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12,237 results for “2019”
ERA5-Land monthly: Total precipitation, monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>ERA5-Land total precipitation monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)</p> <p>Source data:<br>ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Total precipitation:<br>Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.</p> <p>Processing steps:<br>The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate proportion of ERA5-Land / aggregated CHELSA <br>3. interpolate proportion with a Gaussian filter to 30 arc seconds <br>4. multiply the interpolated proportions with CHELSA <br>Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA.</p> <p>The spatially enhanced daily ERA5-Land data has been aggregated to monthly resolution, by calculating the sum of the precipitation per pixel over each month.</p> <p>File naming:<br><code>ERA5_land_monthly_prectot_sum_30sec_YYYY_MM_01T00_00_00_int.tif</code> <br>e.g.:<code>ERA5_land_monthly_prectot_sum_30sec_2023_12_01T00_00_00_int.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.</p> <p>Pixel values:<br>mm * 10<br>Scaled to Integer, example: value 218 = 21.8 mm</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 28:18N<br>south: 14:42N<br>west: 17:05W<br>east: 4:49W</p> <p>Temporal extent:<br>January 2019 - December 2023</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Lineage:<br>Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original ERA5-Land dataset license:<br><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a></p> <p>CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Representation type: Grid</p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
Holotype of Agraptocoris pamiricus Konstantinov, 2019
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Holotype of Agraptocoris pallescens Konstantinov, 2019
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Holotype of Agraptocoris subconcolor Konstantinov, 2019
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Holotype of Agraptocoris nigrisetosus Konstantinov, 2019
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Holotype of Agraptocoris eugeniae Konstantinov, 2019
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MineRL 2019 Datasets
<p>This Zenodo record contains backups of parts of the original datasets published in the 2019 MineRL paper titled "<a href="https://arxiv.org/abs/1907.13440"><em>MineRL: A Large-Scale Dataset of Minecraft Demonstrations</em></a>"<em>. </em>This data is human players playing Minecraft, with the video feed and actions captured and stored. See project documentation on how to use the data.</p> <p>MineRL Github page: <a href="https://github.com/minerllabs/minerl">https://github.com/minerllabs/minerl</a></p> <p>MineRL Documentation: <a href="https://minerl.readthedocs.io/en/latest/">https://minerl.readthedocs.io/en/latest/</a></p> <p>Documentation specifically related on using this data: <a href="https://minerl.readthedocs.io/en/v0.3.7/tutorials/data_sampling.html">https://minerl.readthedocs.io/en/v0.3.7/tutorials/data_sampling.html </a></p>
Crippa et al. 2019 ECAP - Additional data
<p>Heart rate and blood pressure of the participants of the ‘The Effects of DHA on Attention Deficit and Hyperactivity Disorder (DADA)’ study (registered at ClinicalTrials.gov as NCT01796262).</p>
RIFS - Spring 2019 PurpleAir-Grimm experiment at Berliner Str in Potsdam
<p>This is the data that was used as the basis for the manuscript "Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – A field experiment".</p>
Global surface O3, NO2, HCHO, and PM2.5 concentrations estimated from deep learning from 2019 to 2023
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FIGURE 2 in One new species of the genus Alloteratura Hebard, 1922 (Tettigoniidae: Meconematinae) and supplement of Alloteratura (Alloteratura) flabellata Xin & Shi, 2019
FIGURE 2. Alloteratura (Alloteratura) flabellata Xin & Shi, 2019, male: A. genitalia in lateral view (from Guangdong); B–C apex of abdomen (from Xin & Shi, 2019): B. dorsal view; C. ventral view; D–F apex of abdomen (from Guizhou): D. male genitalia in lateral view; E. dorso-lateral view; F. ventral view.
FIGURE 1 in One new species of the genus Alloteratura Hebard, 1922 (Tettigoniidae: Meconematinae) and supplement of Alloteratura (Alloteratura) flabellata Xin & Shi, 2019
FIGURE 1. Alloteratura (Alloteratura) curvata sp. nov.: A–B. head and pronotum: A. dorsal view; B. lateral view; C–G. apex of abdomen: C. lateral view; D. dorsal view; E. latero-apical view; F. apices of cerci in lateral view; G. ventral view; H. ovipositor in lateral view; I. subgenital plate in ventral view. Abbreviation: d. dorsal branch of ninth abdominal tergite; sa. subanal plate; s. subgenital plate; v. ventral branch of ninth abdominal tergite. C–G. male; A–B, H–I. female.
Data from: Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine
<p><strong>Paper Abstract:</strong></p> <p>The 2019–2020 Kangaroo Island bushfires in South Australia burned almost half of the island. To understand how to avoid future severe ‘mega-fires’ and how vegetation may recover from 2019–2020, we can utilize information from the bulk of historical fires in an area. Landsat time-series of vegetation change provide this opportunity, but there has been little analysis of large numbers of fires to build a landscape-level understanding and quantify drivers in an Australian context. In this study, we built a yearly cloud-free surface reflectance normalized burn ratio (NBR) time-series (1988–2020) using all available summer Landsat images over Kangaroo Island. Data were collected in Google Earth Engine and fitted with LandTrendr. Burn severity and post-fire recovery were quantified for 47 fires, with a new recovery metric facilitating comparison where fire frequency is high. Variables representing the current burn, fire history, vegetation structure, and topography were related to severity and yearly recovery with random forest and bivariate analysis. Results show that the 2019–2020 bushfires were the most widespread and severe, followed by 2007–2008. Vegetation recovers quickly, with NBR stabilizing ten years post-fire on average. Severity is most influenced by fire frequency, vegetation capacity and land use with more severe burns in nature conservation areas with dense vegetation and a history of frequent fires. Influence on recovery varied with time since fire, with initial (year 1–3) faster recovery observed in areas with less surviving vegetation. Later (year 6–10) recovery was most influenced by a variable representing burn year and further investigation indicates that precipitation increases in later post-fire years likely facilitated faster recovery. The relative abundance of eucalypt woodlands also has a positive influence on recovery in middle and later years. These results provide valuable information to land managers on Kangaroo Island and in similar environments, who should consider adjusting practices to limit future mega-fire risk and potential ecosystem shifts if severe fires become more frequent with climate change.</p> <p> </p> <p><strong>Data details:</strong></p> <p>See paper: <a href="https://www.mdpi.com/2072-4292/12/23/3942">Remote Sensing | Free Full-Text | Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine (mdpi.com)</a></p> <p>See code on GitHub: <a href="https://github.com/ZZMitch/KangarooIslandFireHistory_1988to2020">ZZMitch/KangarooIslandFireHistory_1988to2020: Code from "Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine" (RS, 2020) (github.com)</a></p> <p> </p> <p><strong>If you use these data, please reference: </strong></p> <p>Bonney, M.T., He, Y., Myint, S.W., 2020. Contextualizing the 2019–2020 Kangaroo Island bushfires: Quantifying landscape-level influences on past severity and recovery with Landsat and Google Earth Engine. Remote Sensing 12(23), <a href="https://doi.org/10.3390/rs12233942" rel="nofollow">https://doi.org/10.3390/rs12233942</a>.</p>
Supplementary movies of the Kaikoura dynamic rupture model published in Ulrich et al. (2019)
<p>Ulrich, T., Gabriel, A. A., Ampuero, J. P., & Xu, W. (2018). Dynamic viability of the 2016 Mw 7.8 Kaikōura earthquake cascade on weak crustal faults.</p>
Data used for the paper Daniel Malko, Yanjun Guo, Pip Jones, George Britovsek, and Anthony Kucernak, "Heterogeneous iron containing carbon catalyst (Fe-N/C) for epoxidation with molecular oxygen|", Journal of Catalysis, 2019, DOI:10.1016/j.jcat.2019.01.008
<p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Daniel Malko, Yanjun Guo, Pip Jones, George Britovsek, and Anthony Kucernak</p> <p>Heterogeneous iron containing carbon catalyst (Fe-N/C) for epoxidation with molecular oxygen</p> <p>Journal of Catalysis</p> <p>DOI:10.1016/j.jcat.2019.01.008</p> <p>Please cite the above reference if you wish to use this data<br> DOI of this data file is: 10.5281/zenodo.2539183</p>
Dataset de análisis para la práctica en grupo de Ciclo de Vida - 2019
Datos de turismo, acoplados para Cantabria y Tenerife
Supplementary material 3 from: Petersen M, Hoffmann J, Glöckler F (2019) Access to Geosciences – Ways and Means to share and publish collection data. Research Ideas and Outcomes 5: e32987. https://doi.org/10.3897/rio.5.e32987
Programm of the two-day workshop Access to Geosciences: sharing and publishing data related to paleontological, mineralogical, and petrological objects using a common data standard. Museum für Naturkunde, 29-30 May 2017
Supplementary material 2 from: Petersen M, Hoffmann J, Glöckler F (2019) Access to Geosciences – Ways and Means to share and publish collection data. Research Ideas and Outcomes 5: e32987. https://doi.org/10.3897/rio.5.e32987
Results of the survey on geoscientific collection data. Names, institutions and contact details are anonymized
Earth's Future 2019 - Bosson et al. - World Heritage Glaciers data
<p>This file gathers the list, the shapefiles and the modeling of future evolution (according to GloGEM model, see Bosson et al., 2019 Earth's Future and Huss & Hock, Frontiers, 2015) of glaciers in natural World Heritage sites in 2018 according to Randolph Glacier Inventory 6.0.</p>
European Robotics League Consumer - Lisbon 2019 contest - FBM 2 - Navigation functionality
<p>Benchmark logs for the FBM2 benchmark: navigation functionality.</p> <p>ERL Consumer competition in Lisbon.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.