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942 results for “scenario”
Raw Data for the article: A retrospective molecular epidemiological scenario of carbapenemase-producing Klebsiella pneumoniae clinical isolates in a Sicilian transplantation hospital shows a swift polyclonal divergence among sequence types, resistome and virulome
<p>In this work, we assessed and characterized the epidemiological scenario of carbapenem-resistant Klebsiella pneumoniae strains (CR-Kp) at IRCCS-ISMETT, a transplantation hospital in Palermo, Italy, from 2008 to 2017. A total of 288 K. pneumoniae clinical isolates were selected based on their resistance to carbapenems. Molecular characterization was also done in terms of the presence of virulence and resistance genes. All patients were inpatients from our facility and clinical isolates were collected from several sources, either from infection or colonization cases. We observed that, in agreement with the Italian epidemiological scenario, initially only ST258 and ST512 clade II (but not from clade I) were identified from 2008 to 2011. From 2012 onwards, other STs have been observed, including the clinically relevant ST101 and ST307, but also others not previously observed in other Italian health settings, such as ST220 and ST753. The presence of genes involved in resistance and virulence was confirmed, and a heterogeneous genetic resistance profile throughout the years was observed. Our work highlights that resistance genes are rapidly disseminating between different and novel K. pneumoniae clones which, combined with resistance to multiple antibiotics, can derive into more aggressive and pathogenic multidrug-resistant strains of clinical importance. Our results stress the importance of continuous surveillance of CR Enterobacterales in health facilities so that novel STs carrying resistance and virulence genes that may become increasingly pathogenic can be identified and adequate therapies to adopted to avoid their dissemination and derived pathologies.</p>
Data from: Moderate climate warming scenarios during embryonic and post-embryonic stages benefit a cold-climate lizard
<p>Warming temperatures caused by climate change are predicted to vary temporally and spatially. For mid- and high-latitude reptiles, the seasonal variation in warming temperatures experienced by embryos and hatchlings may determine offspring fitness, yet this has remained largely unexplored.</p> <p>To evaluate the independent and interactive influence of seasonal variation in warming temperatures on embryonic and hatchling development, we incubated eggs and reared hatchlings of a cold-climate oviparous ectothermic species, the Heilongjiang grass lizard (<em>Takydromus amurensis</em>), following a 2 × 2 factorial design (present climate vs. warming climate for embryos × present climate vs. warming climate for hatchlings). We then evaluated embryonic and hatchling development, including hatching success, incubation period, initial hatchling body size, hatchling metabolic rate, growth rate, and survival in the mesocosms.</p> <p>We found that warming temperatures shortened the incubation period and produced hatchlings with higher survival rates than those incubated under the present climate conditions. Similarly, hatchlings reared under a warming climate had similar growth rates and resting metabolic rates, but higher survival rates than those reared under the present climate. Hatchlings that experienced both warming incubation and warming growth conditions had the highest survival rates.</p> <p>This study revealed that moderate warming temperatures (Representative Concentration Pathway, RCP 4.5, 1.1–2.6 °C) experienced by embryos and hatchlings interact to benefit hatchling fitness in cold-climate oviparous ectotherms. Our study also highlighted the importance of integrating seasonal variation in warming temperatures when evaluating the responses to climate warming in multiple developmental stages in oviparous ectotherms.</p>
Storyline data used in the paper "The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging"
<p>We provide the storyline data (in NetCDF format) used in the paper: “The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging” published in Journal of Climate. The data is structured in four .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climate. The data from the five ensemble members (E1 to E5) have been included separately in 3-months files.</p> <p>Atmospheric variables (Files are named as: {variable}_E{ensemble member}_{starting month}{year}.nc:</p> <ul> <li> <p>Latent heat flux (ahfl)</p> </li> <li> <p>Sensible heat flux (ahfs)</p> </li> <li> <p>Monthly Global Mean 2m Temperature (GMTT2mMonthly)</p> </li> <li> <p>Maximum 2m Temperature (t2max)</p> </li> <li> <p>Mean 2m Temperature (t2mean)</p> </li> <li> <p>Minimum 2m Temperature (t2max)</p> </li> <li> <p>Soil Wetness (ws)</p> </li> </ul> <p> Only for present climate:</p> <ul> <li> <p>850 hPa Temperature (T850)</p> </li> <li> <p>Total Cloud Cover (TCC)</p> </li> <li> <p>500 hPa Geopotential Height (Z500)</p> </li> </ul> <p>Five layers soil moisture (Only for present climate, Files are named as: From20172019in2017Climatessp370{ensemble member}_{year}{starting month}.01_jsbid.nc) </p> <p>Oceanic variables (from FESOM, Files are named as: {variable}_E{ensemble member}_{year}{starting month}01.nc:</p> <ul> <li> <p>Sea Ice Concentration (SIC)</p> </li> <li> <p>Sea Surface Temperature (SST)</p> </li> </ul> <p><strong>Please, note that FESOM uses an unstructured mesh.</strong></p>
Database outcomes systematic review on participatory restoration ecology scenarios
<p><span>Large-scale ecological </span>restoration is <span>crucial for effective </span>biodiversity conservation <span>and combating climate change. However, perspectives on the goals and values of restoration are highly diverse, as are the different approaches to restoration e.g., ranging from the restoration of cultural ecosystems to rewilding. We assess how the future of nature is envisioned in participatory scenarios, focusing on which elements of rewilding and nature contributions to people have been considered in scenario narratives across Europe. We use the Nature Futures Framework to study how different perspectives on the the relationship of people with nature are captured in participatory scenarios. We found that a range of material, regulating and non-materical benefits were well represented in participatory scenarios. The different ecological aspects of rewilding were also present in many participatory scenarios, but not as well represented as nature contributions to people. Nature as culture was the main perspective present in the scenarios, with expected highest positive impacts on non-material benefits and to a lessesr extent on regulating benefits. Nature for nature futures were associated with positive impacts on regulating benefits and negative impacts on material benefits, being the only type of future associate with positive impacts on all three components of rewilding. Nature for society futures were associated with moderate positive impacts on all three types of nature contributions to people. Business as usual futures were associate with negative impacts on regulating and non-material benefits and on all three components of rewilding. Our</span> results <span>also </span>highlight<span> two major gaps that should be addressed in participatory restoration planning and models. Firstly, there is a paucity of spatially explicit approaches, with most studies failing to transform the results of participatory scenario planning into model projections. Secondly, we found scenarios that explored co-benefits between multiple nature perspectives were overall missing from the literature.</span></p>
Exploratory pilot study on resource allocation along the dementia continuum under constrained and unconstrained budget scenarios
<p>Supporting quantitative data for a pilot longitudinal balance of care study. </p>
Toll Dilemma Game Data: Measuring the Cooperation of the People with the Government in Iran in the Form of Seven designed Scenarios
<p>The toll dilemma game was designed in the form of a robot and distributed in the Telegram messenger. More than 1,200 people participated in this game, who acted as testee in the game. Each actor entered one of seven pre-designed scenarios, and how he or she worked with the government was evaluated in a simulated environment. The current data are entered in SPSS software. </p>
Structural changes across thermodynamic maxima in supercooled liquid tellurium: a water-like scenario
<p>This dataset contains all numerical data used to produce the figures in the manuscript, arXiv:2201.06838 by P. Sun et al. (2022).</p> <p>Please see the README file for more information.</p>
The simulated monthly runoff data in the historical period and under future climate scenarios of the Yarlung Zangbo River Basin
<p>This data provides the simulated monthly runoff data under the historical period (1979-2014) and future (2049-2084) climate scenarios for four sub-basins of the Yarlung Zangbo River Basin, including Nugexia, Nuxia, Lasha, and Rikaze.<br> This runoff data is simulated based on the GR4J model coupled with a simple degree-day snow module. The GR4J_SNOW performs parameterization and calculates runoff on each grid cell, and the gridded simulated runoff then converges to the outlet of the sub-basin.<br> Time series of the daily records for meteorological forcing data (precipitation, air temperature, vapor pressure, wind speed, downward long-wave radiation, and downward short-wave radiation) from 1979-2014 was provided by China Meteorological Forcing Dataset (CMFD). <br> Future climate scenarios were generated using the combined climate forcing data together with scaling factors obtained from empirical downscaling of 30 available CMIP5 models (28 GCMs for RCP4.5 and 29 GCMs for RCP8.5). The simulated runoff under RCP4.5 and RCP8.4 are the ensemble averages of 28 and 29 simulated runoff results, respectively.</p>
Data from: Optimal mating of Pinus taeda L. under different scenarios using differential evolution algorithm
<p>A newly developed software, AgMate, was used to perform optimized mating for monoecious <em>Pinus taeda L.</em> breeding. Using a computational optimization procedure called differential evolution (DE), AgMate was applied under different breeding population sizes scenarios (50, 100, 150, 200, 250) and candidate contribution scenarios (max use of each candidate was set to 1 or 8), to assess its efficiency in maximizing the genetic gain while controlling inbreeding. Real pedigree data set from North Carolina State University Tree Improvement Co-op with 962 Pinus taeda were used to optimize objective functions accounting for coancestry of parents and expected genetic gain and inbreeding of the future progeny. AgMate results were compared with those from another widely used mating software called MateSel (Kinghorn, 1999). For the proposed mating list for 200 progenies, AgMate resulted in an 83.7% increase in genetic gain compared with the candidate population. There was evidence that AgMate performed similarly to MateSel in managing coancestry and expected genetic gain, but MateSel was superior in avoiding inbreeding in proposed mate pairs. The developed algorithm was computationally efficient in maximizing the objective functions and flexible for practical application in monoecious diploid conifer breeding.</p>
First responders' mobile phone traces during a search-and-rescue exercise scenario
<p>This dataset is collected during a search-and-rescue exercise scenario in the framework of the ARTION project. </p> <p>The operation took place on the 22nd of May 2022 in Paphos district (near the beach at Mandria village). The exercise was organized and conducted by the Cyprus Civil Defence and data collection was performed by the KIOS Research and Innovation Center of Excellence of the University of Cyprus. </p> <p>The data is saved in an .xlsx file. It consists of 9 first responders' traces captured during a search-and-rescue operation. The responders were moving on foot holding their mobile phones, which were used for capturing their traces. By means of the ARTION mobile app, the locations of the mobile phones were captured by the build-in GPS receiver of the phone approximately every 5 seconds. </p> <p> </p> <p> </p>
Data from "Projections of leaf turgor loss point shifts under future climate change scenarios" (Tordoni et al. 2022 Global Change Biology)
<p>The dataset includes four sheets representing the average turgor loss point (tlp) values at grid cell level (tlp_data) and the climatic variables and related climate change scenarios derived from the three models used in this study (HadGEM2-ES-RACMO22E, EC-EARTH_RACMO22E, EC-EARTH_CCLM4-8-17, respectively).</p> <p>The sheet "tlp_data" reports the cell ID (OGU) and the average tlp values for each taxonomic group considered in this study (gymnosperms, angiosperms, herbaceous and woody angiosperms). </p> <p>Each of the other three sheets reports the cell ID (OGU), coordinates of the cell centroid (Long, Lat) and a set of six climatic variables: 95<sup>th</sup> percentiles of average temperature (BIO1.95, °C), temperature seasonality (BIO4, °C), annual consecutive frost days where temperature was ≤ 0 °C (CFD.ann, n° days), annual consecutive dry days where precipitation was < 1 mm (CDD.ann, n° days), 5<sup>th</sup> percentiles of cumulate annual precipitation (BIO12.5, mm), and precipitation seasonality (BIO15, %). For each model, "hist" refers to historical data encompassing the period 1970-2005, whereas "RCP2.6" and "RCP8.5" reports the average value of future projections for the period 2080-2100 in two representative concentration pathway (RCP) scenarios (RCP2.6 and RCP8.5).</p> <p> </p>
SESMG scenario-files of the study "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution"
<p>This dataset contains model scenario-files belonging to the publication "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution".</p> <p>The individual scenarios can be executed and evaluated with the "Spreadsheet Energy System Model Generator" (<a href="https://github.com/chrklemm/SESMG">SESMG</a>) <a href="https://github.com/chrklemm/SESMG/tree/v0.4.0rc1">v0.4.0rc1</a></p> <p>The respective file names indicate to which model run mentioned in the main study the scenario-files belong. For model runs for which no sepparate scenario file exists, the scenario "reference.xlsx" with adjusted SESMG settings was used.</p> <p> </p>
Monitoring of degradation of 5 organic micropollutant under a multitude of scenarios
<p>The spreadsheet contains HPLC integration peaks monitoring the photolytic and photocatalytic degradation of 5 organic micropollutants (ciprofloxacin, sulfamethoxazole, trimethoprim, venlafaxine and o-desmethyl-venlafaxine) under different conditions. The first sheets show the degradation by UV-A and UV-C photolysis and photocatalysis of each compound in individual solution in milliQ water (initial concentration of each, 2 mg/L). Later, all compounds are present in an initial mixture (concentration of each 2 mg/L, resulting in final mixture solution of 10 mg/L). The impact on degradation of adding 0.1 mM of peroxide in the solution for all 4 processes is studied. Additionally, the impact of: 1) of using simultaneous LED wavelengths (UV-A and UV-C combined); 2) using tap water as matrix; and 3) varying the initial pH for the 1st order kinetic rates of each compound in the mixture is investigated. Plots of the kinetic rate and calculations of EEO values (electrical energy per order consumption) are made. The last spreadsheets contain kinetic monitoring of several experiments performed by altering the composition of the matrix with the addition of nitrates, humic acids and bicarbonates. The data was used to obtain a surface-response box-behnken design of experiments.</p>
Monitoring of ciprofloxacin degradation by UV-A LED photolysis and photocatalysis under different scenarios
<p>The spreadsheet contains the HPLC integration peaks monitoring the photolytic and photocatalytic degradation of the antibiotic ciprofloxacin (CIP) in milliQ water. The initial concentration of CIP is 10 mg/L. In the spreadsheets is shown the obtainment of 1st order kinetic constant rates for different photoreactor designs using 2, 3, 4 or 6 UVA-LED strips around the reactor, distanced 10 or 15 mm from the reactor's walls. The influence of different controlled periodic illumination's duty cycle (0.50 and 0.75) is also shown. Calculations of EEO (electrical energy per order consumption) are also made for each reactor set up.</p> <p>The irradiance values for each photoreactor design calculated by an optical software is presented as planar projections of the cylindrical reactor. Images showing the radiant flux in the photoreactor's middle cross section obtained by the software are also shown</p> <p>Finally, a full factorial design of experiments is made to obtain a model of prediction of kinetic constant rates and EEO values. Predicted vs experimental values are plotted.</p>
Data from: Predicting range shifts of the giant pandas under future climate and land use scenarios
<p><span><strong>Aim</strong>:</span><span> Understanding and predicting how species will respond to global environmental change (i.e., climate and land use change) is essential to efficiently inform conservation and management strategies for authorities and managers. Here, we assessed the combined effect of future climate and land use change on the potential range shifts of the giant pandas (<em>Ailuropoda melanoleuca</em>). </span></p> <p><span><strong>Location</strong>:</span><span> Sichuan Province, China.</span></p> <p><span><strong>Methods</strong>: </span><span>We used ensemble species distribution models (SDMs) to forecast range shifts of the giant pandas by the 2050s and 2070s under four combined climate and land use change scenarios. We also</span><span> compared the differences in </span><span>distributional changes of giant pandas among the five mountains in the study area. </span></p> <p><span><strong>Results</strong>: </span><span>Our ensemble SDMs exhibited good model performance in terms of both AUC (0.931) and TSS (0.747), and suggested that precipitation seasonality, annual mean temperature, the proportion of forest cover and total annual precipitation are the most important factors in shaping the current distribution patterns for the giant pandas. Our projections of future species distribution also suggested a range expansion under an optimistic greenhouse gas emission, while suggesting a range contraction under a pessimistic greenhouse gas emission. Moreover, we found that there is considerable variation in the projected range change patterns among the five mountains in the study area. Especially, the suitable habitat of the giant panda is predicted to increase under all scenarios in Minshan mountains, while is predicted to decrease under all scenarios in Daxiangling and Liangshan mountains, indicating the vulnerability of the giant pandas at low latitudes. </span></p> <p><span><strong>Main conclusions</strong>: </span><span>Our findings highlight the importance of an integrated approach that combines climate and land use change to predict the future species distribution and the need for a spatial explicit consideration of the projected range change patterns of target species for guiding conservation and management strategies. </span></p>
Data and code of Land use scenario for 'Development of common socio-economic scenarios for climate change impact assessments in Japan'
<p>Land use scenario calculation: Executable files, source code files and data files<br> This dataset contains program codes and input data used for reproducing land use scenarios explained in Chapter 5.2 in Yoshikawa et al. (submitted to GMDD).</p> <p>We found a few fatal errors in the following code.<br> These code were fixed from version 2 (http://dx.doi.org/10.5281/zenodo.7090670).<br> /Step3/a01_calc_land_use.py<br> /Step3/a01_calc_land_use_std.py<br> /Step3/a01_calc_land_use_rate.py<br> /Step3/run03.bat</p>
Peak refreezing in the Greenland firn layer under future warming scenarios
<p>This data set includes the materials required to reproduce the figures and tables presented in the study: "Peak refreezing in the Greenland firn layer under future warming scenarios". The data consist of:</p> <p>1. Time series of annual Greenland ice sheet (GrIS) integrated <strong>SMB components</strong> (Gigatons or Gt per year). These time series are available in ASCII format for all simulations presented in the manuscript.</p> <p><strong>RACMO2.3</strong><strong>p2-ERA: </strong>1 member</p> <ul> <li><strong>SMB-components_RACMO2.3p2-ERA_1958-2020_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the ERA-forced RACMO2.3p2 simulation at 5.5 km, statistically downscaled to 1 km spatial resolution (1958-2020).</li> </ul> <p><strong>RACMO2.3</strong><strong>p2</strong><strong>-CESM2</strong>: 3 members</p> <ul> <li><strong>SMB-components_RACMO2.3p2-CESM2-HIST-12_1950</strong><strong>-</strong><strong>2014_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the CESM2-forced RACMO2.3p2 historical reconstruction (HIST) at 11 km, statistically downscaled to 1 km spatial resolution (1950-2014). RACMO2.3p2 was forced by member 12 of the CESM2-HIST ensemble (HIST-12, see <strong>CESM2-HIST</strong> below).</li> <li><strong>SMB-components_RACMO2.3p2-CESM2-SSP126-4_2015-2099_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the CESM2-forced RACMO2.3p2 SSP1-2.6 projection (SSP126) at 11 km, statistically downscaled to 1 km spatial resolution (2015-2099). RACMO2.3p2 was forced by member 4 of the CESM2-SSP126 ensemble (SSP126-4, see <strong>CESM2-SSP126</strong> below).</li> <li><strong>SMB-components_RACMO2.3p2-CESM2-SSP</strong><strong>585</strong><strong>-</strong><strong>3</strong><strong>_2015-2099_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the CESM2-forced RACMO2.3p2 SSP5-8.5 projection (SSP585) at 11 km, statistically downscaled to 1 km spatial resolution (2015-2099). RACMO2.3p2 was forced by member 3 of the CESM2-SSP585 ensemble (SSP585-3, see <strong>CESM2-SSP585</strong> below).</li> </ul> <p><strong>CESM2</strong><strong>-IND: </strong>11 members</p> <ul> <li><strong>SMB-components_CESM2-IND-</strong><strong>X</strong><strong>_1850-1949_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 pre-industrial reconstructions (IND) at ~111 km, statistically downscaled to 1 km spatial resolution (1850-1949). The term “X” in the filename above represents the CESM2 member ranging from 1 to 11.</li> </ul> <p><strong>CESM2</strong><strong>-HIST: </strong>12 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>HIST</strong><strong>-</strong><strong>X</strong><strong>_1</strong><strong>9</strong><strong>50-</strong><strong>2014</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 historical reconstructions (HIST) at ~111 km, statistically downscaled to 1 km spatial resolution (1950-2014). The term “X” in the filename above represents the CESM2 member ranging from 1 to 12.</li> </ul> <p><strong>CESM2</strong><strong>-SSP126: </strong>6 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP126</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP1-2.6 projections (SSP126) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 5.</li> <li><strong>SMB-components_CESM2-</strong><strong>SSP126</strong><strong>-</strong><strong>6</strong><strong>_</strong><strong>2100</strong><strong>-</strong><strong>2299</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native long-term CESM2 SSP1-2.6 projection (SSP126) at ~111 km, statistically downscaled to 1 km spatial resolution (2100-2299).</li> </ul> <p><strong>CESM2</strong><strong>-SSP245: </strong>6 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP245</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP2-4.5 projections (SSP245) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 6.</li> </ul> <p><strong>CESM2</strong><strong>-SSP370: </strong>5 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP370</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP3-7.0 projections (SSP370) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 5.</li> </ul> <p><strong>CESM2</strong><strong>-SSP585: </strong>8 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP585</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP5-8.5 projections (SSP585) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 6.</li> <li><strong>SMB-components_CESM2-</strong><strong>SSP585</strong><strong>-</strong><strong>7</strong><strong>_</strong><strong>2100</strong><strong>-</strong><strong>2299</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native long-term CESM2 SSP5-8.5 projection (SSP585) at ~111 km, statistically downscaled to 1 km spatial resolution (2100-2299). This run (member 7) does not include ice dynamics.</li> <li><strong>SMB-components_CESM2-CISM2-SSP585-8_2100-2299_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native long-term CESM2-CISM2 SSP5-8.5 projection (SSP585) at ~111 km, statistically downscaled to 1 km spatial resolution (2100-2299). This run (member 8) includes ice dynamics.</li> </ul> <p>2. Time series of annual GrIS-wide <strong>runoff line altitude</strong> in meters above sea-level (m a.s.l.). These time series are available in ASCII format for the RACMO2.3p2-ERA simulation (ERA), and as an ensemble mean for all pre-industrial (IND) and historical reconstructions (HIST), and projections (SSP) including both native CESM2 and RACMO2.3p2-CESM2, statistically downscaled to 1 km.</p> <ul> <li><strong>Runoff-line-altitude_ERA_1958-2020_GrIS_1km.txt</strong>: time series of GrIS-wide runoff line altitude (m a.s.l.) derived from the ERA-forced RACMO2.3p2 simulation at 1 km (1958-2020).</li> <li><strong>Runoff-line-altitude_IND-EnsembleMean_1850-1949_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all pre-industrial reconstructions at 1 km (1850-1949, 11 IND members).</li> <li><strong>Runoff-line-altitude_HIST-EnsembleMean_1950-2014_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all historical reconstructions at 1 km (1950-2014, 13 HIST members).</li> <li><strong>Runoff-line-altitude_SSP126-EnsembleMean_2015-2299_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP1-2.6 projections at 1 km (2015-2299, 7 SSP126 members).</li> <li><strong>Runoff-line-altitude_SSP245-EnsembleMean_2015-2099_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP2-4.5 projections at 1 km (2015-2099, 6 SSP245 members).</li> <li><strong>Runoff-line-altitude_SSP370-EnsembleMean_2015-2099_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP3-7.0 projections at 1 km (2015-2099, 5 SSP245 members).</li> <li><strong>Runoff-line-altitude_SSP585-EnsembleMean_2015-2299_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP5-8.5 projections at 1 km (2015-2299, 9 SSP585 members).</li> </ul> <p>3. Time series of annual <strong>500hPa global temperature anomalies</strong> (ºC). These time series are available in ASCII format for the ERA5 reanalysis (ERA5-reanalysis), and as an ensemble mean for all pre-industrial (IND) and historical reconstructions (HIST), and projections (SSP) from the native CESM2 model at ~111 km spatial resolution.</p> <ul> <li><strong>Tglobal-500hPa-anomaly_ERA5-reanalysis_1950-2020.txt</strong>: time series of annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from the ERA5 climate reanalysis (1950-2020). Anomalies are estimated relative to the reference period 1950-1990.</li> <li><strong>Tglobal-500hPa-anomaly_IND-EnsembleMean_1850-1949.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 pre-industrial reconstructions (1850-1949, 11 IND members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_HIST-EnsembleMean_1950-2014.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 historical reconstructions (1950-2014, 12 HIST members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP126-EnsembleMean_2015-2299.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 short/long term SSP1-2.6 projections (2015-2299, 6 SSP126 members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP245-EnsembleMean_2015-2099.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 SSP2-4.5 projections (2015-2099, 6 SSP245 members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP370-EnsembleMean_2015-2099.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 SSP3-7.0 projections (2015-2099, 5 SSP370 members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP585-EnsembleMean_2015-2299.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 short/long term SSP5-8.5 projections (2015-2299, 8 SSP585 members). Anomalies are estimated relative to the reference period 1850-1949.</li> </ul> <p>The gridded, daily downscaled SMB data sets from the ERA-forced RACMO2.3p2 simulation, and the CESM2-forced RACMO2.3p2 projections under a low-end SSP1-2.6 and high-end SSP5-8.5 warming scenario, as well as gridded, monthly downscaled SMB data sets from native CESM2 under pre-industrial (IND), historical (HIST), and short/long term climate projections (SSPs) are freely available from the authors upon request and without conditions (contact: b.p.y.noel@uu.nl). Besides SMB, the data sets include total precipitation (snow and rain), snowfall, total melt (snow and ice), runoff, refreezing and retention and total sublimation (surface and drifting snow) at 1 km horizontal resolution. </p> <p><strong>Abstract</strong>: Firn (compressed snow) covers approximately 90% of the Greenland ice sheet (GrIS) and currently retains about half of rain and meltwater through refreezing, reducing runoff and subsequent mass loss. The loss of firn could mark a tipping point for sustained GrIS mass loss, since decades to centuries of cold summers would be required to rebuild the firn buffer. Here we estimate the warming required for GrIS firn to reach peak refreezing, using 51 climate simulations statistically downscaled to 1 km resolution, that project the long-term firn layer evolution under multiple emission scenarios (1850–2300). We predict that refreezing stabilises under low warming scenarios, whereas under extreme warming, refreezing could peak and permanently decline starting in southwest Greenland by 2100, and further expanding GrIS-wide in the early 22<sup>nd</sup> century. After passing this peak, the GrIS contribution to global sea level rise would increase over twenty-fold compared to the last three decades.</p> <p> </p>
A Dataset of IQ samples in Indoor Jamming Scenarios
<p>This dataset includes physical-layer radio information (IQ samples) acquired from indoor communications affected by different types of jamming techniques. Specifically, it includes data acquired from 7 different Software Defined Radios (SDRs), i.e., the USRP Ettus Research X310, operating in an office environment while the transmitter and receiver communicates without the Line of Sight (nLoS). Each experiment is characterized by a transmitter, a receiver, and a jammer. While the hardware of the transmitter and the receiver are kept the same for all the experiments, the hardware of the jammer is changed adopting 5 different radios of the same model and brand. The dataset includes different jamming types, e.g., no jamming (silent), tone (sinusoidal), and Gaussian noise. Moreover, the dataset includes different transmission distances and jamming power levels. In each experiment, a pre-determined sequence of bits ([0, 255]) has been modulated using the BPSK scheme, and then stored, at the receiver, as a 2-columns matrix of raw I/Q samples.</p>
Schematic and adapted figures from IPBES Sustainable Use of Wild Species Assessment - Chapter 5. Future scenarios of sustainable use of wild species
<p>Schematic and adapted figures from Chapter 5 of the thematic assessment of the sustainable use of wild species of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.</p>
LPJmL4 Potential Natural Vegetation (PNV) simulations for use in MAgPIE for ISIMIP3 scenarios
<p>This data set contains output data from simulations with the model LPJmL version 4 for further use in the MAgPIE model. Simulations are based on the ISIMIP3a/b climate input data. No land use is considered here, simulations are for potential natural vegetation. Geospatial information in files <code>grid.clm</code>, data processing is recommended using <a href="https://github.com/PIK-LPJmL/lpjmlkit" target="_blank" rel="noopener">lpjmlkit</a>.</p>
ScienceDex guides
Understand access before you commit
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.