Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
139
datasets available to search
ShareScore release 0.9.0
Dataset results
139 results for “climate conditions”
Vibration-based Monitoring of a Small-scale Wind Turbine Blade Under Varying Climate Conditions. Part I: An Experimental Benchmark
<p>This repository contains all publicly available data related to the experimental part of <a href="https://onlinelibrary.wiley.com/doi/epdf/10.1002/stc.2660">Sonkyo-Benchmark</a>. The data of each experimental case (R, A, B, C, D, E, F, G, H, I, J, K, L) and temperature point (-15, -10, -5, 0, 5, 10, 15, 20, 25, 30, 35, 40) are stored in a zip file named "Case_<em>X</em>_(<em>T</em>)", where <em>X</em> denotes the case label and <em>T</em> refers to the temperature value. Each file "Case_<em>X</em>_(<em>T</em>).zip" contains two folders "Case_<em>X</em>_(<em>T</em>)_1" and "Case_<em>X</em>_(<em>T</em>)_2", wherein the test results from the two sensor layouts are stored. </p>
An experimental data set on the thermal and fluid dynamic performance of double skin facades (DSFs) subjected to various controlled boundary conditions through the use of a climate simulator facility
<p>Double skin facades (DSFs) are building envelope systems defined by complex phenomena and non-linear-processes that make characterizing their performance a non-trivial task. In an effort to enable the scientific community to access experimental data for further analysis or model validation purposes, we release together with the open-access paper entitled “<strong><em>Laboratory testbed and methods for flexible characterization of the thermal and fluid dynamic behavior of double skin facades” (</em></strong><a href="https://doi.org/10.1016/j.buildenv.2021.108700"><strong><em>https://doi.org/10.1016/j.buildenv.2021.108700</em></strong></a><strong><em>)</em></strong>, a set of experimental data collected during tests carried out with the use of the newly developed testbed. The data contains the results of a series of tests where various configurations of a full-scale DSF mock-up that have been subjected to different boundary conditions replicated in a climate simulator. The database contains a guide in the form of the file ‘Guide.pdf’, which explains how to read data, presents a schematic drawing of sensor layout, and provides more information on sensors’ positions. Further information on the original aims of the experiments, methods, and other data can be found in the article mentioned above, which becomes an essential tool to understand how to read and interpret the experimental data fully. The following collection of experimental data are provided:</p> <ul> <li>32 steady-state measurements where the following factors were changed: ventilation mode (indoor and outdoor air curtain), solar irradiance (0, 400, 600, and 800 Wm<sup>-2</sup>), outdoor chamber temperature (10, 20, 30, and 40 ℃), cavity depth (20, 30, 40 and 60 cm) and venetian blinds position (no blinds, closed blinds, θ=45 º, and open blinds) [file names: ‘Taguchi_4Lx4F_L16_I-I.csv’ and ‘Taguchi 4Lx4F_L16_O-O.csv’],</li> <li>Dynamic profile measurements corresponding to a typical hot summer day [Dynamic_profile_measurements.csv] and</li> <li>Calibration data [Callibration.csv].</li> </ul> <p>Any inquires on the experimental data<em> can be sent </em>to: aleksandar.jankovic@ntnu.no</p>
Interpreting future climate conditions in Brazilian cities – Dashboard and EPW files
<h3>(English)</h3> <h1>1. Introduction</h1> <p>This project aims to address the impacts of climate change on the built environment by developing a set of future Brazilian EPW (Energy Plus Weather Format) files and a dashboard to interpret and evaluate the data. The future climate files were obtained using the Future Weather Generator (FWG) [1] with climate projections for Brazilian cities, integrating these projections into a code pipeline for automation. In this part of the project, thermal comfort indices, such as the Universal Thermal Climate Index (UTCI) and the Discomfort Index (DI), were also evaluated to understand future thermal comfort conditions. The methodology followed the structure available in the <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> repository:</p> <ol> <li>Climate-One-Building (COB) web-scrapping for all available Brazilian EPW files (we recommend doing this carefully so as not to damage the COB infrastructure);</li> <li>Automatic organisation of all EPW files in a folder, extracting them from the ZIP format;</li> <li>Simulation of future climate files using FutureWeatherGenerator [1] in a line of code with default parameters (shown in Table 1);</li> <li>Organisation of all available EPWs (original and simulated) in a single database;</li> <li>Calculation of thermal comfort indices using pythermalcomfort [2].</li> </ol> <p>The main objective is to provide researchers, policymakers and professionals with a comprehensive tool for assessing and mitigating the impacts of climate change in different Brazilian cities, offering accurate data for thermal comfort and energy efficiency modelling. The methodology involves generating future EPW files, validating them against existing literature and visualising the results through a user-friendly dashboard. The study highlights the importance of adaptive and climate-resilient strategies in urban planning and building design. Expected climate changes in Brazil include increased dry bulb temperature and variations in relative humidity, radiation and wind speed in the different bioclimatic zones.</p> <p>The dashboard has been designed to simplify the visualisation of future climate data, focusing on the main climate variables, thermal comfort indices and data visualisation. It allows users to filter by city and automatically calculate all the indices, providing detailed analyses and comparisons of different scenarios. By offering a free, open-access, multi-platform, extensible, customisable and easy-to-maintain tool, the project aims to facilitate continuous updates, new features and corrections. This tool supports decision-making in public policy and urban planning, promoting a more sustainable and resilient built environment in the face of climate change.</p> <p> </p> <h1>2. Further details on the methodology</h1> <p>Details on how the indices were selected and how the study was conducted may be found in Vaz et al. [3]. The GitHub repository in <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> [4] also includes details on the step-by-step procedures.</p> <h3>Table 1 - Parameters used in the FWG simulation:</h3> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Data used in the simulations</strong></p> </td> </tr> <tr> <td> <p>Base files</p> </td> <td> <p>578 cities from COB</p> </td> </tr> <tr> <td> <p>CMIP-6 models</p> </td> <td> <p>BCC-CSM2-MR, CAS-ESM2.0, CMCC-ESM2, CNRM-CM6.1-HR, CNRM-ESM2.1, EC-Earth3, EC-Earth3-Veg, MIROC-ES2H, MIROC6, MRI-ESM2.0, UKESM1.0-LL</p> </td> </tr> <tr> <td> <p>Grid</p> </td> <td> <p>Bilinear interpolation of the four nearest points</p> </td> </tr> <tr> <td> <p>Month transition smoothness</p> </td> <td> <p>72 hours</p> </td> </tr> <tr> <td> <p>Apply variable limits</p> </td> <td> <p>True</p> </td> </tr> <tr> <td> <p>Scenarios</p> </td> <td> <p>A total of nine scenarios: One baseline for 2021 and eight future files (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 for 2050 and 2080)</p> </td> </tr> <tr> <td> <p>Solar hour correction</p> </td> <td> <p>Made by day</p> </td> </tr> <tr> <td> <p>Diffuse irradiation model</p> </td> <td> <p>Engerer, 2015</p> </td> </tr> </tbody> </table> <p> </p> <h1>3. References</h1> <p>[1] E. Rodrigues, M.S. Fernandes, D. Carvalho, Future weather generator for building performance research: An open-source morphing tool and an application, Building and Environment 233 (2023) 110104. https://doi.org/10.1016/j.buildenv.2023.110104.</p> <p>[2] F. Tartarini, S. Schiavon, pythermalcomfort: A Python package for thermal comfort research, SoftwareX 12 (2020) 100578. https://doi.org/10.1016/j.softx.2020.100578.</p> <p>[3] Vaz, I.C.M.; Ghisi, E.; Thives, L.P.; Vieira, A.S.; Rupp, R.F.; da Rosa, A.S.; Flores, R.A.; Bastos, M.B.; Marinoski, D.L.; Silva, A.S.; Weeber, M.; Invidiata, A. (2024). Dashboard for interpreting future climate files used in the simulation of buildings – an outdoor thermal comfort approach. Under submission.</p> <p>[4] Future EPW Analysis - A pipeline of processes aimed at providing future EPW files based on existing models from the literature. Available at: https://github.com/igorcmvaz/future-EPW-analysis.</p> <p> </p> <h1>Current version of the dashboard: 1.0.0.</h1> <h1>Available at <a title="Dashboard comfort - 1.0.0." href="https://app.powerbi.com/view?r=eyJrIjoiNWI0ZTk5YjMtZjA5Ny00ZjE3LTk2ZDUtNDA1OThhNWQ3NWYxIiwidCI6ImZhNzk1MzFjLThjZTUtNGJkMy05N2VlLTI0NWU2ZWUyNjZiOCJ9" target="_blank" rel="noopener">Dashboard Comfort.</a></h1> <p>Suggestions for improvements can be made directly in the GitHub repository at <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> or sent to igorcmvaz@gmail.com.</p> <p> </p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p> </p> <h3>(Português-BR)</h3> <h1>1. Introdução</h1> <p>Este projeto tem como objetivo abordar os impactos das mudanças climáticas no ambiente construído, desenvolvendo um conjunto de futuros arquivos EPW (Energy Plus Weather Format) brasileiros e um <em>dashboard</em> para interpretar e avaliar os dados. Os arquivos climáticos futuros foram obtidos com o Future Weather Generator (FWG) [1] com projeções climáticas para cidades brasileiras, integrando essas projeções a um pipeline de código para automação. Nessa parte do projeto, os índices de conforto térmico, como o Universal Thermal Climate Index (UTCI) e o Discomfort Index (DI), também foram avaliados para entender as condições futuras de conforto térmico. A metodologia seguiu a estrutura que está disponível no repositório <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a>:</p> <ol> <li>Web-scrapping do Climate-One-Building (COB) para todos os arquivos EPW brasileiros disponíveis (recomendamos fazer isso com cuidado para não prejudicar a infraestrutura do COB);</li> <li>Organização automática de todos os arquivos EPW em uma pasta, extraindo-os do formato ZIP;</li> <li>Simulação dos arquivos climáticos futuros por meio do FutureWeatherGenerator [1] em linha de código com parâmetros padrão (mostrados na Tabela 1);</li> <li>Organização de todos os EPW disponíveis (originais e simulados) em um único banco de dados;</li> <li>Cálculo dos índices de conforto térmico com o pythermalcomfort [2].</li> </ol> <p>O objetivo principal é fornecer a pesquisadores, formuladores de políticas e profissionais uma ferramenta abrangente para avaliar e mitigar os impactos das mudanças climáticas em diferentes cidades brasileiras, oferecendo dados precisos para modelagem de conforto térmico e eficiência energética. A metodologia envolve a geração de futuros arquivos EPW, validando-os com a literatura existente e visualizando os resultados por meio de um <em>dashboard</em> de fácil utilização. O estudo destaca a importância de estratégias adaptativas e resistentes ao clima no planejamento urbano e no projeto de edificações. As mudanças climáticas esperadas no Brasil incluem o aumento da temperatura de bulbo seco e variações na umidade relativa, radiação e velocidade do vento nas diferentes zonas bioclimáticas.</p> <p>O <em>dashboard</em> foi projetado para simplificar a visualização dos dados climáticos futuros, concentrando-se nas principais variáveis climáticas, índices de conforto térmico e visualização dos dados. Ele permite que os usuários filtrem por cidade e calculem automaticamente todos os índices, fornecendo análises detalhadas e comparações de diferentes cenários. Ao oferecer uma ferramenta gratuita, de acesso aberto, multiplataforma, extensível, personalizável e de fácil manutenção, o projeto visa a facilitar atualizações contínuas, novos recursos e correções. Essa ferramenta apoia a tomada de decisões em políticas públicas e planejamento urbano, promovendo um ambiente construído mais sustentável e resiliente em face das mudanças climáticas.</p> <p> </p> <h1>2. Mais detalhes sobre a metodologia</h1> <p>Detalhes sobre a seleção dos índices de conforto e como o estudo foi conduzido podem ser encontrados em Vaz et al. [3]. O repositório GitHub em <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> [4] também inclui detalhes sobre os procedimentos passo a passo.</p> <h3>Tabela 1 - Parâmetros usados na simulação do FWG</h3> <table> <tbody> <tr> <td> <p><strong>Parâmetro</strong></p> </td> <td> <p><strong>Dados utilizados na simulação</strong></p> </td> </tr> <tr> <td> <p>Arquivos base</p> </td> <td> <p>578 cidades do COB</p> </td> </tr> <tr> <td> <p>Modelos CMIP-6</p> </td> <td> <p>BCC-CSM2-MR, CAS-ESM2.0, CMCC-ESM2, CNRM-CM6.1-HR, CNRM-ESM2.1, EC-Earth3, EC-Earth3-Veg, MIROC-ES2H, MIROC6, MRI-ESM2.0, UKESM1.0-LL</p> </td> </tr> <tr> <td> <p>Malha</p> </td> <td> <p>Interpolação bilinear dos quatro pontos mais próximos</p> </td> </tr> <tr> <td> <p>Suavização da transição mensal</p> </td> <td> <p>72 horas</p> </td> </tr> <tr> <td> <p>Aplicar limites das variáveis</p> </td> <td> <p>Sim</p> </td> </tr> <tr> <td> <p>Cenários</p> </td> <td> <p>Total de nove cenários: Um arquivo base em 2021 e oito arquivos futuros (SSP1-2.6, SSP2-4.5, SSP3-7.0 e SSP5-8.5 para 2050 e 2080)</p> </td> </tr> <tr> <td> <p>Correção de hora solar</p> </td> <td> <p>Feita por dia</p> </td> </tr> <tr> <td> <p>Modelo de radiação difusa</p> </td> <td> <p>Engerer (2015)</p> </td> </tr> </tbody> </table> <p> </p> <h1>3. Referências</h1> <p>[1] E. Rodrigues, M.S. Fernandes, D. Carvalho, Future weather generator for building performance research: An open-source morphing tool and an application, Building and Environment 233 (2023) 110104. https://doi.org/10.1016/j.buildenv.2023.110104.</p> <p>[2] F. Tartarini, S. Schiavon, pythermalcomfort: A Python package for thermal comfort research, SoftwareX 12 (2020) 100578. https://doi.org/10.1016/j.softx.2020.100578.</p> <p>[3] Vaz, I.C.M.; Ghisi, E.; Thives, L.P.; Vieira, A.S.; Rupp, R.F.; da Rosa, A.S.; Flores, R.A.; Bastos, M.B.; Marinoski, D.L.; Silva, A.S.; Weeber, M.; Invidiata, A. (2024). Dashboard for interpreting future climate files used in the simulation of buildings – an outdoor thermal comfort approach. Under submission.</p> <p>[4] Future EPW Analysis - A pipeline of processes aimed at providing future EPW files based on existing models from the literature. Available at: https://github.com/igorcmvaz/future-EPW-analysis.</p> <p> </p> <h1>Versão atual do <em>dashboard</em>: 1.0.0. </h1> <h1>Disponível em <a title="Dashboard comfort - 1.0.0." href="https://app.powerbi.com/view?r=eyJrIjoiNWI0ZTk5YjMtZjA5Ny00ZjE3LTk2ZDUtNDA1OThhNWQ3NWYxIiwidCI6ImZhNzk1MzFjLThjZTUtNGJkMy05N2VlLTI0NWU2ZWUyNjZiOCJ9" target="_blank" rel="noopener">Dashboard conforto.</a></h1> <p>As sugestões de melhorias podem ser feitas diretamente no repositório do GitHub em <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> ou enviadas para igorcmvaz@gmail.com.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p>
Retrotransposon-based genetic variation of Poa annua populations from contrasting climate conditions
<p>Raw photographs of agarose electrophoresis. Material: six Poa annua populations. Method: inter-Primer Binding Site (iPBS) markers This is the documentation of studies described in the manuscript entitled "Retrotransposon-based genetic variation of Poa annua populations from contrasting climate conditions" accepted for publication in PeerJ journal (decision received on 02.04.2019)</p>
Raw Data for Publication: Fungal colonisation on wood surfaces weathered at diverse climatic conditions
<p>Colour_Changes_Izola.csv</p> <p>This file contains CIE Lab* color coordinates measured on the surface of Scots pine during the natural weathering test in Izola, Slovenia.</p> <p>Colour_Changes_Skelleftea.csv</p> <p>This file contains CIE Lab* color coordinates measured on the surface of Scots pine during the natural weathering test in Skelleftea, Sweden.</p> <p>Contact_Angles_Izola.csv</p> <p>This file contains dynamic contact angle with distilled water measured on the surface of Scots pine during the natural weathering test in Izola, Slovenia.</p> <p>Contact_Angles_Skelleftea.csv</p> <p>This file contains dynamic contact angle with distilled water measured on the surface of Scots pine during the natural weathering test in Skelleftea, Sweden.</p> <p>Gloss.csv</p> <p>This file contains the gloss value measured on the surface of Scots pine during the natural weathering test in Izola, Slovenia and Skelleftea, Sweden.</p> <p><strong>Note:</strong> The sample IDs are structured as follows: The first letter represents the treatment condition, with "R" indicating untreated wood. The second letter (A, B, C) represents the board's ID. The third letter represents the location, with "S" indicating Skelleftea and "I" representing Izola, Slovenia. The number indicates the exposure time in weeks.</p> <p>FUNGAL STRAIS_DNA sequence analysis.xlsx</p> <p>This file contains the Genomic DNA of the fungal strains detected on the surface of Scots pine during the natural weathering test in Izola, Slovenia and Skelleftea, Sweden.</p> <p>Weather_Data_Izola.csv</p> <p>This file contains hourly local weather conditions in Izola, Slovenia</p> <p>Weather_Data_Skelleftea.csv</p> <p>This file contains hourly local weather conditions in Skelleftea, Sweden</p> <p><strong>Note:</strong> The weather conditions including the following parameters:1. Air temperature (°C), 2. Dew point (°C), 3. Relative humidity (%), 4. One-hour precipitation total (mm), 5. Snow depth (mm), 6. Wind direction (°), 7. Average wind speed (km/h), 8.Sea-level air pressure (hPa)</p>
Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations)
<p>This is the outcome data from our study titled "<a href="http://dx.doi.org/10.1016/j.scitotenv.2024.170481" target="_blank" rel="noopener"><em><u>Prediction of global wheat cultivation distribution under climate change and socioeconomic development</u></em></a>" which was published in The Science of The Total Environment. The present study represents a significant extension of our previous research on "<em><a href="http://dx.doi.org/10.1016/j.scitotenv.2019.06.153" target="_blank" rel="noopener">The Potential Distribution and Dynamics of Global Wheat under Multiple Climate Change Scenarios"</a></em>.</p> <p>Socioeconomic and climate change are both critical factors influencing the global distribution of crop cultivation. However, there has been limited exploration of the role of socioeconomic factors in predicting future crop cultivation distribution under climate change.</p> <p>We have proposed the MaxEnt-SPAM approach under the assumption that environmental conditions are the primary determinants of land suitability for cultivating wheat, while socioeconomic factors play a crucial role in influencing farmers' crop choices. In essence, the distribution of wheat cultivation is contingent upon maximizing potential revenue and ensuring suitability for wheat planting.</p> <p>The proposed MaxEnt-SPAM approach was utilized to estimate the distribution of wheat cultivation in three combined Representative Concentration Pathway (RCP) - Shared Socioeconomic Pathway (SSP) scenarios, namely RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3. The methodology involved estimating wheat planting suitability under future RCP scenarios using the MaxEnt model, predicting farmers' crop choices under future SSP scenarios through Time series-Backpropagation (TS-BP) models, and ultimately estimating global wheat cultivation distribution based on the SPAM model. Validation of this approach against major known datasets on the distribution of wheat cultivation demonstrated satisfactory accuracy, with a predictive accuracy exceeding 85% and a significant positive correlation (p < 0.01) between the predicted global wheat cultivation and multiple known datasets.</p> <p>Based on the aforementioned concept and methodology, a global wheat cultivation distribution grid (0.5 degree × 0.5 degree) was projected under the RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios.</p> <p>The findings suggest that RCP8.5-SSP3 may offer the most favorable conditions for wheat cultivation. Additionally, socioeconomic development significantly constrains the potential distribution of wheat cultivation, with estimated areas accounting for an average of 77% of the potential distribution determined by climatic factors under the selected RCP-SSP scenarios. Socioeconomic development appears to have a positive impact on wheat cultivation in Africa.</p> <p>Our results illustrate the influence of socioeconomic factors on crop distribution within a market economy framework, underscoring the importance of integrating socioeconomic factors and climate change for accurate predictions of crop cultivation distribution.</p> <p>We contend that the global wheat cultivation distribution datasets under future climatic and socio-economic conditions (RCP-SSP combinations) are a valuable addition to existing products. This prediction data is among the few products to consider both climate change and socio-economic development, providing a more comprehensive understanding of crop cultivation distribution dynamics.</p> <p>The Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) is expected to enhance our comprehension of the dynamics and distribution of global wheat cultivation under different climate change and socio-economic development paths in the future, potentially supporting research in earth system simulation and agricultural sciences.</p> <p>The dataset for the Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) and the Maxent-SPAM approach code is stored in a zip package named SPAM_MaxEnt.zip, which contains two folders (code and data).</p> <p><strong>code: </strong></p> <p>This sub-folder provides the main program and example data for the MaxEnt-SPAM approach. Codes are written in Matlab language by Puying Zhang. There are also 'read me.txt' files under the code folder to provide the necessary information.</p> <p>The exampleData contains</p> <p>1. h_pri.tif: prior data</p> <p>2. h_res.tif: global C3 crop cultivation proportion</p> <p>Run the main programme: cross_entroy.m</p> <p><strong>data: </strong></p> <p>This sub-folder contains global wheat cultivation distribution stored in GeoTIFF file format.</p> <p><strong>1 Global distribution of the long-term wheat-</strong><strong>c</strong><strong>ultivation area fraction: </strong></p> <p>This sub-folder contains the data for the global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios. The value of each data ranges from 0 to 1, indicating the long-term wheat-cultivation area fraction in each grid, and the higher the value, the more wheat cultivated.</p> <p><strong>r2s1f_sub.tif:</strong> the data for global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1 scenario</p> <p><strong>r4s2f_sub.tif:</strong> the data for global distribution of the long-term wheat-cultivation area fraction in RCP4.5-SSP2 scenario</p> <p><strong>r8s3f_sub.tif:</strong> the data for global distribution of the long-term wheat-cultivation area fraction in RCP8.5-SSP3 scenario</p> <p><strong>2 </strong><strong>S</strong><strong>patial overlap between the long-term period of land suitability for wheat </strong><strong>planting </strong><strong>and wheat cultivation distribution: </strong></p> <p>This sub-folder contains the data for Spatial overlap between the long-term period of land suitability for wheat planting and wheat cultivation distribution in multi-scenarios. The value of each data contains three values:<strong>{1, 2, 3}</strong>, <strong>1</strong> wheat cultivation existed but was predicted to be unsuitable to plant wheat; <strong>2 </strong>presented a reduction in the wheat cultivation area compared to the land's suitability; <strong>3</strong> presented the region that wheat cultivation existed and was predicted to be suitable to plant wheat.</p> <p><strong>com_suit_fra126.tif: </strong>the spatial overlap between the long-term period land suitability for wheat planting and wheat cultivation distribution in (a) RCP2.6-SSP1 scenario and RCP2.6</p> <p><strong>com_suit_fra245.tif: </strong>the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (b) RCP4.5-SSP2 scenario and RCP4.5</p> <p><strong>com_suit_fra385.tif:</strong> the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (c) RCP8.5-SSP3 scenario and RCP8.5</p> <p><strong>3 Differences in the proportion of long-term wheat </strong><strong>c</strong><strong>ultivation: </strong></p> <p>This sub-folder contains the data for the difference in the proportion of long-term wheat cultivation under the RCP-SSP scenarios and the distribution of long-term wheat planting suitability under the same RCP scenarios. The value of each data ranges from -1 to 1, This data is obtained by using the wheat-cultivation area fraction minus planting suitability grid to grid. the negative value indicates that the proportion of wheat cultivation is lower than the wheat planting suitability, while this positive value indicates that the proportion of wheat cultivation is higher than the wheat planting suitability.</p> <p><strong>r2s1_f.tif:</strong> Difference in the proportion of long-term wheat cultivation under the RCP2.6-SSP1 scenario and the distribution of long-term wheat planting suitability under the RCP2.6 scenario</p> <p><strong>r4s2_f.tif:</strong> Differences between the proportion of long-term wheat cultivation in RCP4.5-SSP2 and the suitability of long-term wheat planting under the RCP4.5 scenario</p> <p><strong>r8s3_f.tif:</strong> Differences between the proportion of long-term wheat cultivation in RCP8.5-SSP3 and the suitability of long-term wheat planting under the RCP8.5 scenario </p> <p><strong>References:</strong></p> <p>Yaojie Yue, Puying Zhang, Yanrui Shang. The Potential Distribution and Dynamic of Global Wheat under Multiple Climate Change Scenarios. Science of the Total Environment, 2019, 688: 1308-1318.</p> <p>Xi Guo, Puying Zhang, Yaojie Yue. Prediction of global wheat cultivation distribution under climate change and socio-economic development. Science of the Total Environment, 2024, 919: 170481.</p> <p>For more details on the MaxEnt (Maximum entropy) model, please refer to (Phillips et al., 2006; Elith et al., 2011). SPAM (spatial production allocation model) refers to (You et al., 2009; You et al., 2014).</p> <p>Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E., Yates, C.J., 2011. A statistical explanation of maxent for ecologists. Divers Distrib 17 (1), 43-57. https://coi.org/10.1111/j.1472-4642.2010.00725.x.</p> <p>Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol Model 190 (3-4), 231-259. https://coi.org/10.1016/j.ecolmodel.2005.03.026.</p> <p>You, L.Z., Wood, S., Wood-Sichra, U., 2009. Generating plausible crop distribution maps for sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agr Syst 99 (2-3), 126-140. https://coi.org/10.1016/j.agsy.2008.11.003.</p> <p>You, L.Z., Wood, S., Wood-Sichra, U., Wu, W.B., 2014. Generating global crop distribution maps: from census to grid. Agr Syst 127, 53-60. https://coi.org/10.1016/j.agsy.2014.01.002</p>
Phenotypic trait variation of Herminium monorchis in the Qinghai-Tibetan Plateau with grazing intensity and climatic conditions
This data set contains raw data supporting the research entitled “Livestock grazing outweighs climate in driving trait variation of a widespread alpine plant” (currently under peer review), which documents how phenotypic traits of a widespread herbaceous plant in the Qinghai-Tibetan Plateau, Herminium monorchis, vary with grazing intensity and environmental conditions.
Fig. 6 in Shell Morphology, Growth Pattern And Population Dynamics Of The Land Snail Xerolenta Obvia (Menke, 1828) In Two Areas Of Different Climatic Conditions Within A Temperate Climate Region
Fig. 6. Xerolenta obvia growth model under natural conditions. Size ranges of sexually ma- ture snails are shown in dark grey (4.5–5.4 whorls) and light grey (4.25–4.4 whorls); solid lines = SW population, dashed lines = NE population; 1 = first model variant, 2 = second model variant (details in text)
Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions (v3.0)
<p><strong>Agricultural land resources – a global suitability evaluation (v3.0)</strong></p> <p>Local climate, soil and topography determine the conditions under which agricultural crops are suitable for growth or not. The methodology uses a fuzzy logic approach that is described in Zabel et al. (2014). The approach is based on Liebig's law of the minimum. Accordingly, plant suitability is determined not by total available resources, but by the scarcest resource. The limiting factor depends on the local environmental conditions and the crop-specific requirements, that are taken from literature. </p> <p><strong>Determining Agricultural Suitability</strong></p> <p>Agricultural suitability is calculated for each of 5 climate models (GFDL, HadGEM2, IPSL, MIROC and NorESM1) from the AR5 ISIMIP fast track protocol. Daily climate model data for temperature, precipitation and solar radiation are statistically downscaled to 30 arc seconds spatial resolution. A monthly bias-correction is applied using WorldClim data. The provided suitability data refers to the model median over the 5 climate simulations. Soil data is taken from the Harmonized World Soil Database (HWSD) v1.21. Considered soil properties are texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity. Soil depth is taken into account according to Pelletier et al. (2015). Topography data is applied from the Shuttle Radar Topography Mission (SRTM). Irrigation has strong impact on the suitability of crops and is considered in this approach.</p> <p><strong>Agricultural Suitability</strong></p> <p>The agricultural suitability data is provided at a spatial resolution of 30 arc seconds (approximately 1 km<sup>2</sup> at the equator). The dataset contains four time periods (1980-2009, 2010-2039, 2040-2069, 2070-2099) and two climate change scenarios (RCP2.6 and RCP 8.5). Agricultural suitability is provided for rainfed conditions and for irrigated conditions seperately. Additionally, we provide a dataset in which the current irrigation areas according to Maier et al. (2018) are applied. The suitability is provided for 23 food, feed, fibre, and 1st and 2nd generation bio-energy crops. An 'overall suitability' is provided for all crops that considers the most suitable crop on each pixel. Additionally, we provide a dataset excluding 2nd generation bioenergy crops (18-23) from the overall aggregation of crops.</p> <table> <caption><strong>Food, feed, fiber and first-generation bioenergy crops</strong></caption> <tbody> <tr> <td>Barley</td> <td>Potato</td> <td>Sugarbeet</td> </tr> <tr> <td>Cassava</td> <td>Rapeseed</td> <td>Sugarcane</td> </tr> <tr> <td>Groundnut</td> <td>Rice</td> <td>Sunflower</td> </tr> <tr> <td>Maize</td> <td>Rye</td> <td>Summer wheat</td> </tr> <tr> <td>Millet</td> <td>Sorghum</td> <td>Winter wheat</td> </tr> <tr> <td>Oilpalm</td> <td>Soybean</td> <td> </td> </tr> </tbody> </table> <table> <caption> <p><strong>Second-generation bioenergy crops</strong></p> </caption> <tbody> <tr> <td>Jatropha</td> <td>Reed canary grass</td> </tr> <tr> <td>Miscanthus</td> <td>Eucalyptus</td> </tr> <tr> <td>Switchgrass</td> <td>Willow</td> </tr> </tbody> </table> <p><strong>Growing Season Adaptation</strong></p> <p>The agricultural suitability considers the adaptation of the growing season. For each pixel and crop, the growing season is optimized throughout the year, taking the annual course of precipitation, temperature, and solar radiation as well as their interplay, into account.</p> <p><strong>Most Suitable Crop</strong></p> <p>The most suitable crop for each pixel is provided in the data. Please note that a value of 126 means that no crop suitable and 127 means that multiple crops have the same suitability.</p> <p><strong>Further information</strong></p> <p>Detailled information are available in the following publications:</p> <p>Zabel F, Putzenlechner B, Mauser W (2014) Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions. PLOS ONE 9(9): e107522. doi: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">10.1371/journal.pone.0107522</a></p> <p>Cronin, J., Zabel, F., Dessens, O., Anandarajah, G. (2020): Land suitability for energy crops under scenarios of climate change and land-use. GCB Bioenergy, 12(8). doi: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcbb.12697">10.1111/gcbb.12697</a></p> <p>Schneider. J.M., Zabel, F., Mauser, W. (2022): Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Scientific Data 9, 527. doi: <a href="https://doi.org/10.1038/s41597-022-01632-8">10.1038/s41597-022-01632-8</a></p> <p>Meier, J., Zabel, F., Mauser, W. (2018): A global approach to estimate irrigated areas – a comparison between different data and statistics. Hydrol. Earth Syst. Sci., 22, 1119–1133, 2018. doi: <a href="https://hess.copernicus.org/articles/22/1119/2018/">10.5194/hess-22-1119-201</a></p> <p>Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D. (2016), A gridded global data set of soil, immobile regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, <em>J. Adv. Model. Earth Syst.</em>, 8, 41– 65, doi: <a href="https://doi.org/10.1002/2015MS000526">10.1002/2015MS000526</a>.</p> <p><strong>Improvements in v3.0</strong></p> <p>Compared to the previous version (<a href="https://zenodo.org/record/3748350">v2.0</a>), this version (v3.0) <em>uses updated input data for soil (HWSD v1.21) and high resolution irrigated areas (Maier et al. 2018), and additionally considers soil depth (Pelletier et al. 2016). Moreover, the suitability is calculated for an ensemble of 5 climate models, and is available for more crops, including a number of second generation bioenergy crops.</em></p> <p><strong>Contact</strong></p> <p>Please contact: Dr. Florian Zabel, <a href="mailto:f.zabel@lmu.de">f.zabel@lmu.de</a>, Department of Geography, LMU München (<a href="http://www.geografie.uni-muenchen.de">www.geografie.uni-muenchen.de</a>)</p>
Dataset on Alternaria disease on rocket under simulated climate change conditions
<p>This dataset is related to disease severity caused by the Alternaria spp. isolates tested in different temperature and CO2 combinations on cultivated rocket and published in https://doi.org/10.3920/WMJ2016.2108<em> </em>(Figure 1) and in https://doi.org/10.1007/s42161-018-0125-8,</p>
Data for the Manuscript 'Phenotypic Variation from Waterlogging in Multiple Perennial Ryegrass Varieties under Climate Change Conditions'
<p>Experimental data supporting the findings of the manuscript 'Phenotypic Variation from Waterlogging in Multiple Perennial Ryegrass Varieties under Climate Change Conditions'. This dataset will be made publicly available when the manuscript has been accepted for journal publication unless exceptional conditions become apparent. </p>
Fig. 5. A in Shell Morphology, Growth Pattern And Population Dynamics Of The Land Snail Xerolenta Obvia (Menke, 1828) In Two Areas Of Different Climatic Conditions Within A Temperate Climate Region
Fig. 5. A) Mean monthly whorl increment of Xerolenta obvia in two growth seasons in SW (solid line) and NE (dashed line) populations; B) mean (black lines), maxi- mum and minimum (grey lines) monthly temperature, and C) total monthly precipitation during the study period in SW (solid lines) and NE (dashed lines) sites. Data from nearest meteorological stations in Wrocław and Suwałki (IMGW-PIB data)
Fig. 2 in Shell Morphology, Growth Pattern And Population Dynamics Of The Land Snail Xerolenta Obvia (Menke, 1828) In Two Areas Of Different Climatic Conditions Within A Temperate Climate Region
Fig. 2. Distribution of shell pattern types of Xero- lenta obvia snails in the two populations studied, SW (n = 781) and NE (n = 1387)
Multi-model Hydropower Projections for the United States Federal Power Marketing Areas under CMIP5 Climate Change Conditions
<p>This dataset contains an ensemble of monthly hydropower generation projections for the United States Federal Hydropower plants for the periods of 1966-2005 (historical period) and 2011-2050 (future period). The dataset includes the monthly hydropower projections developed in (Kao et al. 2016) based on the Watershed Runoff-Energy Storage (WRES) model and is complemented with another ensemble based on the process-based Water Management Power (WMP) model.</p> <p>The hydrologic projections are estimated through a cascading modeling toolchain that include ten global climate change model projections (ACCESS1-0, BCC-CSM1-1, CCSM4, CMCC-CM, GFDL-ESM2M, MIROC5, MPI-ESM-MR, MRI-CGCM3, NorESM1-M and IPSL-CM5A-LR) under RCP8.5 scenario, which are dynamically downscaled with a regional climate model (RegCM4) ( Pal et al. 2007, Giorgi et al. 2012)), which then inform the Variable Infiltration Capacity (VIC) hydrology model (Liang et al. 1994). The ensemble of hydrologic projections is then informing two processes to translate runoff into hydropower projections. First, WRES models monthly river routing and employs a non-linear statistical approach relating monthly natural flow to hydropower generation, including processes such as spilling. Second, MOSART-WM (Voisin et al. 2013), a large-scale river routing and water management model, provides daily reservoir storage and regulated release at dam locations as well as regulated flow at run-of-the-river power plants. The WMP model then translates reservoir and regulated river dynamics into hydropower projections (Zhou et al. 2018). Those projections are further calibrated to monthly generation provided by the federal utilities. The US federal hydropower plants analyzed in this study include 132 facilities that were built and/or are operated by the US Army Corps of Engineers (USACE), the Bureau of Reclamation (Reclamation), and the International Boundary and Water Commission (IBWC). The electricity generation projected for these hydropower plants were aggregated by four Power Marketing Administrations (PMAs), including Bonneville Power Administration (BPA), Southeastern Power Administration (SEPA), Southwestern Power Administration (SWPA), and Western Area Power Administration (WAPA), and their associate subregions.</p> <p>The two files, <em>SWA9505V2_Gsim_PMA_WRES.mat</em> and <em>SWA9505V2_Gsim_PMA_WMP.mat</em>, represent model outputs from the two hydropower models, WRES and WMP respectively.</p> <p>Each file contains 6 variables:</p> <p>1) “Models”: the 10 global climate models (GCMs).</p> <p>2) “PMA_areas”: the 18 subregions of PMAs as defined in (Kao et al. 2015).</p> <p>3) “PMA_G_mn_6605”: 1966-2005 projected monthly hydropower generation for each PMA sub-regions. Dimension: (12 [months], 40 [years], 18 [subregions], 10 [GCMs]). Unit: MWH.</p> <p>4) “PMA_G_mn_1150”: Same as “PMA_G_mn_6605”, but for 2011-2050 projected hydropower generation.</p> <p>5) “PMA_G_yr_6605”: 1966-2005 projected annual hydropower generation. Dimension: (40 [years], 18 [subregions], 10 [GCMs]) . Unit: MWH.</p> <p>6) “PMA_G_yr_1150”: Same as “PMA_G_yr_6605”, but for 2011-2050 projected hydropower generation.</p> <p>The following journal paper details the method in creating the dataset:</p> <p><strong>Impacts of Climate Change on Subannual Hydropower Generation: A Multi-model Assessment of the United States Federal Hydropower Plants</strong></p> <p><strong>Zhou et al. (2022) Preparing for submission to Environmental Research Letters.</strong></p>
Neutral processes related to regional bee commonness and dispersal distances are important predictors of plant-pollinator networks along gradients of climate and landscape conditions
<p>Understanding how niche-based and neutral processes contribute to the spatial variation in plant-pollinator interactions is central to designing effective pollination conservation schemes. Such schemes are needed to reverse declines of wild bees and other pollinating insects and to promote pollination services to wild and cultivated plants. We used data on wild bee interactions with plants belonging to the four tribes Loteae, Trifolieae, Anthemideae, and either spring- or summer-flowering Cichorieae, sampled systematically along a 682km latitudinal gradient to build models that allowed us to (a) predict occurrences of pairwise bee-flower interactions across 115 sampling locations, and (b) estimate the contribution of variables hypothesized to be related to niche-based assembly structuring processes (viz. annual mean temperature, landscape diversity, bee sociality, bee phenology, and flower preferences of bees) and neutral processes (viz. regional commonness and dispersal distance to conspecifics). While neutral processes were important predictors of plant-pollinator distributions, niche-based processes were reflected in the contrasting distributions of solitary bee and bumble bees along the temperature gradient, and in the influence of bee flower preferences on the distribution of bee species across plant types. In particular, bee flower preferences separated bees into three main groups, albeit with some overlap: visitors to spring-flowering Cichorieae; visitors to Anthemideae and summer-flowering Cichorieae; and visitors to Trifolieae and Loteae. Our findings suggest that both neutral and niche-based processes are significant contributors to the spatial distribution of plant-pollinator interactions so that conservation actions in our region should be directed towards areas: near high concentrations of known occurrences of regionally rare bees; in mild climatic conditions; and that are surrounded by heterogeneous landscapes. Given the observed niche-based differences, the proportion of functionally distinct plants in flower-mixes could be chosen to target bee species, or guilds, of conservation concern.</p>
Figure 7. The outcomes of the Monitoring and Control of the Greenhouse soil and climate Conditions for tomato crops-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>In the past generation greenhouses it was enough to have one cabled measurement point in<br> the middle to provide the information to the greenhouse automation system. The system itself was<br> usually simple without opportunities to control locally heating, lights, ventilation or some other<br> activity, which was affecting the greenhouse interior climate. The optimal greenhouse climate and<br> soil adjustment can enable us to improve productivity and to achieve remarkable energy savings. In<br> this paper we proposed a multi-agent methodology for integrated management systems in<br> greenhouses. In this regards wireless sensor networks play a vital role to monitor greenhouse and<br> environment parameters. Each controlled process of the greenhouse environment is modeled as an<br> autonomous agent with its own inputs, its own outputs and its own interactions with the other<br> agents. Each agent acts autonomously, as it knows a priori the desired environmental set-points. In<br> this way, any possible conflicting decisions of conventional environmental control methodologies<br> are resolved through negotiations between the agents so that the possible optimal integrated solution<br> is achieved. The developed system is simple, cost effective, and easily installable.</p>
Data from: Climatic conditions and landscape diversity predict plant-bee interactions and pollen deposition in bee-pollinated plants.
<p>Climate change, landscape homogenization and the decline of beneficial insects threaten pollination services to wild plants and crops. Understanding how pollination potential (i.e. the capacity of ecosystems to support pollination of plants) is affected by climate change and landscape homogenization is fundamental for our ability to predict how such anthropogenic stressors affect plant biodiversity. Models of pollinator potential are improved when based on pairwise plant-pollinator interactions and pollinator´s plant preferences. However, whether the sum of predicted pairwise interactions with a plant within a habitat (a proxy for pollination potential) relates to pollen deposition on flowering plants has not yet been investigated. We sampled plant-bee interactions in 68 Scandinavian plant communities in landscapes of varying land-cover heterogeneity along a latitudinal temperature gradient of 4–8 C°, and estimated pollen deposition as the number of pollen grains on flowers of the bee-pollinated plants <em>Lotus corniculatus</em>, and <em>Vicia cracca</em>. We show that plant-bee interactions, and the pollination potential for these bee-pollinated plants increase with landscape diversity, annual mean temperature, plant abundance, and decrease with distances to sand-dominated soils. Furthermore, the pollen deposition in flowers increased with the predicted pollination potential, which was driven by landscape diversity and plant abundance. Our study illustrates that the pollination potential, and thus pollen deposition, for wild plants can be mapped based on spatial models of plant-bee interactions that incorporate pollinator-specific plant preferences. Maps of pollination potential can be used to guide conservation and restoration planning.</p>
Fig. 2 in Characteristic Growing Parameters Of Small-Leaved Lime And Norway Maple Stands In The Climatic Conditions Of Latvia
Fig. 2. Small leaved lime and Norway maple area dynamics in Latvia in 2001–2017 (http:// www.vmd.gov.lv/ Digital Forest Map Database of the State Forest Register [Accessed on March 2017]).
Fig. 3 in Characteristic Growing Parameters Of Small-Leaved Lime And Norway Maple Stands In The Climatic Conditions Of Latvia
Fig. 3. Site location of sample plots of small leaved lime and Norway maple stands (Legend: L 15 (plantation forest); L 16 (plantation forest); L 17 (plantation forest); L 80 (forest stand); L 90 (forest stand); L 115 (forest stand) – lime/age; M 12 – plantation; M 12* – forest stand; M 55 (naturally established plantation forest); M (forest stand); M (forest stand) – lime/age; maple/age).
Data for: Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use
<p>This dataset contains the code and the data files needed to create the figures shown in the paper titled "Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use".</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.