Find research datasets worth reusing

Parametric exploration of zero-energy modes in three-terminal InSb-Al nanowire devices

Files inlcude 1) Raw data for all figure 2) data process file 3) Generated figures

opencc-by-4.0Oct 2021View details →

An effective solution to boost generation from waves: benefits of HESS integration to wave energy converter in grid-connected systems

The dataset here provided concerns the final results and other data for the three studied cases listed in the paper obtained from the simulations carried out in Simulink environment. The data can be opened by means of the free software GNU Octave, that can be downloaded at the following link: https://www.gnu.org/software/octave/index

Source molecular simulation data for calculating energy and friction profiles and permeability coefficients through model lipid membranes

Energy files from GROMACS molecular dynamics simulations with enhanced free energy sampling contain time-dependent evolution of the free energy profiles and friction profiles (and other energies and simulation properties) that were used for calculating permeability coefficients in the publication https://www.biorxiv.org/content/10.1101/2021.07.16.452599v1 Simulation system contains a lipid POPC or DPPC bilayer with a varying amount of cholesterol (specified as mol% in the file name). Hydrophobic level of the permeating particle is specified as "level-I", "level-II" etc. When unspecified in the file name, the particle is hydrophobic level "III". Lipids D-C14-PC denote PC lipids with both tails monounsaturated of length 14 carbon atoms. DOPC is equivalent to D-C18-PC. (Detailed description in the publication) Adaptive Weighted Histogram (AWH) method was used to sample the free energy profile of translocating small molecule through the lipid bilayer. GROMACS tool `gmx awh` reads the files and provides the described profiles. Files were generated by GROMACS `mdrun` simulation engine version 2019.3.   Coarse-grained MARTINI 3.0 model was used for modeling the biomolecular interactions. Scripts to perform the simulations and the files with initial configurations and simulation settings are stored in a public GitHub repository depozited on Zenodo.org: <a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.   Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015). Lindahl, V., Lidmar, J. & Hess, B. Accelerated weight histogram method for exploring free energy landscapes. J. Chem. Phys. 141, 044110 (2014). Souza, P. C. T. et al. Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat. Methods 18, 382–388 (2021). Melcr, J. Git repository with analysis scripts for MD simulations of permeability through lipid membranes. (2021) doi:<a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.

Physiological costs of facultative endosymbionts in aphids assessed from energy metabolism

Using the standard metabolic rate (SMR) as a measure of the energy cost of self-maintenance, we investigated whether two common facultative endosymbionts Hamiltonella defensa or Regiella insecticola increase the maintenance cost of the pea aphid Acyrthosiphon pisum and translate into host fitness reduction (‘compensation hypothesis’). In addition, we tested if there was a link between SMR and the aphid fitness and whether it depended on endosymbiont density and aphid energetic reserves. Finally, we measured SMR at different temperatures to assess the impact of suboptimal thermal conditions on physiological cost of endosymbionts.

Optimal Control of Renewable Energy Communities with Controllable Assets: consumption and production profiles

consumption and production profiles for cases I and II used for computing simulations in Optimal Control of Renewable Energy Communities with Controllable Assets

On the scale dependence in the dynamics of frictional rupture: constant fracture energy versus size-dependent breakdown work

<pre>Potential energy stored during the inter-seismic period by tectonic loading around faults is released during earthquakes as radiated energy, frictional dissipation and fracture energy. The latter is of first importance since it is expected to control the nucleation, the propagation and the arrest of the seismic rupture. On one side, the seismological fracture energy estimated for natural earthquakes (commonly called breakdown work) ranges between 1 $\mathrm{J/m^2}$ and tens of $ \mathrm{MJ/m^2} $ for the largest events, and shows a clear slip dependence. On the other side, recent experimental studies highlighted that, concerning rupture experiments, fracture energy is a material property (energy required to break the fault interface) independently of the size of the event, i.e. of the seismic slip. </pre> <pre>To reconcile these contradictory observations and definitions, we performed stick-slip experiments, as analog for earthquakes, in a bi-axial shear configuration. We estimated fracture energy through both Linear Elastic Fracture Mechanics (LEFM) and a Cohesive Zone Model (CZM) and through the integration of the near-fault stress-slip evolution. We show that, at the scale of our experiments, fault weakening is divided into a near-tip weakening, corresponding to an energy of few $ \mathrm{J/m^2} $, consistent with the one estimated through LEFM and CZM, and a long-tailed weakening corresponding to a larger energy not localized at the rupture tip, increasing with slip.</pre> <pre>Through numerical simulations, we demonstrate that only near-tip weakening controls the rupture initiation and that long-tailed weakening can enhance slip during rupture propagation and allow the rupture to overcome stress heterogeneity along the fault. We conclude that the origin of the seismological estimates of breakdown work could be related to the energy dissipated in the long-tailed weakening rather than to the one dissipated near the tip.</pre>

opencc-by-4.0Mar 2022View details →

Data accompanying the paper "Stable Numerical Implementation of a Turbulence Scheme with Two Prognostic Turbulence Energies".

Data accompanying the paper "Stable Numerical Implementation of a Turbulence Scheme with Two Prognostic Turbulence Energies", submitted to Monthly Weather Review. This repository contains results from the idealized simulations of GABLS1 case, obtained by the OpenIFS single column model version with the TOUCANS two-energy turbulence scheme. Each simulation has its own folder, containing single NetCDF file 'progvar.nc'. Experimental settings, explained in the paper, are following: <pre><code>----------------------------------------------------------------- folder timestep delta beta beta_tau [s] [1] [1] [1] ----------------------------------------------------------------- GABLS1_2TE-R_dt001s_delta00_bt15/ 1 0.00 1.0 1.5 GABLS1_2TE-R_dt045s_delta00_bt15/ 45 0.00 1.0 1.5 GABLS1_2TE-R_dt090s_delta00_bt15/ 90 0.00 1.0 1.5 ----------------------------------------------------------------- GABLS1_2TE-R_dt090s_delta25_bt15/ 90 0.25 1.0 1.5 GABLS1_2TE-R_dt090s_delta25_bt10/ 90 0.25 1.0 1.0 GABLS1_2TE-R_dt180s_delta25_bt10/ 180 0.25 1.0 1.0 -----------------------------------------------------------------</code></pre> Names of the NetCDF fields examined in the paper are: <pre><code>tke - turbulence kinetic energy [J/kg] tte - turbulence total energy [J/kg] efb3 - turbulent heat flux [W/m^2] t - thermodynamic temperature [K] </code></pre> They are all 2D fields, indexed by the timestep number and the model level. Turbulent heat flux is given on the model half levels, remaining quantities on the model full levels. Heights of the model levels are stored in the NetCDF fields: <pre><code>height_f - full level heights [m] height_h - half level heights [m] </code></pre> Simulation time in seconds is stored in the NetCDF field 'time'. CAUTION: Since the simulations were run with the prescribed forcings and only with the turbulence scheme activated, some fields in NetCDF files are meaningless (e.g. skin temperature).

opencc-by-4.0Dec 2021View details →

Data of manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" submitted to Solar Energy

This is the data corresponding to manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" by Kreuwel et al., 2022, submitted to Solar Energy.   The file `basic_stats.tar.gz` contains a broad set of standard statistics of surface meteorology and vertical profiles. The file `sw_flux_dn_xy.tar.gz` contains spatial cross sections of downwelling shortwave radiation.

opencc-by-4.0Mar 2022View details →

Dataset for "Long-term implications of reduced gas imports on the decarbonization of the European energy system"

A dataset containing results for the paper "Long-term implications of reduced gas imports on the decarbonization of the European energy system". See also the associated Github repository: https://github.com/TimToernes/Reduced-gas-imports

opencc-by-4.0Jan 2019View details →

Dance Your Ph.D. 2018 Physics FINALIST: Dark energy and dark matter via probabilistic data analysis

Dark matter (dancer in black mask; brings dancers together) and dark energy (dancer in black mask; pulls dancers apart) are the mysterious constituents of the universe that determine the evolution of the largest structures, clusters of galaxies (dancers in tie dye), and I (dancer in gold mask) want to learn about them them by analyzing a huge set of 3D galaxy positions. The telescope (dancer in silver mask) gives me 2D images of galaxies with no distances, so I have to estimate galaxy locations with "redshift," which is correlated with distance. The exciting telescopes of the next decades will build large samples of galaxies by taking lots of superficial color data that's not informative enough to determine the redshift. One thing that tells us about dark matter and dark energy is the redshift distribution (dancer in red stripes) -- I can still learn about dark energy and dark matter if I can figure out how many galaxies have each redshift. Since I don't actually know the redshifts of any galaxies, I consider the probability of redshift (dancers in white shirts) for each galaxy based on its limited color data. However, the redshift probabilities are biased by prior beliefs (dancer in gray scarf) about physics that are transmitted by the way we derive the probabilities from the galaxy colors. If unaccounted for, the prior beliefs could cause us to draw the wrong conclusions about dark matter and dark energy, meaning we won't learn anything from all this amazing telescope data we're going to have. For my thesis, I developed a method that puts the prior beliefs in their proper place and lets the data guide the inference of the redshift distribution, using an approach called Bayesian statistics. I audition many possible redshift distributions and check how likely each is to have caused the observed galaxy colors, with the redshift probabilities as the judges; this procedure is called Markov chain Monte Carlo (MCMC) sampling, and it leads to an ensemble of possible redshift distributions each with an associated probability of having produced the observed galaxy colors. By doing the math carefully and correctly, we will learn more about dark matter and dark energy even with the coarsest data!

Relative binding free energy between chemically distant compounds using a bidirectional non-equilibrium approach

Data from "Relative binding free energy between chemically distant compounds using a bidirectional nonequilibrium approach" Submitted to J Chem Theory Comput, February  2022. This archive contains the following directories: traj -> This directory contains trajectory files for SAMPL9:         g??_w.pdb.gz file contains ~ 160 snapshots from the 48 ns HREM sampling         of the target bound state of G1-G13 including water solvent.         g??_1.pdb.gz file contains ~ 3500 snapshots (no solvent included) of the     target bound state of G1-G13 work -> This trajectory contains the work data (in kJ/mol) obtained in         the NE trajectories for SAMPL9:     gxx-gyy_b_TIME.wrk is the work sample obtained for the xx->yy transmutation         in the bound state with a duration time of TIME.     gxx-gyy_u_TIME.wrk is the work sample obtained for the xx->yy transmutation         in the unbound state with a duration time of TIME. pdb -> This directory contains the pdb initial structures of the G1-G18 guests and the WP6 host ff ->  This directory contains the force field specification for SAMPL9:     the PrimaDORAC generated (http://www1.chim.unifi.it/orac/primadorac/)         tpg and prm files for the guests and the host in orac format.  (see         http://ftp.chim.unifi.it/orac/MAN/orac-manual.html) bin -> This directory contains the script files to compute all bidirectional and unidirectional RBFE estimates as reported in Table 2 of the paper.        In order to compute an RBFE using the work data in the work dir, do the following:        1) cd into the bin directory        2) issue the command              source source_this_file.bash       (N.B.: gfortran must be installed)        3) cd ../        4) from the main dir, issue the command:              RBFE.bash -b 720 -u 360 B A           where  B and A are the ghost and physical compound, respectively        Results for DG(A->B) are printed to the standard output        Example:                     RBFE.bash -b 720 -u 360 g01 g03 > g03g01       In the g03g01 file, estimates and properties of the work data are       printed in the format      "g05->g02   DG_bar=   -5.13   0.37  DG_ff_G=   -4.2   0.8  DG_ff_J=   -3.0   0.4  sig_AB_u=  0.90 sig_AB_b=  2.16 ADT_AB_u=  0.26 ADT_AB_b=  0.23 DG_rr_G=  130.5  33.5  DG_rr_J=   -7.2   0.8  sig_BA_u=  1.10 sig_BA_b= 13.61 ADT_BA_u=  0.48 ADT_BA_b=  3.40  DG_fr_G=  -4.27  1.04  DG_fr_J=  -3.23  0.58 bias_fr=  0.2  DG_rf_G= 127.88 30.30  DG_rf_J=  -7.16  0.66 bias_rf=  0.0 DG_BAR=  -5.26 0.0 tb=  720 tu=  360"      To compute all DDG estimates of Table 2 launch the script      "do_all.bash" from the main dir         shift-pot -> This directory contains two gnuplot scripts showing the evolution of the       Beutler LJ and elec soft-core potentials (soft.gplt)  and of the shifted LJ and       elec soft-core potentials used in this work  (shift.gplt)

opencc-by-4.0Mar 2022View details →

Transport-Energy Database: Laos

This Transport-Energy Database (TED) covers technology, energy demand and policies relating to the transport-energy system in Laos. This Transport-Energy Database (TED) was compiled by Latsayakone Pholsena funded by the Climate Compatible Growth (CCG) programme. Data sources are linked where possible. This dataset was compiled as part of the CCG programme for future work in constructing transport-energy decarbonisation narratives for partner countries, including Laos.

opencc-by-4.0Dec 2021View details →

Transport-Energy Database: Kenya

This Transport-Energy Database (TED) covers technology, energy demand and policies relating to the transport-energy system in Kenya. It was compiled using data sources from the IEA, World Bank, Kenyan Government, EnergyData.info and the TraCs project (https://www.giz.de/en/worldwide/40579.html). All data sources are linked where possible. Data from the Kenyan Statistical Abstract and the TraCs data were manually transcribed from their sources. This dataset was compiled as part of the Climate Compatible Growth (CCG) programme for future work in constructing transport-energy decarbonisation narratives for partner countries, including Kenya. The data was validated in March/April 2022 by Nam Lolwe Environmental Services.

Energy Market Transformation Survey

This report presents a survey performed with stakeholders and end-users under the scope of PARITY project. The survey explores the overall patterns that will form the energy market transformation and the consumer's perspective. The survey aims to evaluate participants’ opinions on the concepts of LEM and LFM, to identify factors and barriers to the progress of local renewable energy development based on the participants’ knowledge and what would motivate people to participate in such markets. Additionally, the goal of this survey is to analyse the current market needs, as well as investigate what is driving the energy markets transformation and where it is leading.

Data Archive for "Acceleration as a proxy for energy expenditure in a facultative-soaring bird: comparing dynamic body acceleration and time-energy budgets to heart rate"

Heart rate, acceleration, and respirometry data from four wild-caught gulls during climate chamber and treadmill calibration measurements (2018), as well as heart rate and acceleration data from five free-ranging gulls from a colony on Texel, NL during the breeding season (May - July, 2019).

Effects of water source and technology on energy use and environmental impacts of rice production in northern Iran

To analyze the energy flow and greenhouse gas emissions, a total of 200 paddy fields were selected based on the water source utilized for irrigation (river as the surface water source and well as the underground water source), transplanting methods (traditional and mechanical) and grain yield (low- and high-yielding rice cultivars) as a factorial experimental design (2×2×2=8 conditions). Data were collected in 2020 from 200 rice growers in northern Iran using a face-to-face questionnaire survey.

openother-openApr 2022View details →

Strategic low-cost energy investment opportunities and challenges towards achieving universal electricity access (SDG7) in forty-eight African nations.

This dataset underpins the study "Strategic low-cost energy investment opportunities and challenges towards achieving universal electricity access (SDG7) in forty-eight African nations". The study provides insights into energy supply and demand, power generation, investments and total system costs, SD7 indicators, job creation as well as carbon dioxide emissions for each African nation (48 in total). An energy systems model enhanced with geospatial data was developed to evaluate energy supply requirements to cover the energy needs of the African continent during the period 2015-2030 and achieve universal access by 2030. The model was developed using the open-source modeling system for long-term energy planning OSeMOSYS and the geospatial electrification outlook (GEP). The objective function is to minimise the total energy system costs.  The results can be found  https://doi.org/10.5281/zenodo.6468262

Strategic low-cost energy investment opportunities and challenges towards achieving universal electricity access (SDG7) in forty-eight African nations

The attached modeling results underpin the study "Strategic low-cost energy investment opportunities and challenges towards achieving universal electricity access (SDG7) in forty-eight African nations". The study provides insights into energy supply and demand, power generation, investments and total system costs, SD7 indicators, job creation as well as carbon dioxide emissions for each African nation (48 in total). An energy systems model enhanced with geospatial data was developed to evaluate energy supply requirements to cover the energy needs of the African continent during the period 2015-2030 and achieve universal access by 2030. The model was developed using the open-source modeling system for long-term energy planning OSeMOSYS and the geospatial electrification outlook (GEP). The objective function is to minimise the total energy system costs.  The TEMBA model produces aggregate energy, and detailed power system results in each country in the African continent. The power sector results are also reported with power pool aggregation. The OSeMOSYS model and input data used to produce these results can be found at JoPapp/jrc_temba: v1.0.2 [Data set]. Zenodo.https://doi.org/10.5281/zenodo.6468278 (Authors: Ioannis Pappis. (2021)).

opencc-by-4.0Sep 2021View details →

An Innovative Thermo-Energy Harvesting Module for Asphalt Roadway Pavement

The importance of green technologies for generating renewable energy and sustainable development is widely accepted. Road surfaces are exposed to solar radiation that generates thermal gradients and heat flow in the pavement layers. The heat stored can be harvested providing an untapped source of renewable energy. This report presents the design, construction, and assessment of an improved thermoelectric energy prototype for harvesting heat energy from roadway pavements. To accomplish this, various prototype designs were simulated using Finite Element (FE) analysis, followed by design construction and laboratory testing of the most promising prototypes to evaluate their power harvesting capabilities. The main design components of these prototypes are a heat collector/transfer plate, thermoelectric generators (TEG), and a cooling module consisting of a heat sink, phase change material, and an insulation box. The results suggest a direct relationship between thermal gradients and power generation and point out the importance of the cooling module in maintaining the efficiency of the harvester. An optimum harvester design would generate an average power output of 29 mWatt or 835 J over 8 hours per day in South Texas. Extrapolating this output for an installation that covers a length of 1 kilometer of a roadway could produce an average of 23.2 kWh/day, which appears to be a promising independent source of power for roadside signage and sensors.

Co-LivEn : COllective LIVing ENergy Dataset

The dataset contains detailed electricity measurements of appliances in a collective living  (co-living) student apartment at KTH Live-in-Lab. The measurements included are RMS voltage, RMS current, real power, and power factor. The data was collected over a period of 277 days between 28th August 2020 and 31st May 2021 with 1 second time resolution. The compressed file "<a href="../api/files/a7c4dc9b-99f1-4497-ae6c-7a1c2cb1b669/appliance_csv.zip">appliance_csv.zip</a>" contains data in plain CSV file format, whereas "<a href="../api/files/a7c4dc9b-99f1-4497-ae6c-7a1c2cb1b669/appliance_mat.zip">appliance_mat.zip</a>" contains data in MATLAB file format.  For more detailed information and insights into the dataset and the data collection process, refer to the following article. Kindly cite this when using the dataset. <ul> <li>R. R. Avula, T. J. Oechtering and D. Månsson, "Adversarial Inference Control in Cyber-Physical Systems: A Bayesian Approach With Application to Smart Meters," in IEEE Access, vol. 12, pp. 24933-24948, 2024, doi: 10.1109/ACCESS.2024.3365270. </li> </ul>