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709 results for “Coverage”
International media coverage of the Bolivian jaguar trade
<p>The trade in jaguar body parts is viewed as an alarming threat to the jaguar, and there is the assumption that Chinese demand is driving the trade. However, there has been little analysis of the discourses around the trade both with respect to the Chinese public, internationally and in source countries. We analysed 298 media articles in Chinese, English and Spanish languages from 2010 to 2019, to understand the disparities in reporting of this jaguar trade and what impact they would have for jaguar conservation. Temporal analysis showed that this trade did not receive global media attention until 2018, despite having taken place for a couple of years. Only four (teeth, skin, head and claws) out of the 15 body parts reported to have been traded had evidence of trade based on seizures. Jaguars were viewed positively, while the experiences of local people who suffer losses from coexisting with jaguars were rarely captured. The claim that the trade was Chinese-driven was strongly and positively associated with whether seizure and arrest evidence was presented in an article, though articles held different views on if Chinese companies were involved. English and Spanish language articles stated that traditional Chinese medicine was the main use of the jaguar body parts and were more likely to mention that this trade was Chinese-driven; while journalistic investigations and narratives from within China were missing. This research highlights the evidence gap with respect to media coverage of the jaguar trade, and stresses the need to collaborate with stakeholders whose voices are less prominent in the articles analysed, such as Chinese communities and local people who live around jaguars.</p>
Low coverage Cnidaria sequencing GENEWIZ_2021_08_20
<p>These samples were submitted for sequencing but there wasn't adequate DNA available for full coverage:</p> <p>Samples are named by taxid as follows: (two files each, forward and reverse).</p> <p>168719 - Carybdea xaymacana</p> <p>2835545 - Manania auricula</p> <p>645345 - Atolla wyvillei</p> <p>880221 - Periphylla periphylla</p>
Full-coverage 1 km daily ambient PM2.5 and O3 concentrations of China in 2005-2017 based on multi-variable random forest model
<p>The aim of our study was to construct random forest models with high-performance, and estimate daily average PM<sub>2.5</sub> concentration and O<sub>3</sub> daily maximum 8h average concentration (O<sub>3</sub>-8hmax) of China in 2005-2017 at a spatial resolution of 1km×1km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on ten-fold cross validation was established, and spatial and temporal validations were performed to evaluate the model performance. According to our sample-based division method, the daily, monthly and yearly simulations of PM<sub>2.5</sub> gave average model fitting R<sup>2</sup> values of 0.85, 0.88 and 0.90, respectively; these R<sup>2</sup> values were 0.77, 0.77, and 0.69 for O<sub>3</sub>-8hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM<sub>2.5</sub> and O<sub>3</sub>-8hmax simulations. During 2005-2017, PM<sub>2.5</sub> exhibited an overall downward trend, while ambient O<sub>3</sub> experienced an upward trend. Whilst the spatial patterns of PM<sub>2.5</sub> and O<sub>3</sub>-8hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristic.</p> <p>Each dataset is the annual mean concentration of PM<sub>2.5</sub> or O<sub>3</sub>-8hmax based on the standard grid (Grid.csv) for that year. The coordinate system of the grid is WGS-84.</p>
On the influence of Test Smells on Test Coverage
<p>The tests are part of the software quality assurance process, however, they do not have a quality check layer above, ie, there are no tests for the tests, which makes it difficult to guarantee the quality of the tests and for consequence of the software. However, the coverage metric can be applied to the test to measure its effectiveness. Even with high coverage, tests may have poor design choices, called \ textit {test smells}.<br> The aim of this research is to conduct a quantitative study to analyze possible correlations between test coverage, test size and quantification and typing of test smells.<br> Therefore, some improvements have been made to the Test Smell Detector tool, which we applied in eleven open source projects to obtain the search database. The R language and correlation tests were used to analyze the data.<br> From the exploration of the data, the research shows that there is little or no relationship between coverage and quantity of test smell, between coverage and lines of code, but on the other hand, there is a strong relation between number of smells and the size of the test.<br> In addition to the research to make contributions regarding the detection of smells, the data presented can help in a better understanding of the behavior of these bad choices in the test codes.</p>
Efficient Approximation Algorithms for the Diameter-Bounded Max-Coverage Group Steiner Tree Problem
<p> It contains all the data used in our experiments, including 5 real graphs (<code>MONDIAL</code>, <code>OpenCyc</code>, <code>LinkedMDB</code>, <code>YAGO</code>, and <code>DBpedia</code>) and 3 synthetic graphs (<code>LUBM-50K</code>, <code>LUBM-500K</code>, and <code>LUBM-5M</code>).</p> <p>Each real KG directory contains 8 files, including:</p> <ul> <li><code>graph.txt</code>: The first value is the number of vertices. Then each line 'u v' means there is an undirected edge between 'u' and 'v'.</li> <li><code>Weightgraph.txt</code>: The first value is the number of vertices. Then each line 'u v w' means there is an undirected edge between 'u' and 'v' weighted by 'w' which is computed by the Informativeness-based Weighting (IW) scheme.</li> <li><code>nodeName.txt</code>: Mapping from vertex ID to vertex name (i.e., entity URI).</li> <li><code>query.txt</code>: Each line is a keyword query containing a set of keyword names.</li> <li><code>kwName.txt</code>: Mapping from keyword ID to keyword name.</li> <li><code>kwMap.txt</code>: Mapping from keyword ID to vertex IDs. The first value of each line is keyword ID, and the rest are vertex IDs.</li> <li><code>UWHBLL.txt</code>: The HBLL index file which was built based on the Unit Weighting.</li> <li><code>IWHBLL.txt</code>: The HBLL index file which was built based on the Informativeness-based Weighting.</li> </ul> <p>Each synthetic directory contains 6 files, including:</p> <ul> <li><code>graph.txt</code>: same as above.</li> <li><code>Weightgraph.txt</code>: same as above.</li> <li><code>nodeName.txt</code>: same as above.</li> <li><code>queryList.txt</code>: Each line contains a set (separated by ',') of sets of vertex IDs.</li> <li><code>UWHBLL.txt</code>: same as above.</li> <li><code>IWHBLL.txt</code>: same as above.</li> </ul> <p>Apart from that, <code>Dbpedia</code> and <code>LUBM-5M</code> also contain a <code>PLLlabel.txt</code> file which was the supplementary file for the HBLL index.</p>
A 2D seismic reflection dataset of the Caosiyao giant porphyry Mo deposit in the shallow coverage area in Jining, Inner Mongolia, China
<p>Our paper, '<strong>A 2D seismic reflection</strong> <strong>dataset of the Caosiyao giant porphyry Mo deposit in the shallow coverage area in Jining, Inner Mongolia, China</strong>' in <strong>Geoscience Data Journal, </strong>provides a new open dataset for the Caosiyao deposit area to address the paucity of publicly accessible geophysical datasets in shallow covered areas or hardrock areas.</p> <p>Three two-dimensional (2D) seismic reflection exploration profiles (profile 3, 8 and 16) were completed in the Caosiyao deposit area with a total length of 20 km, 488 shots were stimulated by the combination of double-couple seismic sources. In the Caosiyao deposit area, three two-dimensional (2D) seismic reflection exploration profiles (profiles 3, 8 and 16) totaling 20 km in length were completed. The combination of double-couple seismic sources triggered 488 shots. Briefly, the files consist of 488 shots in Caosiyao deposit area: ‘Caosiyao - seismic profile 3’ contains 88 single shot records; ‘Caosiyao - seismic profile 8’ and ‘Caosiyao - seismic profile 16’ both contains 200 single shot records. In addition, all 2D reflection seismic single-shot data are in SEGD format. </p> <p>The international advanced super detector and double controllable seismic source combined excitation instruments were used to guarantee acquisition quality and acquisition depth. The controllable seismic source combination of a 2013 French Nomad-65, weighing 65000 pounds (28 tons), was employed for excitation, and a Sercel 428XL digital seismic acquisition equipment was used for recording.</p> <p>We thoroughly analyze the seismic profile 8, and our dataset afterwards contributes to a better comprehension of the deep geological structures of the research area and delineate the location of the shallow and deep rock masses.</p> <p>Note: The datasets given are protected with password, and the password is available in our published paper.</p>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2019 (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 2019 (001–177). Please <em><a href="https://zenodo.org/record/7500321#.Y7S1dNVBw2y"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2018 (185–361)</strong></em>, and <em><a href="https://zenodo.org/record/7499330#.Y7OTKdVBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2019 (185–361</strong></em><em><strong>)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019 (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 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>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2018 (185–361)
<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 2018 (185–361). <em>Please <a href="https://zenodo.org/record/7502675#.Y7WAotVBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2018 (001–177)</strong> and <em><a href="https://zenodo.org/record/7498056#.Y7OSfdVBw2x"><strong>click here</strong></a> to download the MUSES FVC produ</em>ct <strong>in 2019 (001–177)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2018 (185–361)</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 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>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2018 (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 2018 (001–177). <em>Please <a href="https://zenodo.org/record/7503627#.Y7ZZEn1ByUk"><strong><em>click</em> here</strong></a> to download the MUSES FVC product <strong>in 2017 (185–361)</strong></em>, and <em><a href="https://zenodo.org/record/7500321#.Y7TEIdVBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2018 (185–361</strong></em><em><strong>)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2018 (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 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>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2017 (185–361)
<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 2017 (185–361). <em>Please <a href="https://zenodo.org/record/7505201#.Y7durn1ByUk"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2017 (001–177)</strong> and <em><a href="https://zenodo.org/record/7502675#.Y7WENNVBw2x"><strong>click here</strong></a> to download the MUSES FVC produ</em>ct <strong>in 2018 (001–177)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2017 (185–361)</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 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>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2017 (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 2017 (001–177). <em>Please <a href="https://zenodo.org/record/7508632#.Y7gpc31ByUk"><strong><em>click</em> here</strong></a> to download the MUSES FVC product <strong>in 2016 (185–361)</strong></em>, and <em><a href="http://zenodo.org/record/7503627#.Y7ZZEn1ByUk"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2017 (185–361</strong></em><em><strong>)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2017 (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 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>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2016 (185–361)
<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 2016 (185–361). <em>Please <a href="https://zenodo.org/record/7509954#.Y7jSuH1ByUk"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2016 (001–177)</strong> and <em><a href="https://zenodo.org/record/7505201#.Y7durn1ByUk"><strong>click here</strong></a> to download the MUSES FVC produ</em>ct <strong>in 2017 (001–177)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2016 (185–361)</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 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>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2015 (185–361)
<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 2015 (185–361). <em>Please <a href="https://zenodo.org/record/7513159#.Y7rFIH1ByUk"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2015 (001–177)</strong> and <em><a href="https://zenodo.org/record/7509954#.Y7jSuH1ByUk"><strong>click here</strong></a> to download the MUSES FVC produ</em>ct <strong>in 2016 (001–177)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2015 (185–361)</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 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>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2015 (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 2015 (001–177). <em>Please <a href="https://zenodo.org/record/7513781#.Y7t7Mn1ByUk"><strong><em>click</em> here</strong></a> to download the MUSES FVC product <strong>in 2014 (185–361)</strong></em>, and <em><a href="http://zenodo.org/record/7511224#.Y7oJM_5Bw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2015 (185–361</strong></em><em><strong>)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2015 (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 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>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2014 (185–361)
<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 2014 (185–361). <em>Please <a href="https://zenodo.org/record/7514646#.Y7zlO_5Bw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2014 (001–177)</strong> and <em><a href="https://zenodo.org/record/7513159#.Y7rFIH1ByUk"><strong>click here</strong></a> to download the MUSES FVC produ</em>ct <strong>in 2015 (001–177)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2014 (185–361)</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 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>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2014 (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 2014 (001–177). <em>Please <a href="https://zenodo.org/record/7519197#.Y74Tf31ByUk"><strong><em>click</em> here</strong></a> to download the MUSES FVC product <strong>in 2013 (185–361)</strong></em>, and <em><a href="http://zenodo.org/record/7513781#.Y7t7Mn1ByUk"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2014 (185–361</strong></em><em><strong>)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2014 (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 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>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2013 (185–361)
<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 2013 (185–361). <em>Please <a href="https://zenodo.org/record/7523508#.Y76zH3ZByUk"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2013 (001–177)</strong> and <em><a href="https://zenodo.org/record/7514646#.Y7zlO_5Bw2x"><strong>click here</strong></a> to download the MUSES FVC produ</em>ct <strong>in 2014 (001–177)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2013 (185–361)</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 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>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2012 (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 2012 (001–177). <em>Please <a href="https://zenodo.org/record/7528428#.Y8CZcnZByUk"><strong><em>click</em> here</strong></a> to download the MUSES FVC product <strong>in 2011 (185–361)</strong></em>, and <em><a href="http://zenodo.org/record/7525294#.Y79Ni3ZByUk"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2012 (185–361</strong></em><em><strong>)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2012 (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 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>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2012 (185–361)
<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 2012 (185–361). <em>Please <a href="https://zenodo.org/record/7527547#.Y7_UrHZByUk"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2012 (001–177)</strong> and <em><a href="https://zenodo.org/record/7523508#.Y76zH3ZByUk"><strong>click here</strong></a> to download the MUSES FVC produ</em>ct <strong>in 2013 (001–177)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2012 (185–361)</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 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>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2013 (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 2013 (001–177). <em>Please <a href="https://zenodo.org/record/7525294#.Y79Ni3ZByUk"><strong><em>click</em> here</strong></a> to download the MUSES FVC product <strong>in 2012 (185–361)</strong></em>, and <em><a href="http://zenodo.org/record/7519197#.Y74Tf31ByUk"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2013 (185–361</strong></em><em><strong>)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2013 (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 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>
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.