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This dataset contains the values directly shown in the figures of the article "The impact of aerosol mixing state on immersion freezing: Insights from classical nucleation theory and particle-resolved simulations". This article is in preparation for submission to the journal Atmospheric Chemistry and Physics. The dataset consists of 12 NetCDF files processed from the raw output of the PartMC model. It does not include the theoretical values of frozen fraction, which can be computed using the equations provided in the paper. *New in V2: adding data for a newly included figure (INP_spectrum.nc), removing files that are no longer used in the revised manuscript figures (e.g., UNC_A_ratio=0.9_Dp=0.1.nc, UNC_A_ratio=0.9_Dp=10.0.nc, UNC_A_ratio=0.1_Dp=0.1.nc, and UNC_A_ratio=0.1_Dp=10.0.nc), and updating README.pdf accordingly.

Raw and Processed 4D-STEM datasets organized by particles appeared in each figure in the publication. 1. Figure 1. 2. Figure 2. 3. Figure 3. 4. Figure S7. 5. Readme.txt

This dataset contains the values directly shown in the figures of the article "The impact of aerosol mixing state on immersion freezing: Insights from classical nucleation theory and particle-resolved simulations". This article is in preparation for submission to the journal Atmospheric Chemistry and Physics. The dataset consists of 15 NetCDF files processed from the raw output of the PartMC model. It does not include the theoretical values of frozen fraction, which can be computed using the equations provided in the paper.(These four files — UNC_A_ratio=0.1_Dp=0.1.nc, UNC_A_ratio=0.1_Dp=10.0.nc, UNC_A_ratio=0.9_Dp=0.1.nc, and UNC_A_ratio=0.9_Dp=10.0.nc — were not used in the manuscript. They have the same format and serve the same function as the other UNC_A_ratio=*_Dp=*.nc files, and contain the sensitivity maps for the corresponding combinations of A_ratio and Dp.)

<b>**Data Description:** </b> This dataset provides country- and sector-level estimates of air-pollution–related health impacts, economic externalities, and associated spatial concentration patterns derived from multi-regional input–output (MRIO) modeling and atmospheric simulations (GTAP and EORA frameworks). Files include production- and consumption-based mortality matrices, gridded PM₂.₅ concentration maps, trade-linked net export metrics, externalities, uncertainty analyses, and cross-model correlation summaries used to generate the figures and tables in the manuscript. <b>**Citation Requirement:** </b> If you use this dataset in your research, presentations, or derivative works, please cite both the associated paper and the dataset: Wang, S., Thakrar, S., Johnson, J. et al. International trade and air-quality-related mortality. Nature Communications, 17, 3518 (2026). https://doi.org/10.1038/s41467-026-71408-w Wang, Shiyuan; Christopher, Tessum; Justin, Johnson; Sumil, Thakrar (2026): Data Accompanying "International Trade and Air-Quality-Related Mortality". University of Illinois Urbana-Champaign. https://doi.org/10.13012/B2IDB-0064792_V2

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.

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>