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87 results for “future projections”
Future Projections of Temperature Extremes and Urban Heat Island in Paris using Deep Learning
<p>Future projections of 2-meter maximum and minimum temperature and land surface temperature in Paris, France, using Deep Learning, under four Shared Socioeconomic Pathways. ERA5 and GCM ensemble data at their original resolution are also included. The DL (Convolutional Neural Network) model architecture and trained weights are also available. The Python script to generate the boxplots of the future projections is also included.</p>
Material requirements for future low-carbon electricity projections in Africa - dataset
<p>This dataset is provided to supplement the paper "Material requirements for future low-carbon electricity projections in Africa".</p> <p>The paper calculated the material requirements of proposed electricity systems based on the <a href="https://zenodo.org/record/3521841#.YTHYP44zaUk">JRC-TEMBA </a>projections of African electricity systems, including the embodied emissions resulting from such systems from 2015 to 2065.</p> <p>The data included in this repository covers the total mass of materials, embodied emissions, and jobs created given by country, year and/or region.</p>
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red: area projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). AUC = 0.8333 (average over 10 replicates using cross-validation, standard deviation = 0.001113603). Threshold to transform the logistic model output: 0.3816 (10% omission rate threshold). Maps were built using ESRI ArcGIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable
Fig. 1 Projected future changes for Ixodes ricinus until 2081–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 1 Projected future changes for Ixodes ricinus until 2081–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red:
Fig. 3 Projected future changes for Dermacentor marginatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 3 Projected future changes for Dermacentor marginatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red: area projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). AUC = 0.8229 (average over 10 replicates using cross-validation, standard deviation = 0.001121953). Threshold to transform the logistic model output: 0.4298 (10% omission rate threshold). Maps were built using ESRI ArcGIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic
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>
Plant-hummingbird interactions in the Atlantic Forest: Current and future projections
Open the record for dataset details and reuse information.
Data From: Conservation planning in an uncertain climate: identifying projects that remain valuable and feasible across future scenarios
<p>Conservation actors face the challenge of allocating limited resources despite uncertainty about future climate. A key goal is to minimize the potential for negative outcomes under future scenarios. Thus, we address a global conservation challenge: how to allocate conservation investments given high uncertainty about future climate conditions. To that end, we present a method for identifying projects that remain valuable and feasible across climate scenarios and apply our framework to freshwater biodiversity conservation in the South-Central USA. We combine data from a recent high-resolution hydrologic planning tool and species distribution models to estimate the conservation feasibility and biodiversity value of river reaches below 38 major reservoirs in the Red River basin.We find that only 13% of sites have high conservation priority across all future climate scenarios and that spatial patterns of conservation priority largely reflect patterns of water availability and fish biodiversity.</p>
Dataset for Bukovsky et al. (2021): "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections"
<p>This dataset contains derived data and model data necessary for reproducing the results found in "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections" by Melissa S. Bukovsky, Jing Gao, Linda O. Mearns, and Brian C. O'Neill. This dataset contains data not otherwise available in other public archives, as noted in Bukovsky et al. (2021, Earth's Future; preprint available at https://doi.org/10.1002/essoar.10504141.2). That is, this dataset contains data from the land-use change simulations that are not part of NA-CORDEX (na-cordex.org), but which are complementary to those published in the NA-CORDEX archive.</p>
Data from: Evaluation of different bias correction methods for dynamical downscaled future projections of the California Current Upwelling System
<p class="Abstract">Biases in global Earth System Models (ESMs) are an important source of errors when used to obtain boundary conditions for regional models. Here we examine historical and future conditions in the California Current System (CCS) using three different methods to force the regional model: (1) interpolation of ESM output to the regional grid with no bias correction; (2) a "seasonally-varying" delta method that obtains a season-dependent mean climate change signal from the ESM for a 30-year future period; and (3) a "time-varying" delta method that includes the interannual variability of the ESM over the 1980–2100 period. To compare these methods, we use a high-resolution (0.1˚) physical-biogeochemical regional model to dynamically downscale an ESM projection under the RCP8.5 emission scenario. Using different downscaling methods, the sign of future changes agrees for most of the physical and ecosystem variables, but the spatial patterns and magnitudes of these changes differ, with the seasonal- and time-varying delta simulations showing more similar changes. Not correcting the ESM forcing leads to amplification of biases in some ecosystem variables as well as misrepresentation of the California Undercurrent and CCS source waters. In the non-bias corrected and time-varying delta simulations, most of the ecosystem variables inherit trends and decadal variability from the ESM, while in the seasonally-varying delta simulation, the future variability reflects the observed historical variability (1980–2010). Our results demonstrate that bias correcting the forcing prior to downscaling improves historical simulations and that the bias correction method may impact the spatial and temporal variability of future projections. </p>
Projected future autumn leaf phenology of deciduous trees
<p><span>Autumn leaf phenology (i.e. leaf colouring or leaf senescence) marks the end of the growing season, during which trees assimilate atmospheric CO<sub>2</sub>. Since autumn leaf phenology responds to climatic conditions, climate change affects the length of the growing season. Thus, autumn phenology is often modelled to assess possible climate change effects on future CO<sub>2</sub> mitigating capacities and species compositions of forests.</span></p> <p><span>Here, we give access to the entire dataset of projected autumn phenology analyzed in Meier and Bigler (2023). The data was derived from different combinations of 21 process-oriented phenology models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. The dataset contains the average autumn phenology per site and for the years 2080-2099 according to each combination that led to a successful calibration. Calibration and validation were based on >45 000 observations for common beech (<em>Fagus sylvatica L.</em>), pedunculate oak (<em>Quercus robur L.</em>), and European larch (<em>Larix decidua Mill</em>.) from 500 Central European sites each.</span></p> <p><span>Cite as Meier, M., & Bigler, C. (2023). Process-oriented models of autumn leaf phenology: Ways to sound calibration and implications of uncertain projections. <em>Geoscientific Model Development</em>, 16(23), 7171–7201. https://doi.org/10.5194/gmd-16-7171-2023</span></p>
Calibrated and uncalibrated projection data from the paper "Assessing observational constraints on future European climate in an out-of-sample framework"
<p>Individual uncalibrated and calibrated projections for each of the five methods (A-E) in the following folders:</p> <p>MethodA_proj/</p> <p>MethodB_proj/</p> <p>MethodC_proj/</p> <p>MethodD_proj/</p> <p>MethodE_proj/</p> <p> </p> <p>Also included are the out-of-sample data from the "pseudo-observations" (taken from CMIP6 models) used for the verification (see paper for full details):</p> <p>FUTUREverif/</p>
Personal Online Dosimetry Using Computational Methods: The PODIUM Project and the Future of Active Dosimetry
<p>Individual monitoring of workers exposed to external ionizing radiation is essential to allow application of the ALARA principle and follow up of the official dose limits. However, large uncertainties still exist in personal dosimetry. Also, many practical problems exist for personal dosimetry, with many dosemeters getting lost and the reluctance of many workers to wear one or more dosemeters.</p> <p>Most legal dosimetry is done with passive dosemeters, which are analyzed after the wearing period in an accredited lab. Active dosemeters are also widely used, although mostly only for ALARA purposes or for specific exposure situations. Although the active dosemeters are dosimetrically and technically at least equivalent to passive dosemeters, their higher cost limits their use as only legal dosemeters.</p> <p>In an attempt of reinventing dosimetry by using the modern evolutions in simulations, artificial intelligence and computer vision, the PODIUM project was set up. PODIUM was a short feasibility project, funded by the EC CONCERT programme.</p> <p>The objective of the PODIUM project was to improve personal dosimetry by an innovative approach: the development of an online dosimetry application based on computer simulations without the use of physical dosemeters. Operational quantities, protection quantities and radiosensitive organ doses (e.g. eye lens, brain, heart, extremities) can be calculated based on the use of modern technology such as personal tracking devices, flexible individualized phantoms and scanning of geometry set-up. When combined with fast simulation codes, the aim was to perform personal dosimetry in real-time.</p> <p>We applied and validated the methodology for two situations where improvements in dosimetry are urgently needed: neutron workplaces and interventional radiology. Several validation and test measurements were done in different hospitals, and in 2 workplace fields with significant neutron exposure. Personal doses could be calculated within acceptable simulation times, just based on captured movements of the workers and information of the radiation fields. These doses agreed with the results from physical dosemeters within the standard uncertainties that are accepted in personal dosimetry.</p> <p>This PODIUM dosimetry method can also be used to visualize the radiation in near real time. This will increase awareness of radiation protection among workers and will improve the application of the ALARA principle, and it can also be used in training modules. The use of neural networks and big data will help in further reducing simulation time, making real time simulations and dosimetry without physical dosemeters possible in the near future.</p>
Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations
<p>Data for article "Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations"</p>
Resources for the Future Socioeconomic Projections (RFF-SPs)
<p>RFF-SPs Monte Carlo output data:</p> <p>This dataset includes the socioeconomic and emissions data generated by the Resources for the Future Socioeconomic Projections (RFF-SPs) model as discussed in Rennert et al. (forthcoming) (<a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/</a>). The data take the form of a Monte Carlo simulation with n = 10,000 draws. File structure and column metadata are described here:</p> <p>---</p> <p>death_rates/</p> <p>---</p> <ul> <li>rffsp_death_rates_run_1.feather</li> <li>rffsp_death_rates_run_2.feather</li> <li>rffsp_death_rates_run_2.feather</li> </ul> <p> ...</p> <ul> <li>rffsp_death_rates_run_999.feather</li> <li>rffsp_death_rates_run_1000.feather</li> </ul> <p>This folder contains 1,000 files in the .feather file format (<a href="https://arrow.apache.org/docs/python/feather.html">https://arrow.apache.org/docs/python/feather.html</a>), which is optimized for I/O speed and compressed to minimize storage requirements. Each file in this folder contains 3 columns: ISO3, Year, and DeathRate. For each row:</p> <p>- ISO3 contains the ISO numeric-3 code (<a href="https://www.iso.org/iso-3166-country-codes.html">https://www.iso.org/iso-3166-country-codes.html</a>) of the country whose GDP and population are projected. - Year contains the calendar year of the predictions.</p> <p>- year contains the calendar year of the prediction.</p> <p>- DeathRate contains a death rate in average deaths per 1000 people.</p> <p>Baseline mortality data found in data/mortality was derived from the death_rates.csv provided to the RFF team on October 7th from Hana Sevcikova. In that source file, the column DeathRate is the annual deaths per 1000 people. PopAvg is the denominator (average between two time periods) and PopStart is population at the start of the time interval. Values are average deaths per 1000 people.</p> <p>There is one mortality death rate trajectory for each population trajectory, and each RFF-SP is matched to one of these 1000 trajectories. The file sampled_pop_trajectory_numbers.csv described below maps each of the 10,000 RFF SP scenarios to the baseline mortality scenario (out of 1000) matched to its population draw.</p> <p>---</p> <p>emissions/</p> <p>---</p> <ul> <li>rffsp_co2_emissions.csv</li> <li>rffsp_ch4_emissions.csv</li> <li>rffsp_n2o_emissions.csv</li> </ul> <p>Each file in this folder contains 3 columns: sample, year, and value. For each row:</p> <p>- sample contains the number identifying which draw a prediction belongs to (from 1 to 10,000).</p> <p>- year contains the calendar year of the prediction.</p> <p>- value contains the projected annual global emissions of the gas specified in the filename.</p> <p>The units are as follows:</p> <ul> <li>rffsp_co2_emissions.csv is in gigatons C</li> <li>rffsp_ch4_emissions.csv is in megatons CH4</li> <li>rffsp_n2o_emissions.csv is in megatons N2</li> </ul> <p>---</p> <p>pop_income/</p> <p>---</p> <ul> <li>rffsp_pop_income_run_1.feather</li> <li>rffsp_pop_income_run_2.feather</li> <li>rffsp_pop_income_run_3.feather</li> </ul> <p> ...</p> <ul> <li>rffsp_pop_income_run_9999.feather</li> <li>rffsp_pop_income_run_10000.feather</li> </ul> <p> </p> <p>This folder contains 10,000 files in the .feather file format (<a href="https://arrow.apache.org/docs/python/feather.html">https://arrow.apache.org/docs/python/feather.html</a>), which is optimized for I/O speed and compressed to minimize storage requirements. Each file corresponds to one draw of our socioeconomic data, and contains 4 columns: Country, Year, Pop, and GDP. The number in each filename corresponds to the "sample" column in the emissions data. For each row:</p> <p>- Country contains the ISO Alpha-3 code (<a href="https://www.iso.org/iso-3166-country-codes.html">https://www.iso.org/iso-3166-country-codes.html</a>) of the country whose GDP and population are projected. - Year contains the calendar year of the predictions.</p> <p>- Pop contains the projected population for a given country and year, in units of thousands of people.</p> <p>- GDP contains the projected GDP for a given country and year, in units of millions of 2011 USD.</p> <p>---</p> <p>The probabilistic population projections were produced by Adrian E. Raftery and Hana Ševčíková (University of Washington), using the methods described by Raftery and Ševčíková (2021). Please cite this reference in any publications using these projections. Their research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under NIH grant number R01 HD-070936.</p> <p>The probabilistic economic and emissions projections are from Rennert et al. (forthcoming), which in turn are based in part on Müller, Stock, and Watson (forthcoming).</p> <p>---</p> <p>sample_numbers/</p> <p>---</p> <ul> <li>sampled_gdp_trajectory_numbers.csv</li> <li>sampled_pop_trajectory_numbers.csv</li> </ul> <p>These two files hold the sample IDs corresponding to the 10,000 draws from the dataset described above in the pop_income/ section. For reproducibility, these sample sets are available as inputs to the scripts which created them and used when the script is run in deterministic mode. More information on the weighting and specifications of such sampling is available in the code.</p> <p>---</p> <p>ypc1990/</p> <p>---</p> <ul> <li>rffsp_ypc1990.csv</li> </ul> <p>This CSV file is a matrix of 10,000 rows, pertaining to the 10,000 draws, and 184 columns, pertaining to the 184 countries in this analysis. The columns are labeled with the ISO Alpha-3 code (<a href="https://www.iso.org/iso-3166-country-codes.html">https://www.iso.org/iso-3166-country-codes.html</a>). The values are the GDP for a given country and sample, in units of millions of 2011 USD.</p> <p><strong>References</strong></p> <p>Müller, U.K, Stock, J.H., and Watson, M.W. (forthcoming). An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting. The Review of Economics and Statistics, available online 30 October 2020. URL: <a href="https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth">https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth</a></p> <p>Raftery, A.E. and Ševčíková, H. (2021). Probabilistic population forecasting: Short to very long-term. <em>International Journal of Forecasting</em>, available online 7 October 2021. URL: <a href="https://www.sciencedirect.com/science/article/pii/S0169207021001394">https://www.sciencedirect.com/science/article/pii/S0169207021001394</a></p> <p>Rennert, K., Prest, B.C., Pizer, W., Newell, R.G., Anthoff, D., Kingdon, C., Rennels, L., Cooke, R., Raftery, A.E., Ševčíková, H, and Errickson, F. (forthcoming). The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates. <em>Brookings Papers on Economic Activity.</em> Available online 27 October 2021. URL: <a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/</a></p> <p> </p>
South-East US historical and projected population per county for FUTURES urban growth modeling in GRASS GIS
<p>South East US historical (2001-2019) and projected (2020-2100) population per county for 6 states (NC, SC, TN, GA, AL, FL). Historical data come from The National Vital Statistics System (https://seer.cancer.gov/popdata/download.html) and future data are projected by Hauer 2019 (https://doi.org/10.1038/sdata.2019.5) for SSP2 scenario. Data are formatted for r.futures.demand module, which is a GRASS GIS addon for computing future land demand for FUTURES urban growth model. </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>
ARCHIMEDES Project's Animated Video 'ARCHIMEDES: Pioneering a Sustainable Future'
<div> <p><span>The ARCHIMEDES project, a leading initiative in sustainable innovation, has launched an animated video titled "ARCHIMEDES: Pioneering a Sustainable Future." This video offers a detailed look into the project’s groundbreaking efforts to integrate sustainability with advanced technology across various sectors.</span></p> </div> <div> </div> <div> </div> <div> <p><span>ARCHIMEDES stands at the intersection of innovation and sustainability, aiming to revolutionize the future of mobility, energy, and safety within the framework of Society 5.0. The project focuses on developing state-of-the-art components, models, and methodologies to significantly improve the efficiency and lifespan of propulsion systems, power components, and energy storage devices.</span></p> </div> <div> </div> <div> </div> <div> <p><span>The video highlights how the ARCHIMEDES project spans multiple key industries:</span></p> </div> <div> <ul> <li> <p><span><strong>Automotive:</strong> Pioneering new propulsion systems to make vehicles more efficient and environmentally friendly.</span></p> </li> <li> <p><span><strong>Aviation:</strong> Enhancing energy efficiency and sustainability in the aerospace sector.</span></p> </li> <li> <p><span><strong>Industry:</strong> Transforming industrial processes to be more sustainable and cost-effective.</span></p> </li> </ul> </div> <div> </div> <div> </div> <div> <p><span>The ARCHIMEDES project is committed to creating a sustainable future by leveraging the latest innovations and technologies. By adopting an interdisciplinary approach, the project aims to be at the forefront of advancements in mobility, energy, and industrial processes. The vision is to not only meet current demands but also to anticipate and address future challenges.</span></p> </div> <div> </div> <div> </div> <div> <p><span>The newly released animated video provides a comprehensive overview of how ARCHIMEDES is leading the way toward a more sustainable future. The project invites viewers to join in this transformative journey.</span></p> </div>
Future sea level rise in northwest Mexico is projected to decrease the distribution and habitat quality of the endangered Calidris canutus roselaari (Red Knot)
<p>Sea level rise (SLR) is one of the most unequivocal consequences of climate change, yet the implications for shorebirds and their coastal habitats is not well understood, especially outside of the north temperate zone. Here, we show that by the year 2050, SLR has the potential to cause significant habitat loss and reduce the quality of the remaining coastal wetlands in Northwest Mexico—one of the most important regions for Nearctic breeding migratory shorebirds. Specifically, we used species distribution modelling and a moderate SLR static inundation scenario to assess the effects of future SLR on coastal wetlands in Northwest Mexico and the potential distribution of <em>Calidris canutus roselaari </em>(Red Knot), a threatened long-distance migratory shorebird. Our results suggest that under a moderate SLR scenario, 55% of the current coastal wetland extent in northwest Mexico will be at risk of permanent submergence by 2050, and the high-quality habitat areas that remain will be 20% less suitable for <em>C. c. roselaari</em>. What is more, 8 out of the 10 wetlands currently supporting the largest numbers of <em>C. c. roselaari</em> are predicted to lose — on average — 17.8% of their highly suitable habitat areas, with two sites completely losing all their highly suitable habitat. In combination with increasing levels of coastal development and anthropogenic disturbance in Northwest Mexico, these predicted changes suggest that the potential future distribution of <em>C. c. roselaari</em> (and other shorebirds) will likely contract, exacerbating their ongoing population declines. Our results also make clear that SLR will likely have profound effects on ecosystems outside the north temperate zones, providing a clarion call to natural resource managers. Urgent action is required to begin securing sufficient space to accommodate the natural capacity of wetlands to migrate inland and implement local-scale solutions that strengthen the resilience of wetlands and human populations to SLR.</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.