Find research datasets worth reusing

opencc-by-4.0Feb 2023View details →

Atmospheric boundary layer height and energy over the Tibetan Plateau

Processed data of atmospheric boundary layer height and energy over the Tibetan Plateau.

opencc-by-4.0Jan 2023View details →

Quantum state resolved molecular dipolar collisions over four decades of energy - online data

Data for submission of "Quantum state resolved molecular dipolar collisions over four decades of energy" Authors: Guoqiang Tang1, Matthieu Besemer1, Stach Kuijpers1, Gerrit C. Groenenboom1, Ad van der Avoird1, Tijs Karman1**, Sebastiaan Y.T. van de Meerakker1** 1Radboud University, Institute for Molecules and Materials Heijendaalseweg 135, 6525 AJ Nijmegen, the Netherlands **To whom correspondence should be addressed; E-mail: basvdm@science.ru.nl, t.karman@science.ru.nl

opencc-byJun 2020View details →

Dataset: "The challenge of ionisation chamber dosimetry in ultra-short pulsed high dose-rate Very High Energy Electron beams"

Dataset for the paper entitled "The challenge of ionisation chamber dosimetry in ultra-short pulsed high dose-rate Very High Energy Electron beams"

opencc-by-4.0Feb 2023View details →

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'

Contains all supplementary works mentioned in the manuscript.

Supplementary data for eEDM study in thesis 'Low energy observables and fine-tuning in the MSSM'

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). 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.

Diverse carbon dioxide removal approaches could reduce energy-water-land impacts (output data)

GCAM scenario output data

Data for "Energy Surplus and Atmosphere – Land-Surface "Tug of War" Induced by Climate Change Control Future Evapotranspiration"

USGS gauges used in manuscript "Energy Surplus and An Atmosphere-Land-Surface “Tug of War” Control Future Evapotranspiration". USGS_CTL15_Gage.mat contains the USGS gauge ID, and one can use retrieve_daily_streamflow.m to download the corresponding streamflow time series.

opennotspecifiedJan 2020View details →

Figure 31. Energy dispersive X in Morphological And Molecular Description Of Rhadinorhynchus Hiansi Soota And Bhattacharya, 1981 (Acanthocephala: Rhadinorhynchidae) From Marine Fish Off The Pacific Coast Of Vietnam

Figure 31. Energy dispersive X-ray spectrum of a gallium cut trunk spine of a Rhadinorhynchus hiansi specimen showing high levels of sulfur. The x-ray data are the elemental analysis of the center of the spine; see boldfaced numbers in Table V. Insert: SEM of a lateral longitudinal cut spine.

opennotspecifiedJan 2020View details →

Figure 29. Energy dispersive X in Morphological And Molecular Description Of Rhadinorhynchus Hiansi Soota And Bhattacharya, 1981 (Acanthocephala: Rhadinorhynchidae) From Marine Fish Off The Pacific Coast Of Vietnam

Figure 29. Energy dispersive X-ray spectrum of the base center of a gallium cut large anterior hook of a Rhadinorhynchus hiansi specimen showing high levels of calcium and phosphorus. The x-ray data are the elemental analysis of the hook base (see boldfaced figures in Table III). Insert: SEM of a cross and lateral longitudinal gallium cut hook.

opennotspecifiedJan 2020View details →

Figure 30. Energy dispersive X in Morphological And Molecular Description Of Rhadinorhynchus Hiansi Soota And Bhattacharya, 1981 (Acanthocephala: Rhadinorhynchidae) From Marine Fish Off The Pacific Coast Of Vietnam

Figure 30. Energy dispersive X-ray spectrum of the tip of a gallium cut small base hook of a Rhadinorhynchus hiansi specimen showing high levels of sulfur. The x-ray data are the elemental analysis of the hook tip (see boldfaced figures in Table IV). Insert: SEM of a cross and lateral longitudinal gallium cut hook.

A closer look at high-energy X-ray-induced bubble formation during soft tissue imaging

Improving the scalability of tissue imaging throughput with bright, coherent X-rays requires identifying and mitigating artifacts resulting from the interactions between X-rays and matter. At synchrotron sources, long-term imaging of soft tissues in solution can result in gas bubble formation or cavitation, which dramatically compromises image quality and integrity of the samples. By combining in-line phase-contrast cineradiography with operando gas chromatography, we were able to track the onset and evolution of high-energy X-ray-induced gas bubbles in ethanol-embedded soft tissue samples for tens of minutes (2 to 3 times the typical scan times). We demonstrate quantitatively that vacuum degassing of the sample during preparation can significantly delay bubble formation, offering up to a twofold improvement in dose tolerance, depending on the tissue type. However, once nucleated, bubble growth is faster in degassed than undegassed samples, indicating their distinct metastable states at bubble onset. Gas chromatography analysis shows increased solvent vaporization concurrent with bubble formation, yet the quantities of dissolved gases remain unchanged. Coupling features extracted from the radiographs with computational analysis of bubble characteristics, we uncover dose-controlled kinetics and nucleation site-specific growth. These hallmark signatures provide quantitative constraints on the driving mechanisms of bubble formation and growth. Overall, the observations highlight bubble formation as a critical, yet often overlooked hurdle in upscaling X-ray imaging for biological tissues and soft materials and we offer an empirical foundation for their understanding and imaging protocol optimization. More importantly, our approaches establish a top-down scheme to decipher the complex, multiscale radiation-matter interactions in these applications.

An assessment of energy system transformation pathways to achieve net-zero carbon dioxide emissions in Switzerland. Supplementary Information Assumptions and Results

This dataset accompanies the corresponding article in Communications Earth and Environment. It contains the key assumptions used in the energy system modelling with the Swiss TIMES energy systems model (STEM) for assessing net-zero carbon dioxide emissions scenarios for Switzerland. In addition, contains extensive results from STEM for each one of the core scenarios and variants assessed in the study. The following files are contained in this repository: <ul> <li>Supplementary Data 1: This is the EXCEL file "Supplementary_Information_Assumptions.xslx" which contains key assumptions of the long-term scenarios assessed with STEM. These include among others: the major energy and climate policies in each scenario, the economic and demographic assumptions, key drivers for the residential energy demand (e.g., floor reference area or appliances), key drivers for the energy demand in the services sectors (e.g., Gross Value Added or floor area), key drivers for the energy demand in industry (e.g., production index or Gross Value Added), mobility demands, energy import prices, hourly electricity import prices, net transfer capacities, domestic sustainable renewable resource potentials, energy supply and demand technologies costs and efficiencies</li> <li>Supplementary Data 2: This is the EXCEL file "Supplementary_Information_Results.xlsx" which contains energy balances from the baseline and the net-zero CO2 emissions scenarios. The results for each scenario are: domestic production by fuel, net imports by fuel, primary energy consumption by fuel, input to conversion sectors by fuel, electricity supply and capacities by fuel, district heating supply by fuel, final energy consumption by fuel and sector, CO2 emissions by source, energy system costs, and indicators such as energy consumption per capita, energy intensity of GDP, CO2 emissions per capita and CO2 intensity of GDP. </li> <li>Supplementary Data 3: The ZIP file "Source_code_and_data_for_charts.zip" contains source codes and data for reproducing the figures in the manuscript. </li> </ul>

opencc-by-4.0Sep 2022View details →

Dataset for Energy Resource Management Considering Participation in the Wholesale Day-Ahead Market

This release is associated with a paper entitled "A Novel Bilevel Programming Formulation for Optimizing the Interaction of Distribution System Operators and GENCOs in Day-Ahead Markets". In this document, we provide the mathematical manipulation employed to linearize an MPEC problem that models the market-clearing process considering the participation of generation companies and distribution operators in the day-ahead market. Additionally, we provide a numerical example of the pricing mechanism designed for solving the optimization problem.  Finally, we include information regarding the transmission and distribution power systems adopted to present the results shown in the paper. These systems are modified versions of the IEEE <a href="https://icseg.iti.illinois.edu/ieee-14-bus-system/">14-bus</a> and the IEEE <a href="https://cmte.ieee.org/pes-testfeeders/resources/">34-bus</a>, respectively. Three renewable non-dispatchable generators were added to the original 14-bus system, while 5 dispatchable generators and 3 non-dispatchable generators were added to the distribution system. 13 load shapes were considered as well as 5 scenarios for each uncertain variable (wind velocity, solar irradiance, distribution load oscillation, and transmission load oscillation). The data regarding the costs and physical parameters of each of the power system's elements are provided in this document.

opencc-by-4.0Oct 2022View details →

Data source and projections of maintenance energy gaps for "Caloric reductions needed to achieve obesity goals by 2030 and 2040: A modeling study"

Variables in "data_ENSANUT_waves.xlsx" <table> <thead> <tr> <th scope="col">Name</th> <th scope="col">Variable</th> </tr> </thead> <tbody> <tr> <td>id</td> <td>Identifier for each individual in the data.</td> </tr> <tr> <td>est_var</td> <td>Strata for the estimation of variances, accounting for survey design.</td> </tr> <tr> <td>svy_weights</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/m2).</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/m2). 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/m2). 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/m2). 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/m2). 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/m2). 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/m2). 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/m2). 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/m2). 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>

FIG. 4 in Bat activity rates do not predict bat fatality rates at wind energy facilities

FIG. 4. Bat activity rates measured before and after turbines were built at four wind energy facilities for high- and low-frequency bats during individual seasons. The dashed line indicates a 1:1 relationship between pre- and post-construction activity rates

FIG. 3 in Bat activity rates do not predict bat fatality rates at wind energy facilities

FIG. 3. Regression analysis for high-frequency bat activity rates (Panel A, n = 23) and low-frequency bat activity rates (Panel B, n = 24) recorded by raised microphones versus overall bat fatality rates at paired pre-construction and post-construction studies, grouped by U.S. Fish and Wildlife Service region

FIG. 2 in Bat activity rates do not predict bat fatality rates at wind energy facilities

FIG. 2. Regression analysis for overall bat activity rates recorded by ground microphones (n = 47) versus overall bat fatality rates at paired pre-construction and post-construction studies, grouped by U.S. Fish and Wildlife Service region

FIG. 1 in Bat activity rates do not predict bat fatality rates at wind energy facilities

FIG. 1. The number of paired pre-construction bat activity and post-construction bat fatality studies (first number) and paired post-construction bat activity and post-construction bat fatality studies (second number) used for regression analyses, organized by U.S. Fish and Wildlife Service region. The Mountain-Prairie region includes five studies from southern Alberta, Canada

opencc-zeroApr 2023View details →

dryad32/100

Data for the article: Trophic positions of soil microarthropods in forests increase with elevation, but energy channels remain unchanged

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. 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. 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. We applied three state-of-the-art methods to elucidate trophic positions and basal resource use of both taxa at community level: bulk stable isotope analysis of nitrogen and carbon (SIbulk), compound-specific stable isotope analysis of amino acids (CSSIA-AA) and dietary routing through neutral lipid fatty acids (NLFA). Δ15Nbulk and trophic position calculated using CSSIA-AA (TPCSSIA) both increased with elevation in both taxa. Stable isotope mixing models using δ13C 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. Δ13Cbulk did not respond linearly to the elevational gradient; however, changes with elevations differed between Collembola and Oribatida. A strong linear relationship between δ15N of phenylalanine in consumers and δ15N of litter indicated litter as basal resource of both taxa. 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.