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57 results for “heat map”
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>
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>
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>
Ocean Heat Content Anomalies in the North Atlantic based on mapping Argo data using local Gaussian processes defined over space
<p>Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2005-2022, in the North Atlantic, north of 20N) subtracting the time mean over the period 2005-2021 from the monthly time series of OHC. OHC fields are mapped using a locally stationary Gaussian process (defined over space) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). In this product, mapping is done in latitude and longitude with monthly subsets of data. Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA for 0-2000 dbar. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. </p>
QTL mapping: insights into genomic regions governing component traits of yield under combined heat and drought stress in wheat
<p>The mapping population comprises of 180 RILs developed from a cross between GW322 and KAUZ.</p> <p><strong>Phenotypic data</strong><br>Phenotypic evaluation was conducted across two consecutive crop seasons (2021-22 and 2022-23) under late sown irrigation (LSIR) and late sown restricted irrigation (LSRI) conditions at ICAR-IARI, New Delhi. Various physiological and agronomic traits of importance were measured. The component traits of yield including days to heading (DH), normalized difference vegetation index (NDVI), SPAD chlorophyll content (SPAD), plant height (PH), spike length (SL), thousand-grain weight (TGW), grain weight per spike (GWPS), biomass (BM) and grain yield per plot (PY) were measured under heat and combined stress conditions.</p> <p><strong>Genotypic data</strong><br>DNA was isolated from 21-day-old seedlings using the CTAB method (Murray and Thompson, 1980). DNA quality check was done using 0.8% agarose gel electrophoresis. The Axiom Breeders' array containing 35K Single Nucleotide Polymorphism (SNP) was employed for the genotyping of RILs and parents.</p>
VDF Heat Maps 2021
<p>a) and b) Phase space plots at 440 and 690 ns, respectively,</p> <p>after ablation. Each velocity bin represents a different data run. c)</p> <p>and d) PIC simulation phase space plots at equivalent times.</p>
Global heat map of probable importance of terrestrial ecosystems on meeting local demand of freshwater services
<p>This map (raster dataset, single layer) uses existing datasets to map globally “How important point x is likely to be for meeting the demand of a reliable & useable source of water on a scale of 0 to 1?” This relatively simple approach uses estimated water demand in a given basin as weight to identify pressure for flow regulation and water provisioning services. Precipitation and land cover estimates are then combined with it to give some insight into the hydrologic attributes of “location” and “timing” of flow that the ecosystems may influence. The underlying assumption here is that undisturbed ecosystems everywhere are performing the ecohydrological functions leading to freshwater services. The question is more (at the global scale): how dependent are the populations in the basin on the continued functioning of these services.</p> <p><strong>Input datasets:</strong></p> <ol> <li>Annual surface & groundwater (“blue”) water consumption estimates. URL: <a href="http://waterfootprint.org/en/resources/water-footprint-statistics/">http://waterfootprint.org/en/resources/water-footprint-statistics/</a></li> <li>HydroBasins watershed outline.</li> <li>European Space Agency (ESA) global land cover 2015.</li> <li>WorldClim annual average precipitation (Version 2.0).</li> </ol> <p><strong>Process:</strong></p> <p>Step 1: Calculate average annual water consumption estimates over HydroBasin outlines. This step spreads the demand laterally (in case of small basins) and upstream to the headwaters from (typically) downstream consumer concentration.</p> <p>Step 2: Normalize the demand globally and map the normalized values on to “natural” land cover classes from the land cover dataset [forests, grasslands, etc].</p> <p>Step 3: Normalize annual precipitation layer within basins on the scale 0-1 where 1 is the maximum annual precipitation in that basin. This is also mapped on the “natural” land cover. Precipitation is thus acting as ‘weight’ for importance within the basin. Example, upland headwaters will typically receive more rainfall and can be argued to be important for the flow regulation in the basin.</p> <p>Step 4: Combine the layers from 2 and 3.</p> <p><strong>Caveats:</strong></p> <ol> <li>Identification of what constitutes a “natural” land cover is not trivial, especially from global land cover maps. Example: Forests and plantations are hard to distinguish from these products.</li> <li>Improvement of quality of water is assumed to be implicit for functioning ecosystems.</li> </ol>
Bivariate GWA mapping reveals associations between aliphatic glucosinolates and plant responses to thrips and heat stress
<p>Supplemental data on bivariate GWA mapping of stress phenotypes and metabolomes of Arabidopsis.</p>
QTL mapping: insights into genomic regions governing component traits of yield under combined heat and drought stress in wheat
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Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany (Map-1)
<p>Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany</p> <p>Map-1 (zip.file) ref. to DOI: 10.5281/zenodo.45015</p> <p> </p>
Ng et al. Chi-squared tests & heat map dataset
<p>Ng et al. Chi-squared tests & heat map dataset</p>
Genome-wide association mapping for component traits of drought and heat tolerance in wheat
<p>The study material in GWAS panel with 282 advanced breeding line of bread wheat genotypes from IARI stress breeding program was selected to map the genomic regions responsible for Drought and heat tolerance component traits.</p> <p>Phenotypic data:</p> <p>The GWAS panel was evaluated at multiple locations namely, IARI, New Delhi - DL (28.6550° N, 77.1888° E, MSL 228.61 m), ARI, Pune - PUNE (18.5204° N, 73.8567° E, MSL 560m), IIWBR, Karnal - IIWBR (29.6857° N, 76.9905°E, MSL 243m), IARI, Jharkhand - JR (24.1929° N, 85.3756° E, MSL 580m) and IARI RS, Indore - IND (22.7196° N, 75.8577° E, MSL 553 m) with augmented RCBD design. Three tratments viz, IR (Irrigated), RI (Restricted irrigated) and LS (Late sown) were imposed for control, drought and heat stress, respectively. Data was collected on traits like Days to heading (DH), Days to maturity (DM), Normalized Difference Vegetation Index (NDVI) at anthesis and grain filling stage, chlorophyll content (SPAD) of flag leaf at post anthesis stage, Plant height (PH), Canopy temperature (CT), Grain weight per spike (GWPS), Thousand Grain weight (TGW), Plot Yield (PLTY) and Biomass.</p> <p>Genotypic data:</p> <p>Genomic DNA of the GWAS panel was extracted from the leaves of seedlings by Cetyl Trimethyl Ammonium Bromide (CTAB) method. The panel was genotyped using Axiom Wheat Breeder's Genotyping Array (Affymetrix, Santa Clara, CA, United States) having 35,143 genome-wide SNPs. The monomorphic, markers with minor allele frequency (MAF) of <5%, missing data of >20%, and heterozygote frequency >25% were removed from the analysis. The remaining set of 10546 high-quality SNPs was used in GWAS analysis.</p> <p>The detailed information of the methods and software used, data analysis and GWAS is provided at doi: 10.3389/fpls.2022.943033</p>
FIGURE 3. Heat map showing recovery efficiency for 333 in Delimitation of the new tribe Parartocarpeae (Moraceae) is supported by a 333- gene phylogeny and resolves tribal level Moraceae taxonomy
FIGURE 3. Heat map showing recovery efficiency for 333 genes. Each column is a gene, and each row is one sample. The intensity of color in each cell is determined by the length of sequence recovered divided by the length of the reference gene (maximum of 1.0).
FIGURE. Distribution and heat map of Carex brizoides L. in Poland. Black dots—records before 2000, white dots—records since 2000. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Distribution and heat map of Carex brizoides L. in Poland. Black dots—records before 2000, white dots—records since 2000.
FIGURE. Distribution and heat map of Carex praecox Schreb. in Poland. Black dots—records before 2000, white dots—records since 2000. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Distribution and heat map of Carex praecox Schreb. in Poland. Black dots—records before 2000, white dots—records since 2000.
FIGURE. Distribution and heat map of Carex arenaria L. in Poland. Black dots—records before 2000, white dots—records since 2000. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Distribution and heat map of Carex arenaria L. in Poland. Black dots—records before 2000, white dots—records since 2000.
FIGURE. Distribution and heat map of Carex curvata Knaf in Poland. Black dots—records before 2000, white dots—records since 2000. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Distribution and heat map of Carex curvata Knaf in Poland. Black dots—records before 2000, white dots—records since 2000.
FIGURE. Distribution and heat map of Carex repens Bellardi in Poland. Black dots—records before 2000, white dots—records since 2000. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Distribution and heat map of Carex repens Bellardi in Poland. Black dots—records before 2000, white dots—records since 2000.
FIGURE. Distribution and heat map of Carex pseudobrizoides Clavaud in Poland. Black dots—records before 2000, white dots— records since 2000. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Distribution and heat map of Carex pseudobrizoides Clavaud in Poland. Black dots—records before 2000, white dots— records since 2000.
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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)
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DANDI Archive for NWB datasets
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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.