Find research datasets worth reusing

opencc-by-4.0Apr 2020View details →

ARMOR and NALMA data corresponding to "Observations of anomalous charge structures in supercell thunderstorms in the Southeastern United States"

Dataset includes dual-polarization C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR) data in Raw and quality-controlled Universal Format (UF) from a selected period on 10 April 2009 as well as the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) data in American Standard Code for Information Interchange (ASCII) format from selected period on 10 April 2009.  The ARMOR is located at the Huntsville International Airport in Huntsville, Alabama at 34.64597, -86.77131, 200 m MSL. A set of 15 radar sampling volumes between 1712 UTC and 1821 UTC on 10 April 2009 are included in the dataset. Each of the raw and corrected UF files contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s-1), spectrum width (m s-1), differential phase (°), and total power (dBZ) data. The corrected UF files additionally contain horizontal reflectivity and differential reflectivity data corrected for attenuation and differential attenuation following the methods of Bringi et al. (2001). The corrected files also contain estimated differential propagation phase (°) and computed specific differential phase (° km-1) data (Hubbert and Bringi 1995).    ARMOR file naming conventions are as follows:    RAW_NA_000_125_20090410171216.gz RAW: file format 125: can scan type, where 125 indicates a full or sector volume plan position indicator  20090410171216: date and time in the order of year, month, day, hour, minute, and second   ARMOR_20090410171216_qc1.uf.gz ARMOR: radar name 20090410171216: date and time in the order of year (YYYY), month (MM), day (DD), hour (HH), minute (MM), and second (SS) qc1: denotes ARMOR processed data uf: denotes the file format    NALMA data consist of undecimated VHF source-level lightning measurements in hourly files. The center of the network is located at 34.72461, -86.64533. The network consisted of 11 sensors distributed throughout north Alabama and south-central Tennessee. Information about contributing stations is available in the header of each hourly file, including the station location, status, and the number of sources detected by each station. Further network-specific information documented by Koshak et al. (2004) while Rison et al. (1999) discuss LMA characteristics. Source data include information about the time the source was detected (UTC seconds of the day), latitude and longitude (decimal degrees), altitude (m), reduced chi2 value associated with post-processing (unitless), power (dBW), and a network mask indicating the detecting stations (unitless). The format is (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x).    Hourly file naming conventions are as follows:   LYLOUT_090410_160000_3600.dat.gz LYLOUT: LMA file designator 090410: date in order of last two digits of year (YY), month (MM), and day (DD) 160000: time in order of hour (HH), minute (MM), and second (SS) 3600: length of period covered in file in seconds (3600 s = 1 hour)   Acknowledgments:  Data were collected with support from NASA MSFC Award NNM05AA22A.   References: Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. IEEE Transactions on Geoscience and Remote Sensing, 39(9), 1906–1915. https://doi.org/10.1109/36.951081 Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. Journal of Atmospheric and Oceanic Technology, 12, 643–648.  Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. Journal of Atmospheric and Oceanic Technology, 21(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2 Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. Geophysical Research Letters, 26(23), 3573–3576.

opencc-by-4.0Jul 2020View details →

ARMOR and NALMA data corresponding to 2008 storms analyzed in "Examining conditions supporting the development of anomalous charge structures in supercell thunderstorms in the Southeastern United States"

Total lightning and dual-polarization Doppler velocity data are available from the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) and the C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR), respectively, over selected periods on 6 February 2008 and 11 April 2008. NALMA data are provided in American Standard Code for Information Interchange (ASCII) format and ARMOR data are provided in Raw and quality-controlled Universal Format (UF), where quality control methods are described below.    The NALMA data are provided in hourly files which include undecimated point location (source-level) data corresponding to the detection of very high frequency (VHF) radiation emitted during the breakdown of lightning (Rison et al., 1999; Thomas et al., 2001). Source locations were reported from active sensors configured in an 11-sensor array distributed throughout North Alabama and South Central Tennessee, the center of which is located at 34.72641, -86.64533 (Koshak et al. 2004).  Data files include information on the time that each source was detected (UTC seconds of the day), the latitude, longitude, and altitude of each source’s location (decimal degrees and m, respectively), the reduced chi2 value associated with data processing (unitless), a station mask indicating which sensors contributed to the resolved location of each source (unitless). These data are provided in a line-by-line format of (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x). The 2008 data files additionally include a header section that provides further information about each sensor in the network and its relative contribution to the dataset.    The hourly fine naming conventions are as follows for the February 2008 data: LMA_NA_6.2_125_2008-02-06_10-00-00.dat.gz LMA_NA: LMA file designator corresponding to the NALMA 2008-02-06: year (YYYY)-month (MM)-day (DD) 10-00-00: UTC time, (HH)-minute (MM)-second (SS)   And for the April 2008 data: LYLOUT_080411_180000_3600.dat.gz LYLOUT: LMA file designator 080411: date in order of last two digits of year (YY), month (MM), and day (DD) 180000: UTC time in order of hour (HH), minute (MM), and second (SS) 3600: length of period covered in file in seconds (3600 s = 1 hour)   ARMOR data are provided as sets of 14 (14) sampling volumes corresponding to the 6 February 2008 (11 April 2008) periods between 1002 UTC and 1119 UTC (1844 UTC and 1952 UTC). Each RAW and processed UF file contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s-1), spectrum width (m s-1), differential phase (º), and total power (dBZ) data. Horizontal reflectivity and differential reflectivity data were corrected for attenuation and differential attenuation, differential propagation phase (º) was estimated, and specific differential phase (º km-1) was calculated during post-processing (Hubbert and Bringi 1995, Bringi et al. 2001).   Acknowledgments:  NALMA data were collected with support from NASA MSFC Award NNM05AA22A.   References: Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. IEEE Transactions on Geoscience and Remote Sensing, 39(9), 1906–1915. https://doi.org/10.1109/36.951081 Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. Journal of Atmospheric and Oceanic Technology, 12, 643–648.  Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. Journal of Atmospheric and Oceanic Technology, 21(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2 Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. Geophysical Research Letters, 26(23), 3573–3576. Thomas, R. J., Krehbiel, P. R., Hamlin, T., Harlin, J., & Shown, D. (2001). Observations of VHF source powers radiated by lightning. Geophysical Research Letters, 28(1), 143–146. https://doi.org/10.1029/2000GL011464

opencc-by-nc-4.0Jul 2022View details →

Code and measurement data - State of charge and state of health diagnosis of batteries with voltage-controlled models

This dataset contains the research data (code and measurement data) of the journal article: <a href="https://doi.org/10.1016/j.jpowsour.2022.231828">J. A. Braun, R. Behmann, D. Schmider, W. G. Bessler, "State of charge and state of health diagnosis of batteries with voltage-controlled models", Journal of Power Sources 544 (2022), 231828</a>.   Abstract: The accurate diagnosis of state of charge (SOC) and state of health (SOH) is of utmost importance for battery users and for battery manufacturers. State diagnosis is commonly based on measuring battery current and using it in Coulomb counters or as input for a current-controlled model. Here we introduce a new algorithm based on measuring battery voltage and using it as input for a voltage-controlled model. We demonstrate the algorithm using fresh and pre-aged lithium-ion battery single cells operated under well-defined laboratory conditions on full cycles, shallow cycles, and a dynamic battery electric vehicle load profile. We show that both SOC and SOH are accurately estimated using a simple equivalent circuit model. The new algorithm is self-calibrating, is robust with respect to cell aging, allows to estimate SOH from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.   Intellectual property information: The Matlab codes and the research data provided here are under <a href="https://creativecommons.org/licenses/by-nc/4.0/legalcode">CC-BY-NC-4.0</a> license. Please note that the algorithms themselves are subject to industrial property rights, including, but not necessarily limited to, German patent <a href="https://patents.google.com/patent/DE102019127828B4/en">DE102019127828B4</a> and international patent application <a href="https://patents.google.com/patent/WO2021073690A2/en">WO2021073690A2</a>. Any use of the codes and algorithms presented here is subject to these property rights.   Overview of files: SOC_SOH_simple_model.m: Matlab script performing SOC and SOH diagnosis with the voltage-controlled "simple" equivalent circuit model. The script also reproduces the figures shown in the manuscript. SOC_SOH_simple_extended.m: Matlab script performing SOC and SOH diagnosis with the voltage-controlled "extended" equivalent circuit model. The script also creates figures of additional data not shown in the manuscript. Experimental_data_fresh_cell.csv: Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (99 h total with 1 s resolution) of a fresh lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling. Experimental_data_aged_cell.csv: Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (85 h total with 1 s resolution) of a pre-aged lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling. OCV_vs_SOC_curve.csv: Tabulated experimentally-derived open-circuit voltage (OCV) as function of state of charge (SOC). 1001 data points between SOC = 0 and SOC = 1 in increments of 0.001. readme.txt: Overview of files with a short description.

embargoedcc-by-4.0Aug 2024View details →

Dataset of "Comprehensive Machine Learning Approaches for Modelling the State of Charge of Lithium-ion Batteries"

This paper evaluates three ML approaches for SOC modeling in LIBs: the multilayer perceptron (MLP), long short-term memory (LSTM), and the nonlinear autoregressive with exogenous input (NARX) neural network architectures. These models were tested using an experimental dataset with multiple input variables, including electrochemical impedance spectroscopy (EIS) data, voltage, and capacity readings for commercial LIB cells. Results indicate that MLP and LSTM are more adaptable with a smaller training dataset (14 samples), while the NARX model required more than 34 out of 67 samples to achieve reasonable accuracy. Additionally, the NARX model is more sensitive to changes in the learning rate (α) and exhibits larger output error deviations. The MLP and LSTM models consistently performed well across various hidden layer sizes, showing no upper bound constraints, whereas the NARX model’s performance deteriorated with certain hidden layer configurations.

opencc-by-4.0Aug 2021View details →

Supporting data for "Dispersive sensing of charge states in a bilayer graphene quantum dot"

Supporting data and analysis scripts for all figures in the article "Dispersive sensing of charge states in a bilayer graphene quantum dot", Appl. Phys. Lett. 118, 093104 (2021); <a href="https://doi.org/10.1063/5.0040234">https://doi.org/10.1063/5.0040234</a> The files are sorted according to the figures/panels in the publication with a "0-README.txt" file including further information.  The following versions of Pyhton and the packages have been used: python: 3.6.10 numpy: 1.18.1 matplotlib: 3.1.3 scipy: 1.4.1

zenodo40/100

Data for: Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning

The dataset accompanies the Journal of Energy Storage publication by Shuquan Wang et al. (2024), Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning, DOI 10.1016/j.est.2024.112571.  <h2>Experimental Description:</h2> The dataset comprises results from two experimental tests: pulse testing and driving cycle testing. These tests were conducted on two types of sodium-ion batteries—one with a capacity of 3.2 Ah (battery numbers: 1, 2, and 5) and another with a capacity of 10 Ah (battery numbers: 3, 4, and 6). <h3>Pulse Testing:</h3> The pulse tests were carried out using a battery test platform, consisting of an Arbin battery testing system, a temperature-controlled chamber, and a computer. The tests were performed on two 3.2 Ah and two 10 Ah sodium-ion batteries from Transimage and HiNa, respectively, with a nominal voltage of 3.0 V. The upper and lower cut-off voltages were set at 3.9 V and 1.5 V. Enhanced pulse tests were conducted at six different temperatures: -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. The state-of-charge (SOC) was varied in 10% intervals, with pulse currents escalating incrementally from 0.25C to 3C at 0.25C intervals. Each pulse lasted for 5 seconds, followed by a 15-second rest. After completing each set of pulses, the current was increased, and the process was repeated with a two-minute pause between sets of pulses. <h3>Driving Cycle Testing:</h3> The driving cycle tests were designed to simulate real-world driving conditions using various standard test methods, including the Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), and Dynamic Stress Test (DST). These tests were performed in a temperature-controlled chamber using both the 3.2 Ah and 10 Ah sodium-ion batteries. As with the pulse tests, driving cycle tests were carried out at temperatures of -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. Before each test, the batteries were charged with a 0.5C constant current-constant voltage (CC-CV) charging protocol up to 3.9 V, with a cut-off current of 0.02C. After a 30-minute rest, the driving cycle protocol was performed for seven iterations. <h2>File Naming Conventions:</h2> The dataset files are named based on the experimental conditions, as follows: <ul> <li>Pulse_data_tempX_batY: Data from the pulse tests, where X represents the testing temperature and Y denotes the battery number.</li> <li>Driving_cycle_data_tempX_batY: Data from the driving cycle tests, where X represents the testing temperature and Y denotes the battery number.</li> </ul>

opencc-by-4.0Sep 2024View details →

zenodo40/100

Dataset package for the Manuscript "Absence of bulk charge density wave order in the normal state of UTe2"

The attached dataset contains raw data, normalized to the respective attenuater, reported in the manusript:  "Absence of bulk charge density wave order in the normal state of UTe2". The files "Figure4a.dat", "Figure4b.dat", and "Figure4c.dat" contain data that were presented in Figure 4a, Figure4b, and Figure4c of the manuscript. The first columns contain the x-axis values, the second columns the intensities, and the third column the errorbars. The files "Figure3_N.dat" present the data in Figure 3 c. Here, N labels the (K,L)-coordinates. These are orivuded in "Figure3_KL.dat", where for a number N the N-th row presents the K and L values in the first and second column, respectively. The files "Fig2a.dat" and "Fig2b.dat" contain the datapoints presented in Figure 2a and Figure 2b, where the first column corresponds to the x-axis coordinate and the second column to the recorded intensity.

opencc-by-4.0Oct 2024View details →

zenodo40/100

Data for article "Assembling diuranium complexes in different states of charge with a bridging redox-active ligand"

This upload contains raw data (NMR, X-Ray, Elemental Analysis, AC & DC SQUID, EPR) files for the article.

opencc-by-4.0Aug 2022View details →

opencc-by-4.0Oct 2024View details →

Online data of "Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states"

<h1>Online data of "Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states"</h1> DOI: https://doi.org/10.1038/s41467-024-54520-7 <h2>Authors</h2> <ul> <li>Franco De Palma</li> <li>Fabian Oppliger</li> <li>Wonjin Jang</li> <li>Stefano Bosco</li> <li>Marián Janík</li> <li>Stefano Calcaterra</li> <li>Georgios Katsaros</li> <li>Giovanni Isella</li> <li>Daniel Loss</li> <li>Pasquale Scarlino</li> </ul> <h2>Description</h2> The data for all figures in the main text can be found in csv files in ASCII format in the corresponing folders. For Figures 4-6, the panels are numbered from top to bottom.

opencc-by-4.0Jul 2022View details →

EXAMPLE: Lattice-based equation of state tables with three conserved charges (BSQ)

These table is intended to provide an example format of the equation of state expected for the CCAKE hydrodynamic code which propagates conserved charge densities for baryon number \(B\), net strangeness \(S\), and electric charge \(Q\).  They are included primarily for demonstration purposes and no claim is made as to the accuracy of their contents.

opencc-by-4.0May 2024View details →

Data for "Low-Energy Electronic Structure in the Unconventional Charge-Ordered State of ScV6Sn6"

The files contain the data for the paper "Low-Energy Electronic Structure in the Unconventional Charge-Ordered State of ScV6Sn6". For data Fig1c+d_topography.gwy, the data has been well plotted in a free and commonly used software Gwyddion. The raw data can also be found in Fig1_c_topography.txt and Fig_1c_FFT.txt. For the 2D data, the scales for each axes can be obtained either normalized to Bragg peaks or can be found in the published manuscript online. Any question on the data, please contact the authors.

opencc-by-4.0Mar 2023View details →

Data release for the "First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K"

### On-/Off-Axis Data Release #### (Version 1.0.1, dated 2024/08/12) This tar archive contains the data release for ‘First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K’. It contains the cross-section data points and supporting information in ROOT and text format, which are detailed below: + `onoffaxis_xsec_data.root` This ROOT file contains the extracted cross section and the nominal MC prediction as TH1D histograms for both the flattened 1D array of bins and in the angle binning for the analysis. The ROOT file also contains both the covariance and inverted covariance matrix for the result stored as TH2D histograms. The angle bin numbering and the corresponding bin edges are detailed at the end of the README. + `flux_analysis.root` This ROOT file contains the nominal and post-fit flux histograms for ND280 and INGRID. Two different binnings are included: a fine binned histogram (220 bins) and a coarse binned histogram (20 bins). The coarse binned histogram corresponds to the flux parameters detailed in the paper (and bin edges listed in the appendix). + `xsec_data_mc.csv` The extracted cross-section data points and the nominal MC prediction for each bin is stored as a comma-separated value (CSV) file with header row. + `cov_matrix.csv` and `inv_matrix.csv` The covariance matrix and the inverted covariance matrix are both stored as CSV files with each row stored as a single line and columns separated by commas (there is no header row). Matrix element (0,0) corresponds to the first number in the file. + `nd280_analysis_binning.csv` and `ingrid_analysis_binning.csv` The analysis bin edges are included as CSV files. The columns are labeled with a header row and denote the linear bin index and the lower and upper bin edge for the angle and momentum bins. The units are in cos(angle) for the angle bins and in MeV/c for the momentum bins. + `calc_chisq.cxx` This is an example ROOT script to calculate the chi-square between the data and the nominal MC prediction using the ROOT file in the data release. To run, open ROOT and load the script (`.L calc_chisq.cxx`) and execute the function `calc_chisq("/path/to/file.root")`. + `calc_chisq.py` This is an example Python script to calculate the chi-square between the data and the nominal MC prediction using the text/CSV files in the data release. The code requires NumPy as an external dependency, but otherwise uses built-in modules. To run, execute using a Python3 interpreter and give the file paths to the data/MC text file and the inverse covariance text file as the first and second arguments respectively -- e.g. `python3 calc_chisq.py /path/to/xsec_data_mc.csv /path/to/inv_matrix.csv` + ND280 angle bin numbering     - 0: `-1.0 < cos(#theta) < 0.20`     - 1: `0.20 < cos(#theta) < 0.60`     - 2: `0.60 < cos(#theta) < 0.70`     - 3: `0.70 < cos(#theta) < 0.80`     - 4: `0.80 < cos(#theta) < 0.85`     - 5: `0.85 < cos(#theta) < 0.90`     - 6: `0.90 < cos(#theta) < 0.94`     - 7: `0.94 < cos(#theta) < 0.98`     - 8: `0.98 < cos(#theta) < 1.00` + INGRID angle bin numbering     - 0: `0.50 < cos(#theta) < 0.82`     - 1: `0.82 < cos(#theta) < 0.94`     - 2: `0.94 < cos(#theta) < 1.00`      ### Changelog #### v1.0.1 Fix transcription error in INGRID momentum binning. The lowest momentum bin edge is at 350 MeV/c, not 300 MeV/c.

opencc-by-4.0Oct 2022View details →

Zinc(II) Complexes with Triplet Charge-Transfer Excited States Enabling Energy-Transfer Catalysis, Photoinduced Electron Transfer, and Upconversion

Raw data to the graphs of the publication

dryad36/100

Data from: Charge state-dependent ion condensation near conjugated polymer backbones

Open the record for dataset details and reuse information.

publicNov 2025View details →

opencc-by-4.0Apr 2024View details →

Source Data for the publication "Sub-100-fs energy transfer in coenzyme NADH is a coherent process assisted by a charge-transfer state"

Molecular Structures for solvated NADH.  The folder "QMMM_OPTIMIZED_STRUCTS" contains the pdb files of the six  representatives for the three conformational clusters obtained after REMD used in the Supplementary Information. The folder "SOLVENT_ENSEMBLE_AROUND_FIXED_SOLUTE" conatins AMBER RESTART files for 200 solvent configurations around two cluster reps displayed in Figure 2 of main manuscript.  The folder "PARAMETERS_FOR_MLMCTDH" contains the input file, operator file and parameters for ML-MCTDH dynamics for the structures shown in Main Manuscript and Supplementary.

opencc-by-4.0Mar 2024View details →

Data Related to "Charge-Transfer Contacts for the Measurement of Correlated States in Monolayer WSe2"

Data related to "Charge-Transfer Contacts for the Measurement of Correlated States in Monolayer WSe2"

opencc-by-4.0Sep 2024View details →

Dataset for: "Spectrally Resolving the Phase and Amplitude of Coherent Phonons in the Charge Density Wave State of 1T-TaSe2"

Open the record for dataset details and reuse information.