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1,751 results for “Future”
Reanalysis and future climate projections of the physical state of the Gulf of Riga 1993-2100
<p><strong>Data sets</strong></p> <p>There are two data sets: (1) reanalysis (1993-2021) and (2) future projection (2015-2100). Future projection data set is split into 10 files.<br> The dataset provides gridded monthly mean values of physical parameters in the Gulf of Riga, Baltic Sea. The variables of the dataset of the physical state of the Gulf of Riga are as follows (Long name: <em>acronym</em>, <em>units</em>)</p> <ul> <li>Potential temperature: <em>thetao, </em>°<em>C</em></li> <li>Sea water salinity: <em>s, g/kg</em></li> <li>Eastward sea water velocity: <em>ocu, m/s</em></li> <li>Northward sea water velocity: <em>ocv, m/s</em></li> <li>Deviation of sea-level from the mean sea level: <em>zos, m</em></li> <li>Sea ice area fraction <em>siconc</em>, m<sup>2</sup>/ m<sup>2</sup></li> <li>Sea ice thickness: <em>sithick, m</em></li> <li>Bathymetry: <em>bathymetry, m </em>(included only in reanalysis data set)</li> </ul> <p>The grid size of the dataset is 15 (depth) x 203 (latitude) x 187 (longitude). The horizontal grid spacing is 0.5 nm; the vertical grid has 15 depth layers – 2 m deep surface layer and 4 m step for deeper layers. The time resolution of the dataset is monthly – the monthly mean value is provided in the 1st day of the month in time dimension.<br> The original climatic calculations are based on the University of Latvia (UL) set-up of the Hiromb-BOOS model routinely implemented for the operational oceanography in the Baltic Sea and the Gulf of Riga in Latvia. Its parametrization is empirically suited for climatical reanalysis in the Gulf of Riga domain. The original output of the model run is hourly data series.</p> <p><strong>Reanalysis</strong></p> <p>Time period: 1993-2021 (29 years).<br> The main characteristics of the input data and approach for the reanalysis run are as follows:</p> <ul> <li>EMODNET2020 bathymetry.</li> <li>Initial conditions – bias corrected Copernicus Marine Service (CMS).</li> <li>Atmospheric forcing – ERA5 meteorology with improved cloudiness.</li> <li>Boundary conditions from CMS 1993-2018 reanalysis and CMS operational archive (2019-2021) with bias correction for waterlevel in CMS forecast.</li> <li>River inflow – 15 main rivers taken into consideration according to E-HYPE hydrological model data. E-HYPE discharge multiplied by 0.75.</li> <li>Tides: astronomic calculations.</li> </ul> <p><strong>Future climate projection</strong></p> <p>Time period: 2015-2100 (86 yrs).<br> The main characteristics of the input data and approach for the future climate projections run are as follows:</p> <ul> <li>Emodnet2020 bathymetry.</li> <li>Initial conditions – bias corrected Copernicus Marine Service (CMS).</li> <li>Boundary conditions from downscaled CMIP6 climate projection model NorESM2-MM_ssp585_r1i1p1f1 (search string – project:'CMIP6', source_id:'NorESM2-MM', experiment_id:'ssp585', variant_label:'r1i1p1f1').</li> <li>River inflow – 15 main rivers taken into consideration according to E-HYPE climatological model (SMHI_RCA4_HadGEM2-ES_rcp45). E-HYPE discharge multiplied by 1.093.</li> <li>The past period data was used for the downscaling: <ul> <li>ERA5 reanalysis data was used for the downscaling of the atmospheric forcing time series of CMIP6 climate projection model,</li> <li>CMS reanalysis model data was used for the downscaling of the sea state time series.</li> </ul> </li> <li>Downscaled variables: eastward and northward components of the near surface wind, air temperature, air pressure, water temperature, water salinity, sea level.</li> </ul>
Data from: The impacts of climate change, energy policy, and traditional ecological practices on future firewood availability for Diné (Navajo) People
<p>These data are part of a data portal that accompanies the special issue 'Climate change adaptation needs a science of culture,' published in Philosophical Transactions of the Royal Society B in 2023. To access the data portal, please visit <a href="https://doi.org/10.5061/dryad.bnzs7h4h4"><strong>https://doi.org/10.5061/dryad.bnzs7h4h4</strong></a>.</p> <p>The files consist of the code of an agent-based model (ABM) in a NetLogo, detailed documentation of the ABM in a standard format, and a table of data exported from the simulation experiment reported on in the paper. By downloading the Netlogo file, one could not only rerun the experiment we report on and recreate the data table but toggle parameters or edit the model to explore other dynamics.</p>
FUTURES land change forecasts in response to flooding in Charleston area, South Carolina (2019-2050)
<p>Climate-aware scenarios of FUTURES land change projections in Charleston area (Cherleston, Dorchester, Berkeley) for 2019-2050.</p> <p>Zipped folder <code>results</code> contains FUTURES v3 simulation runs for 3 counties in South Carolina, USA (Charleston, Berkeley, Dorchester) from 2019 to 2050. Included are five <em>climate-aware</em> scenarios (reactive, managed retreat, resist, polarized population, trapped population) and one scenario which does not take future climate conditions into account (<em>dynamic development</em>). Each folder contains 50 monte-carlo simulation results with <code>developed_seed_X.tif</code> and <code>adapted_seed_X.tif</code> where <code>X</code> goes from 1 to 50. Values of <code>developed_seed_X.tif</code> range from -31 to 31, where the positive values represent simulation step when an undeveloped pixel was developed and the negative values represent simulation step when a developed pixel was abandoned. Zero stands for initial development. For example, value -11 means the particular pixel was abandoned in 2030. Note that values of raster files in <code>Dynamic development</code> folder range from -1 to 31, where -1 means undeveloped and the rest is the same as above. Values of <code>adapted_seed_X.tif</code> range from 0 to 100, where 0 means trapped (flooded but no adaptation), the other values represent return period for which the pixel is adapted (e.g. 2-, 5-, 10-, 20-, 50- and 100-year flood). The files' CRS is Albers Conic Equal Area, NAD83(2011) datum.</p> <p>Additionally, we include <code>migration_matrix.csv</code> derived from IRS data, that contains probability values of moving from an origin county in the case study (row) to any other counties (columns).</p>
UKESM1.0-ice simulation output used as test data in Burgard et al., Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks
<p>These files contain NEMO ocean model output and domain definitions for the Southern Ocean from UKESM1.0-ice simulations described in section 6.3.2 of Smith et al. "Coupling the U.K. Earth System Model to Dynamic Models of the Greenland and Antarctic Ice Sheets" , Journal of Advances in Modeling Earth Systems, 2021</p> <p>Files labelled "bf663" are the UKESM simulation referred to in that section as "constant 1970 greenhouse gas and other forcings". Files labelled bi646 are the UKESM simulation referred to in that section as "instantaneously quadrupled 1970 CO<sub>2</sub> concentrations".</p> <p>They were used as test data for the performance of neural networks in Burgard et al., "Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks", Journal of Advances in Modeling Earth Systems 2023.</p>
"It was recorded on Sunday, morning of the 28th of September as some of the slower runners of the Berlin Marathon made it past Torstrasse near my flat. Iwas out to buy some bread for breakfast, but Iusually bring a camera and my Edirol R-1 recorder whenever Igo out. Since Iwas freshly returned to Berlin Iguess Iwas sensitive to the more antiquated sounds which still survive there, like that of the organ grinder. Iam generally interested in how human beings are replacing the presence of Nature with an artificial environment made entirely by human hands (and thus far more understandable, it is hoped). In this new Human Nature, the sounds of Nature are also Human made. Iwrite about these things, but Ialso use the sounds in my videos and my interactive and generative media work, so generally Iam wandering around building up my archive of media documents for use as material in future works." [Baruch/ gottlieb]17 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"It was recorded on Sunday, morning of the 28th of September as some of the slower runners of the Berlin Marathon made it past Torstrasse near my flat. Iwas out to buy some bread for breakfast, but Iusually bring a camera and my Edirol R-1 recorder whenever Igo out. Since Iwas freshly returned to Berlin Iguess Iwas sensitive to the more antiquated sounds which still survive there, like that of the organ grinder. Iam generally interested in how human beings are replacing the presence of Nature with an artificial environment made entirely by human hands (and thus far more understandable, it is hoped). In this new Human Nature, the sounds of Nature are also Human made. Iwrite about these things, but Ialso use the sounds in my videos and my interactive and generative media work, so generally Iam wandering around building up my archive of media documents for use as material in future works." [Baruch/ gottlieb]17
A Medical Research Study Designed to Determine if Venglustat Can be a Future Treatment for ADPKD Patients
ClinicalTrials.gov study NCT03523728. IPD Sharing: YES. Countries: 23. Publications: 3.
Data from: Signatures of local adaptation in candidate genes of oaks (Quercus spp.) in respect to present and future climatic conditions
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Data from: Ability of seedlings to survive heat and drought portends future demographic challenges for five southwestern US conifers
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Data from: Contemporary and future distributions of cobia, Rachycentron canadum
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Phenology-informed decline risk of estuarine fishes and their prey suggests potential for future trophic mismatches
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The greater role of Southern Ocean warming compared to Arctic Ocean warming in shifting future tropical rainfall patterns
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Data from: Model-aided climate adaptation for future maize in the U.S.
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Data for: Curbing global solid waste emissions toward net-zero warming futures
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Rebuilding green infrastructure in boreal production forest given future global wood demand
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Data and R script from: Females prioritize future over current offspring in wild seasonally breeding Assamese macaques
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Plant-hummingbird interactions in the Atlantic Forest: Current and future projections
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Nonbreeding distributions of four declining Nearctic-Neotropical migrants are predicted to contract under future climate and socioeconomic scenarios
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The past, present, and future of predator-prey interactions in a warming world: using species distribution modeling to forecast ectotherm-endotherm niche overlap
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Vulnerability of estuarine systems in the contiguous United States to water quality change under future climate and land-use
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Data from: Immediate genetic augmentation and enhanced habitat connectivity are required to secure the future of an iconic endangered freshwater fish population
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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.