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13,117 results for “Maps”
Joint quantile disease mapping with application to Malaria and G6PD deficiency
Statistical analysis based on quantile regression methods is more comprehensive, flexible, and less sensitive to outliers when compared to mean regression methods. When the link between different diseases are of interest, joint disease mapping is useful for inferring correlation between them. Most studies study this link through multiple correlated mean regressions. In this paper we propose a joint quantile regression framework for multiple diseases where different quantile levels can be considered. We are motivated by the theorized link between the presence of Malaria and the gene deficiency G6PD, where medical scientist have anecdotally discovered a possible link between high levels of G6PD and lower than expected levels of Malaria initially pointing towards the occurrence of G6PD inhibiting the occurrence of Malaria. This link cannot be investigated with mean regressions and thus the need for flexible joint quantile regression in a disease mapping framework arise. Our joint quantile disease mapping model can be used for linear and non-linear effects of covariates by stochastic splines, since we define it as a latent Gaussian model. We perform Bayesian inference of this model using the INLA framework embedded in the R software package INLA, resulting in a very efficient model even for large datasets. Finally, we illustrate the applicability of the model by analyzing the malaria and G6PD deficiency incidences, jointly, in 21 countries.
Analysis of Anomaly Detection for Artificial Intelligence of Things: A Systematic Literature Mapping
<p>This data set contains the characteristics extracted from each of the works selected from the literary review carried out following the PRISMA methodology.</p>
Geostratigraphic map of Rachmaninoff crater and Nathair Facula in the Hokusai Quadrangle (H05) of Mercury
<p>Geostratigraphic map of Rachmaninoff crater and Nathair Facula in the Hokusai Quadrangle (H05) of Mercury. 5 crater classes.</p>
Mapping the Structure and Metasomatic Enrichment of the Lithospheric Mantle Beneath the Kimberley Craton, Western Australia
<p>SUPPLEMENT FILE 1: LA-ICP-MS and EPMA analyses for pyrope garnet and Cr diopside used in study. </p>
Swift/BAT-GUANO upper limits fits map for S230822bm
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Swift/BAT-GUANO upper limits map for S230822bm
<p>Swift/BAT-GUANO upper limits map for S230822bm, the solid and dashed lines indicate the 90% and 50% GW contour levels, respectively.</p>
GTF30: global 30 m tidal flat annual maps from 2000 to 2022
<p><strong>Basic information:</strong></p> <p>Tidal flats are an important part of coastal ecosystems and play an important role in shoreline protection and biodiversity maintenance. <strong>The GTF30 are time-series global 30 m tidal flat annual datasets from 2000 to 2022</strong>, covering the <strong>whole global coasts</strong> especially in the high latitides (>60° N), and generated by<strong> an automatic mapping method</strong> from time-series Landsat observations. The details about the automated method can be seen in the work of <strong>Zhang et al. 2023a</strong>. The quantitative accuracy assessment shows that the GTF30 dataset achieves <strong>an overall accuracy of 90.34% and a kappa coefficient of 0.807</strong>.</p> <p><strong>Notes:</strong></p> <p>The GTF30 annual maps are divided into 588 5°×5° geographical tiles, and each tile contains 23 bands which denotes the tidal flat maps in 2000, 2001, 2002,..., 2021, 2022.</p> <p><strong>Usage Policy:</strong></p> <p>If you plan to use our data in <strong>a scientific analysis paper</strong>, we strongly recommend contacting us in advance to seek opinions, and consider our contributions in the acknowledgments or as co-authors.</p> <p><strong>Citations:</strong></p> <p>Zhang, X., Liu, L., Wang, J., Zhao, T., Liu, W., and Chen, X.: Automated mapping of global 30 m tidal flats using time-series Landsat imagery: algorithm and products, <strong>Journal of Remote Sensing</strong>, 2023a.</p> <p>Zhang, X., Liu, L., Zhao, T., Chen, X., Lin, S., Wang, J., Mi, J., and Liu, W.: GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020, Earth Syst. Sci. Data, 15, 265–293, <a href="https://doi.org/10.5194/essd-15-265-2023">https://doi.org/10.5194/essd-15-265-2023</a>, 2023b.</p>
FIGURE 4. Eocyzicus saharicus. A, head. B, shell pattern. C–D, telson. E, left clasper. F in Mapping the Geographic Distribution of Large Branchiopods in Algeria and a checklist update
FIGURE 4. Eocyzicus saharicus. A, head. B, shell pattern. C–D, telson. E, left clasper. F, dorsal portion of the abdomen.
FIGURE 2 in Mapping the Geographic Distribution of Large Branchiopods in Algeria and a checklist update
FIGURE 2. Distribution map of Algerian large branchiopods recorded in this study and in previous studies.
FIGURE 1 in Mapping the Geographic Distribution of Large Branchiopods in Algeria and a checklist update
FIGURE 1. Map of the study area. A, sampled sites in the Hauts plateau. B, sampled sites in the Northern Sahara. C, sampled sites in the Tassili N'Ajje.
Swift/BAT-GUANO upper limits fits map for S230825k
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Swift/BAT-GUANO upper limits map for S230825k
<p>Swift/BAT-GUANO upper limits map for S230825k. The solid and dashed lines indicate the 90% and 50% GW contour levels, respectively.</p>
Dataset for "Geospatial segmentation of high-resolution photovoltaic production maps for Switzerland", Frontiers Energy
<p>## Notes<br> 1. The following tif files contain the Plain-Of-Array irradiation on south-facing solar panels with different tilts {20,30,40,50,60,70} and during different seasons {summer,winter}.<br> 2. The file pattern is poa\_{season}\_tilt\_{tilt}.tif<br> 3. Read Section 2.2 of the research article for details on how these files were created. <br> 4. These values are POA and need to be converted to energy production via an efficiency factor. This factor was chosen to be a fixed value of 20\% as noted in Section 2.2 of the research article.</p>
Fig. 1. Map showing the collection sites for 5 in Efficiency of RAPD, ISSR, iPBS, SCoT and phytochemical markers in the genetic relationship study of five native and economical important bamboos of North-East India
Fig. 1. Map showing the collection sites for 5 different native bamboos of North-East India. Number indicates the collection sites for the study, 1-Manipur hill; 2- Manipur Valley; 3-Mizoram; 4-Assam and 5-Sikkim. (Mapsource: https://eros.usgs.gov/).
Suspended sediment concentration mapping of a multi-channel system Songzi River of the Yangtze River Basin
<p>We used Google Earth Engine and observed hydrological data to create a multiple linear regression model to map SSC in the multi-channel system Songzi River of the Yangtze. <br> <br> Hydrological data from gauging stations and Landsat surface reflectance data were collected in the study area. Water discharge and suspended sediment data were obtained from four hydrological stations, including Zhicheng, Xinjiangkou, Shadaoguan and Guanwan. Such water and sediment data were obtained from the Changjiang Water Resources Commission (CWRC). Before these datasets are released to the public, they undergo rigorous verification following the relevant government protocols. <br> <br> The Landsat program with nine satellites launched from 1972 to 2021 operated by National Aeronautics and Space Administration (NASA) and United States Geological Survey (USGS) is aimed at to detect changes in the Earth's resources and environments (https://earthexplorer.usgs.gov/). It provides the longest, continuous, moderate resolution earth observation data, which is currently free, globally available. According to the data availability the GEE platform, our study used the surface reflectance data ("LANDSAT/LT05/C01/T1_SR" and "LANDSAT/LC08/C01/ T1_SR") of Landsat 5 and Landsat 8 to investigate the spectral characteristics of SSC for the Songzi River.</p>
Mapping environmental injustices within the U.S. prison system: a nationwide dataset
<p>This open-access geospatial dataset (downloadable in csv or shapefile format) contains a total of 11 environmental indicators calculated for 1865 U.S. prisons. This consists of all active state- and federally-operated prisons according to the Homeland Infrastructure Foundation-Level Data (HIFLD), last updated June 2022. This dataset includes both raw values and percentiles for each indicator. Percentiles denote a way to rank prisons among each other, where the number represents the percentage of prisons that are equal to or have a lower ranking than that prison. Higher percentile values indicate higher vulnerability to that specific environmental burden compared to all the other prisons. Full descriptions of how each indicator was calculated and the datasets used can be found here: <a href="http://https://github.com/GeospatialCentroid/NASA-prison-EJ/blob/main/doc/indicator_metadata.md.">https://github.com/GeospatialCentroid/NASA-prison-EJ/blob/main/doc/indicator_metadata.md.</a></p> <p>From these raw indicator values and percentiles, we also developed three individual component scores to summarize similar indicators, and to then create a single vulnerability index (methods based on other EJ screening tools such as Colorado Enviroscreen, CalEnviroScreen and EPA’s EJ Screen). The three component scores include climate vulnerability, environmental exposures and environmental effects. Climate vulnerability factors reflect climate change risks that have been associated with health impacts and includes flood risk, wildfire risk, heat exposure and canopy cover indicators. Environmental exposures reflect variables of different types of pollution people may come into contact with (but not a real-time exposure to pollution) and includes ozone, particulate matter (PM 2.5), traffic proximity and pesticide use. Environmental effects indicators are based on the proximity of toxic chemical facilities and includes proximity to risk management plan (RMP) facilities, National Priority List (NPL)/Superfund facilities, and hazardous waste facilities. Component scores were calculated by taking the geometric mean of the indicator percentiles. Using the geometric mean was most appropriate for our dataset since many values may be related (e.g., canopy cover and temperature are known to be correlated).</p> <p>To calculate a final, standardized vulnerability score to compare overall environmental burdens at prisons across the U.S., we took the average of each component score and then converted those values to a percentile rank. While this index only compares environmental burdens among prisons and is not comparable to non-prison sites/communities, it will be able to heighten awareness of prisons most vulnerable to negative environmental impacts at county, state and national scales. As an open-access dataset it also provides new opportunities for other researchers, journalists, activists, government officials and others to further analyze the data for their needs and make comparisons between prisons and other communities. This is made even easier as we produced the methodology for this project as an open-source code base so that others can apply the code to calculate individual indicators for any spatial boundaries of interest. The codebase can be found on GitHub (<a href="https://github.com/GeospatialCentroid/NASA-prison-EJ">https://github.com/GeospatialCentroid/NASA-prison-EJ</a>) and is also published via Zenodo (<a href="https://zenodo.org/record/8306856">https://zenodo.org/record/8306856</a>).</p> <p> </p>
FIGURE 36. Distribution maps for A in A taxonomic revision of the genus Pauridia (Hypoxidaceae) in southern Africa
FIGURE 36. Distribution maps for A, Pauridia monticola subsp. monticola (dots) from Northern and Western Cape; P. monticola subsp. nubigena (triangle) from Northern Cape; B, P. affinis from Western Cape, South Africa.
FIGURE 33. Distribution maps for A in A taxonomic revision of the genus Pauridia (Hypoxidaceae) in southern Africa
FIGURE 33. Distribution maps for A, Pauridia ovata from Western Cape; B, P. gracilipes subsp. gracilipes (dots) from Northern and Western Cape; P. gracilipes subsp. speciosa (triangles) from Northern Cape, South Africa.
FIGURE 39. Distribution maps for A in A taxonomic revision of the genus Pauridia (Hypoxidaceae) in southern Africa
FIGURE 39. Distribution maps for A, Pauridia monophylla from Western Cape; B, P. curculigoides from Western Cape, South Africa.
FIGURE 31. Distribution maps for A in A taxonomic revision of the genus Pauridia (Hypoxidaceae) in southern Africa
FIGURE 31. Distribution maps for A, Pauridia serrata subsp. albiflora from Northern Cape; B, P. linearis from Western Cape, South Africa.
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.