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709 results for “Coverage”
Sectoral coverage and prices of carbon pricing mechanisms introduced since 1990
<p>Over the last 30 years, the number of jurisdictions that have implemented a carbon pricing mechanism has grown significantly. Today, 43 national and 32 subnational jurisdictions have such a mechanism in at least one sector. However, a standardized and centralized record of the sectoral scope and prices applied to CO<sub>2</sub> emissions by these mechanisms is lacking.</p> <p>This dataset provides an essential contribution to filling that gap. It covers mechanisms introduced since 1990 at the national and subnational levels and is the most comprehensive attempt at providing a systematic description of carbon pricing mechanisms in terms of their sectoral (and fuel) scope and the associated price signal.</p> <p>A key feature of this dataset is that it provides information structured by territorial jurisdiction, not carbon pricing mechanism. This is achieved by mapping information available for each mechanism onto jurisdictions. It should prove of interest to a wide range of parties, including academic researchers, policy analysts, and interested civil society organizations.</p>
Anisotropic coverage control
<p>This demonstration illustrates the execution of a distributed algorithm for the deployment of a team of Aerial Robotic Workers (ARWs) for coverage of a planar environment. One physical ARW and three simulated ARWs are used in the demonstration.</p> <p>The position and orientation of each ARW is represented as a line with a colored tip.</p> <p>The environment to cover is abstracted into a finite set of landmarks, and each landmark is represented as a colored tile. The color of a landmark corresponds to the ARW which is currently responsible for covering that landmark.</p> <p>The control algorithm prescribes the motion of the ARWs and the distribution of the landmarks among the ARWs. In particular, an ARW is allowed to intermittently yield responsibility of some of its landmarks to a different ARW. The motion and the landmark transfers are programmed in such a way to gradually increment the coverage of the structure. The algorithm is based on a generalization of the concept of Voronoi tessellations.</p> <p>The algorithm is implemented on a ROS architecture, where the controller for each ARW corresponds to a different ROS node. The pose of the physical ARW is measured in real time with a motion capture system.</p> <p>In the demonstration, we can see that three clusters of landmarks emerge, and that the ARWs position themselves in front of the landmarks that they are responsible to cover, at a suitable distance.</p>
Anisotropic coverage control for surveillance of 3D structures
<p>This demonstration illustrates the execution of a distributed algorithm for the deployment of a team of Aerial Robotic Workers (ARWs) for coverage of a 3D structure. One physical ARW and two simulated ARWs are used in the demonstration.</p> <p>The position and orientation of each ARW is represented as a colored circle and arrow.</p> <p>The structure to cover is abstracted into a finite set of landmarks, and each landmark is represented as a colored arrow. The color of a landmark corresponds to the ARW which is currently responsible for covering that landmark. The direction of the arrow corresponds to the outward normal to the surface at the position of the landmark.</p> <p>The control algorithm prescribes the motion of the ARWs and the distribution of the landmarks among the ARWs. In particular, an ARW is allowed to intermittently yield responsibility of some of its landmarks to a different ARW. The motion and the landmark transfers are programmed in such a way to gradually increment the coverage of the structure. The algorithm is based on a generalization of the concept of Voronoi tessellations.</p> <p>The algorithm is implemented on a ROS architecture, where the controller for each ARW corresponds to a different ROS node. The pose of the physical ARW is measured in real time with a motion capture system.</p> <p>In the demonstration, we can see that three clusters of landmarks emerge, and that the ARWs position themselves in front of the landmarks that they are responsible to cover, at a suitable distance.</p>
Genotypes of Aedes aegypti mosquitoes derived from SNP chip and low-coverage whole genome sequencing for platform cross-validation
<p>The mosquito <em>Aedes aegypti </em>is the primary vector of many human arboviruses such as dengue, yellow fever, chikungunya, and Zika, which affect millions of people world-wide. Population genetics studies on this mosquito have been important in understanding its invasion pathways and success as a vector of human disease. The Axiom aegypti1 SNP chip was developed from a sample of geographically diverse <em>Ae. aegypti </em>populations to facilitate genomic studies on this species. Here we evaluate the utility of the Axiom aegypti1 SNP chip for population genetics and compare it with a low-depth shot-gun sequencing approach using mosquitoes from the species' native (Africa) and invasive range (outside Africa). These analyses indicate that the results from the SNP chip are highly reproducible and have a higher sensitivity to capture alternative alleles than a low-coverage whole-genome sequencing approach. Although the SNP chip suffers from ascertainment bias, results from population structure, ancestry, demographic, and phylogenetic analyses using the SNP chip were congruent with those derived from low coverage whole genome sequencing, and consistent with previous reports on Africa and outside Africa populations using microsatellites. More importantly, we identified a subset of SNPs that can be reliably used to generate merged databases, opening the door to combined analyses. We conclude that the Axiom aegypti1 SNP chip is a convenient, more accurate, low-cost alternative to low-depth whole genome sequencing for population genetic studies of <em>Ae. aegypti</em> that do not rely on full allelic frequency spectra. Whole genome sequencing and SNP chip data can be easily merged, extending the usefulness of both approaches. </p>
Exploring the Interaction of Code Coverage and Non-Coverage Objectives in Search-Based Test Generation
<p>Data Package for "Exploring the Interaction of Code Coverage and Non-Coverage Objectives in Search-Based Test Generation"</p> <p>This package contains data generated as part of our experiments on EvoSuite and Defects4J.</p> <p>This paper is currently under submission.</p> <p>This package contains experimental data and the algorithms that generated them in folder "framework\test\". Specifically, in the folder "framework\test\Experiments", the data folders contain the suites generated by each technique for each project and fault ID. </p> <p>If you have questions, please contact Afonso Fontes at afonsohfontes@gmail.com.</p>
Example coverage files of TCRA/TCRB/TCRG/IGH loci
<p>WGS coverage files for the TCRA/TCRB/TCRG and IGH loci to be used as test data</p>
Surface dust coverages on rock targets in Gale crater: Influence of seasonal wind variability, elevation and proximity to aeolian sand fields.
<p>The following dataset accompanies the paper submission to AGU - JGR: Planets for the paper titled: "</p> <p><span>Surface dust coverages on rock targets in Gale crater: Influence of seasonal wind variability, elevation and proximity to aeolian sand fields."</span></p>
Data coverage, biases, and trends in a global citizen-science resource for monitoring avian diversity
<p><strong>Aim:</strong> Understanding and addressing the global biodiversity crisis requires ecological information compiled continuously from across the globe. Data from citizen science initiatives are useful for quantifying species' ecological niches and geographical distributions but can be difficult to apply towards biodiversity monitoring. The presence of fixed geographical locations reduces the opportunistic nature of citizen science data, allowing for more reliable and nuanced trend estimation. The eBird citizen-science programs contains predefined locations whose bird assemblages are sampled across years ('hotspots'). For hotspots to function as a biodiversity monitoring resource, issues related to data coverage, biases, and trends need to be addressed.</p> <p><strong>Location:</strong> Global.</p> <p><strong>Methods:</strong> We estimated the survey completeness of species richness at 300,500 eBird hotspots during the years 2002 to 2022. We documented sampling biases at eBird hotspot and non-hotspot locations during 2022 based on protection status, temperature, precipitation, and landcover.</p> <p><strong>Results:</strong> A total of 10,410 bird species (<em>ca</em>. 96.9% of total) were recorded at hotspots. The number hotspots and the quantity of data and unique participants and quality of species richness estimates has increased worldwide with the Nearctic containing the strongest and most consistent trends. Compared to non-hotspots, hotspots over sampled areas with higher protection status. Hotspots and non-hotspots over sampled warmer and wetter locations in the Antarctic, Nearctic, and Palearctic, and cooler locations in the Afrotropics, Australasia, and the Neotropics. Hotspots and especially non-hotspots over sampled urban areas. Hotspots and non-hotspots under sampled shrublands in Australasia. Hotspots and especially non-hotspots under sampled forests in the Afrotropics, Indomalaya, Neotropics, and Oceania.</p> <p><strong>Main conclusions:</strong> Hotspots have captured a large component of the world's avian diversity but have done so inconsistently across space and time. Data quantity and quality are increasing in many regions, but the presence of sampling biases and spatial uncertainty needs to be addressed when applying the data.</p>
A high coverage Mesolithic aurochs genome and effective leveraging of ancient cattle genomes using whole genome imputation. -- VCF file
<p>This is the open-access VCF file that was created in the article: "<strong>A high coverage Mesolithic aurochs genome and effective leveraging of ancient cattle genomes using whole genome imputation."</strong></p> <p><strong>Information about the filtering steps can be found in the method section.</strong></p> <p>Extra information on the sample IDs can be found in the Supplementary tables.</p>
tev003 r4 Downsampling 2-5X Coverage
This dataset contains 2-5X Coverage genomic sequences downsampled from the tev003 genome.
tev003 r3 Downsampling 2-5X Coverage
This dataset contains 2-5X Coverage genomic sequences downsampled from the tev003 genome.
tev003 r5 Downsampling 2-5X Coverage
This dataset contains 2-5X Coverage genomic sequences downsampled from the tev003 genome.
tev003 r1 Downsampling 2-5X Coverage
This dataset contains 2-5X Coverage genomic sequences downsampled from the tev003 genome.
tev003 r2 Downsampling 2-5X Coverage
This dataset contains 2-5X Coverage genomic sequences downsampled from the tev003 genome.
Data from: Birds influence vegetation coverage and structure on sandy biogeomorphic islands in the Dutch Wadden Sea.
<p><span>Small uninhabited islands form important roosting and breeding habitats for many coastal birds. Here, we assess the role of external nutrient input by coastal birds on the vegetation structure and coverage on sandy biogeomorphic islands in the Dutch Wadden Sea, where island-forming processes depend on vegetation-sedimentation feedbacks. We Used a combination of bird observations and plant stable isotope (<em>δ</em><sup>15</sup>N) analyses, to demonstrate that (i) breeding birds transport large quantities of nutrients via their faecal outputs to these islands annually and that (ii) this external nitrogen source influences vegetation development on these sandy, nutrient-limited, islands. We further discuss how this avian nutrient pump could impact island development and habitat suitability for coastal birds and discuss future directions for research. For the conservation of both threatened coastal birds and sandy back-barrier islands and the design of appropriate management strategies, we argue that three-way interactions between birds, vegetation and sandy island morphodynamics need to be further elucidated. </span></p> <p> </p> <p><span>Methods</span></p> <p><span>Data stored in this repository are bird counts from all three islands [Breeding_total.csv] and the relation between total faecal excretion rates (estimated using bird counts) and average vegetation coverage (estimated using satellite imagery) [Vegetation_bird.csv]. Additionally, we gathered data in the field including vegetation charactersitics [field.data.xls], isotopic data of the vegetation [new_isotopes.csv] and soil charactersitics [Soil_prop.csv]. Detailed description of these methods and the results can found in the accompanying publication (Reijers <em>et al. STOTEN</em>).</span></p>
Molecular surface coverage standards by reference-free GIXRF supporting SERS and SEIRA substrate benchmarking - Dataset
<p>This is the dataset of "Molecular surface coverage standards by reference-free GIXRF supporting SERS and SEIRA substrate benchmarking". </p> <p><a href="https://doi.org/10.1515/nanoph-2024-0222" target="_blank" rel="noopener">https://doi.org/10.1515/nanoph-2024-0222</a></p> <p>Part of this work was supported by the European project OpMetBat, code 21GRD01. The project has received funding from the European Partnership on Metrology, cofinanced from the the European Union's Horizon Europe Research and Innovation Programme, and by Participating States.</p>
Data to "Coverage- and temperature-induced self-metalation of tetraphenyltransdibenzoporphyrin on Cu(111) "
<p>Raw data, meta data, data evaluation and final figures to the corresponding publication.</p>
Unique Property Reference Number (UPRN) indicator: Tree canopy coverage (UPRN_3_1)
<p>This is an indicator with the tree canopy coverage for various distances (15m, 25m, 150m, 300m and 500m) from Unique Property Refence Numbers (UPRNs) in the Office for National Statistics (ONS) UPRN directory v2024.07 (Epoch 111). This metric is based on <em>Bluesky’s National Tree Map™,</em> which is the only tree dataset to include trees in Great Britain and the Republic of Ireland that are 3 metres and taller.</p> <h3>Available files</h3> <ul> <li><strong>UPRN_3_1_tree_canopy_coverage_cm_with_coords.csv</strong>: covers the Cheshire and Merseyside region (England) and includes latitude and longitude for each UPRN.</li> <li><strong>UPRN_3_1_tree_canopy_coverage_cm.csv</strong>: covers the Cheshire and Merseyside region (England).</li> <li><strong>UPRN_3_1_tree_canopy_coverage_lothian_with_coords.csv</strong>: covers the Lothian region (Scotland) and includes latitude and longitude for each UPRN.</li> <li><strong>UPRN_3_1_tree_canopy_coverage_lothian.csv</strong>: covers the Cheshire and Merseyside region (England).</li> </ul> <p> </p> <h3>Fields</h3> <table> <tbody> <tr> <td> <p><strong>Column name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>UPRN</p> </td> <td> <p>Unique Property Reference Number as per the Office for National Statistics UPRN Directory (ONSUD) v2024.07 (Epoch 111).</p> </td> </tr> <tr> <td> <p>n_trees_15m</p> </td> <td> <p>Number of trees (above 3m) within 15m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_15m</p> </td> <td> <p>Percentage of area covered by tree canopy within 15m from the UPRN. From a maximum area of approximately 707m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_25m</p> </td> <td> <p>Number of trees (above 3m) within 25m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_25m</p> </td> <td> <p>Percentage of area covered by tree canopy within 25m from the UPRN. From a maximum area of approximately 1963m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_150m</p> </td> <td> <p>Number of trees (above 3m) within 150m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_150m</p> </td> <td> <p>Percentage of area covered by tree canopy within 150m from the UPRN. From a maximum area of approximately 70686m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_300m</p> </td> <td> <p>Number of trees (above 3m) within 300m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_300m</p> </td> <td> <p>Percentage of area covered by tree canopy within 300m from the UPRN. From a maximum area of approximately 282743m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_500m</p> </td> <td> <p>Number of trees (above 3m) within 500m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_500m</p> </td> <td> <p>Percentage of area covered by tree canopy within 500m from the UPRN. From a maximum area of approximately 785398m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>latitude (only version with coordinates)</p> </td> <td> <p>Latitude of the UPRN, given in decimal degrees, where N is positive and S is negative.</p> </td> </tr> <tr> <td> <p>longitude (only version with coordinates)</p> </td> <td> <p>Longitude of the UPRN, given in decimal degrees, where E is positive and W is negative.</p> </td> </tr> </tbody> </table> <p> </p> <h3><strong>Regions</strong></h3> <p><strong>Cheshire and Merseyside (Endgland)</strong></p> <p>Includes the following local authorities:</p> <ul> <li>Cheshire East</li> <li>Cheshire West and Chester</li> <li>Halton</li> <li>Knowsley</li> <li>Liverpool</li> <li>Sefton</li> <li>St. Helens</li> <li>Warrington</li> <li>Wirral</li> </ul> <p> </p> <p><strong>Lothian (Scotland)</strong></p> <p>Includes the following local authorities:</p> <ul> <li>East Lothian</li> <li>City of Edinburgh</li> <li>Midlothian</li> <li>Westlothian</li> </ul> <p> </p>
Coverage data in males and females, and genetic markers used for genetic mapping of the guppy LG12 (sex chromosome pair)
<p>The study used genetic mapping and coverage data in genome sequences of multiple male and female individuals of <i>M. picta</i> from multiple natural populations to investigate genetic degeneration of the Y chromosome, and quantify gene loss from the Y. The files include coverage results from the sex chromosome that were (i) used for sexing the sequenced individuals, and (ii) combined with autosomal results to analyze M/F, M/A and F/A depth of coverage ratios. Genetic mapping was also used to validate sex linkage, and the data set includes files with genotypes of genetic markers.</p>
Soil Survey of Scotland Staff (1970-1987). Soil maps of Scotland (partial coverage) at a scale of 1:25 000. Digital phase 8 release.
<p>This is the digital dataset which was created by digitising the Soils of Scotland 1:25,000 Soil maps and the Soils of Scotland 1:25,000 Dyeline Masters. The Soils of Scotland 1:25,000 Soil maps were the source documents for the production of the Soils of Scotland 1:63,360 and 1:50,000 published map series. Where no 1:25,000 published maps exist 1:63,360 maps have been digitised for this data set, the field SOURCE_MAP describes the source of the data. The classification is based on Soil Associations, Soil Series and Phases which reflect parent material, major soil group, and soil sub-groups, drainage and (for phases), texture, stoniness, land use, rockiness, topography and organic matter. Phases are not always mapped. In general terms this dataset primarily covers the cultivated land of Scotland but also includes some upland areas. This data set is undergoing a phased revision, the latest (phase 8) was released in August 2021. The digitising of the recently added data was funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at <a href="https://www.hutton.ac.uk/soil-maps/">https://www.hutton.ac.uk/soil-maps/ </a>or viewed at <a href="https://map.environment.gov.scot/Soil_maps/?layer=2">Scotland's Soils - soil maps (environment.gov.scot). </a> This map should be cited as: 'Soil Survey of Scotland Staff (1970-1987). Soil maps of Scotland (partial coverage) at a scale of 1:25 000. Digital phase 8 release. James Hutton Institute, Aberdeen. DOI 10.5281/zenodo.5159133.</p>
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.