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858 results for “Energy Data”
Water, energy and food data in Central Asia
<p>Water, energy and food data in five Central Asian countries that are collected from public data sources.</p>
Data from: Socio-economic impacts of energy access through off-grid systems in rural communities: A case study of South-West Nigeria
<p><span><span>The development of resilient energy systems is important for sustainable cities and communities. However, in countries with insufficient national energy supply, electricity distributors rarely consider remote communities due to their distant settlement, low electricity demand and poor payment capabilities. The United Nations has set a goal to deliver universal energy access by 2030, hence, it has become imperative to deploy clean and affordable off-grid mini-grid solutions to previously abandoned communities. Access to energy in rural communities is expected to result in unlocking their economic potentials. This paper investigates the impact of a solar hybrid mini-grid on the socio-economic growth of local entrepreneurs in Gbamu Gbamu village, Nigeria. A total of 83 Micro and Small Enterprises has been surveyed, descriptive statistics, paired-sample t-test, cross-tabulation, and Chi-square test, were used to assess the performance of businesses before and after electrification. The outcomes include the number of business enterprises created, employment statistics, energy expenses and income generated. Regression analysis was conducted on the relationship between the average income generated by businesses and independent socio-economic variables such as gender, marital status, household size, age, education level, years of business establishment, hours of operation, building tenure, capital source, number of employees, generator ownership and the days of operation. </span></span></p>
Supplementary Data: sampedro-etal_2021_Applied_Energy
<p>Supplementary Information for the following publication:</p> <p>Sampedro, J., Kyle, P., Ramig, C.W., Tanner, D., Huster, J.E., and Wise, M.A. "<em>Dynamic linking of upstream energy and freight demands for bio and fossil energy pathways in the Global Change Analysis Model</em>". Applied Energy. Accepted Manuscript (Aug 2021)</p>
Supplementary Data for "Comprehensive Phase Diagrams of MoS2 Edge Sites Using Dispersion-Corrected DFT Free Energy Calculations"
<p>MoS2 Phase Diagrams to accompany DOI: 10.1021/acs.jpcc.8b02524</p>
Supplementary Data for "Earth's Volatile Depletion Trend Consistent with a High-Energy Moon-Forming Impact"
<p>Vaporization statistics for pyrolite melts containing In, Cd, S, Zn and Hg. Please refer to "Computational Methods" of the main text for details of the simulations and Supplementary Table 2 for the stoichiometry of the melts.</p>
Lacor Hospital's energy community - Power data
<p>The dataset contains power measurements of the St. Mary's hospital community, located in Gulu, Uganda. The variables available are: PV production, the total load, the back-up diesel generator production and the consumption of the sterilization machine (which are part of the total load). Measurements starts from 01/05/2022 until 25/10/2022 with a 5-minutes time-resolution.</p>
Micro data from the Greek Household Budget Survey on energy consumption
<p>The dataset is the end product of compiling a number of datasets from the Greek Household Budget Survey, covering 13 years between 2004 and 2020. The variables included are basic socio-economic, demographic and housing characteristics, along with quantity and cost data of household energy sources.</p>
Quantum state resolved molecular dipolar collisions over four decades of energy - online data
<p>Data for submission of "Quantum state resolved molecular dipolar collisions over four decades of energy"</p> <p>Authors:</p> <p>Guoqiang Tang<sup>1</sup>, Matthieu Besemer<sup>1</sup>, Stach Kuijpers<sup>1</sup>, Gerrit C. Groenenboom<sup>1</sup>,<br> Ad van der Avoird<sup>1</sup>, Tijs Karman<sup>1</sup>**, Sebastiaan Y.T. van de Meerakker<sup>1</sup>**</p> <p><sup>1</sup>Radboud University, Institute for Molecules and Materials<br> Heijendaalseweg 135, 6525 AJ Nijmegen, the Netherlands</p> <p>**To whom correspondence should be addressed;<br> E-mail: basvdm@science.ru.nl, t.karman@science.ru.nl</p>
Supplementary Data for Manuscript : "Application of OSL surface exposure dating with the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy'
<p>Contains all supplementary works mentioned in the manuscript.</p>
Supplementary data for eEDM study in thesis 'Low energy observables and fine-tuning in the MSSM'
<p>For the eEDM study in Chapter 5 of the thesis 'Low energy observables and fine-tuning in the MSSM' we have created several input sets, one for each run. These can be found as 'input_iter[x].csv' and are used to created a SPheno input file. The code to run all the software is found in the directory 'Spheno_to_eEDM'. A description of this code can be found in the thesis (DOI will follow upon succesful defense).</p> <p>For each iteration we have gathered the relevant output (masses, couplings, mixing matrices, observables, fine-tuning etc) from the different output files. These are stored in csv files, gathered in eEDMdata.tar . One line in the output file corresponds to one line in the original input file, where the directory name (dir_name) in the output file is the same as the index of the input file. This is one data point of the study.</p>
Diverse carbon dioxide removal approaches could reduce energy-water-land impacts (output data)
<p>GCAM scenario output data</p>
Data for "Energy Surplus and Atmosphere – Land-Surface "Tug of War" Induced by Climate Change Control Future Evapotranspiration"
<p>USGS gauges used in manuscript "<strong>Energy Surplus and An Atmosphere-Land-Surface “Tug of War” Control Future Evapotranspiration"</strong>. USGS_CTL15_Gage.mat contains the USGS gauge ID, and one can use retrieve_daily_streamflow.m to download the corresponding streamflow time series. </p>
Data source and projections of maintenance energy gaps for "Caloric reductions needed to achieve obesity goals by 2030 and 2040: A modeling study"
<p><strong>Variables in "data_ENSANUT_waves.xlsx"</strong></p> <table> <thead> <tr> <th scope="col">Name</th> <th scope="col">Variable</th> </tr> </thead> <tbody> <tr> <td><em>id</em></td> <td>Identifier for each individual in the data.</td> </tr> <tr> <td><em>est_var</em></td> <td>Strata for the estimation of variances, accounting for survey design.</td> </tr> <tr> <td><em>svy_weights</em></td> <td>Complex survey weight.</td> </tr> <tr> <td>code_upm</td> <td>Identifier of the primary sampling unit.</td> </tr> <tr> <td>sex</td> <td>Sex of the individual (``male'' or ``female'').</td> </tr> <tr> <td>age</td> <td>Age (yrs).</td> </tr> <tr> <td>body_weight</td> <td>Measured body weight (kg).</td> </tr> <tr> <td>height</td> <td>Measured height (cm).</td> </tr> <tr> <td>bmi</td> <td>Body mass index, estimated before the simulation process (kg/m<sup>2</sup>).</td> </tr> <tr> <td>SES</td> <td>Socioeconomic level, divided in tertiles. This variable was constructed using Principal Components Analysis.</td> </tr> <tr> <td>year</td> <td>Indicator for each ENSANUT wave (2000, 2006, 2012, 2016, 2018).</td> </tr> <tr> <td>svy_weights_raking_2030</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2030.</td> </tr> <tr> <td>svy_weights_raking_2040</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2040.</td> </tr> <tr> <td>body_weight_final_2030_Nordpred</td> <td>Simulated body weight by 2030 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Nordpred</td> <td>Simulated body weight by 2040 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Nordpred</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Nordpred</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Nordpred</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Nordpred</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_Gompertz</td> <td>Simulated body weight by 2030 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Gompertz</td> <td>Simulated body weight by 2040 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Gompertz</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Gompertz</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Gompertz</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Gompertz</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_linear</td> <td>Simulated body weight by 2030 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_linear</td> <td>Simulated body weight by 2040 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_linear</td> <td>Simulated body mass index by 2030 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_linear</td> <td>Simulated body mass index by 2040 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_linear</td> <td>Indicator of obesity by 2030, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_linear</td> <td>Indicator of obesity by 2040, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_rootSquare</td> <td>Simulated body weight by 2030 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_rootSquare</td> <td>Simulated body weight by 2040 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_rootSquare</td> <td>Simulated body mass index by 2030 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_rootSquare</td> <td>Simulated body mass index by 2040 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_rootSquare</td> <td>Indicator of obesity by 2030, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_rootSquare</td> <td>Indicator of obesity by 2040, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> </tbody> </table>
Data for the article: Trophic positions of soil microarthropods in forests increase with elevation, but energy channels remain unchanged
<p><span>Mountain forests are at risk as the consequences of climate change will likely lead to altered tree species boundaries. Characterizing food webs along elevational gradients in primary forests may help to predict potential consequences of such changes. The soil food web provides nutrients to plants through decomposition of dead organic matter. Changes in the soil food web with elevation are poorly characterized and most studies focus on community composition of microbes and individual arthropod taxa. </span><span><span> </span></span><span>Here, for the first time we studied trophic variations in two of the species rich microarthropod taxa, Collembola and Oribatida, along an elevational gradient of primary forest in Northeast China, Changbai Mountain. </span><span>Samples were taken at seven elevations of 150 m elevational difference between 800 and 1700 m. At each elevation eight subplots were established and Collembola and Oribatida were extracted from litter samples. </span><span><span> </span></span><span>We applied three state-of-the-art methods to elucidate trophic positions and basal resource use of both taxa at community level: b<span>ulk stable isotope analysis of nitrogen and carbon (SI<sub>bulk</sub>), compound-specific stable isotope analysis of amino acids (CSSIA-AA) and dietary routing through neutral lipid fatty acids (NLFA).</span></span><span><span> </span></span><span>Δ</span><sup><span>15</span></sup><span>N<sub>bulk</sub> and trophic position calculated using CSSIA-AA (TP<sub>CSSIA</sub>) both increased with elevation in both taxa. Stable isotope mixing models</span><span> using δ<sup>13</sup>C of essential amino acids indicated saprotrophic fungi as most important resource at most elevations for both taxa. Also, proportions of marker NLFAs changed little across elevations in both taxa, however, in Collembola the contribution of bacterial markers was generally higher than in Oribatida. Δ</span><sup><span>13</span></sup><span>C<sub>bulk </sub>did not respond linearly to the elevational gradient; however, changes with elevations differed between Collembola and Oribatida. A strong linear relationship between </span><span>δ</span><sup><span>15</span></sup><span>N of phenylalanine in consumers and</span><span> δ</span><sup><span>15</span></sup><span>N of litter indicated litter as basal resource of both taxa. </span><span>Overall, the results suggest that food web functioning likely changes with changing forest types along elevational gradients with microarthropods switching from feeding at the base of the food web to feeding at higher trophic levels potentially compromising their role in litter decomposition and nutrient cycling.</span></p>
Data for: Hunting behavior of a solitary sailfish Istiophorus platypterus and estimated energy gain after prey capture
<p>Foraging behavior and interaction with prey is an integral component of the niche of predators but is inherently difficult to observe for highly mobile animals in the marine environment. Billfish have been described as 'energy speculators', expending a large amount of energy foraging, expecting to offset high costs with periodic high energetic gain. Surface-based group feeding of sailfish, <em>Istiophorus</em> <em>platypterus</em>, is commonly observed, yet sailfish are believed to be solitary roaming predators with high metabolic requirements, suggesting that individual foraging also represents a major component of predator-prey interactions. Here, we use biologging data and video to examine daily activity levels and foraging behavior, estimate metabolic costs, and document a solitary predation event. We estimate a median active metabolic rate of 218.9 ± 70.5 mgO<sub>2</sub> kg<sup>-1</sup> h<sup>-1</sup> which increased to 518.8 ± 586.3 mgO<sub>2</sub> kg<sup>-1</sup> h<sup>-1</sup> during prey pursuit. Assuming a successful predation, we estimate a daily net energy gain of 2.4 MJ (5.1 MJ acquired, 2.7 MJ expended), supporting the energy speculator model. While group hunting may be a common activity used by sailfish to acquire energy, our calculations indicate that opportunistic individual foraging events offer a net energy return that contributes to the fitness of these highly mobile predators. </p>
Input data for executing SELARU 1.0 application for Indonesia energy system optimization case study
<p>Input data for SELARU modelling framework application in a case study to demonstrate the impact of spatially explicit representation for energy system optimization, particularly in depicting incremental grid expansion and its contribution to accumulation of economies of scale. The case study focuses on the electricity sector of Indonesia to represent a highly complex and geographically diverse energy system. SELARU modelling framework is applied using three spatial representations: single-node model, low resolution multi-node model and high resolution multi-node model. Detailed information about the spatial representations can be found in <a href="https://doi.org/10.31219/osf.io/aw4bd">https://doi.org/10.31219/osf.io/aw4bd</a>.</p>
Data set for Chemical bond analysis for the entire periodic table: Energy Decomposition and Natural Orbitals for Chemical Valence in the Four-Component Relativistic Framework
<p>Data related with the publication :Chemical bond analysis for the entire periodic table:<br> Energy Decomposition and Natural Orbitals for Chemical Valence in the Four-Component Relativistic Framework by Diego Sorbelli, Paola Belanzoni, Loriano Storchi, Olivia Bizzarri, Beatrice Bizzarri, Edoardo Mosconi and Leonardo Belpassi</p> <p>Input and data for:</p> <p>Tab.1 --> data in dir "w_dimer_AVDZ" and in dir "ADF/w_dimer".</p> <p>Tab.2 --> data in dir "Agalkyne" and in dir "ADF/Agalkyne"</p> <p>Tab.3 --> data in dir "TMC05ethene"<br> </p>
Data for the study: "Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functional"
<p>This set of files contains the raw data generated by the study titled:</p> <p>"Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functional"</p> <p>Contains:</p> <p>- A set of Jupyter Notebooks to analyzed the data and produce the figures presented in the paper.</p> <p>- runs: Contains the training and validation scripts for each of the systems studied in this work. Also holds the model weights and validation data.</p> <p>Three folders are found: Hchain, noninteracting and Atoms.</p> <p>- datasets: Holds all the datasets generated and employed in this work.</p>
Data repository for "Navigating trade-offs and sustainable development pathways in the Andean water-energy-food-ecosystem nexus"
<p>Data and code supporting the research article: Navigating trade-offs and sustainable development pathways in the Andean water-energy-food-ecosystem nexus.</p>
Ca isotope data and codes for "Moon's high-energy giant-impact origin and differentiation timeline inferred from Ca and Mg stable isotopes"
<p>Ca isotope data for "Moon’s high-energy giant-impact origin and differentiation timeline inferred from Ca and Mg stable isotopes" published in <em>Communications Earth & Environment </em>(2023)</p>
ScienceDex guides
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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.