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709 results for “Coverage”
Attitudes towards influenza vaccination coverage among emergency department personnel
<p>This is a dataset containing answers from Bavarian emergency department personnel to questions related to attitudes towards (influenza) vaccination. The two tables contain questionnaire data from two different time periods (A: 2016/17 and B: 2020/21). Variables and variable names are labelled and should be self explanatory but are in German.</p> <p>The tables contain the original data but variables possibly allowing re-identification of individuals (site, age, number) have been omitted for anonymization reasons.</p> <p>Questions and queries are very welcome!</p> <p>(the questionnaire including the original layout (in German) is available from the authors upon request)</p> <p> </p>
JMLKelinci+: Detecting Semantic Bugs and Covering Branches with Valid Inputs using Coverage-Guided Fuzzing and Runtime Assertion Checking
<p>Testing to detect semantic bugs is essential, especially for critical systems. Coverage-guided fuzzing and runtime assertion checking (RAC) are two well-known approaches for detecting semantic bugs. Coverage-guided fuzzing aims to generate inputs tests with high code coverage. However, while coverage-guided fuzzers are equipped with sanitizers that can detect a fixed set of semantic bugs, they can otherwise only detect bugs that lead to a crash. Thus, the first problem we address is how to help fuzzers detect previously unknown semantic bugs that do not lead to a crash. Moreover, a coverage-guided fuzzer may not necessarily cover all branches with valid inputs, although invalid inputs are useless for detecting semantic bugs. So, the second problem is how to guide a fuzzer to cover all branches in a program using only valid inputs. On the other hand, RAC monitors the expected behavior of a program dynamically and can only detect a semantic bug when a valid input test shows that the program does not satisfy its specification. <br> Thus, the third problem is how to provide high-quality input tests for a RAC that can trigger potential bugs.<br> The combination of a coverage-guided fuzzer and RAC solves these problems and can cover branches with valid inputs and detect semantic bugs effectively. Our study uses RAC to guarantee that only valid inputs reach the program under test using the program's specified preconditions and it also uses RAC to detect semantic bugs using specified postconditions. A prototype tool was developed for this study, named JMLKelinci+. Our results show that combining a coverage-guided fuzzer with RAC will lead to executing the program under test only with valid inputs and that this technique can effectively detect semantic bugs. <br> Also, this idea improves the feedback given to a coverage-guided fuzzer, enabling it to cover all branches faster in programs with non-trivial preconditions.</p>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 500m SIN Grid in 2017
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 500m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2018 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2017. <em>Please <a href="https://zenodo.org/record/7830319#.ZDtHqXZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2016</strong></em>, and <em><a href="https://zenodo.org/record/7496083#.Y7GWb9VBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2018</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2017</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Supplementary material 1 from: Neagu AC, Manolache S, Rozylowicz L (2022) The drums of war are beating louder: Media coverage of brown bears in Romania. Nature Conservation 50: 65-84. https://doi.org/10.3897/natureconservation.50.86019
Appendix S1. Coding categories (adapted from Hughes et al. 2020) and descriptive statistics of analyzed media articles
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2001 (001–177)
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2001 (001–177). <em>Please <a href="https://zenodo.org/record/7563466#.Y8_P3nZByUk"><strong><em>click</em> here</strong></a> to download the MUSES FVC product <strong>in 2000 (185–361)</strong></em>, and <em><a href="https://zenodo.org/record/7559544#.Y8-QO8lBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2001 (185–361</strong></em><em><strong>)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2001 (001–177)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et al. </em>(2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Supplementary data for paper titled "Examining the Effects of Environmental Knowledge and Health Insurance Coverage on Health Status"
<p>Codebook, raw data, Stata dataset, Stata code, Stata results</p>
Evaluation Data For "Feature-Sensitive Coverage for Conformance Testing of Programming Language Implementations"
<p>It contains the evaluation data for PLDI 2023 paper: "Feature-Sensitive Coverage for Conformance Testing of Programming Language Implementations"</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 0.05º Geographic Grid Since 1981
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 0.05º spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on Geographic grid and spans from 1981 to 2018 (continuously updated). It was generated from the MUSES LAI product at 0.05º resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N;</li> <li>Temporal Coverage: 1981 – 2018;</li> <li>Spatial Resolution: 0.05º (approximately 5 km);</li> <li>Temporal Resolution: 1 month;</li> <li>Projection: Geographic;</li> <li>Data Format: HDF;</li> <li>Scale: 0.004;</li> <li>Valid Range: 0 – 250.</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Newspaper Coverage and Framing of Bats, and Their Impact on Readership Engagement
<p>Dataset underpinning the following study: </p> <p>Abstract: The media is a valuable pathway for transforming people’s attitudes towards conservation issues. Understanding how bats are framed in the media is hence essential for bat conservation, particularly considering the recent fearmongering and misinformation about the risks posed by bats. We reviewed bat-related articles published online no later than 2019 (before the recent COVID19 pandemic), in 15 newspapers from the five most populated countries in Western Europe. We examined the extent to which bats were presented as a threat to human health and the assumed general attitudes toward bats that such articles supported. We quantified press coverage on bat conservation values and evaluated whether the country and political stance had any information bias. Finally, we assessed their terminology, and, for the first time, modelled the active response from the readership based on the number of online comments. Out of 1096 articles sampled, 17% focused on bats and diseases, 53% on a range of ecological and conservation topics, and 30% only mention bats anecdotally. While most of the ecological articles did not present bats as a threat (97%), most articles focusing on diseases did so (80%). Ecosystem services were mentioned on very few occasions in both types (<30%), and references to the economic benefits they provide were meagre (<4%). Disease-related concepts were recurrent, and those articles that framed bats as a threat were the ones that garnered the highest number of comments. Therefore, we encourage the media to play a more proactive role in reinforcing positive conservation messaging by presenting the myriad ways in which bats contribute to safeguarding human well-being and ecosystem functioning.</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2015
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2015. <em>Please <a href="https://zenodo.org/record/7928103#.ZF3z6XZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2014</strong></em>, <em>and <a href="https://zenodo.org/record/7927598#.ZF3QURFBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2016</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2015</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Replication Package for SPLC23 Paper Continuous T-Wise Coverage
<p><strong>This package contains the tooling used to evaluate continuous t-wise coverage for the paper submission 9249</strong>.</p> <p>How to use this replication package:</p> <ol> <li>Unzip FeatJar.zip</li> <li>Unzip paper-result.zip</li> <li>Read the readme file for detailed instructions.</li> </ol>
Full-coverage, 1-km atmospheric carbon dioxide (CO2) dataset across China
<p>We employed an enhanced regression-based machine learning model to reconstruct full-coverage daily atmospheric CO2 concentrations in China from 2015 to 2020 at a 0.01° spatial resolution. Utilizing spatiotemporal high-resolution column-averaged dry-air mole fraction of CO2 (XCO2) data from the Orbiting Carbon Observatory 2 (OCO-2) as the dependent variable and multi-source environmental factors as independent variables, we achieved overall, spatial, and temporal cross-validation R2 [RMSE] results of 0.98 [0.74 ppm], 0.95 [1.15 ppm], and 0.93 [1.44 ppm], respectively. </p> <p> </p> <p>The annual mean and monthly mean data are archieved in Geotiff format. If you want to use this dataset, please cite the following publication. If you want to more data (e.g., daily XCO2 estimates), please contact us via qqhe@whut.edu.cn.</p> <p>--He, Q., Ye, T., Chen, X., Dong, H., Wang, W., Liang, Y., & Li, Y. (2023). Full-coverage mapping high-resolution atmospheric CO2 concentrations in China from 2015 to 2020: Spatiotemporal variations and coupled trends with particulate pollution. <em>Journal of Cleaner Production</em>, 139290. [<a href="https://doi.org/10.1016/j.jclepro.2023.139290">url</a>]</p> <p> </p> <p>If you want daily data, please go to <a href="13623590">10.5281/zenodo.13623590</a>. If you have any questions or suggestions, please contact us via qqhe@whut.edu.cn.</p> <p> </p> <p>If you want more atmospheric-related datasets, e.g., full-coverage, 1-km AOD and PM2.5 datasets over China, please go to <a href="https://doi.org/10.5281/zenodo.7229348">10.5281/zenodo.7229348.</a></p>
DETErmination of the Duration of the Dual Antiplatelet Therapy by the Degree of the Coverage of The Struts on Optical Coherence Tomography From the Randomized Comparison Between Everolimus-eluting Ste
ClinicalTrials.gov study NCT01752894. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Expanding Coverage of Continuous Subcutaneous Insulin Infusion in Pediatric Patients With Diabetes
ClinicalTrials.gov study NCT00829062. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Vaccination Coverage Amongst Children/Young People Attending the PED
ClinicalTrials.gov study NCT04485624. IPD Sharing: YES. Countries: 1. Publications: 1.
Increasing the Coverage of Severe Acute Malnutrition (SAM) Treatment in Ethiopia
ClinicalTrials.gov study NCT06380504. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Painful Real-life Experience of the Child of Less Than Three Years During the Removal of the Collecting Bags in the Pediatric Urgency: What Strategy of Coverage?
ClinicalTrials.gov study NCT01659190. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evaluation of Root Coverage by Connective Graft and Different Root Conditioning Methods
ClinicalTrials.gov study NCT03095378. IPD Sharing: NO. Countries: 1. Publications: 2.
Effectiveness of Full-thickness Palatal Graft Technique (FTPGT) in Obtaining Complete Root Coverage
ClinicalTrials.gov study NCT04028037. IPD Sharing: NO. Countries: 1. Publications: 1.
Effect of Digital Payment to Campaign Health Workers on Vaccination Coverage
ClinicalTrials.gov study NCT05684081. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
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.