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12,237 results for “2019”
Fig. 1. Luteicenus congruus Holzschuh, 2019 in To the knowledge of long-horned beetles (Coleoptera: Cerambycidae) of the Oriental Region (with new synonymy). Part 4
Fig. 1. Luteicenus congruus Holzschuh, 2019
A SSP1-Low emission land use scenario based on LCM2019 for Scotland - baseline 2019 and scenario 2050 (nov22)
<p>This set of datasets contains a land use change scenario (2050) for Scotland within the scope of a SSP1 - Low emissions scenario (Shared Socio-Economic Pathways). For achieving a low-emission scenario, simulated land use change targeted woodland expansion (including silvo-arable and silvo-pastoral) and decreased grazing intensity, both land use changes also aimed at benefitting four aspects of ecosystem services: carbon storage through tree planting, emission reduction through deintensification, biodiversity enhancement through tree planting, and pollination to support food production.</p> <p>The baseline dataset is based on the Land Cover Map 2019 (Morton et al, 2020) aggregated at 100m resolution. Grazing intensity was added to it by using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). From the baseline dataset, the land use scenario map was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). The attached land use scenario map for 2050 is not an optimised result, but it is only one possibility that meets all the constraints stipulated for the scenario.</p> <p><strong>For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a></strong></p> <p>This analysis was conducted as part of the Land use Transformations (<a href="https://landusetransformations.hutton.ac.uk/">https://landusetransformations.hutton.ac.uk/</a>) project (JHI-C3-1) in the Scottish Government funded Strategic Research Programme 2022-27.</p> <p> </p> <p><strong>-------------------------</strong></p> <p><strong>Datasets accessible here : <a href="https://openscience.hutton.ac.uk/dataset/low-emission-land-use-scenarios">SSP1-Low Emission Land Use Scenarios - Dataset - Natural Asset Register Data Portal (hutton.ac.uk)</a></strong></p> <p><strong>License</strong>: CC BY-NC 4.0 namely “Creative Commons Attribution-NonCommercial 4.0 International“ <a name="_Hlk161153952"></a>(https://creativecommons.org/licenses/by-nc/4.0/)</p> <p><strong>Copyright to display of the datasets</strong>: <br>“Contains Data owned by UK Centre for Ecology & Hydrology © Database Right/Copyright UKCEH. Based on Data from LPIS and JAC (Scottish Government, 2019).”</p> <p><strong>2 Main files :</strong></p> <ul> <li><strong>SSP1LEonLCM19_2019.tif </strong>: original land uses (2019) on which the scenario is based on. This land use map, of a resolution of 100m, is based on the Land Cover Map 2019 (Morton et al, 2020), estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021).<br><br><strong>Contributions </strong>to the baseline dataset (SSP1LEonLCM19_2019.tif) : <ul> <li>100% of 100m cells: Land Cover Map 2019 (Morton et al, 2020)</li> <li>66.84% of 100m cells: the LCM 2019 was subdivided by grazing intensity using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This impacts the grasslands, heathers, bogs and arable classes.</li> <li>Estimated overall contributions: 90% UKCEH, 10% JHI</li> </ul> </li> </ul> <ul> <li><strong>SSP1LEonLCM19_2050.tif</strong> : land use scenario (2050), which is within the scope of a SSP1 - Low emissions scenario (Shared Scocio-Economic Pathways). The scenario was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a><br><br><strong>Contributions</strong> to the scenario dataset (SSP1LEonLCM19_2050.tif) : <ul> <li>cf. contribution to the baseline (above)</li> <li>14% of 100m cells: modelled land use change</li> <li>Estimated overall contributions: 85% UKCEH, 15% JHI</li> </ul> </li> </ul> <p> </p> <p><strong>Main references:</strong></p> <p>Morton, R. D., Marston, C. G., O’Neil, A. W., & Rowland, C. S. (2020). Land Cover Map 2019 (25m rasterised land parcels, GB) [Data set]. NERC Environmental Information Data Centre. <a href="https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC">https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC</a></p> <p>Wardell-Johnson, D. (2022) Stocking rates derived from IACS 2019 version 4. <br>Based on data from Land Parcel Information System (2019) courtesy of Rural Payments and Inspections Division, Scottish Government.<br>Based on data from the June Agricultural Census (2019) courtesy of Rural and Environment Science and Analytical Services, Agricultural Statistics team, Scottish Government.</p> <p>Chapman, P. (2007) Conservation Grazing of Semi-natural Habitats. Technical note TN586. SAC tn586-conservation.pdf (sruc.ac.uk)</p> <p>FAS (2021) Practical Guide: Managing Peatlands and Upland Habitats. <a href="https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/">https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/ </a>(author: Paul Chapman)</p> <p>Castellazzi, M.S.; Gimona, A. (2021) SLM-OptionsTool, a land use change tool for Ecosystem Services (arcgis toolbox and user manual included, part of RESAS Deliverable-O1.4.2ciiD27).</p> <p>Castellazzi, M.S., Matthews, J., Angevin, F., Sausse, C., Wood, G.A., Burgess, P.J., Brown I., Conrad, K.F., Perry J.N. (2010). Simulation scenarios of spatio-temporal arrangement of crops at the landscape scale . Environmental Modelling and Software 25, 1881-1889. <a href="https://doi.org/10.1016/j.envsoft.2010.04.006">https://doi.org/10.1016/j.envsoft.2010.04.006</a> </p> <p><a href="https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts">https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts</a></p>
Global anthropogenic NOx emissions from 2019 to 2022 based on satellite NO2 observations and GEOS-Chem model
<p><a href="../api/records/10947114/draft/files/emissions.nc/content" target="_blank" rel="noopener noreferrer">emissions.nc</a><a href="10052904"> includes the monthly data of global anthropogenic nox emissions from 2019 to 2022, using the GEOS-Chem model combined with TROPOMI satellite observation data</a></p>
Figure 1 in First record of Sphaerotheca magadha Prasad et al., 2019 (Amphibia: Anura: Dicroglossidae) From West Bengal, India
Figure 1. Distribution map of Sphaerotheca magadha Prasad et al., 2019.
Predicted time‐to‐recover (days) for southeast Australia forested regions affected by the 2019–2020 bushfires.
<p>Model prediction of time-to-recover of southeast Australian eucalypt forests that burned during the megafires of 2019-2020. See the following publication for details regarding its creation.</p> <p>Rifai, S. W., De Kauwe, M. G., Gallagher, R. V., Cernusak, L. A., Meir, P., & Pitman, A. J. (2024). Burn severity and post‐fire<br>weather are key to predicting time‐to‐recover from Australian forest fires. Earth's Future, 12, e2023EF003780. https://doi.org/10.1029/2023EF003780</p>
Supporting Materials for: The calm before the (next) storm: no third outburst in 2019--2020, and ongoing monitoring of the transient AGN IC 3599
<p>This deposit contains supporting materials for the article "The calm before the (next) storm: no third outburst in 2019--2020, and ongoing monitoring of the transient AGN IC 3599" by Grupe, Komossa, and Wolsing (2024), ApJ. </p> <p>The deposit has the following machine-readable tables:</p> <p><a href="../api/records/10899673/draft/files/MRT_longterm_xray_fig1.txt/content" target="_blank" rel="noopener noreferrer">MRT_longterm_xray_fig1.txt</a>: contains the data used to create Figure 1. These are 0.2-2.0 keV fluxes of IC 3599 fromm ROSAT, Chandra, and Swift observations. </p> <div> </div> <div><a href="../api/records/10899673/draft/files/MRT_swift_observations.txt/content" target="_blank" rel="noopener noreferrer">MRT_swift_observations.txt </a>: contains the information about the Swift XRT and UVOT observations, including the Target ID, segement, start and end times in UT, the MJD of the middle of the exposure times in the XRET and each of the UVOT filters. This table is Table 1 in the Appendix of the paper</div> <div> </div> <div> </div> <div><a href="../api/records/10899673/draft/files/MRT_Swift_results.txt/content" target="_blank" rel="noopener noreferrer">MRT_Swift_results.txt</a> : contains the fluxes measured in the XRT and UVOT filters. These results were used to create Figures 2 - 7. This table is Table 3 in the Appendix of the paper. </div> <div> </div> <p>The deposit conatins the UV W2 image that was use to create Figure 10. This is <a href="../api/records/10899673/draft/files/uvw2_sum_low_2013_2023.fits/content" target="_blank" rel="noopener noreferrer">uvw2_sum_low_2013_2023.fits</a></p> <p> </p> <p>The files <a href="../api/records/10899673/draft/files/arf_pc_2013_2023.fits/content" target="_blank" rel="noopener noreferrer">arf_pc_2013_2023.fits</a> , <a href="../api/records/10899673/draft/files/backgr_spec_pc_2013_2023.fits/content" target="_blank" rel="noopener noreferrer">backgr_spec_pc_2013_2023.fits</a> , <a href="../api/records/10899673/draft/files/source_spec_pc_2013_2023.fits/content" target="_blank" rel="noopener noreferrer">source_spec_pc_2013_2023.fits </a>were used for the X-ray spectral analysis of the low state data between 2013-2023. This low state spectrum is displayed in Figures 8 and 9, and was used for the</p> <p>spectral energy distribution in Fugure 11. </p> <p> </p> <p>The files <span><a href="../api/records/10899673/draft/files/arf_pc_high_2010.fits/content" target="_blank" rel="noopener noreferrer">arf_pc_high_2010.fits</a></span> , <span><a href="../api/records/10899673/draft/files/backgr_spec_high_2010.fits/content" target="_blank" rel="noopener noreferrer">backgr_spec_high_2010.fits</a></span> , <span><a href="../api/records/10899673/draft/files/source_spec_high_2010.fits/content" target="_blank" rel="noopener noreferrer">source_spec_high_2010.fits</a></span> were used to do the X-ray spectral</p> <p>analysis of the 2010 high state data. This spectrum is displayed in Figure 9 and was used in Figure 11 to show the spectral energy distribution. </p> <p> </p>
Fault zone material files for dynamic rupture modeling of the 2019 Ridgecrest earthquakes
<p>This repository contains the material files used to add a low-velocity fault zone to a dynamic rupture model of the 2019 Ridgecrest sequence (Taufiqurrahman et al., 2023, Nature).</p>
ECMWF/HRES - 2019 to 2021 - Great Britain
<p>Dataset of single-value Numerical Weather Predictions used as input for the probabilistic wind power forecasting tool developed for the paper: <strong>Seamless short- to mid-term probabilistic wind power forecasting</strong>.</p>
16545_2019_07_16_aggregates_CTP_switch_2_0glu_0_0glu_URA7young_URA8young_URA8old_secondRun_01 (part 2)
High throughput time lapse experiment. Details can be found on the 'txt' files inside.
16545_2019_07_16_aggregates_CTP_switch_2_0glu_0_0glu_URA7young_URA8young_URA8old_secondRun_01 (part 1)
High throughput time lapse experiment. Details can be found on the 'txt' files inside.
World Pain Day 2019 SEFAC
<p>Dataset containing surveys conducted with patients experiencing pain in various Spanish pharmacies, on the occasion of World Pain Day 2019. The initiative was carried out by the Spanish Society of Clinical, Family, and Community Pharmacy (SEFAC).</p> <p> </p> <p>Dataset que recopila encuestas realizadas a pacientes con dolor en diversas farmacias españolas, con motivo del Día Mundial del Dolor 2019. La iniciativa fue llevada a cabo por la Sociedad Española de Farmacia Clínica, Familiar y Comunitaria (SEFAC).</p> <p> </p> <p> </p>
Sentinel-5p/TROPOMI SO2 Layer Height dataset covering the Raikoke volcanic eruption 2019
<p>Sentinel-5p/TROPOMI SO2 Layer Height product generated by DLR as part of the INPULS project using the retrieval algorithm developed in the framework of the ESA Sentinel-5p Innovations: SO2 LH (S5P+I: SO2LH) project</p> <p>The dataset contains SO2LH results for the timeframe 2019-06-22 until 2019-07-30 covering the eruptive period of the Raikoke volcanic eruption. This dataset was used as input for the paper of Inness et al. "The CAMS volcanic forecasting system utilizing near-real time data assimilation of S5P/TROPOMI SO2 retrievals" (2021, submitted to GMD)</p> <p>The dataset contains modified Copernicus Sentinel data processed by DLR</p>
Population-based age-stratified seroepidemiological investigation protocol for coronavirus 2019 (COVID-19) infection in the Federation of Bosnia and Herzegovina
<p>Results of population-based age stratified seroepidemiological investigation in the Federation of Bosnia and Herzegovina</p>
Terms from the SPARC Term Request Pipeline (Oct 2019 - Aug 2021)
<p>Terms analyzed for the paper "Extending and using anatomical vocabularies in the Stimulating Peripheral Activity to Relieve Conditions (SPARC) project". These terms were submitted by SPARC investigators to the SPARC term request pipeline. The SPARC anatomical term request pipeline is an iterative curation process that consists of three main steps: term request, term review, and term engineering. <strong> </strong>This work was supported by NIH grant 3OT2OD030541 from the Office of the Director through the Stimulating Peripheral Activity to Relieve Conditions (SPARC) program.</p>
A spatiotemporal reconstruction of daily ambient temperature using satellite data in the Megalopolis of Central Mexico from 2003–2019
<p>In this project, we built a model to predict the mean, maximum, and minimum temperature on each day at each square in a 1-km grid for an area around Mexico City.</p> <p>See the README at <a href="https://github.com/justlab/Mexico_temperature/blob/master/README.rst">https://github.com/justlab/Mexico_temperature/blob/master/README.rst</a> for more information and instructions, or see the publication at:</p> <p>Gutiérrez-Avila, I., Arfer, K. B., Wong, S., Rush, J., Kloog, I., & Just, A. C. (2021). A spatiotemporal reconstruction of daily ambient temperature using satellite data in the Megalopolis of Central Mexico from 2003–2019. <em>International Journal of Climatology, 41</em>, 4095–4111. <code>doi:10.1002/joc.7060</code></p>
2019_0105_Karukonda
<p>A small rock-cut monastery to the east of Kothaguden. It consists of a stupa and two image shrines. The stupa opens deep recesses in four directions. The back wall of each recess carves a seated image of the Buddha. Rock-cut cells, which could also be used as monastic residences, carve a seated Buddha with attendants on the back wall and standing bodhisattvas on the side walls.</p>
Svalbox-DOM_2019-0013_Konusdalen-West
<p><strong>Model description: </strong><br> Extended model for Konusdalen West (Maaykedalen) featuring both extensional faults in the main outcrop and contractional pop-up structures in the outcrop further up.Refer to the MSc theses of Karoline Løvlie and Lise Nakken for further details on the interpretation.<br> <br> <strong>Data availability:</strong><br> All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.7.1.11797).<br> Exported model data use CRS epsg:32633.<br> <br> <strong>Show and visualise on <a href="http://svalbox.no/?page_id=3745">Svalbox.no</a>:</strong><br> Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community.<br> <br> For more information, contact the Svalbox team at svalbox[at]gmail.com.</p>
Electron-capture supernova progenitor models of Zha et al. ApJ 886:22 (2019)
<p>We provide 6 progenitor models of electron-capture supernova of Zha et al. ApJ 886:22 (2019). The progenitor models feature updated weak-interaction rates and two-dimensional hydrodynamic simulations for the deflagration phase.</p>
ASIT Wind and Waves 16-17 October 2019
<p>Measurements made from the Air-Sea Interaction Tower south of Martha's Vineyard on 16-17 October 2019.</p> <p>Waves were measured by Lidar at 10 Hz.</p> <p>Wind speed at 10 m height is a 20 min average.</p>
Output of aquaplanet idealized GCM simulations under perpetual solstice-like forcing from Singh 2019, J. Atmos. Sci.
<p># Overview</p> <p>This directory contains the output from simulations in the idealized aquaplanet GCM that were originally reported in Singh 2019, J. Atmos Sci. The contents are as follows:</p> <p>```<br> ├── Omega0.125_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega0.25_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega0.5_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega0.66_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega0.75_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega1.5_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega1_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega2_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega3_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega4_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega8_y0005-y0010.mean.1x5days.zonal_avg.nc<br> └── README.md<br> ```</p> <p>where `Omega*x` indicates the rotation rate multiplicative factor, with 1x being Earth's rotation rate. All simulations are performed with time-invariant northern solstice insolation. A full description of the simulation setups is available in Singh 2019.</p> <p># References</p> <p>Idealized GCM formulation: Frierson, Dargan M. W., Isaac M. Held, and Pablo Zurita-Gotor. “A Gray-Radiation Aquaplanet Moist GCM. Part I: Static Stability and Eddy Scale.” Journal of the Atmospheric Sciences 63, no. 10 (October 1, 2006): 2548–66. https://doi.org/10.1175/JAS3753.1.</p> <p>Simulations: Singh, Martin S. “Limits on the Extent of the Solsticial Hadley Cell: The Role of Planetary Rotation.” Journal of the Atmospheric Sciences 76, no. 7 (April 30, 2019): 1989–2004. https://doi.org/10.1175/JAS-D-18-0341.1.</p>
ScienceDex guides
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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.