Dataset Search

The dataset contains unbiased molecular dynamics (MD) trajectories in XTC format for anandamide binding in cannabinoid receptors, along with the files containing corresponding parameter and topology. All simulations employed the CHARMM36m force field for proteins, while endocannabinoids were parameterized using the CGenFF force field. Unbiased simulations were performed with OpenMM v7.7.

This dataset contains simulation results from PartMC-MOSAIC and WRF-PartMC that used in the journal article: Quantifying the Impact of Surfactants on Cloud Condensation Nuclei Activity Using a Particle-Resolved Model. Two compressed folder are uploaded here, one is for the data that used in this article, the other folder is the python scripts to process the data. For more details of the uploaded files, please check the README file.

This dataset accompanies the research paper "The single edge notch fracture test for viscoelastic elastomers" by Kamarei, Sozio, and Lopez-Pamies, published in the Journal of Theoretical, Computational and Applied Mechanics (2026). Making use of the Griffith criticality condition introduced by Shrimali and Lopez-Pamies (Extreme Mechanics Letters 58: 101944, 2023), the paper presents a comprehensive analysis of the single edge notch fracture test for viscoelastic elastomers — combining a parametric study with direct comparisons against experiments — to reveal how non-Gaussian elasticity, nonlinear viscosity, and intrinsic fracture energy interact to govern fracture nucleation from a pre-existing crack. The dataset contains figure data, numerical results, and supporting materials for reproducing the findings of the paper.

Data for the publication Protostellar Outflows Shed Light on the Dominant Close Companion Star Formation Pathways (Sponzilli et al). Contains the fits files, data files, and python scripts. The entire analysis is containerized with Docker. The `Dockerfile` in the root folder can be used to build the image. <b>Note:</b> __MACOSX folder or files starting with dot can be safely ignored or removed.

This folder contains the data and analysis code used to produce the results reported in "Quantifying classical and quantum bounds for resolving closely spaced, non-interacting, simultaneously emitting dipole sources in optical microscopy", (accepted, J. Chem. Phys. 2026).

This dataset contains all the raw and processed data used to generate the figures presented in the main text and the appendix of the paper "Fluxonium as a control qubit for bosonic quantum information". It also includes code for data analysis and figure generation.

This dateset contains data files necessary to replicate figures from "Idealized Particle-Resolved Large-Eddy Simulations to Evaluate the Impact of Emissions Spatial Heterogeneity on CCN Activity" submitted to Atmospheric Chemistry and Physics. Within the compressed folder data.zip are two subdirectories, "processed_data" and "spatial-het". The "processed_data" directory contains netCDF files which contain a subset of simulation output used in figure generation. The "spatial-het" subdirectory contains a .csv file with spatial heterogeneity values computed via an exact algorithm of the spatial heterogeneity metric described by Mohebalhojeh et al. 2025. The subdirectory "sh-patterns" contains .csv files for each emissions scenario. Each entry corresponds to a single grid cell over a domain of dimension 100x100 (lateral resolution of the computational domain employed in this paper). Within scripts.zip are python notebooks for generating figures. Additional python modules are included which contain helper functions for notebooks. Furthermore, a Fortran version of the spatial heterogeneity metric is included alongside shells scripts for creating a python environment in which the code can be compiled and convert into a Python module. Note that the create_env.sh and compile_nsh.sh scripts must be run prior to executing cells in notebooks to make use of the spatial heterogeneity subroutines. <b>*Note*:</b> New in this V3: During review, a bug regarding vertical diffusion of particles was discovered in WRF-PartMC which necessitated re-running simulations. We present new simulations with diffusion fixed. Furthermore, we have run additional simulations in response to reviewer comments--simulations with emissions turned off at t = 4 h to investigate reversible partitioning and simulations with the RH raised near saturation throughout the domain to model the effects of co-condensation. The README PDF has been updated to reflect changes to the dataset collection. Also, we have added a shell script in scripts_v3.zip which was used to process simulation output and create the data subsets contained in data_v3.zip. Lastly, notebooks were re-run with updated datasets to create manuscript figures and additional plotting routines were added for new figures pertaining to the requested simulations.

This repository contains source data for key plots presented in the manuscript "Plasmon-driven exciton formation in a non-equilibrium Fermi liquid." Experimental data that was analyzed in Igor Pro 8 are presented as the .pxp files used to generate individual sub-plots. Electronic spectral function calculations are provided as .txt files, in which consecutive rows refer to the meshgrid x coordinate, y coordinate, spectral function (and, where relevant, axis-projected local angular momentum). We additionally include the Wannier model and DFT-obtained bulk band structure on which the Wannier model was based. Files are named as the number of the figure in the manuscript to which they correspond, with additional details included where necessary. <b>Details of file names:</b> 2a_DOS_Lxz_Ek_KGM_40layer_xnum_800kpt_tot.txt: Density of states, xz-axis projected local orbital angular momentum, for 800 points along the K-Gamma-M path, for a 40-layer model. 2c_composite_y.pxp: ARPES (angle-resolved photoemission spectroscopy) spectra along the ky axis, including both a scan near the Fermi level and a scan at high kinetic energies. 2d_LCP_RCP_diff_Sect_20K.pxp: difference between ARPES constant energy cuts at T=20 K at E0 + 0.23 eV taken with left- and right-circularly polarized photons. The polarization-integrated intensity at the constant energy cut is also included. 2e_DOS_L45_E11pt79_m0pt25to0pt25_xnum_800kpt_tot.txt: Density of states, xz-projected local orbital angular momentum, and corresponding k-points in two dimensions from ab-initio electronic structure calculations for a constant-energy cut. 3a_[x]_[y]ps: ARPES cut under excitation at a fluence of x uJ/cm2, measured y ps after photoexcitation. Measurements were performed at 9 K. 3b_[x]: Energy distribution curves under excitation at a fluence x uJ/cm2 at selected delay times after photoexcitation. 4a_ImSigma_vs_temperature.pxp: Imaginary self energy (extracted from ARPES linewidths) at different energies above E0 for selected lattice temperatures. 4b_EELS_lowE.pxp: Electron energy loss spectrum over a low energy range 5b_diff_55m15.pxp: Difference between momentum-integrated Tr-ARPES traces at 55 uJ/cm2 and 15 uJ/cm2 photoexcitation. Time-dependent intensity at each energy level has been normalized to a maximum of 1 for each individual fluence prior to subtraction. 5d_invtau_at_EX_vs_fluence.pxp: decay rate at a specified energy EX for different excitation fluences, from single exponential fits. <b>NOTE: Analyses based on the Wannier model presented here should cite both the associated Article and this dataset. For all other files in the repository, citing the dataset alone is sufficient.</b>

This dataset contains all the raw and processed data used to generate the figures presented in the main text and the appendix of the paper "Readout-induced leakage of the fluxonium qubit", Physical Review Applied, 2026 (https://doi.org/10.1103/wjdb-4814) . It also includes code for data analysis and code for generating the figures.

This dataset provides estimates of annual agricultural and food commodity flows (in kg) between all county pairs within the United States from 2018 to 2022. The database provides 343.7 million data points, since pairwise information is provided between 3134 counties, for 7 commodity categories, and 5 time periods. The commodity categories correspond to the Standardized Classification of Transported Goods and are: - SCTG 1: Iive animals and fish - SCTG 2: cereal grains - SCTG 3: agricultural products (except for animal feed, cereal grains, and forage products) - SCTG 4: animal feed, eggs, honey, and other products of animal origin - SCTG 5: meat, poultry, fish, seafood, and their preparations - SCTG 6: milled grain products and preparations, and bakery products - SCTG 7: other prepared foodstuffs, fats and oils For additional information, please see the related paper by Arnav et al. (2026) in Environmental Research: Food Systems. http://iopscience.iop.org/article/10.1088/2976-601X/ae487c.

These codes implement the master equation microkinetic modeling (ME-MKM) calculations of Adams et al. (J. Phys. Chem. C 2025, 129, 15, 7285–7294), as well as the automatic derivatives for activation energies and reaction orders in their follow-up work (in review).

Raw data from the article "Locus Coeruleus-Amygdala Circuit Disrupts Prefrontal Control to Impair Fear Extinction", which is accepted for publication in PNAS.

Ground based radar data sets collected during the 2013 NASA EVEX Campaign conducted in Roi-Namur island of the Kwajalein Atoll in the Republic of Marshall Islands are deposited in this databank. Radar data were collected with IRIS VHF and ALTAIR VHF/UHF systems.

This dataset contains variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5; Hersbach et al., 2020). These data were used for the analysis in “The impact of large-scale land surface conditions on the South American low-level jet” published in Geophysical Research Letters. Acknowledgments: This work was supported by NSF Award AGS-1852709. We thank Dr. Zhuo Wang and Dr. Divyansh Chug for their valuable feedback and insightful discussions. References: Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis. Q J R Meteorol Soc. 2020; 146: 1999–2049. https://doi.org/10.1002/qj.3803

Incoherent scatter radar datasets collected during the September 2016 campaign at Arecibo have been deposited in this databank. The lag products of the ISR data are stored as lag profile matrices with 5 minutes of integration time. The data is organized in a Python dictionary format, with each file containing 12 lag profile matrices representing one hour of observation. A sample Python script is provided to illustrate its usage.

Data related to a publication, "Emulating 2D Materials with magnons" to be published, but also as a preprint on arXiv https://arxiv.org/abs/2601.03210. It contains scripts for the simulation program Mumax3, and python scripts for conversion and analysis.

Note: The GTAP dataset includes a total of 140 regions, some of which are aggregated regions. For all map-related supplementary files (S11, S12, S13), we assign values to each individual country to enhance visualization. Countries within the same aggregated region are assigned the same regional value to maintain consistency across the map. <b>Data S1 (separate file): S1.csv</b>- CSV file detailing production-related deaths for the GTAP dataset. Rows: Each row represents a country where deaths occur as a result of production activities. Columns: Each column represents a country-sector pair on the production side. Values: The values indicate the number of deaths caused by production activities in the country-sector listed in each column and occurring in the country listed in each row. <b>Data S2 (separate file): S2.csv</b>- CSV file detailing production-related deaths for the EORA dataset. Structure: The file has the same structure as S1.csv. <b>Data S3 (separate file): S3.csv</b>- CSV file detailing consumption-related deaths for the GTAP dataset. Rows: Each row represents a country where deaths occur as a result of consumption activities. Columns: Each column represents a consumption country. Values: The values indicate the number of deaths caused by consumption activities in the country listed in the column and occurring in the country listed in the row. <b>Data S4 (separate file): S4.csv</b>- CSV file detailing consumption-related deaths for the EORA dataset. Structure: The file has the same structure as S3.csv. <b>Data S5 (folder of files): S5.zip</b>- a folder containing 141 CSV files, each named after a country's 3-digit code (e.g., USA.csv, CHN.csv), representing production-related spatial PM₂.₅ concentration patterns for all GTAP countries. Rows: Each row corresponds to a grid cell. Columns: Each column represents an industrial sector. The final column, "geometry," contains the spatial coordinates (latitude and longitude) for each grid cell. Values: Each value indicates the PM₂.₅ concentration level (in µg/m³) attributable to emissions from the specified sector in the given country, as they occur in each grid cell. <b>Data S6 (folder of files): S6.zip</b>- a folder containing 188 CSV files, each named after a country's 3-digit code, representing production-related spatial PM₂.₅ concentration patterns for all EORA countries. Structure: Each file follows the same format as those in S5.zip, with rows representing grid cells and columns representing industrial sectors, plus a "geometry" column containing spatial coordinates. <b>Data S7 (separate file): S7.csv</b>- CSV file containing consumption-related spatial PM₂.₅ concentration patterns for all GTAP countries. Rows: Each row represents a grid cell. Columns: Apart from the last column ("geometry"), which contains spatial information for each grid cell in latitude-longitude coordinates, each column represents a consumption country. Values: Each value indicates the PM₂.₅ concentration level caused by each country’s consumption process and occurring in each grid cell, measured in µg/m³. <b>Data S8 (separate file): S8.csv</b>- CSV file containing consumption-related spatial PM₂.₅ concentration patterns for all EORA countries. Structure: The file has the same structure as S7.csv. <b>Data S9 (separate file): S9.csv</b>- CSV file listing the total net bidirectional export of deaths for all countries in GTAP, displaying only positive values. Columns: "from": The country that exports more consumption-related deaths. "to": The country that imports more consumption-related deaths. "values": The net export of deaths between these two countries, calculated as the difference between the deaths flowing from "from" to "to" and those from "to" to "from." <b>Data S10 (separate file): S10.csv</b>- CSV file listing the total net bidirectional export of deaths for all countries in EORA, displaying only positive values. Structure: The file has the same structure as S9.csv. <b>Data S11 (separate file): S11.csv</b>- CSV file listing the Value of Statistical Lives (VSLs), and consumption-related externalities under three scenarios—Business as Usual (BAU), Global Community (GC), and Fair Trade in Deaths (FTD)—along with externalities per GDP and their differences for GTAP countries. Columns: VSL, BAU_Externality, GC_Externality, FTD_Externality BAU_Ext_perGDP, GC_Ext_perGDP, FTD_Ext_perGDP Diff_GC_BAU, Diff_FTD_BAU, Diff_FTD_GC <b>Data S12 (separate file): S12.csv</b>- Same as S11.csv, but for EORA countries. Structure: Identical to S11.csv. <b>Data S13 (separate file): S13.csv</b>- purpose: Includes data used to generate Figures 1, 2, 3, and 5 in the main text. Columns: country_code: 3-letter country code GTAP_region, continent, population, GDP, GDP_capita, VSL export_of_death, import_of_death, net_export, net_export_capita allforeign_world, G50foreign_world, G100foreign_world cause_allforeign_world, cause_L30foreign_world, cause_L50foreign_world BAU_Externality, GC_Externality, FTD_Externality BAU_Ext_perGDP, GC_Ext_perGDP, FTD_Ext_perGDP Diff_GC_BAU, Diff_FTD_BAU, Diff_FTD_GC geometry (used for visualization) <b>Data S14 (separate file): S14.xlsx</b>- this Excel file contains six sheets summarizing cross-model Pearson correlation coefficients between sectoral economic activity fractions and transboundary mortality impact metrics, based on both GTAP and EORA datasets. Sheets: Output_fraction_GTAP Direct_demand_fraction_GTAP Final_demand_fraction_GTAP Output_fraction_EORA Direct_demand_fraction_EORA Final_demand_fraction_EORA Rows: Each row represents an economic sector. Columns: G50foreign_world: Fraction of deaths attributable to final demand from regions where demand per capita is more than 50% higher than in the current country. cause_L50foreign_world: Fraction of deaths caused by consumption within the current country but occurring in countries with more than 50% lower demand per capita. Values: Each value represents the Pearson correlation between the sectoral fraction and the corresponding transboundary mortality metric. <b>Data S15 (separate file): S15.csv</b>- CSV file derived from the GTAP dataset, containing Monte Carlo simulation results (500 draws) for the uncertainty analysis of production-based premature deaths. Column Producer: The producing country–sector pair responsible for the emissions leading to health impacts. Column Affected Country: The country where the resulting premature deaths occur. Column Deaths: The estimated number of deaths corresponding to the one used in the main analysis. Columns Deaths_median, Deaths_low95, Deaths_high95: The median, 2.5th percentile, and 97.5th percentile values across 500 Monte Carlo draws of the GEMM θ parameter, representing the 95% confidence interval for each producer–affected country pair. <b>Data S16 (separate file): S16.csv</b>- CSV file derived from the GTAP dataset, containing Monte Carlo simulation results (500 draws) for the uncertainty analysis of consumption-based premature deaths. Column Consumer: The consuming country whose final demand drives the global production and associated health impacts. Column Affected Country: The country where the resulting premature deaths occur. Column Deaths: The estimated number of deaths corresponding to the one used in the main analysis. Columns Deaths_median, Deaths_low95, Deaths_high95: The median, 2.5th percentile, and 97.5th percentile values across 500 Monte Carlo draws of the GEMM θ parameter, representing the 95% confidence interval for each consumer–affected country combination.

This dataset includes the FITS files for all ALMA images used in the ApJ publication "Multiband ALMA Polarization Observations of BHB 07-11 Reveal Aligned Dust Grains in Complex Spiral Arm Structures". Additionally, this dataset includes details regarding the data reduction process so that interested users can perform the reduction and imaging themselves.

Data for 'Atomic-scale imaging and charge state manipulation of NV centers by scanning tunneling microscopy' to be published in Nature Communications.

This dataset contains the following to replicate figures from "TChem-atm (v2.0.0): Scalable Performance-Portable Multiphase Atmospheric Chemistry" submitted to Geophysical Model Development (GMD). It contains (1) the simulation inputs, outputs and analysis notebook for recreating the PartMC-CAMP and PartMC-TChem-atm comparison and (2) scripts, timing results and analysis tools for recreating the performance evaluation. Users can either inspect the raw output to verify the results of the manuscript or rerun simulations using the provided inputs. Additionally, modifiying the inputs allows for for further exploration of both model simulation and performance characteristics.

This dataset supports the analysis presented in the study on curbside electric vehicle (EV) charging infrastructure planning in San Francisco and the published paper titled "Urban electric vehicle infrastructure: Strategic planning for curbside charging." It includes spatial data layers and tabular data used to evaluate location suitability under multiple criteria, such as demand, accessibility, and environmental benefits. This dataset can be used to replicate the multi-criteria decision-making framework, perform additional spatial analyses, or inform policy decisions related to EV infrastructure siting in urban environments. The paper's DOI is https://doi.org/10.1016/j.jtrangeo.2025.104328.

This is the dataset that accompanies the paper titled "A Dual-Frequency Radar Retrieval of Snowfall Properties Using a Neural Network", submitted for peer review in August 2020. Please see the github for the most up-to-date data after the revision process: https://github.com/dopplerchase/Chase_et_al_2021_NN Authors: Randy J. Chase, Stephen W. Nesbitt and Greg M. McFarquhar Corresponding author: Randy J. Chase (randyjc2@illinois.edu) Here we have the data used in the manuscript. Please email me if you have specific questions about units etc. 1) DDA/GMM database of scattering properties: base_df_DDA.csv This is the combined dataset from the following papers: Leinonen & Moisseev, 2015; Leinonen & Szyrmer, 2015; Lu et al., 2016; Kuo et al., 2016; Eriksson et al., 2018. The column names are D: Maximum dimension in meters, M: particle mass in grams kg, sigma_ku: backscatter cross-section at ku in m^2, sigma_ka: backscatter cross-section at ka in m^2, sigma_w: backscatter cross-section at w in m^2. The first column is just an index column. 2) Synthetic Data used to train and test the neural network: Unrimed_simulation_wholespecturm_train_V2.nc, Unrimed_simulation_wholespecturm_test_V2.nc This was the result of combining the PSDs and DDA/GMM particles randomly to build the training and test dataset. 3) Notebook for training the network using the synthetic database and Google Colab (tensorflow): Train_Neural_Network_Chase2020.ipynb This is the notebook used to train the neural network. 4)Trained tensorflow neural network: NN_6by8.h5 This is the hdf5 tensorflow model that resulted from the training. You will need this to run the retrieval. 5) Scalers needed to apply the neural network: scaler_X_V2.pkl, scaler_y_V2.pkl These are the sklearn scalers used in training the neural network. You will need these to scale your data if you wish to run the retrieval. 6) <b>New in this version</b> - Example notebook of how to run the trained neural network on Ku- Ka- band observations. We showed this with the 3rd case in the paper: Run_Chase2021_NN.ipynb 7) <b>New in this version</b> - APR data used to show how to run the neural network retrieval: Chase_2021_NN_APR03Dec2015.nc The data for the analysis on the observations are not provided here because of the size of the radar data. Please see the GHRC website (<a href="https://ghrc.nsstc.nasa.gov/home/">https://ghrc.nsstc.nasa.gov/home/</a>) if you wish to download the radar and in-situ data or contact me. We can coordinate transferring the exact datafiles used. The GPM-DPR data are avail. here: <a href="http://dx.doi.org/10.5067/GPM/DPR/GPM/2A/05">http://dx.doi.org/10.5067/GPM/DPR/GPM/2A/05</a>

Transport and MFM data of brickwork artificial spin ice composed of permalloy are included, which are reproductions of the data in an article named "Magnetic response of brickwork artificial spin ice". Transport data represent magnetic response of connected brickwork artificial spin ice, and MFM data represent how both connected and disconnected brickwork artificial spin ice react to external magnetic fields. SEM images of typical samples are included, where individual nanowire leg (island) is approximately 660 nm long and 140 nm wide with a 40 nm thickness. For the transport, each sample was measured in a longitudinal and a transverse geometry. Red curves are the 2500 Oe to -2500 Oe sweeps and the blue curves are -2500 Oe to 2500 Oe sweeps. Transport measurements were taken by using a standard 4-wire technique. Each plot was saved in pdf format.

Important Note: the raw transient files need to be downloaded through this separate link: https://uofi.box.com/s/oagdxhea1wi8tvfij4robj0z0w8wq7j4. Once downloaded, place the file within the within the .d folder in the unzipped 20210930_ShortTransient_S3_5 folder to perform reconstruction step. The minimal datasets to run the computational pipeline MEISTER introduced in the manuscript titled "Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry". The key steps of our computational pipeline include (1) tissue mass spectrometry imaging (MSI) reconstruction; (2) multimodal image registration and 3D reconstruction; (3) regional analysis; and (4) single-cell and tissue data integration. Detailed protocols to reproduce our results in the manuscript are provided with an example data set shared for learning the protocols. Our computational processing codes are implemented mostly in Python as well as MATLAB (for image registration).

This is a collection of 31 quasi-linear convective system (QLCS) mesovortices (MVs) that were first manually identified and analyzed using the lowest elevation scan of the nearest relevant Weather Surveillance Radar–1988 Doppler (WSR-88D) during the two years (springs of 2022 and 2023) of the Propagation, Evolution, and Rotation in Linear Storms (PERiLS) field campaign. This analysis was completed using the Gibson Ridge radar-viewing software (GR2Analyst). Throughout the two years of PERiLS, a total of nine intensive observing periods (IOPs) occurred (see https://catalog.eol.ucar.edu/perils_2022/missions and https://catalog.eol.ucar.edu/perils_2023/missions for exact IOP dates/times). However, only six of these IOPs (specifically, IOPs 2, 3, and 4 from both years) are included in this dataset. The inclusion criteria were based on the presence of strictly QLCS MVs that from a cursory analysis were within the C-band On Wheels (COW) domain, one of the research radars deployed in the field for the PERiLS project. The 31 QLCS MVs identified using WSR-88D data were also examined using data from the COW radar (using Solo3 software). The lowest elevation angle was not always useable in the COW data, and sometimes the second lowest elevation angle was used. Further details on how MVs were identified are provided below, and a very detailed methodology is published in Blind-Doskocil et al. (2025). Each MV had to be produced by a QLCS, defined as a continuous area of 35 dBZ radar reflectivity over at least 100 km when viewed from the lowest elevation scan. The MVs analyzed also had to pass through/near the COW’s domain at some point during their lifetimes to allow for additional analysis using the COW data. Tornadic (TOR), wind-damaging (WD), and non-damaging (ND) MVs were analyzed over their entire lifetime and subsequently during the pretornadic, predamaging (wind damage), and prewarning phase (classified altogether as the prephase) of each MV. The prephase MVs were classified based on the first damage report or lack thereof associated with them. ND MVs were ones that usually had a tornado warning placed on them (all but one case) but did not produce any damage and persisted for five or more radar scans; this was done to target the strongest MVs that forecasters thought could be tornadic. The QLCS MVs were identified using objective criteria, which included the existence of a circulation with a maximum differential velocity (dV; i.e., the difference between the maximum outbound and minimum inbound velocities at a constant range) of at least 20 kt over a distance ≤ 7 km. The following radar-based characteristics were catalogued for each QLCS MV at the lowest elevation angle of the nearest WSR-88D: latitude and longitude locations of the MV, the genesis to decay time of the MV, the maximum dV across the MV, the maximum rotational velocity (Vrot; i.e., dV divided by two), diameter of the MV, the range from the radar of the MV center, and the height above radar level of the MV center. In the Excel workbook titled “nexrad_analyzed_mvs_perils_illinois_data_bank”, there are a total of 36 sheets. 31 of the 36 sheets are for each MV that was examined. The 31 MV sheets that were used to calculate MV statistics are labeled following the convention 'mv#_iop#_qlcs'. ‘mv#’ is the unique number that was assigned to each MV for clear identification, 'iop#' is the IOP in which the MV occurred, 'qlcs' denotes that the MV was produced by a QLCS, and the 2023 IOPs are denoted by ‘_2023’ after ‘qlcs’ in the sheet name. In these sheets, there are notes on what was visually seen in the radar data, damage associated with each MV (using the National Centers for Environmental Information (NCEI) database), and the characteristics of the MV at each time step of its lifetime. The yellow rows in each of the sheets indicate the last row of data included in the prephase statistics. The orange boxes in the notes column indicate any reports that were in NCEI but not in GR2Analyst. There are also sheets that examine pretornadic and predamaging diameter trends; box and whisker plot statistics of the overall characteristics of the different types of MVs; and the overall characteristics of each MV, with one Excel sheet (‘combined_qlcs_mvs’) examining the characteristics of each MV over its entire lifetime and one Excel sheet (‘combined_qlcs_mvs_before_report’) examining the characteristics of each MV before it first produced damage or had a tornado warning placed on it. In the Excel workbook titled “cow_analyzed_mvs_perils_illinois_data_bank”, there are a total of 33 sheets. 31 of the 33 sheets are for each MV that was examined, with a similar naming convention to those analyzed using WSR-88D data. The data documented in each sheet is also similar to that in the WSR-88D sheets. Due to the very tedious and time-consuming nature of analyzing radar data manually, we mainly focused on cataloging only the times where the MVs were detectable in the COW data during the prephase. In the WSR-88D data, we examined the MVs over their entire lifetimes and during their prephases. Not all the MVs analyzed in the WSR-88D data ended up being detectable in the COW data, and we focused on comparing the prephase MVs in the COW data and WSR-88D data. Therefore, there are sheets that are missing values and note that the MV was not in the COW’s domain, not detectable during the prephase, only focused on cataloging the prephase, etc. There are also sheets that examine characteristics of each MV during the prephase (‘combined_qlcs_mvs_before_report’) and box and whisker plot statistics of the prephase characteristics of the MVs (‘box_whisker_stats).