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1,751 results for “Future”
Fig. 1 in Advertisement calls of six glassfrog species in the Colombian Andes, and comments on priorities for future research and conservation
Fig. 1. Fieldwork localities in the Central and Eastern Cordilleras where the advertisement calls were obtained.
Supported data for manuscript: 'Future hydrogen economies imply environmental trade-offs and a supply-demand mismatch' (DOI: 10.1038/s41467-024-51251-7)
<p>After unpacking the ZIP file, this repository contains the following files:</p> <p><strong>results_lcoh_ecoinvent_391_reference.tif:</strong> grid-specific hydrogen production cost (euro/kg H2) for the reference scenario.</p> <p><strong>results_lcoh_SSP2-Base_2050_base.tif:</strong> grid-specific hydrogen production cost (euro/kg H2) for the baseline scenario in 2050.</p> <p><strong>results_lcoh_SSP2-PkBudg1150_2050_base.tif:</strong> grid-specific hydrogen production cost (euro/kg H2) for the 2°C scenario in 2050.</p> <p><strong>results_lcoh_SSP2-PkBudg500_2050_base.tif:</strong> grid-specific hydrogen production cost (euro/kg H2) for the 1.5°C scenario in 2050.</p> <p><strong>results_ghg_kg_h2_ecoinvent_391_reference.tif:</strong> grid-specific GHG emissions (kg CO2-eq./kg H2) from hydrogen production for the reference scenario.</p> <p><strong>results_ghg_kg_h2_SSP2-Base_2050_base.tif:</strong> grid-specific GHG emissions (kg CO2-eq./kg H2) from hydrogen production for the baseline scenario in 2050.</p> <p><strong>results_ghg_kg_h2_SSP2-PkBudg1150_2050_base.tif:</strong> grid-specific GHG emissions (kg CO2-eq./kg H2) from hydrogen production for the 2°C scenario in 2050.</p> <p><strong>results_ghg_kg_h2_SSP2-PkBudg500_2050_base.tif:</strong> grid-specific GHG emissions (kg CO2-eq./kg H2) from hydrogen production for the 1.5°C scenario in 2050.</p> <p><strong>main_gen_figures.ipynb: </strong>Jupyter Notebook script used to generate the main figures of the manuscript.</p>
Estimating future climate change impacts on human mortality and crop yields via air pollution: supplemental files
<p>Atmospheric chemistry model output and other gridded data sets necessary to estimate human mortality and crop yield losses associated with future climate change, as used in Murray et al. [PNAS, 2024] doi:10.1073/pnas.2400117121.</p>
Brazil - Future weather files for building energy simulation
<p>This dataset contains future weather files for energy simulation of buildings of the state capitals of Brazil plus the Federal District. The weather data is provided in EPW format, commonly used in <a href="https://energyplus.net/">EnergyPlus inputs</a>. The periods of 2010, 2050, and 2090 are considered for generating the Typical Meteorological Years (TMY) for each location. Additionally, different climate model projections were used to enable an analysis of uncertainties in the simulation results. <strong>In this updated version, we have included the complete individual years used to develop the TMYs. This data is provided in CSV format and has been bias-corrected.</strong></p> <p>The future climate data was derived from various regional climate model projections from the <a href="https://cordex.org/">Coordinated Regional Downscaling Experiment (CORDEX) project</a>. These projections are part of the CORDEX-CORE experiment, which includes three GCMs (HadGEM2, MPI-ESM, and NorESM1) as driving models, and two nested RCMs (regcm and remo) for dynamical downscaling, totaling an ensemble of six members at a spatial resolution of approximately 25km. The <a href="https://doi.org/10.1007/s10584-011-0148-z">representative concentration pathways </a>RCP 8.5 and RCP 2.6 were the available scenarios for the region, and both were considered for developing the weather files. The future climatic variable values were interpolated and formatted to an hourly resolution, commonly used in building energy simulation tools. Subsequently, different bias correction methods were applied to specific climate variables based on historical weather series. These data series consist of hourly data measured at weather stations located in each city between the years 2001 and 2021. The historical series files were made available by Dru Crawley and Linda Lawrie and served as the basis for developing the TMYx weather files available on the <a href="https://climate.onebuilding.org/">OneClimate Building website</a>.</p> <p>It is crucial to recognize that the historical data is based on measurements taken at airports, which are often situated far from urban centers. As a result, urban overheating was not factored into these developed weather files. It is also important to note that the developed weather files represent a typical meteorological year for each period, and do not include the most extreme periods, such as heatwaves and atypical summers.</p> <p>The weather files were developed specifically for use with the EnergyPlus engine. Therefore, climatic variables not used by the engine (e.g., precipitation and ceiling height), even if available in EPW format, should not be considered for other studies.</p> <p>It is important to emphasize the higher internal operative temperature results obtained when using the REGCM model compared to the REMO model in Building Energy Simulations. <strong>Caution is recommended when using files developed using a single combination of climate models, especially with weather files based on the REGCM model.</strong></p> <p><strong>Suggestions for corrections can be sent to matheus.bracht@posgrad.ufsc.br</strong></p>
VEClim's initial assessment of Aedes albopictus activity and disease risk in three decades: historical (1980-1990), recent (2010-2020), and future (2090-2100).
<p><span>This is the initial release of VEClim, an early warning decision support system for climate-sensitive vector activity and vector-borne disease risk assessment, comprising an assessment of the seasonal dynamics of <em>Aedes albopictus</em>, the Asian tiger mosquito, and the associated risk of chikungunya virus transmission. This analysis compares three decades, i.e., historical (1980-1990), </span><span>recent <span>(2010-2020), and future (2090-2100), in terms of seasonal and geospatial averages. </span></span></p>
Data sets accompanying "Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning"
<p>This data sets accompany the publication "Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning" in One Earth.</p> <ul> <li>lur-db-output-zenodo.xlsx: contains all land use requirement estimates for renewable energies reviewed in the publication. Meta-data is reported in one sheet, the other sheet contains the original data.</li> <li>authorship-tree-zenodo.xlsx: contains the information necessary to derive the authorship tree shown in the supplementary information. Meta-data is reported in one sheet, the other sheet contains the original data.<br><br><br></li> </ul>
Complementary data for Iqbal et al. (2024): Slopes along Apollo EVAs: Astronaut experience as input for future mission planning
<p>Complementary data for Iqbal et al. (2024): Slopes along Apollo EVAs: Astronaut experience as input for future mission planning</p> <p>Data contains shapefiles that can be used in any geoinformation system (GIS).</p> <p><strong>If you use these data, please cite BOTH the <em>JOURNAL NAME</em> publication and the Zenodo dataset.</strong></p> <p>Iqbal, W., Head III, J. W., van der Bogert, C. H., Frueh, T., Henriksen, M., Bickel, V., Kring, D., Hiesinger, H., Scott, D. R., & Heyer, T. (2024). Slopes along Apollo EVAs: Astronaut experience as input for future mission planning. Acta Astronautica, 223, 184-196. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.actaastro.2024.07.006" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.actaastro.2024.07.006</a></p> <p>Iqbal, W., Head, J. W., van der Bogert, C., Frueh, T., Henriksen, M., Bickel, V., Kring, D., Hiesinger, H., Scott, D. R., & Heyer, T. (2024). Complementary data for Iqbal et al. (2024): Slopes along Apollo EVAs: Astronaut experience as input for future mission planning [Data set]. In Acta Astronautica (Bd. 223, S. 184–196). Zenodo. <a href="https://doi.org/10.5281/zenodo.13790204" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13790204</a></p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Structure</p> <p>-> File "Apollo_Traverses_Iqbal_24" - It includes six subfolders for each landing site that contain the shapefiles of traverses.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further questions contact <a href="mailto:lwueller@uni-muenster.de" rel="noopener noreferrer nofollow">iqbalw@uni-muenster.de</a></p> <p>Wajiha Iqbal, Institut für Planetologie, Universität Münster, Germany.</p>
Offshore wind turbine damage probability maps and hub height TC wind speeds for U.S. Atlantic and Gulf Coasts exposed to historical and future tropical cyclones
<p>Damage probability maps for offshore wind turbines exposed to tropical cyclones (TCs) under both historical and future climate scenarios along the U.S. Atlantic and Gulf Coasts are presented in this dataset. TCs are generated using <a href="../records/10392725" target="_blank" rel="noopener">The Risk Analysis Framework for Tropical Cyclones (RAFT)</a>, forced by <a href="https://pcmdi.llnl.gov/CMIP6/" target="_blank" rel="noopener">CMIP6</a> historical and future global climate simulations. Maximum wind speeds for 20- and 50-year TCs are processed through a <a href="https://www.sciencedirect.com/science/article/pii/S0960148120311423">fragility function</a> specific to offshore wind (OSW) turbines in order to estimate the probability of damage – specifically yielding and buckling – based on wind speed intensity. </p> <p><strong>Included data:</strong></p> <ul> <li><strong>TC wind speeds:</strong> Peak 10-min mean hub height (90m) TC wind speed maps</li> <li><strong>Damage states:</strong> Yielding and Buckling probability maps for OSW turbines</li> <li><strong>Geographic coverage:</strong> U.S. Atlantic and Gulf Coasts (up to 200km from the shoreline)</li> <li><strong>Time periods:</strong> Historic (1980-2014) and Future (2066-2100)</li> </ul> <p><strong>Methodology:</strong></p> <ul> <li><strong>Tropical cyclone simulation:</strong> The RAFT TC model is used to simulate storms for historical and future climates using CMIP6 environmental conditions.</li> <li><strong>TC impact metric:</strong> Wind speeds associated with 20- and 50-year return period TCs are used to estimate the aerodynamic and sea wave loading on OSW turbines.</li> <li><strong>Fragility functions:</strong> Wind speeds are input into a fragility function developed for OSW turbines, estimating the probability of yielding and buckling damage.</li> <li><strong>Damage probability maps:</strong> The results consist of eight (8) gridded damage probability maps representing the likelihoods of yielding and buckling to OSW turbines from 20- and 50-year TCs under historical and future climatic conditions.</li> </ul> <p><strong>Potential Uses:</strong></p> <ul> <li>Assessing the spatial vulnerability of OSW infrastructure to TCs</li> <li>Supporting decision-making for the design and siting of turbines</li> <li>Evaluating the impact of climate change on the risk of damage to OSW infrastructure</li> </ul> <p>For further insights into this dataset, users are encouraged to refer to the associated paper: <a href="https://www.nature.com/articles/s43247-024-01887-6">https://www.nature.com/articles/s43247-024-01887-6</a></p> <p>This dataset offers valuable insights into the potential impact of TCs on offshore wind infrastructure, aiding in risk assessment and resilience planning for the renewable energy sector.</p> <p> </p>
F I G U R E 2 in Current progress and future prospects for understanding genetic diversity of seed plants in China
F I G U R E 2 Number of articles on genetic diversity for seed plants in different fields. The data comes from the results of Web of Science (www.webofscience.com/wos/alldb/basic‐search, accessed: January 17th, 2024) using the search rule: TS = (seed plant genetic diversity) OR TS = (flowering plant genetic diversity) OR TS = (germplasm genetic diversity) OR TS = (angiosperm genetic diversity) OR TS = (gymnosperm genetic diversity). The numbers in parentheses represent the number of articles published in different fields. The overlapping areas show studies that cover multiple fields.
F I G U R E 1 in Current progress and future prospects for understanding genetic diversity of seed plants in China
F I G U R E 1 Number of articles on genetic diversity in different sequencing stages, with lines of red, blue, and green representing studies for all organisms, plants, and seed plants, respectively. Molecular markers and their first published time are provided in the blue boxes. Three major public databases (NCBI, EMBL, and BioSino) and their established time are shown in the red boxes. The data comes from the results of Web of Science (www.webofscience.com/wos/alldb/basic‐search, accessed: January 17th, 2024) using the search rules: TS = (genetic diversity) for all, TS = (plants genetic diversity) OR TS = (ferns genetic diversity) OR TS = (moss genetic diversity) OR TS = (angiosperm genetic diversity) OR TS = (gymnosperm genetic diversity) for plants, and TS = (seed plant genetic diversity) OR TS = (flowering plant genetic diversity) OR TS = (germplasm genetic diversity) OR TS = (angiosperm genetic diversity) OR TS = (gymnosperm genetic diversity) for seed plants.
Baseline and Future (2050s and 2090s) Climate Suitability Scores for 116 Useful Tree Species and 220 locations from Côte d'Ivoire, Ghana and Guinea
<p>Climate suitability scores were calculated for 116 Useful Tree Species identified by filtering Top830+ native tree species from Côte d'Ivoire, Ghana and Guinea via the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database and checking for the availability of globally observed environmental ranges from the <a href="https://doi.org/10.5281/zenodo.13132613">TreeGOER</a> database.</p> <ul> <li>Score = 3 means that in 'environmental space' the planting site occurs within the 25% - 75% species's range (as documented in the <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16914" target="_blank" rel="noopener">TreeGOER</a> ) for all variables</li> <li>Score = 2 corresponds to the 5% - 95% species's range for all variables. For some variables, the planting site occurs outside the 25% - 75% species's range.</li> <li>Score = 1 corresponds to the 0% - 100% species's range for all variables. For some variables, the planting site occurs outside the 5% - 95% species's range.</li> <li>Score = 0 means that the planting site occurs outside the 0% - 100% species's range for some of the variables</li> <li>Score = -1 means that the species is not documented by TreeGOER</li> </ul> <p>Locations corresponded to cities and weather stations from the three target countries sourced from the <a href="https://doi.org/10.5281/zenodo.10004594">CitiesGOER</a> and <a href="https://doi.org/10.5281/zenodo.12679832">ClimateForecasts</a> databases, respectively. Both these databases provide bioclimatic conditions for the historical (baseline) and three future climate change scenarios. Bioclimatic variables for future climates correspond to the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 for the 2050s (2041-2060), from 21 GCMs for SSP 3-7.0 for the 2050s and from 13 GCMs for SSP 5-8.5 for the 2090s.</p> <p>Investigations were made for two different sets of bioclimatic variables, allowing for sensitivity analysis:</p> <ul> <li>One set of bioclimatic variables included BIO01 (mean annual temperature), BIO12 (total annual precipitation), climaticMoistureIndex, monthCountByTemp10 (number of months with average temperature above 10 degrees), growingDegDays5, BIO05 (maximum temperature of the warmest month), BIO06 (minimum temperature of teh coldest month), BIO16 (precipitation of the wettest quarter), BIO17 (precipitation of the driest quarter) and MCWD (Maximum Climatological Water Deficit). These are the same bioclimatic variables available internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> for climate filtering.</li> <li>One set only included BIO01 (mean annual temperature), which is the single bioclimatic variables available for the BGCI <a href="https://cat.bgci.org/">Climate Assessment Tool</a>.</li> </ul> <p>Calculations were made with similar scripting pipelines in the <em>R</em> statistical environment as documented here: <a href="https://rpubs.com/Roeland-KINDT/1168650">https://rpubs.com/Roeland-KINDT/1168650</a>. These scripts use similar calculations methods as those used for the global case studies of the TreeGOER manuscript (Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>), and used internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> online database. Interested readers should especially refer to the manuscript for further details on methods used and their justification.</p> <p>The maps show the frequency distribution of tree species with climate scores 3, 2, 1 and 0, excluding 18 species not documented by the TreeGOER.</p> <p> </p> <p><strong>References</strong></p> <ul> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13132613" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13132613</a></li> <li>Kindt, R., Graudal, L., Lillesø, JP.B. <em>et al.</em> (2023). GlobalUsefulNativeTrees, a database documenting 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in landscape restoration. <em>Sci Rep</em> <strong>13</strong>, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a></li> <li>Kindt, R. (2023). CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000 (2023.10) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10004594" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10004594</a></li> <li>Kindt, R. (2024). ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12679832" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12679832</a></li> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Opendatasoft (2023) Geonames - All Cities with a population > 1000. <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name</a> (accessed 22-JULY-2023)</li> <li>Meteostat (2024) Weather stations: Lite dump with active weather stations. <a href="https://github.com/meteostat/weather-stations">https://github.com/meteostat/weather-stations</a> (accessed 17-FEB-2024)</li> </ul> <p> </p> <p><strong>Funding</strong></p> <p>The data sets and maps available in this archive were created within the context of an agreement between The International Centre for Research in Agroforestry (ICRAF) and WORLD UNIVERSITY SERVICE OF CANADA (WUSC) for a <em><a href="https://ceci.org/en/projects/nature-based-climate-adaptation-guinean-forest-west-africa-sbn-guinean-forests">Nature-based climate adaptation project in the Guinean forests of West Africa (NbS Guinean Forests)</a></em> funded by <a href="https://www.international.gc.ca/global-affairs-affaires-mondiales/home-accueil.aspx?lang=eng">Global Affairs Canada</a>.</p>
Future winter blocking data and codes
<p>Here, you can find data and codes required to reproduce analyses from Michel et al. (in rev.). Please refer to the readMe file.</p>
Moana Ocean Future Climate (Sample Daily Fields)
<p>The <strong>Moana Ocean Future Climate </strong>downscaling is a set of dynamical downscalings for New Zealand waters combining boundary and surface forcing from the New Zealand Earth System Model with the Moana Ocean Hindcast ROMS configuration. MOFC has been run for a 20 year reference period (1990-2010) and three future climate experiments (SSP3-70 2030-2060, SSP3-70 2070-2099 and SSP2-45 2070-2099). While this repository contains sample files, the full dataset can be downloaded from the project webpage at https://www.moanaproject.org/</p> <p><br>Model results are available as hourly and daily average values for temperature, salinity, velocity (u, v, w), and sea surface elevation.</p> <p> </p> <p>Full descriptions of these datasets are provided in Roach et al. (submitted to GMD).</p>
Planning for the Future: How ICT Professors Approach Retirement and Post-Career Life
<p><strong>Context</strong>: Professors' attitudes toward retirement vary widely, ranging from enthusiasm to reluctance. Some see it as a new beginning, while others perceive it as an intensified continuation of their academic efforts. As an evolving process, professor retirement is becoming increasingly relevant due to the aging academic workforce in higher education institutions worldwide. <strong>Goal</strong>: This study aims to investigate the human, organizational, legal, and regulatory factors that may affect the retirement process of Information and Communication Technology (ICT) professors. <strong> Method</strong>: We conducted a survey with 176 ICT professors from various universities across the country to gather data on their perceptions and preparations for retirement. <strong>Results</strong>: Our findings reveal that most professors begin to consider retirement early (31.3%) or mid-career (21.6%). Over 64% plan to engage in leisure activities and travel after retirement, while continuing to contribute to academic research. Additionally, many reported building a financial reserve during their active professional life to supplement their income and maintain their standard of living. <strong>Conclusion</strong>: ICT professors are increasingly aware of the importance of retirement planning, with many adopting strategies to ensure a comfortable future and continue their academic contributions. Financial reserves and post-career activity planning are key aspects of this process. However, most universities do not offer training courses to prepare them for retirement.</p> <p><strong>Keywords</strong>: Retirement, ICT Professors, Post-Career Life</p>
Main output data used in "Exploring the Greenland Ice Sheet's response to future atmospheric warming-threshold scenarios over 200 years" (Delhasse et al., 2025)
<p>Outputs used in:</p> <p>Delhasse, A., Kittel, C. and Beckmann, J.: Exploring the Greenland Ice Sheet's response to future atmospheric warming-threshold scenarios over 200 years, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-709, 2025.</p> <p>Each MAR-PISM coupling experiment (1991-2200) is related to the Greenland warming over a 10-year period compared to our reference period (1961-1990) at which climate is stabilized until 2200. The last experiment is the Reverse one, where the climate is year by year reversed after 2100 to go back to 2000-climate as forcing in 2200, the last year of the simulation. Please refer to Delhasse et al. (2024) for the coupling description.</p> <div> <table> <tbody> <tr> <th> <p>Experiment </p> </th> <th> <p>Exact Greenland warming at 600hPa (°C)</p> </th> <th> <p>10-years period</p> </th> </tr> </tbody> <tbody> <tr> <td> <p>CTRL</p> </td> <td> <p>+0.00</p> </td> <td> <p>1961-1990</p> </td> </tr> <tr> <td> <p>+1</p> </td> <td> <p>+1.04</p> </td> <td> <p>1995-2004</p> </td> </tr> <tr> <td> <p>+1.5</p> </td> <td> <p>+1.51</p> </td> <td> <p>2010-2019</p> </td> </tr> <tr> <td> <p>+2</p> </td> <td> <p>+2.04</p> </td> <td> <p>2021-2030</p> </td> </tr> <tr> <td> <p>+3</p> </td> <td> <p>+2.98</p> </td> <td> <p>2040-2049</p> </td> </tr> <tr> <td> <p>+4</p> </td> <td> <p>+4.04</p> </td> <td> <p>2058-2067</p> </td> </tr> <tr> <td> <p>+5</p> </td> <td> <p>+5.00</p> </td> <td> <p>2074-2083</p> </td> </tr> <tr> <td> <p>+6</p> </td> <td> <p>+5.96</p> </td> <td> <p>2083-2092</p> </td> </tr> <tr> <td> <p>+7</p> </td> <td> <p>+6.85</p> </td> <td> <p>2091-2100</p> </td> </tr> </tbody> </table> </div> <p><strong>Table 1. Greenland warmings at 600hPa since 1961-1990 used to define our experiments and the corresponding 10-years periods over which warmings are determined. </strong></p> <p>For each experiment, 3 types of output are available (where <em>EXP</em> corresponds to the name of the experiment as referenced in Table 1) : </p> <ul> <li> <p>EXP-PISM-thk-msk-1991-2200.nc: contain yearly ice thickness (THK) and ice mask (MASK) as simulated by PISM (PISM grid, 4.5 km);</p> </li> <li> <p>EXP-SMB-ME-RU-MAPI-CESM2-1991-2200.nc: contain yearly SMB (surface mass balance), ME (melt), and RU (runoff) on the MAR grid (25 km);</p> </li> <li> <p>EXP-ts-MB-D-SMB-1991-2200.nc: contain time series of the total MB (mass balance), D (discharge), and SMB (surface mass balance) integrated over the all ice sheet mask from PISM.</p> </li> </ul> <p>The MAR code used in this dataset is tagged as v3.11.3 on https://gitlab.com/Mar-Group/MARv3/-/tree/v3.11.3 (last access: 24 October 2024) (MARTeam, 2024). The PISM code used is tagged as PISMv1.2.2 on <a href="https://github.com/pism/pism/releases/tag/v1.2.2">https://github.com/pism/pism/releases/tag/v1.2.2</a> (last access: 24 October 2024).</p> <p>If you need other variables from MAR or PISM, send us an email (alison.delhasse@uliege.be) and we will be glad to help you. We will also be happy to share the scripts we have developed to analyze the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR-PISM outputs.<br><br><strong><em>Data usage notice:</em></strong></p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgments should be similar to the one below that contains information related to MAR and PISM. To document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact me to add their works to the list of MAR-related publications. </p> <p>"We thank A. Delhasse, C. Kittel, and J. Beckmann, as well as the MAR and PISM teams which make available the model outputs. We also thank agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR-PISM simulations. "</p> <p>You should also refer to and cite the following paper in its latest version:</p> <p>Delhasse, A., Kittel, C. and Beckmann, J.: Exploring the Greenland Ice Sheet’s response to future warming-threshold scenarios over 200 years, [JOURNAL UNDER REVIEW], 2024.</p> <p><strong><em>References</em></strong></p> <p>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</p> <p>MARTeam: MARv3.11, GitLab [data set], <a href="https://gitlab.com/Mar-Group/MARv3">https://gitlab.com/Mar-Group/MARv3#</a> (last access: 24 October 2024), 2024.</p> <p> </p>
Current Applications and Future Prospects of Artificial Intelligence in Higher Medical Education: A Bibliometric Analysis
<p>This dataset supports the study titled <em>"Current Applications and Future Prospects of Artificial Intelligence in Higher Medical Education: A Bibliometric Analysis"</em>. It includes bibliometric data from Web of Science on the use of artificial intelligence in higher medical and pharmaceutical education. The dataset covers relevant literature, keywords, and citation analysis to provide insights into research trends, institutional contributions, and future directions in the field. This data can be valuable for researchers interested in the intersection of AI and medical education, as well as for those conducting further bibliometric analyses.</p>
Dataset for the article 'Estimating countries' additional carbon accountability for closing the mitigation gap based on past and future emissions'.
<p>Dataset for the article 'Estimating countries’ additional carbon accountability for closing the mitigation gap based on past and future emissions', published in Nature Communications. DOI: <a href="https://doi.org/10.1038/s41467-024-54039-x">10.1038/s41467-024-54039-x</a></p> <p>TablesInManuscriptandCalculations.xlsx includes a calculations sheet where the main results can be estimated using only Excel, and each respective table found in the article.</p> <p>PlannedEmissions.xlsx includes estimated pathways for all analyzed countries during 2023-2070. Results are given in million tonnes of carbon dioxide (MtCO₂).</p> <p>DataForSensitivityAnalysis.xlsx is a full database with all the results used in the article, both the main approach and sensitivity cases.</p> <p>These files are generated using R-code available at:</p> <p><a href="https://github.com/morfeldt/AdditionalCarbonAccountability">https://github.com/morfeldt/AdditionalCarbonAccountability</a></p> <p> </p>
Present and future distribution models for chestnut in the Iberian Peninsula
<p>Current and future distribution of chestnut trees in the Iberian Peninsula developed in the manuscript "<strong>Impact of climate change over distribution and potential range of chestnut in the Iberian Peninsula</strong>"</p> <p>These predictions were derived through computational modeling utilizing various environmental parameters. Specifically, the model integrated topographical features such as slope, northness (cosine of aspect), or eastness (sine of aspect), soil attributes including pH and soil organic carbon content (SOC), and bioclimatic variables sourced from the CHELSA V.2.1 dataset.</p> <p><br>File Naming Convention Explanation:</p> <p>"C_sativa_current.tif" denotes the model representing the current distribution of C. sativa.</p> <p>In other instances, such as "C_sativa_md_ensemble_ssp370_2011-2040.tif":</p> <ul> <li>"md_ensemble" indicates the median ensemble, while "mn_ensemble" signifies the mean ensemble.</li> <li>In cases where the model is solely based on a General Circulation Model (GCM), the term "md_ensemble" or "mn_ensemble" is replaced by the specific model used.</li> <li>"ssp370" or "ssp585" corresponds to the climate change scenario derived from CMIP6 GCMs.</li> <li>The timeframe "2011-2040," "2041-2070," or "2071-2100" denotes the period under evaluation.</li> </ul>
Oriental Honey-Buzzards Dataset | Climate change leads to range contraction for the Oriental Honey-Buzzards: How to point out the future conservation strategies?
<p>This dataset contains raster data (.TIF) in probability and binary outputs of oriental honey-buzzards distribution within the wintering and breeding areas under changing climate.</p> <p><strong>File Size</strong>: ~184 MB (13.8 MB in compressed ZIP file)</p> <p><strong>Format File</strong>:</p> <p><em>ohb_A_B_C</em>.tif (.tfw; .XML; .dbf)</p> <p><strong>A</strong>: breeding or wintering</p> <p><strong>B</strong>: timepoint and scenario. e.g., 2050ssp5 or 2010ssp2</p> <p><strong>C</strong>: binary or probability outputs. e.g., bin or prob. <em>Note: for binary maps, value 0: non-suitable areas for OHB and value 1: suitable areas for OHB</em></p> <p>For further inquiries. Please contact: aryo_acondro@apps.ipb.ac.id</p>
Yes! We're open. Open science and the future of academic practices in translation and interpreting studies - Supplementary material
<p>Supplementary material to the article "<em>Yes! We’re open</em>. Open science and the future of academic practices in translation and interpreting studies" by Christian Olalla-Soler. </p> <ul> <li>Sheet 1: Translation and Interpreting Studies journals and bibliometric indicators.</li> <li>Sheet 2: Translation and Interpreting Studies articles in Scopus.</li> <li>Sheet 3: Pre-registrations related to translation and interpreting.</li> </ul> <p>Reference:</p> <p>Olalla-Soler, Christian (2021). "<em>Yes! We’re open</em>. Open science and the future of academic practices in translation and interpreting studies". <em>Translation & Interpreting</em> 13 (2): 1-28. <a href="https://doi.org/10.12807/ti.113202.2021.a01">https://doi.org/10.12807/ti.113202.2021.a01</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.