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12,237 results for “2019”
FIGURES 11−16. Extraordinarius alicecooperi spec. nov. 11−13 in New species and records of the genus Extraordinarius Rheims, 2019 (Araneae Sparassidae: Sparianthinae)
FIGURES 11−16. Extraordinarius alicecooperi spec. nov. 11−13 Male (holotype), left palp (11 prolateral, 12 ventral, 13 retrolateral); 14−16 Female (paratype), epigyne/vulva (14 ventral, 15 dorsal, 16 schematic course of internal duct system). C—conductor; CO—copulatory opening; E—embolus; FD—fertilization duct; LL—lateral lobe; MA—median apophysis; MS—median septum; RbP—retrobasal cymbial projection; RTA—retrolateral tibial apophysis; TBC—tegular projection close to conductor base; TBE—tegular projection close to embolus base; VTA—ventral tibial apophysis. Scale lines: 1 mm.
FIGURES 7−10 in New species and records of the genus Extraordinarius Rheims, 2019 (Araneae Sparassidae: Sparianthinae)
FIGURES 7−10. Extraordinarius alicecooperi spec. nov., female (paratype). 7−8 Habitus (7 dorsal, 8 ventral); 9−10 epigyne/ vulva (9 ventral, 10 dorsal). Scale lines: 7−8: 2 mm; 9−10: 1 mm.
FIGURE 31 in New species and records of the genus Extraordinarius Rheims, 2019 (Araneae Sparassidae: Sparianthinae)
FIGURE 31. Distribution map for all known species of Extraordinarius Rheims, 2019. Doubtful record for E. alicecooperi spec. nov. is marked with a "?".
Aerosol and meteorological data measured at LACMS from December 2019 to November 2020
<p>The data in this meteorological data set include concentration data of major pollutants, ion concentration data, meteorological elements data, etc. Detailed information can be seen in the 'Data Ddescription'.</p>
Four-dimensional wind fields retrieved from FY4A GIIRS during 30-minute interval intensified observations on typhoons Ampil (2018), Mangkhut(2018) and Lekima(2019)
<p>This dataset includes the four-dimensional (time, pressure, u, v) wind fields retrieved from FY4A GIIRS with 30-minute interval during Ampil (2018), Mangkhut(2018) and Lekima(2019).</p>
MUSES Leaf Area Index (LAI) 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 LAI product at 500m spatial resolution and 8-day 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. Please <em><a href="https://zenodo.org/record/7488190#.Y6zTA31ByUl"><strong>click here</strong></a> to download the MUSES LAI product <strong>in 2018</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: 0.01</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>
Data Analysis of Adolescent Unintended Pregnancy and Pre-marital Sex in East Java (BKKBN, 2019)
<p>The data used is secondary data in the form of raw data from SKAP (Program Performance and Accountability Survey) for East Java in 2019 conducted by BKKBN (National Population and Family Planning Agency). SKAP is a survey conducted by the BKKBN in order to measure the success of population development programs, family planning, and family development.<br> The data consist of women aged 15-18 years were selected, totaling 1815 women of childbearing age and adolescents. The adolescents included in this study were residents of East Java.</p>
Funka Bay observation bottle data (Nutrients, chlorophyll-a, and oxygen) from February to April of 2019
<p>Bottle data of Nutrients, chlorophyll-a, and oxygen in seawater collected from the Funka Bay, Hokkaido, Japan, from February to April of 2019. Methods are presented in Umezawa et al., Significant nutrient consumption in the dark subsurface layer during a diatom bloom: a case study on Funka Bay, Hokkaido, Japan, Biogeosciences, 2023.</p>
Cartographie de l'occupation du sol de la Région de la Mé en 2016, 2019 et 2022
<p>Cartographie de la dynamique des affectations des terres (2016-2019-2022) dans la Région de la Mé basée sur le traitement par classification supervisée (méthode Random Forest) des images Sentinel-2A.<br> L'UMC adoptée pour produire les différentes cartes est de 0,1 ha conformément au nouveau code forestier de Côte d'Ivoire qui fixe la superficie minimale des forêts à 0,1 ha.</p>
Cartographie de l'occupation du sol de la Région de la Mé en 2016, 2019, 2022
<p>Cartographie de la dynamique des affectations des terres (2016-2019-2022) dans la Région de la Mé basée sur le traitement par classification supervisée (méthode Random Forest) des images Sentinel-2A.<br> L'UMC adoptée pour produire les différentes cartes est de 0,1 ha conformément au nouveau code forestier de Côte d'Ivoire qui fixe la superficie minimale des forêts à 0,1 ha.</p>
Ukraine's PM2.5 in 2019
<p>Global (GL) Annual PM2.5 Grids from MODIS, MISR, and SeaWiFS Aerosol Optical Depth (AOD), v4.03 (1998 – 2019) is a dataset provided by NASA Socioeconomic Data and Applications Center (SEDAC) and is part of the SEDAC Environmental Indicators (SEI).</p>
Ukraine's population density in 2019
<p>Data used for determining population density consists of several datasets: Natural Earth data and data on the population in Ukraine. Firstly, the Natural Earth quick start kit is a collection of vector and raster data sets that provide base map information for the world. Administrative boundaries, railways, aquatic bodies, and other elements that are frequently found on maps are all included in the data sets. Secondly, population data can be found in and consists of data taken from main departments of statistics in Vinnytsia, Volyn, Dnipropetrovsk, Donetsk, Zhytomyr, Zakarpattia, Zaporizhzhya, Ivano-Frankivsk, Kyiv, Kirovohrad, Luhansk, Lviv, Mykolaiv, Odesa, Poltava, Rivne, Sumy, Ternopil, Kharkiv, Kherson, Khmelnytskyi, Cherkasy, Chernivtsi, Chernihiv oblast and Kyiv city.</p>
Ukraine's nighttime data in 2019
<p>The EOG Nighttime Light data is a dataset that offers details on how artificial lights are dispersed over the world during nighttime computed by The National Centers for Environmental Information of the National Oceanic and Atmospheric Administration. </p>
Ukraine's gross regional product (GRP) in 2019
<p>The data used is provided by the State Statistics Service of Ukraine (UkrStat) which is the Ukrainian government agency in charge of gathering and disseminating statistical data.</p>
Supporting CESM output for "The effect of plant physiological responses to rising CO2 on global streamflow" (Fowler et al.; 2019)
<p>Thirty years of CESM soil moisture, rain, and snow data is collected here based on each experiment contained within Fowler et al. (2019). These data were used to produce Figure 3 of that work. Other supporting data including daily runoff and river discharge is archived elsewhere, while analysis scripts and more information can be found here: https://github.com/megandevlan/Physiology-Streamflow.git. </p>
UCYN-A1 nifH gene abundance data from SCOPE-Gradients 2019 cruise
<p>Surface UCYN-A1 nifH gene abundance data from the April 2019 SCOPE-Gradients 2019 cruise (http://scope.soest.hawaii.edu/data/gradients/gradients.html). Sample collection followed methods described in Gradoville et al. 2020 (Limnology and Oceanography), but included near-surface water sampling from the ship's clean underway line ("Underway") and trace-metal clean tow-fish (Tow-fish) in addition to CTD sampling. nifH genes of the UCYN-A1 phylotype were enumerated using ddPCR according to the methods described by Gradoville et al. 2021 (Environmental Microbiology). Average and standard deviations from two replicate bottles are provided.</p>
TROPOMI SIF data in July, 2019
<p>TROPOMI SIF global data in July, 2019 (Floating type-binary data. Geographic projection at a spatial resolution of 0.05 degree. Samples = 7200, Lines = 3600) </p>
Figure 1 in Mammalian species and the twofold nature of taxonomy: a comment on Taylor et al. 2019
Figure 1: Relationship of population lineages and species delimited according to different species concepts. The only level in the hierarchy of population-level lineages that can be delimited non-arbitrarily is the (allopatric) lineage itself (L). No species concept used in taxonomic practice, however, considers each such lineage to be a separate species. Different concepts lump lineages to varying degrees. The illustration shows three such concepts of increasing inclusiveness: the Phylogenetic Species Concept (PSC), the Biological Species Concept (BSC) and the Hennigian Species Concept (HSC). The latter is based on absolute reproductive isolation and thus may comprise several taxa that, under the BSC, are treated as separate species.
Amundsen Sea seal-tag CTD data (2014, 2019, 2020)
<p>1. Seal-tag hydrographic CTD measurements collected in the Amundsen Sea in 2014, 2019, and 2020.</p> <p>Columns are: Latitude [<sup>o</sup>], Longitude [<sup>o</sup>], Temperature [<sup>o</sup>C], Salinity [PSU], depth, z [m], absolute salinity, SA [g/kg], conservative temperature, CT [<sup>o</sup>C], potential temperature, PT [<sup>o</sup>C], potential density, PD [kg/m<sup>3</sup>], N<sub>2</sub>, Datetime [yyyy-mm-dd hh:mm:ss], Station/seal name. </p> <p>The official seal tag dataset can be found here: https://www.meop.net/database/meop-databases/.</p> <p>2. Remote sensing imagery (MODIS, Landsat) dates and links.</p>
Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"
<p>The .zip file contains temporal-spatial averaged metrics for evaluating simulations against observed ice thickness, concentration, volume, and drift. These quantities are presented in the manuscript "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"</p> <p>Subfolders are named by the experiment IDs, including metrics obtained from the relevant experimental results and observations.</p> <p>In case information is missing, do not hesitate to contact chengsukun@hotmail.com</p> <p>We thank Pavel Sakov for helpful discussions and improvement regarding the EnKF-C code and Jiping Xie for contributing the TOPAZ interface to sea ice observations. We are grateful for the support from Timothy Williams and Anton Korosov regarding the environments of neXtSIM and its analysis tools. The work is funded by the DASIM-II grant from ONR (grant nos. N00014-18-1-2493 and N00014-18-1-2204). Alberto Carrassi, Christopher K. R. T. Jones, Ali Aydo ̆gdu, and Pierre Rampal acknowledge the support of the project SASIP funded by Schmidt Futures – a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologies. Sukun Cheng and Laurent Bertino were co-funded by the FOCUS project from the Research Council of Norway (grant no. 301450), and Alberto Carrassi and Yumeng Chen are also supported by the UK National Centre for Earth Observation (grant no. NCEO02004). Computations were carried out on the Norwegian Supercomputing InfrastructureSigma2 (grants nn2993k for computing and NS2993K for data storage)</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.