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739 results for “data loss”
Data and code for figures: Temperature dependence of microwave losses in lumped-element resonators made from superconducting nanowires with high kinetic inductance
<p>This directory contains the datasets and code (if applicable) for generating the figures in the research article: Temperature dependence of microwave losses in lumped-element resonators made from superconducting nanowires with high kinetic inductance, <em>Supercond. Sci. Technol.</em> <strong>37</strong> 075013</p>
Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing
<p>The dataset contains supporting data for the paper submitted to The Cryosphere "Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing".<br><br>OGGM_area_projections.nc contains data for Figure 3.<br>OGGM_volume_projections contains data for Figure 4.</p> <p>OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5.</p> <p>OGGM_solid_ice_discharge_regions.nc contains data for Figure 6.</p> <p>OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7.</p> <p>OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.</p>
Data and analysis supplement for: Functional imagery training versus motivational interviewing for weight loss: a randomised controlled trial of brief individual interventions for overweight and obesity.
<p>This submission provides the data and code for analyses reported in our publication.</p>
Data supporting 'Ice loss in the European Alps until 2050 using a fully assimilated, deep-learning-aided 3D ice-flow model'
<p>The dataset supporting our publication '<strong>Ice loss in the European Alps until 2050 using a fully assimilated, deep-learning-aided 3D ice-flow model</strong>' in <em>Geophysical Research Letters.</em></p> <p>The main .zip archive contains a set of NetCDF files detailing:</p> <ul> <li>Initial optimised glacier states (geology-optimized...)</li> <li>Simulation results (Prog20...)</li> </ul> <p>Initial states and results are given by cluster (see Figure 1 in the paper), as shown in all filenames (C1 through to C12). Prognostic simulation filenames additionally distinguish between runs between 1999 and 2019 (Prog2020) and between 2020 and 2050 (Prog2050). 'NV'/'NoVel' and 'NT'/'NoThk' refer to simulations using the partial optimisation (optimisation without including velocity/thickness observations) as detailed in the paper. 'AV' at the end of the filename denotes the integrated area/volume results file, as opposed to the 2D raster results file. A 'V' before the cluster designation shows that the simulation used the variable SMB as opposed to the fixed SMB (see the paper for details). 'ID' before the cluster designation shows that the simulation was using extrapolated SMB based on the trend in SMB since 2000, instead of assuming the continuation of the current SMB. 'ID' on its own denotes linear extrapolation and 'IDQ' denotes quadratic extrapolation (not used in the published paper). 'SMBF' in the filename shows that the simulation used the SMB-elevation feedback.</p> <p>The additional .zip archive contains the code of IGM v1.0 used to produce the model results. For details on installing and using IGM, please see the Github page at <a href="https://github.com/jouvetg/igm.The">https://github.com/jouvetg/igm</a>.</p> <p>A further .zip archive (in version 3 - Sims2010-2022.zip) contains the simulations based on linear extrapolation of the observed trend in SMB between 2010 and 2022, following the same nomenclature as in the principal archive (see above).</p> <p>Version 4 contains an additional mosaicked DEM of the results for the whole Alps with the ice removed to give the complete basal topography (kindly processed by T. Léger at UNIL) using the Japan Aerospace Exploration Agency (2021) ALOS World 3D 30 meter DEM. V3.2, Jan 2021. Distributed by OpenTopography. <a title="https://doi.org/10.5069/G94M92HB" href="https://doi.org/10.5069/G94M92HB" target="_blank" rel="noreferrer noopener">https://doi.org/10.5069/G94M92HB</a>. Accessed: 2024-09-09.</p>
Supplementary Data for "Climate change may induce connectivity loss and mountaintop extinction in Central American forests"
<p>Supplementary data underlying the main figures presented in the publication "<strong>Climate change may induce connectivity loss and mountaintop extinction in Central American forests</strong>"</p>
raw data of Gut microbiota remodeling and intestinal adaptation to lipid malabsorption after enteroendocrine cell loss in adult mice
<p>Microbiome dataset for "Gut microbiota remodeling and intestinal adaptation to lipid malabsorption after enteroendocrine cell loss in adult mice" publication</p> <p>https://doi.org/10.1016/j.jcmgh.2023.02.013</p> <p> </p>
Data on eye movements of glaucoma patients with asymmetrical visual field loss during free viewing.
<p>Raw eye tracking data and processed eye movement data were recorded from fifteen participants with assymmetrical visual field loss (visual field worse in one eye) while they freely viewed 270 images of nature with each eye monocularly.</p>
Supplementary Data for Low-loss stable storage of 1.2 Angstrom X-ray pulses in a 14 m Bragg cavity
<p>Supplementary Data for Margraf, R. et al. "Low-loss stable storage of 1.2 Angstrom X-ray pulses in a 14 m Bragg cavity," Nature Photonics, 2023.</p>
Termite foraging data from bait mass loss at eleven locations at the Jornada Basin LTER site, 1988-2000
This data package contains data on yearly mass loss of termite baits at the Jornada Basin LTER site in southern New Mexico, USA. Termites are important to litter decomposition and nutrient cycling in desert grasslands. This study measured annual feeding activity on paper baits by subterranean termites in desert shrubland and black-grama (Bouteloua eriopoda) grassland ecosystems over twelve years. Eleven sites, known as the "consumer plots" were included in the study. Toilet paper roll termite baits were placed on grids on each consumer plot. Data include initial bait weights before deployment, and bait weights after they were retrieved from the field each year. Mass loss of the baits was calculated as a measure of termite foraging activity. This study is complete.
Data set: "A 21st Century Warming Threshold for Sustained Greenland Ice Sheet Mass Loss"
<p>This data set includes the materials required to reproduce the figures and tables presented in the study: "A 21<sup>st</sup> Century Warming Threshold for Sustained Greenland Ice Sheet Mass Loss". The data consist of:</p> <ul> <li>Maps of annual historical and projected surface mass balance (SMB) of the Greenland ice sheet (GrIS) from RACMO2.3p2 at 1 km spatial resolution in NetCDF format.</li> </ul> <ol> <li><strong>smb_rec.1950-2014.BN_RACMO2.3p2-CESM2-Historical.1km.YY.nc</strong>: annual cumulative GrIS SMB (kg m<sup>-2</sup> or mm w.e. per year) from RACMO2.3p2 forced by CESM2 for the historical period 1950-2014, further statistically downscaled to 1 km spatial resolution.</li> <li><strong>smb_rec.2015-2099.BN_RACMO2.3p2-CESM2-SSP5-85.1km.YY.nc</strong>: annual cumulative GrIS SMB (kg m<sup>-2</sup> or mm w.e. per year) from RACMO2.3p2 forced by CESM2 under a high-end warming scenario SSP5-8.5 for the period 2015-2099, further statistically downscaled to 1 km spatial resolution.</li> <li><strong>Icemask_Topography_lon_lat_average_1km_GrIS.nc</strong>: mask file including an ice mask (Promicemask) separating Greenland's peripheral glaciers and ice caps (values 1 and 2) from the main ice sheet (value 3), surface topography derived from the GIMP DEM, and longitude/latitude coordinates on the 1 km grid. </li> </ol> <p>NB: the NetCDF files above use a Polar Stereographic North (EPSG:3413) projection with a horizontal resolution of 1 km x 1 km. The reference point is located at 45ºW longitude and 70ºN latitude.</p> <ul> <li>Time series of historical and projected annual GrIS-integrated SMB (Gigatons or Gt per year) and annual mean GrIS temperature (TGrIS; K) from RACMO2.3p2 at 1 km spatial resolution in ASCII format.</li> </ul> <ol> <li><strong>SMB_TGrIS_RACMO2.3p2-CESM2_Historical_1950-2014.dat</strong>: time series of annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) and mean TGrIS (K) from the CESM2-forced RACMO2.3p2 simulation for the historical period 1950-2014.</li> <li><strong>SMB_TGrIS_RACMO2.3p2-CESM2_SSP5-8.5_2015-2099.dat</strong>: time series of annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) and mean TGrIS (K) from the CESM2-forced RACMO2.3p2 projection under a high-end warming scenario SSP5-8.5 (2015-2099).</li> </ol> <ul> <li>Time series of historical and projected reconstruction of annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) and mean TGrIS (K) from 12 CESM2 historical members and 10 CESM2 projections under various warming scenarios, namely SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.</li> </ul> <ol> <li><strong>Reconstructed_SMB_CESM2_Historical_1950-2014.dat</strong>: time series of reconstructed annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) from the parent historical CESM2 simulation (HIST-parent) used to force RACMO2.3p2 and 11 additional historical CESM2 members (HIST-X) for 1950-2014. </li> <li><strong>Reconstructed_SMB_CESM2_Projections_2015-2099.dat</strong>: time series of reconstructed annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) from the parent CESM2 projection (SSP5-8.5-parent) used to force RACMO2.3p2 and 9 additional CESM2 projection members (SSP1-2.6-X to SSP5-8.5-X ) for 2015-2099. </li> <li><strong>TGrIS_CESM2_Historical_1950-2014.dat</strong>: time series of annual mean TGrIS (K) from the parent historical CESM2 simulation (HIST-parent) used to force RACMO2.3p2 and 11 additional historical CESM2 members (HIST-X) for 1950-2014.</li> <li><strong>TGrIS_CESM2_Projections_2015-2099.dat</strong>: time series of annual mean TGrIS (K) from the parent CESM2 projection (SSP5-8.5-parent) used to force RACMO2.3p2 and 9 additional CESM2 projection members (SSP1-2.6-X to SSP5-8.5-X ) for 2015-2099. </li> </ol> <p>NB: the file <strong>Crossref_sim_names.txt </strong>cross-references the simulation abbreviations in the above .dat files to official simulation names from the National Center for Atmospheric Research (NCAR).</p> <p>The daily downscaled SMB data set from the CESM2-forced RACMO2.3p2 historical simulation and SSP5-8.5 projection are freely available from the authors upon request and without conditions (contact: <strong>b.p.y.noel@uu.nl</strong>). Besides SMB, the data set includes daily total precipitation (snow and rain), snowfall, total melt (snow and ice), meltwater runoff, retention and refreezing, total sublimation (surface and drifting snow), snow drift erosion, as well as 2 m air temperature at 1 km horizontal resolution. </p> <p>Abstract: "Under anticipated future warming, the Greenland ice sheet (GrIS) will pass a threshold when meltwater runoff exceeds the accumulation of snow, resulting in a negative surface mass balance (SMB < 0) and sustained mass loss. In spite of several recent warm summers with high melt rates, SMB < 0 has not been reached since at least the year 1958. Here we dynamically and statistically downscale the outputs of an Earth system model to 1 km resolution to infer that a Greenland near-surface atmospheric warming of 4.5 ± 0.3 °C—relative to pre-industrial—is required for GrIS SMB to become persistently negative. Climate models from CMIP5 and CMIP6 translate this regional temperature change to a global warming threshold of 2.7 ± 0.2 °C. Under a high-end warming scenario, this threshold may be reached around 2055, while for a strong mitigation scenario it will likely not be passed."</p>
Raw dataset and additional data for article "Nonmotor symptoms associated with progressive loss of dopaminergic neurons in a mouse model of Parkinson's disease"
<p>Dataset from the project investigating the presence of nonmotor symptoms of Parkinson's disease in a mouse model of progressive loss of dopaminergic neurons (namely,TIF-IADATCreERT2 strain). Mice were tested for executive and cognitive functions (males: Operant Sensation Seeking test, OSS; females: Probabilistic Reversal Learning Task in Intellicages), olfactory acuity (males: buried food test), saccharin preference (males and females), and motor performance (males and females: test using CatWalk apparatus).</p><p>The dataset includes files used to perform statistical analyses but their names may vary from the ones used in the scripts. For the purpose of recreating our analyses, please refer to the GitHub page, where both scripts and input data file names (in 'Raw data files' section) are compliant: https://github.com/annaradli/tif-pd-behavior.</p><p><strong>Description of files:</strong></p><p><i>Raw data files:</i></p><ul><li>animals_info.csv - animals data: genotype, sex, age, Intellicage tag identifier</li><li>catwalk_run_statistics_all_females.csv - data recorded in CatWalk apparatus for females</li><li>catwalk_run_statistics_all_males.csv - data recorded in CatWalk apparatus for males</li><li>females_weight_raw_data_revised.csv - females' body weight (revised for containing Polish words)</li><li>intellicage_raw_data.csv - data recorded in IntelliCage exported to .csv format</li><li>intellicage_raw_data_R.RData - data recorded in IntelliCage in .RData format</li><li>males_weight_raw_data.csv - males' body weight</li><li>olfactory_time_digging_raw_data.csv - time to start digging at the right place in the buried food test</li><li>olfactory_time_retrieve_raw_data.csv- time to retrieve cracker in the buried food test</li><li>oss_raw_data.csv - data recorded in the OSS test</li><li>saccharin_preference_males_raw_data.csv - saccharin preference test results for males</li><li>snvta_cells_count.csv - number of TH+ cells in SN and VTA in male mice (3+3) 14 weeks after tamoxifen treatment</li></ul><p><i>Additional data files:</i></p><ul><li>all_anova.xlsx - summary of two-way ANOVAs of all behavioral tests and weight measurements for males and females</li><li>catwalk_complete.xlsx - CatWalk complete dataset with datapoints</li><li>catwalk_correlation_between_paws.xlsx - correlation coefficients of CatWalk parameters between the left and right paws</li><li>catwalk_reduced.xlsx - CatWalk parameters used in linear regression model reduction of data</li><li>intelli.xlsx - IntelliCage data summarized in bins</li><li>oss.xlsx - operant sensation-seeking data</li></ul><p>v2 contains the corrected 'animals_info.csv' file without an unnecessary column.</p><p>v3 has a revised version of file containing females' weight measurements and also added a file with midbrain cell counts</p><p>v4 has a whole section of 'Additional data files' added</p>
Single-cell mouse and PC9 data for "TP53 loss with whole genome doubling mediates heterogeneous intra-patient therapy response through Chromosomal Instability"
<p>This repository includes the processed data (including copy number profiles and related analysis) for the E/EP mouse tumors and for the PC9 resistance cell lines for all the analyses of the manuscript "TP53 loss with whole genome doubling mediates heterogeneous intra-patient therapy response through Chromosomal Instability".</p><p>The code for the related analyses is available in GitHub at https://github.com/zaccaria-lab/TP53loss_WGD</p>
Data from: Mating tactic influences body condition loss in Rocky Mountain bighorn rams (Ovis canadensis)
<p>In polygynous mating systems, males often employ alternative mating tactics to enhance reproductive success. In Rocky Mountain bighorn sheep, the primary tactics are coursing, involving mating chases, and tending, involving mate guarding. While both tactics are energetically costly and can diminish body condition, it remains unclear whether the associated costs significantly differ and to what extent. Our study investigated the impact of mating tactics, specifically the proportion of time allocated to each, on body condition loss during the rutting season in bighorn sheep. Using a non-invasive photographic method to estimate body condition loss, we found that the proportion of time a male spent tending significantly correlated with body condition loss. In contrast, the percentage of time spent coursing did not show a significant effect. Age was associated with the choice of tactic, with younger males predominantly coursing, older males primarily tending, and some intermediate-aged males employing both tactics concurrently. Despite the higher energetic costs, our results reveal the flexibility in tactic usage and indicate that tending, while demanding, is a high-cost, high-gain strategy, as tending rams are known to sire more offspring.</p>
Data from: Climatic damage cause variations of agricultural insurance loss for the Pacific Northwest region of the United States
<p>Agricultural crop insurance is an important component for mitigating farm risk, particularly given the potential for unexpected climatic events. Using a 2.8 million nationwide insurance claim dataset from the United States Department of Agriculture (USDA), this research study examines spatiotemporal variations of over 31,000 agricultural insurance loss claims across the 24-county region of the inland Pacific Northwest (iPNW) portion of the United States, from 2001 to 2022. Wheat is the dominant insurance loss crop for the region, accounting for over 2.8 billion dollars in indemnities, with over 1.5 billion dollars resulting in claims due to drought (across the 22 year time period). While fruit production generates considerably lesser insurance losses (400 million dollars) as a primary result of freeze, frost, and hail, overall revenue ranks number one for the region, with 2 billion dollars in sales, across the same time range. Principal components analysis of crop insurance claims showed distinct spatial and temporal differentiation in wheat and apple insurance losses using the range of damage causes as factor loadings. The first two factor loadings for wheat accounts for approximately 50 percent of total variance for the region, while a separate analysis of apples accounts for over 60 percent of total variance. These distinct orthogonal differences in losses by year and commodity in relationship to damage causes suggest that insurance loss analysis may serve as an effective barometer in gauging climatic influences.</p>
Data for "Future global mangrove losses and the key role of protected areas in their conservation"
<p>Content (spatial resolution, data info)</p> <ol> <li>Mangrove table input to the machine learning model (1 table)</li> <li>Results of mangrove cumulative loss proportion under SSP1 and SPP5 scenarios (1km, 1 ZIP)</li> <li>All processing R codes can be accessed: https://github.com/2pangp/Mangrove-TidalFlat_Distribution</li> </ol>
Supplementary data for: "Historical glacier change on Svalbard predicts doubling of mass loss by 2100"
<p>Supplementary datasets for:</p> <p>Geyman, E.C., van Pelt, W.J.J., Maloof, A.C., Faste Aas, H., and Kohler, J., 2022. "Historical glacier change on Svalbard predicts doubling of mass loss by 2100." Nature.</p> <p>Abstract:</p> <p>The melting of glaciers and ice caps accounts for about one-third of current sea-level rise, exceeding the mass loss from the more voluminous Greenland or Antarctic Ice Sheets. The Arctic archipelago of Svalbard, which hosts spatial climate gradients that are larger than the expected temporal climate shifts over the next century, is a natural laboratory to constrain the climate sensitivity of glaciers and predict their response to future warming. Here we link historical and modern glacier observations to predict that twenty-first century glacier thinning rates will more than double those from 1936 to 2010. Making use of an archive of historical aerial imagery from 1936 and 1938, we use structure-from-motion photogrammetry to reconstruct the three-dimensional geometry of 1,594 glaciers across Svalbard. We compare these reconstructions to modern ice elevation data to derive the spatial pattern of mass balance over a more than 70-year timespan, enabling us to see through the noise of annual and decadal variability to quantify how variables such as temperature and precipitation control ice loss. We find a robust temperature dependence of melt rates, whereby a 1°C rise in mean summer temperature corresponds to a decrease in area-normalized mass balance of -0.28 m yr<sup>-1</sup> of water equivalent. Finally, we design a space-for-time substitution8 to combine our historical glacier observations with climate projections and make first-order predictions of twenty-first century glacier change across Svalbard.</p> <p> </p> <p>Dataset description: </p> <p><br> This dataset contains the digital elevation models (DEMs), elevation change maps, point clouds, orthophotos, and vector outlines of glacier extents based on the Norwegian Polar Institute's collection of 5,507 high-oblique aerial images captured over Svalbard in 1936/1938. The photographs were analyzed through structure-from-motion (SfM) photogrammetry to generate 3D models. We also provide an .xlsx spreadsheet containing glacier-by-glacier statistics of ice loss and climate fields. Note that all of the raster and point cloud files listed below have been georeferenced in Metashape using the ground control points (GCPs) illustrated in Main Text, Fig. 2e, but have not undergone the co-registration and bias-correction following the methods of Nuth & Kaab (2011), which was done on a glacier-by-glacier basis. However, the glacier change budgets in the .xlsx file [#5 below] do reflect the values from the glacier-by-glacier co-registered and bias-corrected DEMs. See below for descriptions of each dataset (each number below corresponds to a different zipped folder).</p> <p>------------------------------------------------------------------------------------- </p> <p><strong>Svalbard-wide datasets [all georeferenced Svalbard-wide datasets are in the coordinate system UTM 33N]: </strong></p> <p><br> 1. Svalbard-wide 1936 DEM (20 m and 50 m resolution) [georeferenced .tif file] </p> <p>2. Svalbard-wide 1936 orthophotomosaic (20 m resolution) [georeferenced .tif file] </p> <p>3. Svalbard-wide dh (1936-2010) (20 m and 50 m resolution) [georeferenced .tif file] </p> <p>4. Shapefile of 1936 glacier extents [ESRI .shp file] </p> <p>5. Glacier-by-glacier statistics [.xlsx file] </p> <p>------------------------------------------------------------------------------------- </p> <p><strong>Regional-datasets: </strong></p> <p><em>Due to file size limitations, the high-resolution (5 m) datasets are split into the 8 regions illustrated in Main Text, Fig. 2d: </em></p> <p><em>Zone 1 - South Spitsbergen</em></p> <p><em>Zone 2 - Barentsoya-Edgeoya</em></p> <p><em>Zone 3 - Austfonna</em></p> <p><em>Zone 4 - Vestfonna</em></p> <p><em>Zone 5 - Northeast Spitsbergen</em></p> <p><em>Zone 6 - Central Spitsbergen</em></p> <p><em>Zone 7 - Northwest Spitsbergen</em></p> <p><em>Zone 8 - North Spitsbergen</em></p> <p><br> 6. Regional 1936 DEMs (5 m resolution) [georeferenced .tif files] </p> <p>7. Regional dh (1936-2010) (5 m resolution) [georeferenced .tif files] </p> <p>8. Local 1936 orthomosaics (5 m resolution) [georeferenced .tif files] </p> <p>9. Unprocessed point clouds [.laz files]. These files represent the raw 3D point clouds (x,y,z) generated in Agisoft Metashape for each of the 17 local models described in Extended Data Figure 3.</p> <p>10. Thumbnail-sized copies of the 5,507 historical aerial images (1936 and 1938) analyzed in this study, along with a .csv file labeling the approximate location of each photograph.</p>
Data set: "North Atlantic cooling is slowing down mass loss of Icelandic glaciers"
<p>This data set includes the materials required to reproduce the figures and tables presented in the study: "North Atlantic cooling is slowing down mass loss of Icelandic glaciers". The data consist of:</p> <p>1. Maps of annual surface mass balance (SMB) of Icelandic glaciers and ice caps (ICL) from RACMO2.3 at 500 m spatial resolution in NetCDF format.</p> <ul> <li><strong>smb_rec.1958-2019.RACMO2.3-ERA.ICL-0.5km.YY.nc</strong>: annual cumulative SMB of Icelandic glaciers and ice caps (kg m<sup>-2</sup> or mm w.e. per year) from RACMO2.3 forced by ERA reanalyses for the period 1958-2019, and further statistically downscaled to 500 m spatial resolution. Forcing includes ERA-40 (1958-1978), ERA-Interim (1979-2018) and ERA5 (2019) reanalyses.</li> <li><strong>smb_rec.1958-2099.RACMO2.3-CESM2-SSP85.ICL-0.5km.YY.nc</strong>: annual cumulative SMB of Icelandic glaciers and ice caps (kg m<sup>-2</sup> or mm w.e. per year) from RACMO2.3 forced by CESM2 for the historical period 1958-2014 and by CESM2 under a high-end warming scenario SSP5-8.5 for the period 2015-2099, further statistically downscaled to 500 m spatial resolution.</li> <li><strong>Topo_icemask_lsm_lon_lat_ICL-0.5km.nc</strong>: mask file including an ice mask, land/sea mask and surface topography derived from the ArcticDEM, and longitude/latitude coordinates on the 500 m grid.</li> </ul> <p><strong>NB</strong>: the NetCDF files above use a Polar Stereographic North (EPSG:3413) projection with a horizontal resolution of 500 m x 500 m. The reference point is located at 45ºW longitude and 70ºN latitude.</p> <p>2. Time series of annual ICL-integrated SMB components (Gigatons or Gt per year), annual mean 2 m air temperature above Icelandic glaciers and ice caps (T2m; K), annual mean sea surface temperature (SST) in the Northern Blue Blob. These time series are available in ASCII format for the RACMO2.3 simulation forced by ERA reanalyses (1958-2019) and the RACMO2.3 projection forced by CESM2 under a high-end warming scenario SSP5-8.5 (1958-2099).</p> <p><strong>RACMO2.3-ERA</strong></p> <ul> <li><strong>SMB-components-RACMO2.3-ERA-1958-2019.txt</strong>: time series of annual integrated SMB, snowfall, rainfall, runoff, total melt, refreezing and retention (Gt per year) from the ERA-forced RACMO2.3 simulation (1958-2019).</li> <li><strong>T2m-glacier-RACMO2.3-ERA-1958-2019.txt</strong>: time series of annual mean glacier T2m and anomalies relative to the period 1958-1994 (K) from the ERA-forced RACMO2.3 simulation (1958-2019).</li> <li><strong>SST-Northern-Blue-Blob-RACMO2.3-ERA-1958-2019.txt</strong>: time series of annual mean Northern Blue Blob SST and anomalies relative to the period 1958-1994 (K) derived from the ERA reanalyses (1958-2019).The reanalyses include ERA-40 (1958-1978), ERA-Interim (1979-2018) and ERA5 (2019).</li> </ul> <p><strong>RACMO2.3-CESM2</strong></p> <ul> <li><strong>SMB-components-RACMO2.3-CESM2-SSP85-1958-2099.txt</strong>: time series of annual integrated SMB, snowfall, rainfall, runoff, total melt, refreezing and retention (Gt per year) from the CESM2-forced RACMO2.3 projection under a SSP5-8.5 scenario (1958-2099).</li> <li><strong>T2m-glacier-RACMO2.3-CESM2-SSP85-1958-2099.txt</strong>: time series of annual mean glacier T2m and anomalies relative to the period 1958-1994 (K) from the CESM2-forced RACMO2.3 projection under a SSP5-8.5 scenario (1958-2099).</li> <li><strong>SST-Northern-Blue-Blob-RACMO2.3-CESM2-SSP85-1958-2099.txt</strong>: time series of annual mean Northern Blue Blob SST and anomalies relative to the period 1958-1994 (K) derived from the CESM2 projection under a SSP5-8.5 scenario (1958-2099).</li> </ul> <p>3. Time series of monthly ICL-integrated SMB (Gt per month) for the period 1958-2099. </p> <ul> <li><strong>SMB-monthly-RACMO2.3-1958-2099.txt</strong>: time series of monthly integrated SMB (Gt per month) combining RACMO2.3-ERA (January 1958 - December 2019) with RACMO2.3-CESM2 under a SSP5-8.5 scenario (January 2020 - December 2099) at 500 m horizontal resolution.</li> </ul> <p>The daily downscaled SMB data sets from the ERA-forced RACMO2.3 simulation and the CESM2-forced RACMO2.3 projection under a SSP5-8.5 scenario are freely available from the authors upon request and without conditions (contact: b.p.y.noel@uu.nl). Besides SMB, the data sets include daily total precipitation (snow and rain), snowfall, total melt (snow and ice), runoff, refreezing and retention, total sublimation (surface and drifting snow), snow drift erosion, as well as 2 m air temperature at 500 m horizontal resolution. </p> <p><strong>Abstract</strong>: Icelandic glaciers have been losing mass since the Little Ice Age in the mid-to-late 1800s, with higher mass loss rates in the early 21<sup>st </sup>century, followed by a slowdown since 2011. As of yet, it remains unclear whether this mass loss slowdown will persist in the future. By reconstructing the contemporary (1958-2019) surface mass balance of Icelandic glaciers, we show that the post-2011 mass loss slowdown coincides with the development of the Blue Blob, an area of regional cooling in the North Atlantic Ocean to the south of Greenland. This regional cooling signal mitigates atmospheric warming in Iceland since 2011, in turn decreasing glacier mass loss through reduced meltwater runoff. In a future high-end warming scenario, North Atlantic cooling is projected to mitigate mass loss of Icelandic glaciers until the mid-2050s. High mass loss rates resume thereafter as the regional cooling signal weakens. </p>
Time changes everything: A multispecies analyses of temporal patterns in evaporative water loss - data
<p>The dataset was analysed in the manuscript “Žagar A., Carretero, M.A., de Groot M. (accepted) Time changes everything: A multispecies analyses of temporal patterns in evaporative water loss. Oecologia”</p> <p>The dataset consisted out of water loss by 23 populations of lizards from 16 different species and three families which was compiled from several different studies. All studies used the same standardized protocols. During the experiment every hour for 12 hours, the body weight of the lizard was measured (in total 13 measurements per lizard). The species name (SP), the snout-vent length of the animal (SVL, in millimetres), altitude (m a.s.l.), sampling location (site name, latitude and longitude), weight (in grams), sex (M=male, F=female), code of the individual lizard (CODE), date of experiment (DATE_H) and the reference of the study were noted down (full references are available in the manuscript). Per column the instantaneous water loss values (EWLi) were recorded per hour measured. First hour was EWLi8, second hour was EWLi9, etc. The EWLi was calculated by the weight minus the weight in the next hour divided by the weight multiplied by 100 ((W<sub>n</sub> – W<sub>n+1 </sub>/ W<sub>n</sub>) × 100).</p>
Code and data for "Global warming generates predictable extinctions of warm- and cold-water marine benthic invertebrates via thermal habitat loss"
<pre>This repository contains the following information: Datasets S1 to S4 can all be loaded, manipulated, and analysed in R using script provided in Data S5 to obtain the results of the paper, Reddin et al. 2022, "Global warming generates predictable extinctions of warm and cold-water marine benthic invertebrates via thermal habitat loss". Data S1. (separate file) The original downloaded PaleoDB dataset. Data S2. (separate file) The pre-prepared dataset of occurrences. Data S3. (separate file) The finished environmental dataset. Data S4. (separate file) Additional environmental dataset. Data S5. (separate file) The R-code for the main analysis. Data S6. (compressed directory) Output data and code from the simulations. Table S7 (separate file). List of data source publications for PaleoDB data used in our study. Listed are the data source author list (ref_author), year (ref_pubyr), and reference number as appears in the PaleoDB (reference_no). </pre>
Input data for: Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.
<p>This repository includes input data used in the following article:</p> <p><strong>Vieilledent G., C. Grinand, F. A. Rakotomalala, R. Ranaivosoa, J.-R. Rakotoarijaona, T. F. Allnutt, and F. Achard.</strong> Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.</p> <p>For this article, data have been processed with a R/GRASS script. The development version of this script is available on GitHub at https://github.com/ghislainv/deforestation-maps-Mada. The last release of this script is archived on Zenodo: [DOI: 10.5281/zenodo.1118484].</p>
ScienceDex guides
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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.