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TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"
<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> time = 3672 ;<br> stations = 6 ;<br> name_strlen = 6 ;<br> depth = 3 ;<br> height = 201 ;<br> variables:<br> double time(time) ;<br> time:standard_name = "time" ;<br> time:long_name = "time of measurement" ;<br> time:units = "hours since 2016-06-01 00:00:00" ;<br> time:timezone = "UTC" ;<br> time:calendar = "proleptic_gregorian" ;<br> double lat(stations) ;<br> lat:standard_name = "latitude" ;<br> lat:long_name = "station_latitude" ;<br> lat:units = "degrees_north" ;<br> double lon(stations) ;<br> lon:standard_name = "longitude" ;<br> lon:long_name = "station_longitude" ;<br> lon:units = "degrees_east" ;<br> double elev(stations) ;<br> elev:standard_name = "altitude" ;<br> elev:long_name = "station_altitude" ;<br> elev:units = "m ASL" ;<br> double height(height) ;<br> height:standard_name = "altitude" ;<br> height:long_name = "station_altitude" ;<br> height:units = "m ASL" ;<br> double depth(depth) ;<br> depth:standard_name = "soil_depth" ;<br> depth:long_name = "soil sensor depth" ;<br> depth:units = "cm" ;<br> char station_name(name_strlen, stations) ;<br> station_name:long_name = "station_name" ;<br> station_name:cf_role = "timeseries_id" ;<br> double T(time, stations) ;<br> T:_FillValue = -9999. ;<br> T:standard_name = "temperature" ;<br> T:long_name = "2m air temperature" ;<br> T:units = "degree_Celsius" ;<br> T:source = "TERENO-preAlpine" ;<br> double Q(time, stations) ;<br> Q:_FillValue = -9999. ;<br> Q:standard_name = "mixing_ratio" ;<br> Q:long_name = "2m mixing ratio" ;<br> Q:units = "g kg-1" ;<br> Q:source = "TERENO-preAlpine" ;<br> double ET_i(time, stations) ;<br> ET_i:_FillValue = -9999. ;<br> ET_i:standard_name = "evapotranspiration_intensive" ;<br> ET_i:long_name = "lysimeter evapotranspiration intensive management" ;<br> ET_i:units = "g kg-1 h-1" ;<br> ET_i:source = "TERENO-preAlpine" ;<br> double ET_e(time, stations) ;<br> ET_e:_FillValue = -9999. ;<br> ET_e:standard_name = "evapotranspiration_extensive" ;<br> ET_e:long_name = "lysimeter evapotranspiration extensive management" ;<br> ET_e:units = "g kg-1 h-1" ;<br> ET_e:source = "TERENO-preAlpine" ;<br> double LvE_cor(time, stations) ;<br> LvE_cor:_FillValue = -9999. ;<br> LvE_cor:standard_name = "latent_heat_flux" ;<br> LvE_cor:long_name = "energy balance corrected flux tower latent heat flux" ;<br> LvE_cor:units = "W m-2" ;<br> LvE_cor:source = "TERENO-preAlpine" ;<br> double HTs_cor(time, stations) ;<br> HTs_cor:_FillValue = -9999. ;<br> HTs_cor:standard_name = "sensible_heat_flux" ;<br> HTs_cor:long_name = "energy balance corrected flux tower sensible heat flux" ;<br> HTs_cor:units = "W m-2" ;<br> HTs_cor:source = "TERENO-preAlpine" ;<br> double GHF(time, stations) ;<br> GHF:_FillValue = -9999. ;<br> GHF:standard_name = "ground_heat_flux" ;<br> GHF:long_name = "flux tower ground heat flux" ;<br> GHF:units = "W m-2" ;<br> GHF:positive = "up" ;<br> GHF:source = "TERENO-preAlpine" ;<br> double SW(time, stations) ;<br> SW:_FillValue = -9999. ;<br> SW:standard_name = "short_wave_radiation" ;<br> SW:long_name = "downward short wave radiation" ;<br> SW:units = "W m-2" ;<br> SW:source = "TERENO-preAlpine" ;<br> double LW(time, stations) ;<br> LW:_FillValue = -9999. ;<br> LW:standard_name = "long_wave_radiation" ;<br> LW:long_name = "downward long wave radiation" ;<br> LW:units = "W m-2" ;<br> LW:source = "TERENO-preAlpine" ;<br> double VWC_25(time, depth) ;<br> VWC_25:_FillValue = -9999. ;<br> VWC_25:standard_name = "volumetric_water_content" ;<br> VWC_25:long_name = "DE-Fen SoilNet volumetric water content first quartile" ;<br> VWC_25:units = "vol. %" ;<br> VWC_25:source = "TERENO-preAlpine" ;<br> double VWC_50(time, depth) ;<br> VWC_50:_FillValue = -9999. ;<br> VWC_50:standard_name = "volumetric_water_content" ;<br> VWC_50:long_name = "DE-Fen SoilNet volumetric water content second quartile" ;<br> VWC_50:units = "vol. %" ;<br> VWC_50:source = "TERENO-preAlpine" ;<br> double VWC_75(time, depth) ;<br> VWC_75:_FillValue = -9999. ;<br> VWC_75:standard_name = "volumetric_water_content" ;<br> VWC_75:long_name = "DE-Fen SoilNet volumetric water content third quartile" ;<br> VWC_75:units = "vol. %" ;<br> VWC_75:source = "TERENO-preAlpine" ;<br> double T_prof(time, height) ;<br> T_prof:_FillValue = -9999. ;<br> T_prof:standard_name = "temperature_profile" ;<br> T_prof:long_name = "DE-Fen HATPRO spline interpolated temperature profile" ;<br> T_prof:units = "K" ;<br> T_prof:source = "scaleX campaign 2016" ;<br> double A_prof(time, height) ;<br> A_prof:_FillValue = -9999. ;<br> A_prof:standard_name = "humidity_profile" ;<br> A_prof:long_name = "DE-Fen HATPRO spline interpolated absolute humidity profile" ;<br> A_prof:units = "kg m-3" ;<br> A_prof:source = "scaleX campaign 2016" ;<br> double PRW(time) ;<br> PRW:_FillValue = -9999. ;<br> PRW:standard_name = "precipitable_water" ;<br> PRW:long_name = "DE-Fen HATPRO column precipitable water" ;<br> PRW:units = "kg m-2" ;<br> PRW:source = "scaleX campaign 2016" ;</p> <p>// global attributes:<br> :history = "2019-09-12: File created." ;<br> :institution = "Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research" ;<br> :Contact_person = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :Author = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :source = "https://www.tereno.net, https://scalex.imk-ifu.kit.edu" ;<br> :Conventions = "CF-1.6" ;<br> :License = "Creative Commons Attribution Non Commercial Share Alike 4.0 International" ;</p> <p> </p>
Data and figures for Characterization of 30 ^{76}Ge enriched Broad Energy Ge detectors for GERDA Phase II
<p>Data and figures for Characterization of 30 <sup>76</sup>Ge enriched Broad Energy Ge detectors for GERDA Phase II</p>
X-ray tomography (CT) image data of tungsten fusion energy heat exchange components
<p>X-ray tomography (CT) image data of tungsten fusion energy heat exchange components.</p> <p>The dataset includes images of four samples:</p> <ul> <li>CCFE_MB_ROI (Culham Centre for Fusion Energy thermal break concept monoblock, region of interest sample)</li> <li>IPP_Wf-Cu (Max-Planck-Institut für Plasmaphysik tungsten fibre / copper matrix coolant pipe)</li> <li>ITER_HHFT_ROI (ITER reference monoblock which has undergone high heat flux testing, region of interest sample)</li> <li>ITER_MB_ROI (ITER reference monoblock, region of interest sample)</li> </ul> <p>This data was used originally for the following publication (please cite if re-using the data) where further details on the data may be obtained:</p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Each of the sample directories include reconstructed slices in Tiff format. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). CCFE_MB_ROI also includes raw radiographs; scan & reconstruction parameter settings file.</p> <p>A Neutron CT version of this data is available for comparison: <a href="https://doi.org/10.5281/zenodo.3533418">https://doi.org/10.5281/zenodo.3533418</a></p> <p>Image-based simulation (IBSim) meshes were generated directly from these datasets: <a href="https://doi.org/10.5281/zenodo.3533422">https://doi.org/10.5281/zenodo.3533422</a></p>
Neutron tomography (CT) image data of tungsten fusion energy heat exchange components
<p>Neutron tomography (CT) image data of tungsten fusion energy heat exchange components.</p> <p>The dataset includes three sets of images:</p> <ul> <li>ITER_171T-WA-0002_MB (ITER reference monoblock)</li> <li>CCFE_ThBr_MB (Culham Centre for Fusion Energy thermal break concept monoblock)</li> <li>ROIsamples_Stack (A stack of four region of interest samples*)</li> </ul> <p>The region of interest samples within the stack are as below:</p> <ul> <li>CCFE_ThBr_ROI (Culham Centre for Fusion Energy thermal break concept monoblock)</li> <li>IPP_Wf-Cu_p5_s1 (Max-Planck-Institut für Plasmaphysik tungsten fibre / copper matrix coolant pipe)</li> <li>ITER_HHFT_ROI (ITER reference monoblock which has undergone high heat flux testing)</li> <li>ITER_17IT-WA-0002_ROI (ITER reference monoblock)</li> </ul> <p>This data was used originally for the following publication (please cite if re-using the data) where further details on the data may be obtained:</p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Each of the sample directories include reconstructed slices in Tiff format. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads).</p> <p>CCFE_ThBr_ROI and ROIsamples_Stack include raw radiographs; dark and flat field images; scan & reconstruction parameter settings file.</p> <p>ITER_171T-WA-0002_MB includes data relating to the modulation transfer function (MTF) measurement.</p> <p>An X-Ray CT version of the ROI data is available for comparison: <a href="https://doi.org/10.5281/zenodo.3533420">https://doi.org/10.5281/zenodo.3533420</a></p> <p>Image-based simulation (IBSim) meshes were generated directly from these datasets: <a href="https://doi.org/10.5281/zenodo.3533422">https://doi.org/10.5281/zenodo.3533422</a></p>
Data sets accompanying "Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning"
<p>This data sets accompany the publication "Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning" in One Earth.</p> <ul> <li>lur-db-output-zenodo.xlsx: contains all land use requirement estimates for renewable energies reviewed in the publication. Meta-data is reported in one sheet, the other sheet contains the original data.</li> <li>authorship-tree-zenodo.xlsx: contains the information necessary to derive the authorship tree shown in the supplementary information. Meta-data is reported in one sheet, the other sheet contains the original data.<br><br><br></li> </ul>
Data Set for the Development and Testing of the MC23 Nonclassical-Energy Functional
<p>This dataset contains files used to train and test the Multi-Configuration 23 (MC23) functional and to compare the results to other methods. It includes files to carry out electronic structure calculations. These include molecular geometries in xyz format, <em>OpenMolcas</em> input files for CASSCF calculations, converged CASSCF natural orbitals, <em>OpenMolcas</em> basis set files, and <em>Gaussian 16</em> formatted checkpoint files for KS-DFT calculations. It also includes data used for data processing such as stoichiometries, absolute energies, and reference energies.</p> <p>Each file in this dataset is a .tar.xz archive. One can extract them by the following command:</p> <pre>tar -xJf name_of_archive.tar.xz</pre> <p>Below is a description of the content of each archive.</p> <p><strong>gaussian_16_fchk.tar.xz</strong> contains <em>Gaussian 16</em> formatted checkpoint files for all KS-DFT calculations used in this work. The files in the archive are named as <em>functional</em>/<em>database</em>/<em>system</em>.fchk</p> <p><strong>openmolcas_basis_set.tar.xz</strong> contains <em>OpenMolcas</em> basis set files used for multireference calculations. To reproduce the results in this work, the basis set files should be placed in the “basis_library” directory in the <em>OpenMolcas</em> installation location.</p> <p><strong>openmolcas_wave_function.tar.xz</strong> contains files needed by <em>OpenMolcas</em> to reproduce the CASSCF wave function used in this work. The files in the archive are named <em>database</em>/<em>system</em>.*.</p> <ul> <li>The file <em>system</em>.xyz contains the Cartesian coordinates. Note that for Data Set 2, the coordinates are in the input files <em>system</em>.inp.</li> <li>The file <em>system</em>.inp contains the <em>OpenMolca</em>s input file to perform CASSCF calculations.</li> <li>The files <em>system</em>.RasOrb, <em>system</em>.rasscf.h5, and <em>system</em>.rasscf.molden contain the converged CASSCF natural orbitals.</li> </ul> <p><strong>gaussian_16_stoichiometry_energy.tar.xz</strong> and <strong>openmolcas_stoichiometry_energy.tar.xz</strong> contain files used for data processing.</p> <ul> <li>Files with names like <em>database</em>.ref contain information used to calculate the final energies and errors. They are tab-delimited files. Each row represents an energy difference (e.g. atomization energy, barrier height, etc.). The first column contains the name of the energy difference (note: spaces may be present in this column). This is followed by the file names of each electronic structure calculation and the stoichiometries used to calculate the energy difference from the absolute energies. Each name or stoichiometry occupies one column. The second from the last column contains the reference value in kcal/mol. The reference values contain spin–orbit coupling. The last column contains the factor by which the final energy should be divided. This factor usually equals 1, but it can be greater than 1 for databases calculating atomization energies per bond or per atom.</li> <li>Files with names like <em>method</em>.elist contain the absolute energies of each electronic structure calculation. They are tab-delimited files. Each row represents an electronic structure calculation, and each row always contains two columns. The first column is the file name of the calculation in the format <em>database</em>/<em>system</em>. The second column is the absolute energy in atomic units extracted from the output file of electronic structure programs.</li> <li>The file named SOC.dat contains the spin–orbit coupling term in kcal/mol to be added to each electronic structure calculation prior to calculating energy differences. It has the same format as files with names like <em>method</em>.elist.</li> </ul> <p>The database names in the directory names use a slightly different convention than the ones in the article describing MC23. A prefix DS2_ or DS3_ is used to indicate the data set to which a database belongs, and the number of data points is removed from the database name. For example, the MR-MGN-BE8 database from Data Set 2 has a file name DS2_MR-MGN-BE.</p>
Raw data for High-speed shear mixing: a versatile energy-efficient ultra-fast strategy for solvent-free amine-functionalised solid CO2 adsorbents for direct air capture
<p><strong>Specification of affiliations:</strong></p> <ul> <li>Pavol Suly - Centre of Polymer Systems</li> <li>Barbora Hanulikova - Centre of Polymer Systems</li> <li>Abdulkadir Bozarslan - Centre of Polymer Systems</li> <li>Milan Masar - Centre of Polymer Systems</li> <li>Michal Urbanek - Centre of Polymer Systems</li> <li>Eva Domincova Bergerova - Centre of Polymer Systems</li> <li>Michal Machovsky - Centre of Polymer Systems</li> <li>Ivo Kuritka - Centre of Polymer Systems</li> </ul> <p> </p> <p>Raw data for the research paper. Information on the data collection are described in the manuscript. </p>
Safeguarding the energy transition against political backlash to carbon markets - figure raw data
<p>Figure raw data for the article "Safeguarding the energy transition against political backlash to carbon markets" (published in Nature Energy).</p>
Load Shifting Optimization with Genetic Algorithms for Energy Cost Minimization in Households - Case Study Data
<p>The case study of this dataset uses real household data, representing five days from 0h00 to 23h59. This dataset uses a period of 15 minutes for all loads execution time and energy data. The case study considers twenty unique houses that can have up to five different shiftable appliances, each executing three process cycles.<br> <br> File Description:</p> <ul> <li>Case_Studies_Data-BAU_and_Load_Shifting - Excel containing appliances energy profile, load execution preferences, BAU consumption, and other house data</li> <li>Houses_Input_JSONs - Zip containing the input files, from each house, for the proposed system</li> </ul>
Original Data for Manuscript "Energy and Momentum Distribution of Surface Plasmon-induced Hot Carriers Isolated via Spatiotemporal Separation"
<p>Raw data of the time-dependent energy density calculation and time-resolved photoemission electron microscopy (TR-PEEM) measurements used in the manuscript.</p> <p>A preprint of the manuscript is available on arXiv: <a href="https://arxiv.org/abs/2107.14277">2107.14277</a></p> <p>The manuscript was published in <em>ACS Nano</em> 2021, 15, 12, 19559-19569 <a href="https://doi.org/10.1021/acsnano.1c06586">10.1021/acsnano.1c06586</a></p> <p>The data is provided in hdf5 files, which were produced using the snomtools python package (<a href="https://github.com/hartelt/snomtools">availale on github</a>) and can be read with any <a href="https://support.hdfgroup.org/HDF5/tools5desc.html">hdf5 compatible software</a>. The calculated data was produced as described in Ref.1 with the parameters given in the manuscript. For the experimental data, each zip file contains the raw data (hdf5 Files) as well as the PEEM settings used (sav Files as output from the experiment control software) for the respective measurement.</p> <p>Parts of the manuscript that are generated from the calculated data [calculation_energy_density.zip]:</p> <ul> <li>Figure 2 A</li> <li>Figure S2</li> </ul> <p>Parts of the manuscript that are evaluated from the PEEM real space dataset [PEEM_realspace.zip]:</p> <ul> <li>Figure 2 B</li> <li>Figure 3</li> <li>Figure S1</li> <li>Figure S3</li> <li>Figure S4</li> <li>Movie S2 [timeseries_binned_fermi.avi] in the supplementary material</li> </ul> <p>Parts of the manuscript that are evaluated from the PEEM momentum space (momentum microscopy) SPP dataset [PEEM_k-space_timesteps_SPP.zip]:</p> <ul> <li>Figure 4 (in combination with the pump pulse reference dataset)</li> <li>Figure S5 B</li> <li>Figure S6 B</li> <li>Figure S7 B</li> </ul> <p>Parts of the manuscript that are evaluated from the PEEM momentum space (momentum microscopy) pump pulse reference dataset [PEEM_k-space_timesteps_pump.zip]:</p> <ul> <li>Figure 4 (in combination with the SPP dataset)</li> <li>Figure S5 A</li> <li>Figure S6 A</li> <li>Figure S7 A</li> </ul> <p>Parts of the manuscript that are evaluated from the PEEM momentum space (momentum microscopy) data of the full timetrace, combining SPP dataset [PEEM_k-space_full-timetrace_SPP.zip] and pump pulse reference dataset [PEEM_k-space_full-timetrace_pump.zip]:</p> <ul> <li>Figure S8</li> </ul>
The LSST Dark Energy Science Collaboration (DESC) Science Requirements Document v1 Released Data Products
<p>This tarball includes software and data products associated with the DESC Science Requirements Document (SRD) v1. See the "Executive Summary and User Guide" in the enclosed PDF of the DESC SRD for instructions on how to use and cite those products. The DESC SRD is described on <a href="https://arxiv.org/abs/1809.01669">arXiv</a> as follows:</p> <p>The Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration (DESC) will use five cosmological probes: galaxy clusters, large scale structure, supernovae, strong lensing, and weak lensing. The Science Requirements Document (SRD) quantifies the expected dark energy constraining power of these probes individually and together, with conservative assumptions about analysis methodology and follow-up observational resources based on our current understanding and the expected evolution within the field in the coming years. We then define requirements on analysis pipelines that will enable us to achieve our goal of carrying out a dark energy analysis consistent with the Dark Energy Task Force definition of a Stage IV dark energy experiment.</p>
Research Data and Software (re)Use Indications in High Energy Physics related Scholarly Works (Full dataset)
<p>This dataset contains research data and software (re)use indications (formal citations, informal mentions) in scholarly works related to High Energy Physics. 1,411 research and software indications were identified by a mix of approaches: use of citation discovery services and multiple search approaches in Google Scholar. The dataset contains indications by what approach the (re)use indications were found. All identified research data and software (re)use indications were classified according to their purpose, location, and elements.</p> <p>The data was collected in 2018 for a PhD thesis on research data and software (re)use indications in scholarly works.</p>
Identification of Research Data and Software (re)Use Indications in High Energy Physics related Scholarly Works (1951 - 2018)
<p>This dataset contains research data and software (re)use indications in High Energy Physics related scholarly works. A minimal random sample of scholarly works that contain highly processed research data was taken from HEPData and manually read for research data and software (re)use indications. The sample contains 368 works, which were published between 1951 and 2018.</p> <p>The data was collected in 2019 for a PhD thesis on research data and software (re)use indications in scholarly works.</p>
Experimental Data of Edge Energy Management System (EEMS)
<p>Readme>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>><br> This data repository contains the experimental data of low-voltage and <br> industrial settings. Separate folders are provided for both data artefacts.</p> <p>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>></p> <p>Low Voltage Residential Settings >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>><br> Different sub-Folders are provided with respect to the different <br> combination of electrical load</p> <p>Each folder contains .txt file</p> <p>This File Contains Timestamp such that date and time are separated by "!".<br> After parsing time, Voltage and Current are separated by the "Current".<br> Then each voltage and current waveforms can be further parsed by "@@@"<br> because non-consecutive waveform signals are separated using this symbol in <br> project. Furthermore, the variable slab during data acquisition is provided <br> at the end of .txt file and can be parsed using the term "Const"</p> <p>This data parsing process is also provided in all phase calculation files.</p> <p>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>></p> <p>Industrial Islanded Power System >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>><br> This Folder contains the data acquired from 15 diesel gensets. Their <br> operational parameters are logged in MySQL database and are presented using<br> LAMP server. This MySQL data is provided as CSV file in this folder.</p> <p>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>></p> <p> </p>
Optimal Dynamic Service Restoration of Distribution Networks Considering Energy Storage System Data
<p>The power distribution system presented is composed with 53 node and 61 branches and can be employed in multi-time service restoration problem, islading operation and energy storage system optimal operation. The system was designed based on a 53 node system (available <a href="https://ieee-dataport.org/documents/optimal-service-restoration-active-distribution-networks-considering-microgrid-formation">here</a>). The dataset was modified to include 6 photovoltaic generation, 3 energy storage system and time-changing demand load nodes.</p>
Supporting Information (software and data) for: Client-side energy and GHGs assessment of advertising and tracking in the news websites
<p>This is the open data and free/libre and open source software repository for the article "Client-side energy and GHGs assessment of advertising and tracking in the news websites" by Fabio Pesari, Giovanni Lagioia, Annarita Paiano.</p>
Data for: "Market Power and Price Exposure: Learning from Changes in Renewable Energy Regulation"
<p>Given the key role of renewable energies in current and future electricity markets, it is important to understand how they affect firms' pricing incentives in these markets. In this paper, we study whether renewables depress electricity market prices, and how this effect depends on their degree of market price exposure. Our theoretical analysis shows that paying renewables with fixed prices, rather than with market-based prices, is relatively more effective at curbing market power when the dominant electricity firms own large shares of the renewable capacity, and <em>vice-versa</em>. To test this prediction, our empirical analysis leverages several short-lived changes to renewable energy pricing mechanisms in the Spanish electricity market. In this context, we find that the switch from full price exposure to fixed prices caused a 2-4% reduction in the average price-cost markup.</p>
Data analysis of Maamela et al. 2023 The effect of temperature and dietary energy content on female maturation and egg nutritional content in Atlantic salmon
<p>This folder includes the data and R scripts used in the data analysis of the Maamela et al. 2023 paper in Journal of Fish Biology.</p>
NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies
<p><strong>NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies</strong></p> <p>This dataset contains the raw energy measurements as well as R scripts to reproduce the energy consumption plot for the corresponding paper.</p> <p>Each .csv file contains a specific set of measurements and we provide a script to read, process and plot the contained data.</p> <p><strong>Figure 3</strong></p> <p>Mean energy consumption of the different phases for Authentication for NB-IoT and LTE-M.</p> <p>Due to the fact that the duration of <em>Idle Connected</em> in the measurement scripts was 30 seconds and 60 seconds for <em>Idle Not Connected</em>, the D-value and the mean power consumption are divided by 2.</p> <ul> <li>Data – energy_measurements_fig3.csv</li> <li>Code – fig3.R</li> </ul> <p><strong>Figure 4</strong></p> <p>Mean energy consumption of the different phases for Data Connection and Download for NB-IoT and LTE-M for 1KB of data in HTTP.</p> <p>The delay between the measurements for Figure 4 were all 30 seconds long, but the identified <em>Standby</em> and <em>Idle</em> phases have different lengths. Therefore, the <em>Idle</em> phase values for both access technologies have been normalized and calculated for 20 seconds each.</p> <ul> <li>Data – energy_measurements_fig4.csv</li> <li>Code – fig4.R</li> </ul> <p><strong>Figure 5</strong></p> <p>Mean energy consumption of the different phases for Data Connection and Download for HTTP and MQTT for 1KB of data in NB-IoT.</p> <p>In this scenario the delay between the measurements were different again. For <em>MQTT</em> the delay was 150 seconds and for <em>HTTP</em> 30 seconds. Therefore, the data during the <em>Idle</em> and <em>Standby</em> (only for <em>MQTT</em>) phase is normalized and calculated for 20 seconds and 10 seconds, respectively. During the <em>MQTT</em> <em>Idle</em> phase measurements, the device disconnects. This is not taken into account for the evaluation, which is why these energy values are discarded for this figure.</p> <ul> <li>Data – energy_measurements_fig5.csv</li> <li>Code – fig5.R</li> </ul> <p><strong>Contact</strong></p> <p>For questions or issues with this code, please contact Viktoria Vomhoff (viktoria.vomhoff@uni-wuerzburg.de) or any of the authors of the related publication.</p>
Energy consumption data of office building and energy production data of a 185KW PV plant
<p>The dataset includes two-year monitoring data from the energy consumption of and office building located in center of Italy. The building has HVAC system, heat pumps for space heating /cooling (overall 120-140 KW load) and lighting subsystems controlled individually and/or overall by BMS.</p> <p>The building is a part of a small smart-grid which includes a PV plant (180KW). Energy production data are monitored and a two-year dataset is provided as well. </p>
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