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12,237 results for “2019”
Data on female oviposition behaviour of the Pararge aegeria butterfly in outdoor cages (2019–2020)
<p><span>We studied oviposition behavior in a butterfly (</span><em><span>Pararge aegeria</span></em><span>)</span><span> that used to be confined to forest, but recently colonized anthropogenic areas too. This provides an ideal study system when trying to understand the underlying processes of niche expansion and colonisation success. The dataset produced allows us to test to what extent ecotype origin (agricultural vs. forest landscape types) and larval developmental conditions (open field vs. canopy-covered forest floors) affect multiple features of oviposition behahaviour. We also performed multiple behavioural trials per individual and considered changes in oviposition site preference within and over trials. </span></p> <p>The two provided files contain all behavioral or independent variables that were obtained during behavioural tracking of the oviposition behaviour of the Speckled Wood butterfly (<em>Pararge aegeria</em>) in an outdoor cage in 2019 and 2020.</p> <p>In the<strong> 'femdata' </strong>file, each line contains all dependent (e.g., proportion of time spent active) and independent variables (e.g. ecotype) that apply for a single observation trial of 30 minutes of a single individual.In total, 211 observation trials were performed, by 110 different female individuals. </p> <p>In the <strong>'ovimdata' </strong>file, each line contains all variables for a single oviposition bout (i.e., if a butterfly curls its abdomen on a surface and lays eggs). Examples include number of eggs per bout, the height above ground where an egg was laid and temperature at the egg laying site. These data were for example used to track changes over consecutive bouts within a single trial. In total 275 oviposition bouts were observe, laid by 85 different female individuals.</p> <p>Data of the ovimdata file were also implemented in the femdata file. For example, in the femdata file information can be obtained about the properties of the first oviposition bout an individual made during that trial (starting with 'b1').</p>
Cartes d'occupation et d'usage des terres dans la région de la Mé (Côte d'Ivoire) en 2016, 2019 et 2022
<p>Ce répertoire contient trois cartes d'occupations du sol couvrant la région de la Mé (Côte d'Ivoire) respectivement pour les années de référence 2016, 2019 et 2022.</p> <p>La méthodologie utilisée pour produire ces cartes est basée à la fois sur l’utilisation d’images Sentinel-2 à 10 mètres de résolution (2016 et 2019) et de composites d'images Planet (2022) sur une zone couvrant 8 000 km2 avec seize catégories d'occupation et d'usage des terres. Après prétraitements, les images Sentinel-2 et Planet ont été soumises à une analyse automatisée sous R en plusieurs étapes dont la classification supervisée avec l’algorithme Random Forest et l’évaluation de la qualité du modèle de classification. Les cartes présentant des précisions globales satisfaisantes (92% pour la carte de 2016, 87% pour celle de 2019 et 80% pour celle de 2022) ont ensuite servi à produire les cartes des conversions des diverses formations forestières. </p> <p>Ces cartes ont été produites dans le cadre des travaux de recherche sur les conversions forestières dans la région de la Mé (Côte d'Ivoire) et l'analyse des causes de la déforestation dans le cadre du Projet PRM2 (Préserver la biodiversité de Mabi-Yaya et renforcer le développement socio-économique de la Mé).</p>
MUSES Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) 8-Day Global 500m SIN Grid in 2019
<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 FAPAR product at 500m spatial resolution and 8-day temporal resolution. The MUSES FAPAR product is provided on a Sinusoidal grid and spans from 2000 to 2019 (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 PAR through the entire canopy (Xiao <em>et al</em>., 2015). The MUSES FAPAR values are the instantaneous values at 10:30 am local time, close approximation of daily average PAPAR values, and they are physically consistent with the corresponding MUSES LAI values. The MUSES FAPAR product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FAPAR product in 2019. <em>Please <a href="https://zenodo.org/record/7793505#.ZCt_1XZBypo"><strong>click here</strong></a> to download the MUSES FAPAR </em><em>product <strong>in 2018</strong></em>, and <em><strong>click here</strong> to download the MUSES FAPAR </em><em>product <strong>in 2020</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019</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 al</em>., Estimating the fraction of absorbed photosynthetically active radiation from the MODIS databased GLASS leaf area index product. <em>Remote Sensing of Environment</em>, 171,105-117, 2015.</li> <li>Xiao Zhiqiang, <em>et al</em>., Evaluation of Three Long Time Series for Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 56, 5509-5524, 2018.</li> <li>Zheng Y., Xiao Z., Li J., Yang H., Song J., Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products at 500 m Spatial Resolution. <em>Remote Sensing</em>, 14, 3304, 2022.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Sonification of the atmospheric carbon record: 1988–1992 and 2014–2019
<p><span>These two pieces are part of a collection of sound compositions called <em>Timescales</em>, which sonifies datasets spanning different time scales of the atmospheric carbon record. Sonification is analogous to visualization. In visualizations, data is mapped to an image; in sonification data is mapped to sound. <em>In Situ 1988</em> and <em>In Situ 2014</em> both sonify five years of atmospheric carbon data from the Mauna Loa Observatory, commonly known as the Keeling Curve. The pieces present the carbon record at weekly, monthly, and yearly timescales with a musical scale and translate historical time to musical time. Unlike standard graphical presentations of data, these sound compositions allow the listener to hear the data as physical resonance over time. </span></p>
Change in Antarctic Ice Shelf Area from 2009 to 2019
<p>Antarctic Ice Shelves provide buttressing support to the ice sheet, stabilising the flow of grounded ice and its contribution to global sea levels. Over the past 50-years satellite observations have shown ice shelves collapse, thin and retreat, however, there are few measurements of the Antarctic wide change in ice shelf area. Here, we use MODIS satellite data to measure the change in ice shelf calving front position and area on 34 ice shelves in Antarctica, from 2009 to 2019. Over the last decade, a reduction in area on the Antarctic Peninsula (6,692.5 km2) and West Antarctica (5,563.1 km2), has been outweighed by area growth in East Antarctica (3,532.1 km2) and the large Ross and Ronne-Filchner Ice Shelves (14,027.9 km2). The largest retreat was observed on Larsen-C Ice Shelf where 5,916.6 km2 of ice was lost during an individual calving event in 2017, and the largest area increase was observed on Ronne Ice Shelf in East Antarctica, where gradual advance over the past decade (535.3 km2/yr) led to a 5,888.6 km2 area gain from 2009–2019. Overall, the Antarctic Ice Shelf area has grown by 5,304.5 km2 since 2009, with 18 ice shelves retreating and 16 larger shelves growing in area. Our observations show that Antarctic Ice Shelves gained 660.6 Gt of ice mass over the decade whereas the steady state approach would estimate substantial ice loss over the same period, demonstrating the importance of using time-variable calving flux observations to measure change.</p>
North Pacific Subtropical Gyre 2018–2019 invertebrate biodiversity on macroplastic
<p>We show that the high seas are colonized by a diverse array of coastal species, which survive and reproduce in the open ocean, contributing strongly to its floating community composition. Analysis of rafting plastic debris in the Eastern North Pacific Subtropical Gyre revealed 37 coastal invertebrate taxa in total, largely of Western Pacific origin, exceeding pelagic taxa richness by three-fold. Coastal taxa, including diverse taxonomic groups and life history traits, occurred on 70.5% of debris items. Most coastal taxa possessed either direct development or asexual reproduction, possibly facilitating long-term persistence on rafts. Our results suggest that the historical lack of available substrate limited colonization of the open ocean by coastal species, rather than physiological or ecological constraints as previously assumed. It appears that coastal species persist now in the open ocean as a substantial component of a neopelagic community sustained by the vast and expanding sea of plastic debris.</p>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 500m SIN Grid in 2019
<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 2019 (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 2019. Please <em><a href="https://zenodo.org/record/7496083#.ZDk-tbpBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2018</strong></em>, and <em><strong>click here</strong> to download the MUSES FVC product <strong>in 2020</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019</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>
Wind Farm Survey Ireland 2019
<p>A survey of windfarms in Ireland as of 2019 as part of my introductory period in my PhD research at the University of Limerick funded by Sustainable Energy Authority of Ireland.</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 500m SIN Grid in 2019
<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 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 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 2019. <em>Please <a href="https://zenodo.org/record/7880335#.ZE5_M3ZByUn"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2018</strong></em>, and <em><strong>click here</strong> to download the MUSES FVC product <strong>in 2020</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019</li> <li>Spatial Resolution: 500m</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>
MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2019
<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 LAI product at 1km spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES LAI product in 2019. <em>Please <a href="https://zenodo.org/record/7880588#.ZE52B3ZByUk"><strong>click here</strong></a> to download the MUSES LAI product <strong>in 2018</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2020</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019</li> <li>Spatial Resolution: 1km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</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>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Policy and programmatic directions for the Lesotho tuberculosis programme: Findings of the national tuberculosis prevalence survey, 2019
<p><strong>Introduction</strong></p> <p>The Kingdom of Lesotho has one of the highest burdens of tuberculosis (TB) in the world. A national TB prevalence survey was conducted to estimate the prevalence of bacteriologically confirmed pulmonary TB disease among those ≥15 years of age in 2019.</p> <p><strong>Method</strong></p> <p>A multistage cluster-based cross-sectional survey where residents ≥15 years in 54 clusters sampled from across the country were eligible to participate. Survey participants were screened using a symptom screen questionnaire and digital chest X-ray (CXR). Respondents who acknowledged cough of any duration, fever, weight loss, night sweats and/or had any CXR abnormality in the lungs were asked to provide two spot sputum specimens. All sputum testing was conducted at the National TB Reference Laboratory (NTRL), where samples underwent Xpert MTB/RIF Ultra (1st sample) and MGIT culture (2nd sample). HIV counselling and testing was offered to all survey participants. TB cases were those with Mycobacterium tuberculosis complex-positive samples with culture; and where culture was not positive, Xpert MTB/RIF Ultra (Xpert Ultra) was positive with a CXR suggestive of active TB and no current or prior history of TB.</p> <p><strong>Result</strong></p> <p>A total of 39,902 individuals were enumerated, and of these, 26,857 (67.3%) were eligible to participate; 21,719 (80.9%) participated in the survey of which 8,599 (40%) were males and 13,120 (60%) were females. All 21,719 (100%) survey participants underwent symptom screening and a total of 21,344 participants (98.3%) had a CXR. Of the 7,584 (34.9%) participants who were eligible for sputum examination, 4,190 (55.2%) were eligible by CXR only, 1,455 (19.2%) by symptom screening, 1,630 by both, and 309 by CXR exemption. A total of 6,780 (89.4%) submitted two sputum specimens, and 311 (4.1%) submitted one sample only. From the 21,719 survey participants, HIV counseling and testing was offered to 17,048, and 3,915 (23.0%) were documented as HIV-positive. The survey identified 132 participants with bacteriologically confirmed pulmonary TB thus providing an estimated prevalence of 581 per 100,000 population (95% CI 466–696) for those ≥15 years in 2019. Using the survey results, TB incidence was re-estimated to be 654 per 100,000 (95% CI 406–959), which was comparable to the 2018 TB incidence rate of 611 per 100,000 (95% CI 395–872) reported by the World Health Organization (WHO). The highest TB burden was found in those ≥55 years and among men. The ratio of prevalence to case notification was estimated at 1.22. TB/HIV coinfection was identified in 39 (29.6%) participants. Out of the 1,825 participants who reported a cough, 50% of these participants, mostly men, did not seek care. Those who sought care predominantly went to the public health facilities.</p> <p><strong>Conclusion</strong></p> <p>The TB prevalence survey results confirmed that burden of TB and TB/HIV coinfection remains very high in Lesotho. Given that TB prevalence remains high, and there is a significant proportion of participants with confirmed TB that did not report TB suggestive symptoms. The National TB Programme will need to update its TB screening and treatment algorithms to achieve the End TB targets. A major focus will need to be placed on finding the "missing cases" i.e., undiagnosed or under-reported TB cases, or ensuring that not only TB symptomatic but also those who do not present with typical TB symptoms are promptly identified to reduce further onward transmission.</p>
River monitoring, particulate Cs-137 measurements and rainfall monintoring of the Mano Dam catchment (Japan) from 2014 to 2019
<p>This dataset is composed of river monitoring, laboratory particulate Cs-137 measurements and rainfall monitoring used in the article: Vandromme, R., Hayashi, S.,Tsuji, H., Evrard, O., Grangeon, T., Landemaine, V., Laceby, J.P., Wakiyama, Y., Cerdan, O. The unprecedented soil decontamination program in Fukushima only reduced radioactive fluxes in rivers by 17% (PNAS, under review) </p> <p>River measurements were conducted by Hayashi S. and Tsuji H. <br> A hydrological station was installed immediately upstream of Mano Dam Lake (Lake Hayama) in June 2014. The water height in the river was measured using an AquiStar PT12 pressure/temperature sensor (INW, Kirkland, WA, USA) at 5-min intervals and then converted into flow rate data using gage curves. Water and SS samples were also collected from the river section using an ISCO 6712 automatic water sampler (Teledyne Technologies, Lincoln, NE, USA) at 1-h intervals during flood events. The SS concentration was determined in the laboratory by filtration using a Whatman GF/F filter with 0.7-µm pores (Cytiva, Tokyo, Japan), and these concentrations were then used to convert the turbidity logs measured by a DTS-12 digital turbidity sensor (FTS Inc., Victoria, BC CANADA) at 5-min intervals into SS logs. <br> This station was damaged by Typhoon Etau in September 2015. Following this damage, the data analysis and discovery of anomalies that persisted until the end of 2016 prompted us to omit the data acquired during this period. The hydrological station was subsequently moved a few hundred meters upstream in 2017. Water flows and sediment concentrations were subsequently acquired using the same systems described above at 10-min intervals until the end of 2019. </p> <p>River water samples were punctually collected to measure 137Cs concentration in Suspended Sediment (SS) (66 measurements during floods) from June 2014 to December 2019 (this chronicle ends in the middle of a flood).</p> <p>Station location before 2017 (coordinate system:WGS84) : lat 37.738391, long 140.803943<br> Station location after 2017 (coordinate system:WGS84) : lat 37.733191, long 140.807709</p> <p>Rainfall record was acquired by the Fukushima Prefecture (Maenori station, lat 37.73644, long 140.74233, coordinate system:WGS84) at 10-minute time steps.</p>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 1km SIN Grid in 2019
<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 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 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 2019. <em>Please <a href="https://zenodo.org/record/7903706#.ZFhGtnZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2018</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2020</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019</li> <li>Spatial Resolution: 1 km</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>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2019
<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 2019. <em>Please <a href="https://zenodo.org/record/7880335#.ZE5_M3ZByUn"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2018</strong></em>, and <em><strong>click here</strong> to download the MUSES FVC product <strong>in 2020</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019</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>
Twitter links to tweets from Flemish parties and chairmen in advance of the 2019 elections
<p>This is a list of al tweets used in the Master's Thesis of Toon Tabruyn to obtain the degree of Master of Sociology.</p>
Martin Bergmann pCO2 dataset 2016 - 2019
<p>pCO2 dataset presented in Sims, R. P., Ahmed, M., Butterworth, B. J., Duke, P. J., Gonski, S. F., Jones, S. F., Brown, K. A., Mundy, C. J., Williams, W. J., and Else, B. G. T.: High interannual surface pCO2 variability in the Southern Canadian Arctic Archipelago's Kitikmeot Sea, <a href="https://doi.org/10.5194/egusphere-2022-710">https://doi.org/10.5194/egusphere-2022-710</a>, 2023.</p>
Absolute Gravity Measurements at the Alpine Research Centre in Obergurgl (Austria) in June 2019
<p> This report contains the results of absolute gravity measurements carried out at Alpine Research Centre (University of Innsbruck) in Obergurgl in June 2019. The absolute gravimeter FG5X#216 was operated by Olivier Francis and Sajad Tabibi from the University of Luxembourg. The measurements are part of an on-going research collaboration between the Geophysics Laboratory of the University of Luxembourg and Christian Ullrich from the Bundesamt für Eich- und Vermessungswesen (BEV, Federal Office of Metrology and Surveying) in Vienna, Austria. It is the third time we measured at this site. Measurements were previously performed in June and September 2018.</p>
Weibliche Larve der Grossen Höckerschrecke (Arcyptera fusca). (Foto Daniel Roesti) in Jahresausflug des Entomologischen Vereins Bern 2019
Weibliche Larve der Grossen Höckerschrecke (Arcyptera fusca). (Foto Daniel Roesti)
Rotklee-Bläuling (Polyommatus semiargus). (Foto Hans-Peter Wymann) in Jahresausflug des Entomologischen Vereins Bern 2019
Rotklee-Bläuling (Polyommatus semiargus). (Foto Hans-Peter Wymann)
Männchen der Kleinen Goldschrecke (Euthystira brachyptera). (Foto Daniel Roesti) in Jahresausflug des Entomologischen Vereins Bern 2019
Männchen der Kleinen Goldschrecke (Euthystira brachyptera). (Foto Daniel Roesti)
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.