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4,230 results for “Energie”
Figure 1 in Meat quality of different beef cattle breeds fed high energy forage
Figure 1. The pH of meat from cattle of different breeds
Code and data used in "Health co-benefits of sub-national renewable energy policy in the US"
<p>Archive of modeling, inputs, and results.</p>
Development and demonstration of the next generation of renewable energy-driven technologies for buildings and industrial processes heating and cooling - João Soares 6 year plan
<p>This figure illustrates the six years research plan and methods of João Soares, with the main goal to develop, evaluate and demonstrate the next generation of RES (solar, biomass or hybrid) driven technologies for heating and cooling (H/C) in buildings and industrial processes. </p>
SLABCC: Total energy correction code for charged periodic slab models
<p>Test set for SLABCC (SLAB Charge Correction) including the input files, input parameters and the expected output.<br> The geometries correspond to the positively charged Cl-vacancy on the surface of the NaCl slab with different vacuum thickness. The compiler type/compilation flags, linked libraries, and the hardware architecture may influence the optimization results but the effects on the correction energies should be negligible.</p> <p>The latest version of SLABCC can be downloaded from <a href="https://github.com/MFTabriz/slabcc">https://github.com/MFTabriz/slabcc</a></p> <p> </p>
The potential of sector coupling in future European energy systems soft linking between the Dispa-SET and JRC-EU-TIMES models - Dataset
<p>Supporting dataset and Dispa-SET version used within "The potential of sector coupling in future European energy systems soft linking between the Dispa-SET and JRC-EU-TIMES models" paper.</p>
Fig. 3 in Divergence in energy sources for Prochilodus lineatus (Characiformes: Prochilodontidae) in Neotropical floodplains
Fig. 3. δ13C values of food sources (A to D) and of P. lineatus (E). The box-whisker-plots show the lower and upper quartile (25% - 75%), median (□), minimum and maximum (vertical bars). The small letters represent significant differences between the components studied in Paraná, Baía and Ivinheima subsystems of the floodplain of the Upper Paraná River (n = number of samples).
Supplemental information for "Creating thin magnetic layers at the surface of 2 Sb2Te3 topological insulators using a low-energy chromium ion beam"
<p>A test upload of the data repository to Zenodo</p>
The effect of renewable and nuclear energy consumption on decoupling economic growth from CO2 emissions in Spain
<p>This study examines the relationship between renewable and nuclear energy consumption, carbon dioxide emissions and economic growth by using the Granger causality and non-linear impulse response function in a business cycle in Spain. We estimate the threshold vector autoregression (TVAR) model on the basis of annual data from the period 1970‒2018, which are disaggregated into quarterly data. Our analysis reveals that economic growth and CO<sub>2</sub> emissions are positively correlated during expansions but not during recessions. Moreover, we find that rising nuclear energy consumption leads to decreased CO<sub>2</sub> emissions during expansions, while the impact of increasing renewable energy consumption on emissions is negative but insignificant. In addition, there is a positive feedback between nuclear energy consumption and economic growth, but unidirectional positive causality running from renewable energy consumption to economic growth in upturns. Our findings do indicate that both nuclear and renewable energy consumption contribute to a reduction in emissions; however, the rise in economic activity, leading to a greater increase in emissions, offsets this positive impact of green energy. Therefore, a decoupling of economic growth from CO<sub>2</sub> emissions is not observed. These results demand some crucial changes in legislation targeted at reducing emissions, as green energy alone is insufficient to reach this goal.</p>
Comparison of turbulent structures and energy fluxes over exposed and debris-covered glacier ice: Datasets
<p>This repository contains data of near surface data of turbulence conditions measured simultaneously over exposed ice and a 0.08 m thick supraglacial debris cover on Suldenferner, a small glacier in the Italian Alps. It is related to the following publication:</p> <p>Nicholson, L. and Stiperski, I. (2020) Comparison of turbulent structures and energy fluxes over exposed and debris-covered glacier ice. Journal of Glaciology.</p> <p><strong>The repository contains the following files</strong></p> <p>(1) Overview figure of the locations of the installed weather stations collecting data used in the analysis, and images of the eddy covariance installations </p> <ul> <li><strong>Filename:</strong> overview.tif</li> </ul> <p>(2) 30 minute average meteorological data from the automatic weather station (AWS) on the debris-covered glacier surface with ca. 0.09 m thick debris cover.</p> <ul> <li><strong>Filename:</strong> aws.csv</li> <li><strong>Location:</strong> 46.496 °N / 10.569 °E / ~2625 m</li> <li><strong>Time period:</strong> 11.08.2015 13:30 – 14.08.2015 21:00</li> <li><strong>Variables - unit:</strong> listed in variables&units.pdf</li> </ul> <p>(3) 5 minute data from the eddy covariance station on the clean ice glacier surface.</p> <ul> <li><strong>Filename:</strong> ecci.csv</li> <li><strong>Location:</strong> 46.498°N /10.560°E / ~ 2780 m</li> <li><strong>Time period:</strong> 11.08.2015 13:42 – 14.08.2015 20:57</li> <li><strong>Variables - unit:</strong> listed in variables&units.pdf</li> </ul> <p>(4) 5 minute data from the eddy covariance station on the debris-covered glacier surface with ca. 0.08 m thick debris cover.</p> <ul> <li><strong>Variables - unit:</strong> listed in variables&units.pdf</li> <li><strong>Location:</strong> 46.495 °N / 10.572 °E / ~ 2600 m</li> <li><strong>Time period:</strong> 11.08.2015 13:42 – 14.08.2015 20:57</li> <li><strong>Variables - unit:</strong> listed in variables&units.pdf</li> </ul> <p>(5) A list of the variables and units used in the datafiles.</p> <ul> <li><strong>Filename:</strong> variables&units.pdf</li> </ul> <p> </p> <p>The<strong> instrumentation locations</strong> can be seen in the overview figure. </p> <p>The <strong>automatic weather station </strong>consists of a Kipp and Zonen CNR1 4-way radiation sensor, a shielded Vaisala HMP45c temperature and relative humidity sensor, and a Young 05103 anemometer. 30-minute averages and standard deviations of variables were recorded by a Campbell C3000 datalogger. Temperature and relative humidity are also sampled at 30-minute intervals allowing the vapor pressure to be calculated at this interval. </p> <p>The <strong>eddy covariance</strong><strong> instrumentation</strong> was identical at both stations (ecci and ecdc) and consisted of two segmented masts drilled into the ice with sensors mounted at a height of 1.6 m on a cross arm spanning the vertical masts. A CSAT 3D sonic anemometer and KH20 hygrometer sampling data at a frequency of 20Hz were mounted parallel to the surface and facing obliquely across-glacier at a bearing of 255° so as to capture both up and downglacier winds. A shielded Vaisala HMP45 was installed on the EC mast to record 1-minute averages of air temperature, relative humidity and vapor pressure. Data were recorded using Campbell Scientific CR1000 data loggers with compact flash card storage modules. Power was provided by 60Ah deep cycle batteries connected to 20 W solar panels. </p> <p>The <strong>data provided</strong> here is gap-filled data. The eddy covariance station over debris-covered ice was installed on 10 August 2015, and the one over clean ice was installed on 11 August 2015. At ecdc, a faulty solar panel regulator resulted in this station losing power on 15 August. At ecci two instrument failures occurred; the Vaisala instrument on the afternoon of 12 August and the KH20 at the end of 14 August. Air pressure was not recorded at any of the three stations. Missing data was filled on the basis of multiple regression transformation of data measured at nearby stations, as described fully in the publication. Pressure data were filled in with data from the nearby Madritsch weather station, operated by the Autonomous Province of Bozen. Missing temperature and vapor pressure data spanning 12-14 August at the eddy station over clean ice was filled in with data from the glacier automatic weather station.</p> <p><strong>Thanks</strong> are due to the Gutgsell family at the Hintergrathütte for continued support of our research activities and (in alphabetical order) Michael Adamer, Federico Covi, Costanza del Gobbo, Lukas Hammerer, Irmgard Juen, Marius Massimo, Kristin Richter, Reto Stauffer and Anna Wirbel for assistance in the field. Permission to work on Suldenferner is granted by Stelvio National Park. This research was funded by the Austrian Science Fund Grant numbers V309, P28521 and T781-N32.</p> <p> </p>
Data: The effects of general fatigue induced by incremental exercise test and active recovery modes on energy cost, gait variability and stability in male soccer players
<p>Data, code, and movies for Mahaki et al. (2020); The effects of general fatigue induced by incremental exercise test and active recovery modes on energy cost, gait variability and stability in male soccer players. Published in The Journal of Biomechanics (<a href="https://doi.org/10.1016/j.jbiomech.2020.109823">https://doi.org/10.1016/j.jbiomech.2020.109823</a>).</p> <p><strong>Gas data</strong> - contains all Oxygen data plus a script to run the data.</p> <p> - Subjectxx - folder with oxygen data.</p> <p> - GasdataMohammad [type of file: MATLAB code (.m)] - script to analyze the oxygen data. The outcomes have been saved as RestECnet, PWSECnet, and IVTECnet [type of files: MATLAB data (.mat)].</p> <p> - EC_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of energetic cost, to calculate the effect sizes of main effects of Time, Recovery and Interaction effect (Time vs Recovery) and to show the outcome in boxplot together with individual data points (The results have been presented in <strong>Fig 2.</strong>)</p> <p> </p> <p><strong>Kinematic Data</strong> - contains all Kinematic data plus scripts to run the data:</p> <p> - Subjectxx - folder with kinematics data.</p> <p> - Rest_VAR [type of file: MATLAB code (.m)] - script to analyze the kinematics data in the Rest recovery session. The outcomes have been saved as RestVAR [type of file: MATLAB data (.mat)]. The corresponding plots, per subject & per trial, have been saved in the Plots folder.</p> <p> - PWS_VAR [type of file: MATLAB code (.m)] - script to analyze the kinematics data in the PWS recovery session. The outcomes have been saved as PWSVAR [type of file: MATLAB data (.mat)]. The corresponding plots, per subject & per trial, have been saved in the Plots folder.</p> <p> - IVT_VAR [type of file: MATLAB code (.m)] - script to analyze the kinematics data in the IVT recovery session. The outcomes have been saved as IVTVAR [type of file: MATLAB data (.mat)]. The corresponding plots, per subject & per trial, have been saved in the Plots folder.</p> <p> - VAR_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of gait variability (VAR sagittal, frontal, and horizontal) and to show the outcome in boxplot together with individual data points (The results have been presented in <strong>Fig 3.</strong>).</p> <p> - VAR_Sagittal_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of gait variability in sagittal plane, to calculate the effect sizes of main effects of Time, Recovery and Interaction effect (Time vs Recovery), and to show the outcome in boxplot together with individual data points.</p> <p> - VAR_Frontal_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of gait variability in frontal plane, to calculate the effect sizes of main effects of Time, Recovery and Interaction effect (Time vs Recovery), and to show the outcome in boxplot together with individual data points.</p> <p> - VAR_Horizontal_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of gait variability in horizontal plane, to calculate the effect sizes of main effects of Time, Recovery and Interaction effect (Time vs Recovery), and to show the outcome in boxplot together with individual data points.</p> <p> - normalizetimebase [type of file: MATLAB code (.m)] – function to normalize VAR from 0 to 100% of the gait cycle.</p> <p> - VAR_Sagittal [type of file: JASP/Jamovie files (.jasp/.omv)] - file to statistically test gait variability in sagittal plane. Imported data have been saved as VAR_Sagittal [type of file: Excell file (.csv)].</p> <p> - VAR_Frontal [type of file: JASP/Jamovie files (.jasp/.omv)] - file to statistically test gait variability in frontal plane. Imported data have been saved as VAR_Frontal [type of file: Excell files (.csv)].</p> <p> - VAR_Horizontal [type of file: JASP/Jamovie files (.jasp/.omv)] - file to statistically test gait variability in horizontal plane. Imported data have been saved as VAR_Horizontal [type of file: Excell file (.csv)].</p> <p> + Trunk Stability - folder includes all outcomes and codes in terms of gait stability:</p> <p> -Thoracic [type of file: Excell file (.xlsx)] - general file includes all outcomes in terms of gait stability.</p> <p> - LDS_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of gait stability (λ sagittal, λ frontal, and λ horizontal) and to show the outcome in boxplot together with individual data points (The results have been presented in <strong>Fig 5.</strong>).</p> <p> - LDS_Sagittal_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses inn terms of gait stability in sagittal plane, to calculate the effect sizes of main effects of Time, Recovery and Interaction effect (Time vs Recovery), and to show the outcome in boxplot together with individual data points.</p> <p> - LDS_Frontal_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of gait stability in frontal plane, to calculate the effect sizes of main effects of Time, Recovery and Interaction effect (Time vs Recovery), and to show the outcome in boxplot together with individual data points.</p> <p> - LDS_Horizontal_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of gait stability in horizontal plane, to calculate the effect sizes of main effects of Time, Recovery and Interaction effect (Time vs Recovery), and to show the outcome in boxplot together with individual data points.</p> <p> - Rest_LDS_Sagittal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the Rest recovery session and in the sagittal plane.</p> <p> - Rest_LDS_Frontal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the Rest recovery session and in the frontal plane.</p> <p> - Rest_LDS_Horizontal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the Rest recovery session and in the horizontal plane.</p> <p> - PWS_LDS_Sagittal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the PWS recovery session and in the sagittal plane.</p> <p> - PWS_LDS_Frontal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the PWS recovery session and in the frontal plane.</p> <p> - PWS_LDS_Horizontal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the PWS recovery session and in the horizontal plane.</p> <p> - IVT_LDS_Sagittal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the IVT recovery session and in the sagittal plane.</p> <p> - IVT_LDS_Frontal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the IVT recovery session and in the frontal plane.</p> <p> - IVT_LDS_Horizontal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the IVT recovery session and in the horizontal plane.</p> <p> - LDS_sagittal [type of files: JASP/Jamovie files (.jasp/.omv)] - file to statistically test gait stability in sagittal plane. Imported data have been saved as LDS_sagittal [type of file: Excell file (.csv)].</p> <p> - LDS_frontal [type of files: JASP/Jamovie files (.jasp/.omv)] - file to statistically test gait stability in frontal plane. Imported data have been saved as LDS_frontal [type of file: Excell file (.csv)].</p> <p> - LDS_horizontal [type of files: JASP/Jamovie file (.jasp/.omv)] - file to statistically test gait stability in horizontal plane. Imported data have been saved as LDS_horizontal [type of file: Excell file (.csv)].</p> <p> </p> <p><strong>Movies</strong> –contains some movies [type of files: .mp4] to show the Noraxon calibration, IMUs placement on body segments, fatigue protocol, walking at PWS, and resting metabolic cost measurement.</p> <p> </p> <p><strong>The characteristics of participants</strong> [type of file: Excell file (.xlsx)] – file includes 4 sheets:</p> <p> - Sheet 1 includes height (cm), weight (kg), age (year), BMI, and body fat percentage of each participant.</p> <p> - Sheets 2-4 include the physiological parameters of each participant during IVT, PWS, and Rest recovery modes. The physiological parameters such as Vo2, Vo2max, HR, and RER of each participant have been reported during rest (i.e. sitting position) and incremental exercise test.</p> <p> </p>
Data archive for paper "WRF‐TEB: Implementation and Evaluation of the Coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) Model"
<p><strong>WRF-TEB data archive</strong></p> <p>This archive contains data and tools to reproduce results as included in <a href="https://doi.org/10.1029/2019ms001961">Meyer et al. (2020)</a>.</p> <p><strong>Prerequisites</strong></p> <ul> <li><a href="https://sylabs.io/">Singularity</a> version >= 3.</li> </ul> <p><strong>Usage</strong></p> <p>To run all models and plotting scripts included in integration test and meteorological evaluation, run the following command from your command-line interface.</p> <pre><code>NPROC=8 TYPE=evaluate tools/singularity/run.sh</code></pre> <p>where <code>NPROC=8</code> is the maximum number of processes to use. The output can be found in the <code>work/</code> folder.</p> <p><strong>HPC</strong></p> <p>If you want to use this in an HPC environment, use <code>tools/hpc</code> as a template. As an example, to run the evaluation on Imperial HPC using PBS (Portable Batch System), use:</p> <pre><code>qsub -v REPO_ROOT=$(pwd),TYPE=evaluate tools/hpc/job_imperial.sh</code></pre> <p><strong>Copyright and License</strong></p> <p>Copyright and licensing information are included at the top of source files or as separate files in folders.</p> <p><strong>References</strong></p> <p>Meyer, D., Schoetter, R., Riechert, M., Verrelle, A., Tewari, M., Dudhia, J., Masson, V., Reeuwijk, M., & Grimmond, S. (2020). WRF‐TEB: implementation and evaluation of the coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) model. Journal of Advances in Modeling Earth Systems. <a href="https://doi.org/10.1029/2019ms001961">https://doi.org/10.1029/2019ms001961</a></p>
Appliances Energy Dataset
<p>This dataset is part of the Monash, UEA & UCR time series regression repository. <a href="http://tseregression.org/">http://tseregression.org/</a></p> <p>The goal of this dataset is to predict total energy usage in kWh of a house. This dataset contains 138 time series obtained from the Appliances Energy Prediction dataset from the UCI repository. The time series has 24 dimensions. This includes temperature and humidity measurements of 9 rooms in a house, monitored with a ZigBee wireless sensor network. It also includes weather and climate data such as temperature, pressure, humidity, wind speed, visibility and dewpoint measured from Chievres airport. The data set is averaged for 10 minutes period and spanning 4.5 months.</p> <p>Please refer to <a href="https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction">https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction</a> for more details<br> <br> Relevant papers<br> Luis M. Candanedo, Veronique Feldheim, Dominique Deramaix, Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788<br> <br> Citation request<br> Luis M. Candanedo, Veronique Feldheim, Dominique Deramaix, Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788</p>
Codes and datasets associated with the paper "Parameterizing the Energy Dissipation Rate in Stably Stratified Flows"
<p>Here, you will find some of the codes and datasets utilized in the article: "Parameterizing the Energy Dissipation Rate in Stably Stratified Flows". (<a href="https://arxiv.org/abs/2001.02255">https://arxiv.org/abs/2001.02255</a>)</p> <p>The direct numerical simulations were performed using the HERCULES code (<a href="https://github.com/friedenhe/HERCULES">https://github.com/friedenhe/HERCULES</a>). For all the simulations, the bulk Reynolds number is kept at 20,000. The bulk Richardson number is varied from 0.1 to 0.5. The zip files Re20000RiXX.zip contains the various turbulence statistics. </p> <p>Re20000Ri10: Ri_b = 0.1</p> <p>Re20000Ri20: Ri_b = 0.2</p> <p>Re20000Ri30: Ri_b = 0.3</p> <p>Re20000Ri40: Ri_b = 0.4</p> <p>Re20000Ri50: Ri_b = 0.5</p> <p>Every dat file (within the zip archives) is in ascii format and has 10 rows and 286 columns. Each row corresponds to normalized time (ranging from 1 to 10). Each column represents vertical model levels. </p> <p> </p>
Characterizing Energy Consumption of Third-Party API Libraries using API Utilization Profiles (ESEM'2020 Dataset)
<p><strong>Motivation</strong></p> <p>This repository contains the data-set used as a basis for our ESEM'2020 paper <em>Characterizing Energy Consumption of Third-Party API Libraries using API Utilization Profiles</em> The dataset is comprised of 2 test scenarios: The first one is based on the paper from Rocha et al. (2019), the second one is based on the commonly known Google Gson library.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset is stored in a file *.csv and contains the following data:</p> <ul> <li>id - an individual identifier</li> <li>name - the name of the library examined</li> <li>className - the class name as an abbreviation</li> <li>method - the name of the executed method</li> <li>duration - duration of method execution</li> <li>energyConsumption - computed energy consumption</li> <li>watts - recorded wattage</li> <li>uApi - the computed uAPI profile value</li> </ul> <p>Besides the data, this repository also contains the result images from the ESEM'2020 paper in pdf-file format.</p> <p><em><strong>Android I/O Experiment</strong></em></p> <p>For the Android I/O Experiment we examined the correlation between API utilization and energy consumption. For the dataset, we took inspiration from Rocha et al. (2019). The dataset uses abbreviations for the examined classes which are further described in the <em>readme.md</em> file</p> <ul> <li>Filename: io_test_052020.csv</li> </ul> <p><em><strong>Google Gson Experiment</strong></em></p> <p>The Google Gson dataset contains recordings for Google Gson version 2.8.5. Again, the dataset was used as a foundation for examining the correlation between the computed API profiles and energy consumption.</p> <ul> <li>Filename: gson_test_052020.csv</li> </ul> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p>
Effects of low-carbon energy adoption on airborne particulate matter concentrations with feedbacks to future climate over California
<p>California plans to reduce emissions of long-lived greenhouse gases (GHGs) through adoption of new energy systems that will also lower concentrations of short-lived absorbing soot contained in airborne particulate matter (PM). Here we examine the direct and indirect effects of reduced PM concentrations under a low-carbon energy (GHG-Step) scenario on radiative forcing in California. Simulations were carried out using the source-oriented WRF/Chem (SOWC) model over California with 12 km spatial resolution for the year 2054. The avoided aerosol emissions due to technology advances in the GHG-step scenario reduce ground level PM concentrations by ~8.85% over land compared to the Business as Usual (BAU) scenario, but changes to meteorological parameters are more modest. Top of atmospheric forcing predicted by the SOWC model increased by 0.15 W m<sup>-2</sup>, surface temperature warmed by 0.001 K, and planetary boundary layer height (PBLH) increased by 2.20 cm in the GHG-Step scenario compared to the BAU scenario. PM climate feedbacks are small because the significant changes in ground level PM concentrations associated with the GHG-Step scenario are limited to the first few hundred meters of the atmosphere, with little change for the majority of the vertical column above that level. As an order-of-magnitude comparison, the long-term effects of global reductions in GHG emissions (RCP8.5 – RCP4.5) lowered average surface temperature over the California study domain by approximately 0.76 K. The effects of long-lived climate pollutants such as CO<sub>2</sub> are much stronger than the effects of short-lived climate pollutants such as PM soot over California in the year 2054. </p>
Phase portrait and potential energy function of the dynamical system corresponding to modified ZK equation for ion-acoustic waves in a magnetized electron-ion plasma with generalized (r, q) distributed electrons
<p>These figures (a) and (b) represent phase portrait and potential energy function of the dynamical system corresponding to the mZK equation for ion-acoustic waves in a magnetized electron-ion plasma with generalized (r, q) distributed electrons for q = 5 and r = 0.1. These figures contain three fixed points P<sub>0,</sub> P<sub>1 </sub>and P<sub>2</sub> with one separatrix. The figure (a) contains one family of supernonlinear periodic orbits, two families of periodic orbits and one pair of homoclinic orbits at P<sub>0. </sub>The figure (b) contains the potential energy function with two local minima and one maxima which is necessary condition for supernonlinear wave. </p>
Docking Adenosine Receptor Ligands to SARS-CoV2 mRNA Cap 2'-O-Methyltransferase: Energy Minimized Structures
<p>Energy minimized structures associated with a study docking adenosine receptor binders and related ligands to the adenosine binding site on SARS-CoV2 nsp16. The associated paper is under review, but we have a previous version of the paper available as a preprint at <a href="https://chemrxiv.org/articles/preprint/Docking_Adenosine_Receptor_Ligands_to_SARS-CoV2_mRNA_Cap_Guanine-N7_Methyltransferase/12462080/2">https://chemrxiv.org/articles/preprint/Docking_Adenosine_Receptor_Ligands_to_SARS-CoV2_mRNA_Cap_Guanine-N7_Methyltransferase/12462080/2</a>.</p>
Sub-picosecond to Sub-nanosecond Vibrational Energy Transfer Dynamics in Pentaerythritol Tetranitrate
<p><strong>Experimental Data</strong></p> <p>All data are saved as a .csv file. Each transient .csv file is labeled according to molecule, file contents, polarization, pump laser power, spectrometer grating position, and a date (<em>e.g</em>. PETN_TA_para_1.8mW_11.1um_06Feb2020.csv). The first column contains frequencies (in cm<sup>-1</sup>) while the first row indexes each time delay (in ps). Transient spectra at select time delays are similarly labeled (<em>e.g</em>. PETN_spectra_7.8um_FTIRcorr_07Feb2020.csv) with the same data organization within each file. The select transient spectra shown at 1660 cm-1 were taken from the 6.1 µm "TA" dataset. The static PETN FTIR and 33°C ThDIR spectra are given as two column .csv files: the first column is frequency (in cm<sup>-1</sup>) while the second column is either transmission<br> or <span class="math-tex">\(\Delta \mathrm{OD}\)</span>.</p> <p><strong>Theory Data</strong></p> <p>Included in the data repository are all of the necessary inputs to perform the vibrational analysis using the Phonopy software package. The minimized PETN crystal used in the nite displacement calculations is given as a POSCAR file format to be used in VASP, the enumeration of all displacements used in the first anharmonic expansion is given in disp_fc3.yaml. The collection of all individual VASP calculations into second- and third-order force constants are contained in the .hdf5 files. The remaining files contain all of the details for the Phonopy calculations performed, per-mode population adjustments in the JDOS are achieved by modifying the phono3py/phonon3/triplets.py source code file which is distributed with all versions of the code. Short movies of each IR mode and all phonon modes between 0–800 cm<sup>-1</sup> are presented in .gif format; filenames are either the calculated frequency or, in the case of several modes, the approximate center frequency.</p>
Data sets used in the manuscript titled "Ecosystem-level energy and water budgets are resilient to canopy mortality in sparse semi-arid biomes"
<p><span>This data set reports water and energy fluxes, soil water content and sap fluxes measured at two adjacent pinon-juniper woodlands in central New Mexico from January 2009 to December 2016. This data set was used for the analysis in the manuscript titled "Ecosystem-level energy and water budgets are resilient to canopy mortality in sparse semi-arid biomes" submitted to JGR-Biogeosciences. </span></p>
Figure 14 in The biology and functional morphology of the high-energy beach dwelling Paphies elongata (Bivalvia: Mactroidea: Mesodesmatidae). Convergence with the surf clams (Donax: Tellinoidea: Donacidae)
Figure 14. Paphies elongata. An interior view of the fused ventral mantle margin posterior to the pedal gape and showing the rejectory tract. This figure also illustrates the posterior adductor muscle and the visceral ganglia beneath it. AN, anus; AVG, accessory visceral ganglia; FIMF(2), fused inner mantle folds (outer component); IMF(1), inner mantle fold (inner component); IS, inhalant siphon; PA (1), posterior adductor muscle (anterior component); PA(2), posterior adductor muscle (posterior component); PGA, pedal gape; R, rectum; RT, rejectory tract; SN, siphonal nerve; SRM, siphonal retractor muscles; VG, visceral ganglia; VG-AVG-CONN, visceral ganglia-accessory visceral ganglia connective.
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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.