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176 results for “evaporation”
Level A Pan Europe Solar Index for estimation of Potential evaporation February
Solar Index for estimation of Potential evaporation February. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation August
Solar Index for estimation of Potential evaporation August. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation March
Solar Index for estimation of Potential evaporation March. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module)
Level A Pan Europe Solar Index for estimation of Potential evaporation July
Solar Index for estimation of Potential evaporation July. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation Apr
Solar Index for estimation of Potential evaporation April. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation November
Solar Index for estimation of Potential evaporation November. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation September
Solar Index for estimation of Potential evaporation September. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation May
Solar Index for estimation of Potential evaporation May. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation June
Solar Index for estimation of Potential evaporation June. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation October
Solar Index for estimation of Potential evaporation October. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation January
Solar Index for estimation of Potential evaporation January. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation December
Solar Index for estimation of Potential evaporation December. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Dataset for publication "Deposition of Sn-Zr-Se precursor by thermal evaporation and PLD for the synthesis of SnZrSe3 thin films"
<p>This dataset entails various structural material data that was used to provide additional evidence for arguments presented in publication "Deposition of Sn-Zr-Se precursor by thermal evaporation and PLD for the synthesis of SnZrSe3 thin films". </p><p>Mainly data consists of: SEM, XRD, Raman, Auger and TGA raw data.</p><p>Summary of results is provided in Extended_data.pdf file </p>
A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation
<p>This repository contains the datasets used in the research article "A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation".</p> <p>The repository contains the following files: 1) Input - contains all the processed input used for training the deep learning models and the datasets used for creating the figures in the article. 2) Output - contains the final deep learning models and the outputs (evaporation and transpiration stress factor) outputs from the hybrid model developed in the study.</p> <p>Formats: All scripts are in the programming language Python. The datasets are in HDF5 and NetCDF file formats.</p> <p>The codes related to the research article and deep learning model are available in the following repository: https://github.com/akashkoppa/StressNet</p>
MSR SIMULATION WITH CGEMS: SALT AND FISSION PRODUCT EVAPORATION
<p>Conference proceedings: 10th Europen Review Meeting on Severe Accident Research, ERMSAR 2022</p>
Experimental assessment of thermal effectiveness of a regenerative indirect evaporative cooler
<p>Heating, ventilation and air-conditioning, HVAC, systems represent a significant energy use in Europe, around 50% of total energy use in buildings. Conventional HVAC systems are mainly based on direct expansion units, whose use of 100% outdoor air leads to high energy use. Then, different innovative and efficient air-cooling systems could be an interesting alternative to approach Nearly Zero Energy Buildings, nZEB. One of these efficient solutions is the technology of indirect evaporative cooling. This work was based on the experimental evaluation of a regenerative indirect evaporative cooler, RIEC. Several empirical tests were carried out under different inlet conditions: inlet air temperature values between 29 °C and 43 °C, <em>T<sub>OA</sub></em>, and inlet air humidity ratio values between 9 g/kg and 13 g/kg, 𝜔<em><sub>OA</sub></em>, were considered. A constant inlet air stream, V<em><sub>0A</sub></em>, and a constant supply air stream, <em><sub>SA</sub></em>, were adjusted during these tests for a steady-state period of thirty minutes each. The response variables which evaluated the thermal behaviour of this RIEC system were: (i) dew point effectiveness, <em>ε<sub>dp</sub></em>; (ii) wet bulb effectiveness, <em>ε<sub>wb</sub></em>. High values of <em>ε<sub>dp</sub></em> and <em>ε<sub>wb </sub></em>were reached when the inlet air humidity ratio was 9 g/kg, around 0.87 and 0.92, respectively. However, low values of dew point effectiveness, 0.71, and wet bulb effectiveness, 0.78, were showed when the inlet air temperature was 29 °C and the inlet air humidity ratio was 13 g/kg. According to the results that this study showed, the RIEC system could be an interesting alternative in spaces where improved indoor air quality is required by using 100% outdoor air. This type of systems could achieve high values of thermal performance, specially under hot-dry climatic conditions.</p>
Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon (Open)
<p>This work was carried out in the scope and with the support of the project: Climate Services Through Knowledge Co-Production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events (CLIMAX).</p> <p>The project consortium includes the following institutions: Centre National de la Recherche Scientifique CNRS/Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI-IFAECI) (Argentina-France); General Coordination of Earth Sciences /National Institute for Space Research (INPE) (Brazil); Institut de Recherche pour le Développement (IRD)/ Unité Mixte de Recherche (UMR 245) (France); Le Laboratoire des Sciences du Climat et de l'Environnement (LSCE) (France); Potsdam Institute for Climate Impact Research (PIK) (Germany); Technical University of Munich (TUM) (Germany) and Wageningen University and Research (WUR) Netherlands). The project is sponsored by the Collaborative Research Action (CRA) on “Climate Predictability and Inter-Regional Linkages” of the Belmont Forum, launched in 2015.</p> <p>Climate variability patterns linking the South American Monsoon region, including Amazonia, with southeastern South America influence climate extremes and impact several societal sectors. More than 200 million people live in the study region, which is also one of the largest agricultural production regions of the world and home to the world’s second largest hydroelectric power plant.</p> <p>The objectives of CLIMAX include better understanding the combined role of remote and local drivers on South American climate variability from sub-seasonal to decadal timescales, and its impact on the occurrence and intensity of extreme events. Special focus is given to an improved understanding of the effects of land use changes from the Amazon to the subtropics and their impact on climate.</p> <ol> <li> <p><strong>EXPERIMENT DESIGN</strong></p> </li> </ol> <p>We used four models that are classified as Dynamic Global Vegetation Models (DGVMs) (Prentice et al., 2007; Rezende et al., 2015): Integrated Model of Land Surface Processes (INLAND) (Tourigny, 2014); Lund-Potsdam-Jena managed Land model version 4 (LPJmL4) (Schaphoff et al., 2018), Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) (Smith et al. 2001, Hickler et al., 2012), and Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE) (Krinner et al., 2005).</p> <p>We used three forcings with climate data (GLDAS, GSWP3, and WATCH+WFDEI), Land Use Change (LUC) data and validation data (FLUXCOM (Remote sensor+meteorological data+artificial neural network approach), FLUXCOM (eddy covariance), MODIS (Light Use Efficiency), GLEAM, and TerraClimate (Rezende et al., 2022).</p> <p>We conducted two sets of simulation experiments with different values of CO2: 1) increasing CO2 from the pre-industrial period to 2010 named <strong>historical CO</strong><strong>2</strong> (<strong>hist CO</strong><strong>2</strong>); 2) constant concentration of 278 ppm of (pre-industrial) atmospheric CO2 named <strong>constant CO</strong><strong>2</strong><strong> (const CO</strong><strong>2</strong><strong>)</strong>. We ran both CO2 experiments under <strong>Land Use Change</strong> (<strong>LUC</strong>) and <strong>Potential Natural Vegetation </strong>(<strong>PNV</strong>) conditions. All combinations of CO2 and land use change resulted in four sets of simulation experiments per climate input: 1. <strong>LUC historical CO</strong><strong>2</strong>; 2. <strong>LUC constant CO</strong><strong>2</strong>; 3. <strong>PNV historical CO</strong><strong>2</strong>; 4. <strong>PNV constant CO</strong><strong>2 </strong> (Rezende et al., 2022).</p> <p><strong>2. DATA DESCRIPTION</strong></p> <p>The complete description of the data, including the climate forcing, LUC, the validation datasets, methodology, simulations, discussion and conclusion is in Rezende et al. (2022). This archive contains only the data description from the simulations (outputs) by the DGVMs.</p> <p><strong>2.1 SOFTWARE </strong></p> <p><br> The data were manipulated, worked, standardized, converted using the software: <strong>Climate Data Operators (cdo) version 1.7.0</strong>, <strong>Grid Analysis and Display System (Grads) (</strong><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a><strong>) version 2.0.2, and RStudio Desktop version</strong> 1.3.1093 <strong>(R Core Team, 2020)</strong>, through command lines and several scripts developed for this purpose. The figures were generated with <strong>Grads,</strong> and <strong>RStudio</strong>, and some images were enhanced with <strong>Gimp version 2.8.22</strong>. All the software used is freeware.</p> <p><strong> 2.2 PRIMARY DATA FROM SIMULATIONS</strong></p> <p>Data originating from the simulations are in monthly resolution, covering South America, with all the forcings. Despite data spanning over 1948-2010 or 1950-2010 our study focuses on the period 1981-2010.</p> <p><strong>Variables</strong>: Gross Primary Productivity (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p>The naming of the files is according to the following rules:</p> <p><strong>DGVM_forcing_vegetation cover_CO2 concentration_attribute</strong></p> <p><strong>DGVMs</strong>;</p> <p> InLand (INLAND)</p> <p> LPJ-G (LPJ-GUESS)</p> <p> LPJmL (LPJmL4)</p> <p> ORCHI (ORCHIDEE)</p> <p>forcings: </p> <p> gld - GLDAS</p> <p> gsw – GSWP3</p> <p> wat – WATCH+WFDEI</p> <p> </p> <p>vegetation cover:</p> <p> LU – Land Use Change</p> <p> PNV – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration:</p> <p> CO2 – historical CO2</p> <p> noCO2 – constant CO2 = 278 ppm</p> <p> </p> <p>variables:</p> <p> E – evaporation</p> <p> Et – transpiration</p> <p> gpp – Gross Primary Productivity</p> <p> npp - Net Primary Productivity (not used in the experiment)</p> <p> </p> <p><strong>Example</strong>:</p> <p> inLand_gld_LU_noCO2_E.nc</p> <p> </p> <p><strong> 2.3 SUPPLEMENTARY DATA </strong></p> <p>These interception loss data (mm month-1) were requested by a reviewer to complement the analysis and are available only for the LUC CO2 scenario and for the study region: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).</p> <p>Files are named according to the following rules:</p> <p>variable_season_forcing_DGVM_vegetation cover CO2 concentration_region</p> <p>variable:</p> <p> inter – loss by interception</p> <p> </p> <p>season:</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>forcings: </p> <p> gl - GLDAS</p> <p> gs – GSWP3</p> <p> wa – WATCH+WFDEI</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or – ORCHIDEE</p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p> inter_D_gl_in_LC_SA.nc</p> <p><strong>2.4 PROCESSED DATA</strong></p> <p>Processed data cover all scenarios and input data sets and are restricted to the study area: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).They are in seasonal resolution with averages for January-February-March-April (JFMA) (rainy season) and averages for June-July-August-September (JJAS). Each of the files contains the Gross Primary Productivity variables (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p> </p> <p>Files are named according to the following rules:</p> <p> </p> <p><strong>season_DGVM_forcing_vegetation cover CO2 concentration_region</strong></p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>Example:</p> <p>D_in_Gs_LN.nc</p> <p><strong>2.5 DERIVED DATA</strong></p> <p> </p> <p>The variable Water Use Efficiency (WUE) (kg m-2 mm-1 month-1) results from rate: GPP / Tr (transpiration) (Eq. 1). </p> <p> </p> <table> <tbody> <tr> <td> <p> WUE = GPP / Tr</p> </td> <td> <p>(Eq. 1)</p> </td> </tr> </tbody> </table> <p> </p> <p>These data refer to the study region: southern Amazon and apply to only one scenario: Land Use Change and historic CO2. Files are named naming of according to the following rules:</p> <p> </p> <p>Variable:</p> <p> WUE – Water Use Efficiency</p> <p> </p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p>wue_D_lm_Gl_LC_SA.nc</p> <p> </p> <p><strong>How to cite this work</strong>:</p> <p>Rezende, Luiz F. C., Aline Castro, Celso Von Randow, Romina Ruscica, Boris Sakschewski, Phillip Papastefanou, Nicolas Viovy, Kirsten Thonicke, Anna Sörensson, Anja Rammig, Iracema F. A. Cavalcanti. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p><strong>References</strong></p> <p><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>. Center for Ocean-Land-Atmosphere Studies, Institute of Global Environment and Society,<a href="https://en.wikipedia.org/wiki/George_Mason_University"> George Mason University</a>. Archived from<a href="http://www.iges.org/grads/gadoc/"> the original</a> on 7 April 2015. Retrieved 14 March 2015.</p> <p>Hickler T. et al., 2012. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species based dynamic vegetation model. Glob Ecol Biogeograp 21:50–63, <a href="https://doi.org/10.1111/j.1466-8238.2010.00613.x">https://doi.org/10.1111/j.1466-8238.2010.00613.x</a></p> <p>Krinner, G.et al., 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochemical Cycles, 19, GB1015, doi:10.1029/2003GB002199.</p> <p>R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. </p> <p>Prentice IC (2007) Dynamic global vegetation modeling: quantifying terrestrial ecosystem responses to large-scale environmental change. In: Canadell J, Pataki D, Pitelka L (eds) Terrestrial ecosystems in a changing world. Springer, Berlin Heidelberg.</p> <p>Rezende, Luiz F. C. et al., 2015. Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2. Int J Biometeorol., 2015, doi: 10.1007/s00484-015-1087-6.</p> <p>Rezende, Luiz F. C. et al., 2022. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research - Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p>Schaphoff, S. et al., 2018. LPJmL4 – a dynamic global vegetation model with managed land –Part 1: Model description. Geosci. Model Dev., 11, 1343–1375, 2018, <a href="https://doi.org/10.5194/gmd-11-1343-2018">https://doi.org/10.5194/gmd-11-1343-2018</a>.</p> <p>Smith B. et al (2001) Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob Ecol Biogeograp 10:621–637</p> <p>Tourigny, E. (2014). Multi-scale fire modeling in the neotropics: coupling a land surface model to a high resolution fire spread model, considering land cover heterogeneity. Phd dissertation, Meteorology. INPE. Retrieved from http://urlib.net/sid.inpe.br/mtc-m21b/2014/05.30.00.36</p> <p><br> </p> <p> </p>
Water Droplet Evaporation Profiles Calculated Using SADKAT Model
<p>Evaporation profiles of pure water droplets calculated using SADKAT model.</p> <p>Initial size: 25 µm</p> <p>Temperature range: 278.15 - 343.15 K</p> <p>RH range: 0 - 100 %</p> <p>Particle motion is ignored (gravity = 0 m^2/s) in these calculations.</p> <p>Evaporation profiles are saved individually as comma separated .txt files.</p> <p>Data is also saved as .npy files, containing lists of evaporation profiles. These may be accessed using Python 3 with the Numpy and Pandas libraries.</p>
Time changes everything: A multispecies analyses of temporal patterns in evaporative water loss - data
<p>The dataset was analysed in the manuscript “Žagar A., Carretero, M.A., de Groot M. (accepted) Time changes everything: A multispecies analyses of temporal patterns in evaporative water loss. Oecologia”</p> <p>The dataset consisted out of water loss by 23 populations of lizards from 16 different species and three families which was compiled from several different studies. All studies used the same standardized protocols. During the experiment every hour for 12 hours, the body weight of the lizard was measured (in total 13 measurements per lizard). The species name (SP), the snout-vent length of the animal (SVL, in millimetres), altitude (m a.s.l.), sampling location (site name, latitude and longitude), weight (in grams), sex (M=male, F=female), code of the individual lizard (CODE), date of experiment (DATE_H) and the reference of the study were noted down (full references are available in the manuscript). Per column the instantaneous water loss values (EWLi) were recorded per hour measured. First hour was EWLi8, second hour was EWLi9, etc. The EWLi was calculated by the weight minus the weight in the next hour divided by the weight multiplied by 100 ((W<sub>n</sub> – W<sub>n+1 </sub>/ W<sub>n</sub>) × 100).</p>
Data generated by the model presented in the research article entitled "Simulation of mass and heat transfer in an evaporatively cooled PEM fuel cell"
<p>This repository provides all the data and scripts necessary to reproduce the line plots shown in the manuscript entitled "Simulation of mass and heat transfer in an evaporatively cooled PEM fuel cell".</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.