Find research datasets worth reusing

NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies

NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies This dataset contains the raw energy measurements as well as R scripts to reproduce the energy consumption plot for the corresponding paper. Each .csv file contains a specific set of measurements and we provide a script to read, process and plot the contained data. Figure 3 Mean energy consumption of the different phases for Authentication for NB-IoT and LTE-M. Due to the fact that the duration of Idle Connected in the measurement scripts was 30 seconds and 60 seconds for Idle Not Connected, the D-value and the mean power consumption are divided by 2. <ul> <li>Data – energy_measurements_fig3.csv</li> <li>Code – fig3.R</li> </ul> Figure 4 Mean energy consumption of the different phases for Data Connection and Download for NB-IoT and LTE-M for 1KB of data in HTTP. The delay between the measurements for Figure 4 were all 30 seconds long, but the identified Standby and Idle phases have different lengths. Therefore, the Idle phase values for both access technologies have been normalized and calculated for 20 seconds each. <ul> <li>Data – energy_measurements_fig4.csv</li> <li>Code – fig4.R</li> </ul> Figure 5 Mean energy consumption of the different phases for Data Connection and Download for HTTP and MQTT for 1KB of data in NB-IoT. In this scenario the delay between the measurements were different again. For MQTT the delay was 150 seconds and for HTTP 30 seconds. Therefore, the data during the Idle and Standby (only for MQTT) phase is normalized and calculated for 20 seconds and 10 seconds, respectively. During the MQTT Idle 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. <ul> <li>Data – energy_measurements_fig5.csv</li> <li>Code – fig5.R</li> </ul> Contact 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.

opencc-by-4.0Nov 2022View details →

Free Energy Differences from Molecular Simulations: Exact Confidence Intervals from Transition Counts

Supporting data for Free Energy Differences from Molecular Simulations: Exact Confidence Intervals from Transition Counts Molecular simulations make it possible to predict equilibrium constants and corresponding free energy differences. For a system that exists in two states A and B, the equilibrium constant K can be predicted as K = t_B / t_A, where t_B and t_A are times spent in states B and A, respectively. The free energy can be calculated as Delta G = -kT log(K). Here we propose a new method for calculation of confidence intervals for K and Delta G. The ratio of the true value of K and estimated K follows the F-distribution with degrees of freedom df1 = number of B to A transitions and df2 = number of A to B transitions. This makes it possible to calculated the confidence interval of K solely from the number of transitions. The code in the directory errors was used to calculate Table 1 of the article. The code in the directory type1error was used to generate 10000 first time passage times for a transition from A to B and B to A as random numbers with exponential distribution. This was done for different combinations of number of transition and values of K. Number of confidence intervals not spanning the predefined value of K (type 1 errors) was expected to be 5 % for 95-% confidence intervals. This was in agreement with the result. The code in the directory type1errorodd was used to run similar experiment as type1error, but with number of A to B transitions higher than B to A by one. The code in the directory threestates was used to run similar experiment as type1error and  type1errorodd but for a system with three states A, B and C. The directory glycerol contains a trajectory, evolution of values of torsion angles and the code for analysis of the simulation of glycerol in water. The directory ffmp contains evolution of values of RMSD from the native structure, manual assignments of folded and unfolded states and the code for analysis of simulations of fast folding miniproteins (original data from Lindorf-Larsen et al. Science 2011, 334(6055) 517-520). The directory se contains the code for calculation of standard errors numerically and by the method presented in the article. The directory parallel presents the code for calculations supporting our method to calculate rate and equilibrium constants in parallel simulations. Codes written in R were executed using R version 3.4.4 by running: $ R –no-save < code.R > code.log File md5sums contains md5sum codes for all files.

Energy consumption data of office building and energy production data of a 185KW PV plant

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. 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.

opencc-by-4.0Nov 2022View details →

Sustainable recovery of critical elements from seawater saltworks bitterns by integration of high selective sorbents and reactive precipitation and crystallisation: Developing the probe of concept with on-site produced chemicals and energy

The availability of raw mineral resources containing elements included in the Critical Raw Materials (CRMs) list is a growing concern for the European Union. Sea mining has been identified as a promising secondary source. In particular, brines obtained in solar saltworks (bitterns) contain relevant amounts of valuable CRMs such as Mg(II), B(III), other alkaline/alkaline earth metals (Rb(I), Cs(I), Sr(II)) and transition/post-transition elements (Co(II), Ga(III), Ge(IV)). However, the low concentration of some of these elements (µg/L) requires an effort to develop recovery routes that are sustainable and economically feasible where the required chemicals and energy are produced on-site from the saltworks bitterns (i.e. HCl and NaOH). Even the conventional recovery processes such as ion exchange, sorption and precipitation, which have proved to be competitive for metals recovery, are challenged in the case of Trace Elements (TEs). This work studies the recovery of TEs included in the CRMs list from saltworks bitterns after ion exchange processes. First, batch crystallisation and reactive precipitation were tested for some target elements in single-component solutions: Sr(II), Co(II), Ga(III), Ge(IV) and B(III). Then, the experiments were carried out with multi-component synthetic solutions assuming different scenarios of bittern streams coming out a selective extraction stage using sorption and ion exchange processes. The targeted elements were recovered except for Ge(IV), where alternative routes need to be evaluated, as its precipitation involves the use of tannic acid or sulphide solutions that could not be produced from the bitterns. However, a further concentration step would be necessary to achieve element concentrations closer to the mineral phases saturation. Moreover, model simulations were performed using the PHREEQC program, which provided a good prediction of the experimental trends obtained in most cases.

Data files of the paper "Energy conversion by magnetic reconnection in multiple ion temperature plasmas"

Reconnection rate and energy budget files for the simulations used in for the paper "Energy conversion by magnetic reconnection in multiple ion temperature plasmas". The paper has two simulations. The first one, without cold ions, has his reconnection rate data stored in "rate_147.dat" and the energy budget data in "Ebudget_symm_nocold_Xframe.h5". The second one, with cold ions, has his reconnection rate data stored in "rate.dat" and the energy budget data in "Ebudget_symm_cold_Xframe.h5". On top of that is a datafile from simulation 1 at time 144.5 (corresponding to the time of the picture A in figure 1) with all the output fields from the simulation at this given time.

Dataset Related to "Evaluating the Energy Measurements of the IBM POWER9 On-Chip Controller"

Dataset (and programs used to create it) for the publication "Evaluating the Energy Measurements of the IBM POWER9 On-Chip Controller": Hannes Tröpgen, Mario Bielert, and Thomas Ilsche. 2023. Evaluating the Energy Measurements of the IBM POWER9 On-Chip Controller. In Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering (ICPE ’23), April 15–19, 2023, Coimbra, Portugal. ACM, New York, NY, USA, 10 pages. <a href="https://doi.org/10.1145/3578244.3583729">https://doi.org/10.1145/3578244.3583729</a> Find additional descriptions of the data in the included readme files.   This work is supported in part by the German National High Performance Computing (NHR@TUD). The authors are grateful to the Center for Information Services and High Performance Computing at TU Dresden for providing the Power9 Systems used in the measurements and the support during them.

Optical data for Cu2O, CuO and Cu in the 1 eV to ~ 100 eV energy range.

Our optical data in (J. Phys.: Condensed Matter.24 (2012) 175002;  DOI10.1088/0953-8984/24/17/175002) for Cu2O, CuO, and Cu in the 1 eV to ~ 100 eV energy range has received a strong and continued interest and we have had requests to share the data in digital form. Here we therefore make the data available for n and k in Fig. 8 of the paper in two Excel files.

Supporting data set for: On the challenge of obtaining an accurate solvation energy estimate in simulations of electrocatalysis

The data set generated for the article: "On the challenge of obtaining an accurate solvation energy estimate in simulations of electrocatalysis". Consists of subfolders for various sets of calculations. The data analysis procedure is shown in detail on <a href="https://bjk24.gitlab.io/bg-solvation/intro.html">this website</a>. If you want to peform the data analysis yourself, follow the instructions on the <a href="https://bjk24.gitlab.io/bg-solvation/docs/setup.html">setup page</a> of the website to download the repository, insert this data set into it, and run the Jupyter book.

LOFAR Carbon Footprint and Energy Consumption

The LOw Frequency ARray (LOFAR) is a European radio telescope operating since 2010 in the frequency bands 10 - 80 MHz and 110 - 250 MHz. This Excel model provides an analysis of the energy consumption and the carbon footprint of LOFAR. The analysis uses a Life Cycle Analysis following the Green House Gas protocol. Results include the footprint stemming from operations of all LOFAR stations and central processing. The impact of a number of typical science projects is analyzed as well. This model provides a transparent baseline to the sustainability of LOFAR and can serve as a blueprint for the analysis of other research infrastructures.

opencc-by-3.0-usMar 2023View details →

Supporting information for a multifidelity neural network formulation for molecular potential energy surfaces

This is a supplementary information for our paper titled "Multifidelity neural network formulations for prediction of quantum chemistry potential energy surfaces" Supplemental information includes two data files corresponding to the complete sets of low and high fidelity training data used in numerical experiments.  Format is JavaScript Object Notation (JSON). 1. low_fidelity_training_data.json contains 74000 records 2. high_fidelity_training_data.json contains 36988 records Each record consists of a numerical id ("id"), (x,y,z) position tuples ("geometry") for C5H5 ordered as 5 carbon atoms followed by 5 hydrogen atoms, and corresponding potential energy ("energy"). Source: normal mode sampling around 2 wells, 1 transition state, and a set of IRCs as depicted in Figure 2. Usage: subsets of this data were used as needed to define different data amounts and different subset randomizations in Figures 5 through 8.

opencc-by-4.0Mar 2021View details →

U.S. building energy efficiency and flexibility as an electric grid resource (Data and Code)

* New in Version 2.1 * <ul> <li> All residential measure savings shapes data (Latest_Res_Shapes.zip and residential measures in Latest_BM_Shapes.zip) were updated to correct post-processing errors present in version 2. </li> <li> The raw baseline-case data that are used in Scout to estimate sector-level baseline hourly loads (file <a href="https://github.com/trynthink/scout/blob/master/supporting_data/tsv_data/tsv_load.gz">tsv_load</a>) are now included in this data resource (see files Latest_Res_Baselines.zip and Latest_Com_Baselines.zip). </li> <li> Additional residential measure run documentation is available (<a href="https://github.com/NREL/resstock/blob/e2a98b7345d5c453ba35341b70af2f8859dd22fe/GEB_Potential.yml">here</a> for all except water heating efficiency plus flexibility (EE+DF) measure and <a href="https://github.com/NREL/resstock/blob/9611d92388e1e23466c9dc451e115c21321b4012/GEB_Potential_v2.5.0_appl_ee_dr.yml">here</a> for the water heating EE+DF measure). </li> <li>A guide to reading and/or preparing savings shapes CSVs is available <a href="https://scout-bto.readthedocs.io/_/downloads/en/latest/pdf/">in the Scout documentation</a>, p. 36. The documentation also summarizes the net system load conditions that measures with flexibility (DF) characteristics respond to (Table 1, p. 37).</li> </ul> * New in Version 2 * All hourly savings shapes CSV files that support the original <a href="https://doi.org/10.1016/j.joule.2021.06.002">analysis</a> have been updated to reflect the following improvements: <ul> <li> Generate residential data using ResStock v2.5.0 and commercial data using DOE Commercial Prototypes generated with OpenStudio v3.3.0. </li> <li> Residential and commercial measures with flexibility (DF) features respond to updated grid conditions (net peak/low load periods) that are consistent with projections from the EIA 2022 Annual Energy Outlook (AEO) “Low renewables cost” <a href="https://www.eia.gov/outlooks/aeo/tables_side_xls.php">side case</a>. </li> <li> Residential baseline loads and load savings are now distinguished by three building types (single family, multi family, and mobile homes). </li> </ul> Updated savings shape CSVs are organized into three ZIP files that may be separately downloaded depending on user interests: Latest_BM_Shapes.zip includes only the subset of savings shape CSVs needed to execute the <a href="https://doi.org/10.5281/zenodo.3158929">Scout Benchmark Scenarios</a>. Latest_Res_Shapes.zip includes all residential savings shape CSVs. Latest_Com_Shapes.zip includes all commercial savings shape CSVs. Baseline load shapes in Scout have also been updated based on the same versions of ResStock and the DOE Commercial Prototypes, and peak/take period impact calculations have been updated to reflect the 2022 AEO system conditions. These updated data are contained in <a href="https://github.com/trynthink/scout/releases/tag/v0.8">Scout v0.8</a> (see ./supporting_data/tsv_data). Summary of Original Data Files These data underpin an analysis of the near- and long-term technical potential bulk power grid resource offered by best available U.S. building efficiency and flexibility measures. Using multiple openly-available modeling frameworks supported by the U.S. Department of Energy, including <a href="https://scout.energy.gov/">Scout</a>, <a href="https://resstock.nrel.gov/">ResStock</a>, and the <a href="https://www.energycodes.gov/development/commercial/prototype_models">Commercial Building Prototype Models</a>, we pair bottom-up simulations of measures' building-level impacts with regional representations of the building stock and its projected electricity use to estimate the impacts of multiple building efficiency and flexibility scenarios on hourly regional system loads across the contiguous U.S. in 2030 and 2050. We find that demand-side management via building efficiency and flexibility could avoid up to nearly ⅓ of annual fossil-fired generation and ½ of fossil-fired capacity additions after 2020. Results are reported at both the national and regional scales and are disaggregated by building type and end use, facilitating a quantitative understanding of the role that buildings as a whole and specific building technologies or operational approaches can play in the future evolution of the U.S. electricity system. The four ZIP files that make up this data record are interpreted as follows: Measure_Data.zip: Includes the Scout energy conservation measure (ECM) JSON definitions that were used to generate the main baseline and efficient/flexible scenario results ("Baseline_Measures" and "Efficiency_Flexibility_Measures", respectively), as well as side cases that assess the sensitivity of results to higher levels of variable renewable penetration ("High_RE_Sensitivity_Analysis") and a high degree of building load electrification ("High_Electrification_Measures"). Each measure set includes supporting 8760 load savings shapes in the sub-folder "Savings_Shapes". Additional details about defining and interpreting Scout measures with time-sensitive analysis features are available <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#time-sensitive-valuation">here</a>. Results_Data.zip: Includes the main and side case results data. Baseline-case outcomes, which are consistent with the <a href="https://www.eia.gov/outlooks/archive/aeo19/">EIA 2019 Annual Energy Outlook</a>, are stored in "Baseline_Loads". Efficient/flexible scenario results are stored in "Efficiency_Flexibility_Measure_Impacts_Individual" and "Efficiency_Flexibility_Measure_Impacts_Portfolio," respectively, where the former includes results for individual measures in our analysis without considering any interactions across measures, and the latter includes results for aggregations of energy efficiency (EE), demand flexibility (DF), and efficiency and flexibility (EE+DF) portfolios that do consider interactions across measures in each portfolio. Results for the high electrification side case are stored in the "High_Electrification" sub-folder in the EE+DF case only. Results for the high renewable sensitivity analysis are stored in "High_RE_Sensitivity_Analysis", and residential and commercial 8760 savings shape outcomes for each of the EE, DF, and EE+DF measure portfolios and five of the 2019 EIA Electricity Market Module (EMM) <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">regions</a> (p.6) of focus are stored in "Sector_Level_8760s". Source_Code.zip: Includes the source code needed to translate the measure inputs provided in "Measures_Data.zip" into the outputs provided in "Results_Data.zip". The core set of files required to execute the main analysis results is stored in "Base_Code_Package", while variants to certain files in the core package needed to execute the high renewable sensitivity and high electrification side cases are stored in "Code_Variants". In general, the process of running an analysis is as described in the Scout <a href="https://scout-bto.readthedocs.io/en/latest/quick_start_guide.html">Quick Start Guide</a>; however, the file "ecm_prep_batch.py" should be substituted for "ecm_prep.py" and the file "run_batch.py" should be substituted for "run.py". These batch files execute multiple versions of "ecm_prep.py" and "run.py" that are tailored to generate individual measure and whole portfolio results for annual, net peak summer and winter, and net off-peak summer and winter metrics (individual measures: "ecm_prep.json," "ecm_prep_spa," "ecm_prep_wpa," "ecm_prep_sta," "ecm_prep_wta"; whole portfolio: "ecm_results.json," "ecm_results_spa.json," "ecm_results_wpa.json," and "ecm_results_sta.json," and "ecm_results_wta.json"). Results for the side cases are generated by replacing the versions of the "ecm_prep" and "run" files included in the "Base_Code_Package" folder with those in the "Code_Variants" folder. Sector-level 8760 shapes are generated using the "--sect_shapes" command line option as described <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#sector-level-hourly-energy-loads">here</a>. See Scout's <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#local-execution-tutorials">Local Execution Tutorials</a> for more details on how to develop Scout inputs and outputs. Supporting_Data.zip: Includes supplemental data files provided by EIA that describe key inputs and outputs to the <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">Electricity Market Module</a> in the AEO 2019 run of the National Energy Modeling System ("EIA EMM Data (AEO 2019)"), as well as raw EnergyPlus outputs that were used to develop the baseline Scout hourly load shape file found in "./Source_Code/Base_Code_Package/supporting_data/tsv_data/tsv_load.json".

The cost of movement: assessing energy expenditure in a long-distant ectothermic migrant under climate change

Functions to simulate monarch migration under set weather conditions. Data for repsirometry measurements and weather stations are also included in ZIP folders. Functions include working example of movement based on literature values for thresholds. Functions can be modified for other species as needed. Weather station data were collected from NOAA LCD stations. Alternative data sources include Wunderground Personal Weather Station datasets. However, Wunderground requires an API to access their data unless you have a PWS in their system. Connecting a PWS to wunderground provides you an API key for accessing data.

Photon Showers in a High Granularity Calorimeter with Varying Incident Energy and Angle

Dataset of photon showers in the Si-W ILD Electromagnetic calorimeter, consisting of 30 layers of active silicon sensors sandwiched between tungsten absorber layers. The incident energies vary uniformly in the range of 10-100 GeV, along with the incident angle which varies in the range of 90-30 degrees from the axis orthogonal to the calorimeter face. The incident point to the calorimeter face is fixed. The cells are projected to a regular grid of shape (z, x, y) = (30, 30, 60), where the z axis points into the calorimeter face, giving a total of 54k channels. In total, the file contains approximately 500k showers. The structure of the file is as follows: <ul> <li>Group name ecal <ul> <li>energy        : Dataset{500k, 1}</li> <li>theta          : Dataset{500k, 1}</li> <li>layers          : Dataset{500k, 30, 30, 60}</li> </ul> </li> </ul> The energy is the energy of the initial incident photon in units of GeV, theta is the incident angle of the incoming photon in units of radians and layers is the energy deposited in each cell in units of MeV

Virtual sensors for wind energy applications benchmark study data - preliminary version

Test version of the time series data for the wind energy virtual sensing benchmark study data.

The Long-term energy planning with highly detailed demand modelling for Egypt: an IOA-MAED-OSeMOSYS soft-linking approach

These files contain an updated model built for Egypt's power system as of 2023, with detailed demand simulation in 3 different scenarios; business as usual, high economic growth and industrial energy efficiency. Also, a multi region model built for Egypt, Sudan and Ethiopia power sector technologies for future cooperation scenarios.

Energy Saving from Reduced building Consumption in Valladolid city

Climate change can cause overheating in city centers, especially through the “heat island effect”. Green urban infrastructure can play a role in climate change adaptation through reducing air and surface temperature by providing shading and enhancing evapo-transpiration, which leads to energy and carbon savings from reduced building energy consumption especially in summer. On the other hand, insulating effect of plants reduces heating energy consumption and associated carbon emissions in winter.  This indicator was calculated for the UrbanGreenUP monitoring program.

Core-Hole Spectroscopy of Energy Conversion and Storage Related-Phosphorus Compounds Using Soft and Hard X rays

Dear reader,   Please find attached the input files (.xml) and their corresponding outputs for the Exciting calculations of the imaginary component of the dielectric tensor ("XAS".dat) for: InP, GaP, red P and InPO4 which have been used in our publication:"Core-Hole Spectroscopy of Energy Conversion and Storage Related-Phosphorus Compounds Using Soft and Hard X rays".

The Energy consumption and Carbon Footprint of the LOFAR Telescope V2.0

The LOw Frequency ARray (LOFAR) is a European radio telescope operating since 2010 in the frequency bands 10 - 80 MHz and 110 - 250 MHz. This article provides an analysis of the energy consumption and the carbon footprint of LOFAR. The approach used is a Life Cycle Analysis (LCA). We find that one year of LOFAR operations requires 3,627 MWh of electricity, 48,714 m3 gas and 135,497 liters of fuel. The associated carbon emission is 2,624 tCO2e/year. Results include the footprint stemming from operations of all LOFAR stations and central processing, but exclude scientific post-processing and activities. The potential recovery of  embodied footprint in construction materials at the end of life equals 17%. The electrical energy required for scientific processing is assessed separately. It ranges from 1% (standard The Energy Consumption and Carbon Footprint of the LOFAR Telescope imaging and time-domain), to 40% (wide field long baseline imaging) of the energy consumption for the observation. The outcome provides a transparent baseline in making LOFAR more sustainable and can serve as a blueprint for the analysis of other research  infrastructures.

Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - sample treated_213nm

RHEED intensity image dataset of sample "treated_213nm" in work "Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."