD, SCED and SCD. The snow phenological parameters are calculated based on Northern Hemisphere EASE Grid 2.0 Weekly Snow and Sea Ice Extent (NHSCE).</p>
A multi-system comparison of forecast flooding extent using a scale-selective approach [data]
<p>A multi-system comparison of forecast flooding extent using a scale-selective approach, data.</p> <p>Creator: Helen Hooker[1] Publication Year: 2023</p> <p>Organisation(s): 1. Department of Meteorology, University of Reading, U.K</p> <p>Description: This dataset contains:</p> <p>- SAR-derived observed flood maps used in the study.</p> <p>- CSM validation maps for forecast flood maps from Food Foresight (FF), GloFAS Rapid Flood Mapping (RFM) and FFWC Super Model (FFWC) predicting flooding extent on 25 July 2020 for the Jamuna River, Bangladesh compared against Sentinel-1 SAR-derived flooding extent. File format is e.g. CSM_FF_forecastdate_rundate.tif. CSM maps for Flood Foresight following system improvement are labelled with ..._reas.tif.</p> <p>Helen Hooker. (2023). A multi-system comparison of forecast flooding extent using a scale-selective approach (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7509980</p> <p>Related publications:</p> <p>A multi-system comparison of forecast flooding extent using a scale-selective approach; 2023; Hydrology Research (in preparation) Helen Hooker[1], Sarah L. Dance[1,2,3], David C. Mason[4], John Bevington[5], and Kay Shelton[5]</p> <ol> <li>Department of Meteorology, University of Reading, UK.</li> <li>Department of Mathematics and Statistics, University of Reading, UK.</li> <li>NCEO, University of Reading, UK.</li> <li>Department of Geography and Environmental Science, University of Reading, UK.</li> <li>JBA Consulting, UK.</li> </ol> <p>Correspondence: Helen Hooker (<a href="mailto:h.hooker@pgr.reading.ac.uk">h.hooker@pgr.reading.ac.uk</a>)</p>
Active gas seepage in western Spitsbergen fjords, Svalbard archipelago: spatial extent and geological controls
<p>The following digital data package is provided as part of the submission of the publication Rodes et al. (2023) <em>Active gas seepage in western Spitsbergen fjords, Svalbard archipelago: spatial extent and geological controls</em>, considered for publication in the journal Frontiers in Earth Science. The dataset contains the distribution and characteristics of the gas flares observed during the HE-449 cruise in 2015 and the GASGEM cruise in 2021. Moreover, it contains the interpreted outcropping areas of the geological units in Isfjorden and Van Mijenfjorden. Each dataset is provided in GeoJSON, Geopackage and Shapefile file extensions. Finally, we provide a minimal working example to calculate the flare density per multibeam area of outcropping geological units.</p>
Global reference data set for validating ESA WorldCereal temporary cropland extent
<p>IIASA team has created a new validation data set, which is completely independent from all other existing maps or reference data sets, and which is in line with the cropland definitions and mapping period of the WorldCereal products. To decide if this is an active cropland in each period, the experts looked at very high-resolution Google historical imagery and Google Street level images, Microsoft Bing images, ESRI imagery, Planet historical data, Sentinel-2 time series, and Modis NDVI time series. The experts were asked to label 5 by 5 Sentinel pixels (each pixel 10m by 10m) either as winter crops, or as summer crops, or as maize (if this was possible), or as active crops (where it was not possible to confirm a growing season, e.g. overlap between seasons was too big or crop fields were too small in size), or as no crops, or as not sure where was too little information available for 2021. There was additional question on irrigation system, either circle, or other irrigation, or rainfed, or not sure. </p> <p>Fields:</p> <p>"rowid" -unique row identifier</p> <p>"submissionid" – unique submission id, which consists of 25 single pixels (rows)</p> <p>"timestamp" – time stamp of each submission</p> <p>"sampleid" – unique sample site, which could have a few submission ids</p> <p>"submission_itemid" – unique single pixel submission id</p> <p>"enhancement" – unique legend identifier in the Geo-Wiki database</p> <p>"question" – question asked (either on crop presence or irrigation)</p> <p>"answer" – answer to a question asked</p> <p>"sub_pixel_x","sub_pixel_y" – centroids of a single pixel in WGS84</p>
An unprecedented sea ice retreat in the Weddell Sea driving an overall decrease of the Antarctic sea-ice extent over the 20th century
<p>This archive contains the sea-level pressure and sea ice reconstructions in the Antarctic over 1700–2000 CE associated with the following study:</p> <p>Dalaiden, Q., Rezsohazy, J., Goosse, H., Thomas, E.R., Vladimirova, D. O. and Tetzner, D. (2023). An unprecedented sea ice retreat in the Weddell Sea driving an overall decrease of the Antarctic sea-ice extent over the 20th century, Geophysical Research Letters (submitted).</p> <p>Each NetCDF file corresponds to one variable. For each variable, along with the reconstruction, an estimation of the uncertainty is provided. All fields are anomalies relative to the 1961–1990 CE period. The variables included in the archive are:</p> <p>BAS-SIE: Sea-ice extent averaged over the Bellingshausen and Amundsen Sea sector<br> Ross-SIE: Sea-ice extent averaged over the Ross Sea sector<br> Weddell-SIE: Sea-ice extent averaged over the Weddell Sea sector<br> King_Hakon_VII-SIE: Sea-ice extent averaged over the King Hakon VII sector<br> Western_Pacific_Ocean-SIE: Sea-ice extent averaged over the Western Pacific Ocean sector<br> SH-SIE: Sea-ice extent averaged over the Southern Hemisphere<br> SIC: Sea-ice concentration<br> SLP: Sea-level pressure</p> <p>Please contact Quentin Dalaiden (quentin.dalaiden@uclouvain.be) for more information.</p>
A Global Lake/Reservoir Surface Extent Dataset (GLRSED)
<p>Global lake/reservoir surface water extent is the basic input data for many studies. Although there are some datasets at present, there are problems such as incomplete or spatial inconsistency exist among them due to various reasons like different data sources and dynamic change characteristics of the surface water. Here, a new Global Lake/Reservoir Surface Extent Dataset (GLRSED) that contains spatial extent and basic attributes (e.g., name, area, lake type and source) of 2.17 million lakes/reservoirs was produced based on HydroLAKES, GRanD and OpenStreetMap. By spatially overlaying GLRSED with other auxiliary data, we identified mountain lakes, endorheic lakes, reservoirs, glacier-fed lakes and permafrost-fed lakes, etc. In addition, we calculated the Surface Water and Ocean Topography (SWOT) orbits passing through each lake. The dataset could provide basic data for global lake/reservoir monitoring, as well as the study on the impact of human actions and climate changes on lake/reservoir freshwater availability, etc.</p>
The extent of rapid colour change in male agamid lizards is unrelated to overall sexual dichromatism
<p><span>Dynamic colour change is widespread in ectothermic animals, but has primarily been studied in the context of background matching. For most species, we lack quantitative data on the extent of colour change across different contexts. It is also unclear whether and how colour change varies across body regions, and how overall sexual dichromatism relates to the extent of individual colour change. In this study, we obtained reflectance measures in response to different stimuli for males and females of six species of agamid lizards (<em>Agamidae</em>, sister family to <em>Chameleonidae</em>) comprising three closely related species pairs. We computed the colour volume in a lizard-vision colour space occupied by males and females of each species and estimated overall sexual dichromatism based on the area of non-overlapping male and female colour volumes. As expected, males had larger colour volumes than females, but the extent of colour change in males differed between species and between body regions. Notably, species that were most sexually dichromatic were not necessarily those in which males showed the greatest individual colour change. Our results indicate that the extent of colour change is independent of the degree of sexual dichromatism and demonstrate that colour change on different body regions can vary substantially even between pairs of closely related species.</span></p>
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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.