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This dataset encompasses experimental results supporting the upcoming journal paper, "Hydro-mechanical-chemical behavior of sedimentary rock during CO2 injection". The dataset includes the measurements and analyses conducted under controlled laboratory conditions, capturing changes in poroviscoelastic properties and pore structure after CO2 treatment.

This dataset contains all the raw and processed data used to generate the figures presented in the main text and the supplementary information of the paper "Operation of a high frequency, phase slip qubit." It also includes code for data analysis and code for generating the figures. <b>Note:</b> V2 includes time domain analysis that also accounts for the thermal dephasing from the f state (see readme in Time domain Device A).

SCiLS MSI data files, images used in the figures and table contents for the tables found in the manuscript. The figures are labeled by figure and by their title on each figure set, including those found in the Supplementary Information. The tables are in an MS Excel sheet with the corresponding contents. The tables list the metabolites found in the images. To reduce the number of images in the manuscript, the tables complete the metabolite information not observed in the images. The images can be found using the SCiLS data files. A software license is needed to open these files. The SCiLS data files contains the processed MSI data for all obtained images. All files in the corresponding SCiLS data file must be present to open the individual data file. The feature list used for MSI analysis should be saved on the attached bookmark inside the SCiLS file so it should be available once the file is opened. SCiLS files can only be opened with the Bruker SCiLS software. If using an outdated version (before Version 13.01.17218), the files may not open or show poor quality.

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

This dataset contains the raw transmission electron microscopy (TEM) and scanning electron microscopy (SEM) images used to calculate the synthesis yield of patchy nanoparticles (NPs), as described in Supplementary Table 1 of the paper “Patchy Nanoparticles by Atomic “Stencilling” (2025).” All the images were taken at the Materials Research Laboratory, University of Illinois at Urbana-Champaign by Qian Chen group. 1. We have 21 subfolders, each with a name corresponding to one of the 21 patchy NPs listed in Supplementary Table 1 of the paper “Patchy Nanoparticles by Atomic “Stencilling” (2025)." 2. In TEM images, the bright and dark regions indicate the polymer patches and NP cores, respectively. 3. In SEM images, the bright and dark regions indicate the NP cores and polymer patches, respectively. 4. Each subfolder contains a “readme (subfolder name).txt” file with more detailed information about each sample.

This dataset contains the PartMC-MOSAIC simulations used in the article "Quantifying Errors in the Aerosol Mixing-State Index Based on Limited Particle Sample Size". The 1000 simulations of output data is organized into a series of archived folders, each containing 100 scenarios. Within each scenario directory are 25 NetCDF files, which are the hourly output of a PartMC-MOSAIC simulation containing all information regarding the environment, particle and gas state. This dataset was used to investigate the impact of sample size on determining aerosol mixing state. This data may be useful as a data set for applying different types of estimators.

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).

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 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.

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 V2: Following an initial review, an additional figure was requested, which required updates to both data.zip (adding one new NC file: no-heterogeneity_met-vars_subset.nc) and scripts.zip (a minor addition to a Python notebook). A README in PDF format has also been uploaded to provide a summary of the dataset.

This repository provides the data and code used to reproduce key plots from the manuscript and to extend discussions that were only briefly covered therein. All MATLAB scripts were developed and tested in MATLAB R2024a. All Python scripts were developed and tested in Python 3.11.2. * <b> NOTE:</b> New in this V3: 1. 2 new MATLAB files (ChiralPointGroups.m and THz_current_estimation.m), ChiralPointGroups.pdf (a compiled version of ChiralPointGroups.m) and theoretical model code (theoretical_model.zip) are added. More information can be found in the readme. 2. Updated and renamed "publication_data.zip" (in V2) to "data_and_analysis.zip" 3. Change License from CC BY to "Other license". Licensing Terms: Data (all .mat files) is under CC BY and Code is released under MIT license. Therefore, V3 is bound to this new license. V2 is still under CC BY. <b>→ Data and analysis code (data_and_analysis.zip):</b> The dataset is organized into five subfolders. Each subfolder corresponds to a unique combination of experimental conditions, including: • Magnetic field orientation (B ∥ c or B ⟂ c) • Scan parameter (magnetic field or temperature) • Pump laser polarization (linear s, linear p, or circular) • Detection polarization (linear s) Each folder contains: • The raw time-domain data files (.mat) • Oscillator parameters extracted via linear prediction algorithm (.mat) • MATLAB scripts (.m) that generate plots of the raw data, processed fits, and amplified modes. Each script should be run within its corresponding folder to ensure proper loading of the associated data files. Folder summary: 1. B_parallel_c_linear_spump_sprobe_field: B ∥ c, s-polarized pump, s-polarized THz detection, magnetic field dependence 2. B_parallel_c_linear_spump_sprobe_temperature: B ∥ c, s-polarized pump, s-polarized THz detection, temperature dependence 3. B_perp_c_linear_spump_sprobe_field: B ⟂ c, s-polarized pump, s-polarized THz detection, magnetic field dependence 4. B_perp_c_linear_spump_sprobe_temperature: B ⟂ c, s-polarized pump, s-polarized THz detection, temperature dependence 5. B_parallel_c_LCPRCP_pump_sprobe_field: B ∥ c, circularly polarized pump (LCP & RCP), s-polarized THz detection, magnetic field dependence <b>→Theoretical model code (theoretical_model.zip):</b> The Python script depends on packages “numpy” and “matplotlib”. The script generates a plot of the dispersion relations of the theoretical model introduced in the Main Text. More precisely, it plots the real (red) and imaginary (blue) parts of the frequency (ω) as a function of wavenumber (k) as obtained by solving the characteristic equation, equation (6) of the Supplemental Information, with σ_E and σ_Μ given respectively by equations (3) and (2) of the Main Text. All branches of the dispersion relations are plotted simultaneously. All model parameters are adjustable. The included Mathematica notebook (printout also provided in .pdf format) was used to obtain symbolic expressions for the coefficients of powers of ω appearing in the characteristic determinant. These coefficients were copied directly into the Python function detCoeffs(). <b>→ Standalone scripts (not in subfolders):</b> • ChiralPointGroups.m Outputs a table summarizing the 2D matrix representation of σ_Μ in the 11 enantiomorphic point groups. ChiralPointGroups.pdf is a compiled version of chiral point groups table, identical to the output of ChiralPointGroups.m. • THz_current_estimation.m Estimates the photoinduced THz current in tellurium under magnetic field. The script evaluates a phenomenological resonant contribution to the magnetoelectric coupling (with negligible dependence on NIR polarization), leading to excitation of s-polarized, B-antisymmetric mode S_odd at ~0.37 THz. These standalone scripts provide additional physical discussion and calculation detail that are intentionally streamlined or omitted from the published manuscript and its supplementary materials for clarity and space.

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).

4D-STEM datasets for solution-treated (CrCoNi)93Al4Ti2Nb MEA in [111], [112], and [114] zone. Data used for Ultramicroscopy article "Differentiating electron diffuse scattering via 4D-STEM spatial fluctuation and correlation analysis in complex FCC alloys". Experiment details can be found in the paper. Data-specific details are listed in the Readme file.

This dataset contains all raw and processed data used to generate the figures in the main text and supplementary material of the paper "High dynamic-range quantum sensing of magnons and their dynamics using a superconducting qubit." The data can be used to reproduce the plots and validate the analysis. Accompanying Jupyter notebooks provide step-by-step analysis pipelines for figure generation. The dataset also includes drawings for the mechanical samples used to perform the experiment. In addition, the dataset provides ANSYS HFSS electromagnetic simulation files used to design and analyze the resonator structures and estimate field distributions.

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.

Physiological and yield data from a three year field experiment of soybean exposed to elevated ozone stress and reduced soil moisture at the SoyFACE experiment.

Data and code for the paper titled "Electric Vehicle Charging Stations at Risk from Hazardous Events and Power Outages: Analytics and Resilience Implications" published in Renewable and Sustainable Energy Reviews journal (https://doi.org/10.1016/j.rser.2025.116144).

Data accompanying the article "Physics of Unraveling and Micromechanics of Hagfish Threads". Abstract of the article: Hagfish slime is a unique biological material composed of mucus and protein threads that rapidly deploy into a cohesive network when deployed in seawater. The forces involved in thread deployment and interactions among mucus and threads are key to understanding how hagfish slime rapidly assembles into a cohesive, functional network. Despite extensive interest in its biophysical properties, the mechanical forces governing thread deployment and interaction remain poorly quantified. Here, we present the first direct in situ measurements of the micromechanical forces involved in hagfish slime formation, including mucus mechanical properties, skein peeling force, thread–mucus adhesion, and thread–thread cohesion. Using a custom glass-rod force sensing system, we show that thread deployment initiates when peeling forces exceed a threshold of approximately 6.8 nN. To understand the flow strength required for unraveling, we used a rheo-optic setup to impose controlled shear flow, enabling us to directly observe unraveling dynamics and determine the critical shear rate for unraveling of the skeins, which we then interpreted using an updated peeling-based force balance model. Our results reveal that thread–mucus adhesion dominates over thread–thread adhesion and that deployed threads contribute minimally to bulk shear rheology at constant flow rate. These findings clarify the physics underlying the rapid, flow-triggered assembly of hagfish slime and inform future designs of synthetic deployable fiber–gel systems.

These data support Ruess et al (2025) "Drought impacts to water footprints and virtual water transfers of counties of the United States", Water Resources Research, 61, e2024WR037715, https://doi.org/10.1029/2024WR037715. The dataset contains estimates for Virtual Water Content (VWC) and Virtual Water Trade (VWT) for nine unique combinations of three crop categories (cereal grains, produce, and animal feed) and three water sources (surface water withdrawals, groundwater withdrawals, and groundwater depletion) for the years 2012 and 2017 within the Continental United States. The VWC is calculated by dividing irrigation withdrawal estimates (m3) by the production (tons) at the county resolution. The VWT is calculated by multiplying the VWC by the estimated county level food flows (tons) from Karakoc et al. (2022). All VWC estimates are provided at the county resolution according to county GEOID and are given in units of m3/ton. All VWT estimates are given in pairs of origin and destination GEOID’s and provided in units of m3. When using, please cite as: Ruess, P.J., Hanley, J., and Konar, M. (2025) "Drought impacts to water footprints and virtual water transfers of counties of the United States", Water Resources Research, 61, e2024WR037715, doi: 10.1029/2024WR037715.

This repository includes data files and analysis and plotting codes for reproducing the figures in the paper "A scanning resonator for probing quantum coherent devices" arXiv:2506.22620

This repository includes HRLDAS Noah-MP model output generated as part of Bieri et al. (2025) - Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): Impact on Amazon dry-season transpiration. These data are distributed in two different formats: Raw model output files and subsetted files that include data for a specific variable. All files are .nc format (NetCDF) and aggregated into .tar files to facilitate download. Given the size of these datasets, Globus transfer is the best way to download them. Raw model output for four model experiments is available: FD (control), GW, SOIL, and ROOT. See the associated publication for information on the different experiments. These data span an approximately 20 year period from 01 Jun 2000 to 31 Dec 2019. The data have a spatial resolution of 4 km and a temporal frequency of 3 hours. These data are for a domain in the southern Amazon basin (see Figure 1 in the associated publication). Data for each experiment is available as a .tar file which includes 3-hourly NetCDF files. All default Noah-MP output variables are included in each file. As a result, the .tar files are quite large and may take many hours or even days to transfer depending on your network speed and local configurations. These files are named 'noahmp_output_2000_2019_EXP.tar', where EXP is the name of the experiment (FD, GW, SOIL, or ROOT). Subsetted model output at a daily temporal resolution for all four model experiments is also available. These .tar files include the following variables: water table depth (ZWT), latent heat flux (LH), sensible heat flux (HFX), soil moisture (SOIL_M), canopy evaporation (ECAN), ground evaporation (EDIR), transpiration (ETRAN), rainfall rate at the surface (QRAIN), and two variables that are specific to the ROOT experiment: ROOTACTIVITY (root activity function) and GWRD (active root water uptake depth). There is one file for each variable within the tarred files. These files are named 'noahmp_output_subset_2000_2019_EXP.tar', where EXP is the name of the experiment (FD, GW, SOIL, or ROOT). Finally, there is a sample dataset with raw 3-hourly output from the ROOT experiment for one day. The purpose of this sample dataset is to allow users to confirm if these data meet their needs before initiating a full transfer via Globus. This file is named 'noahmp_output_sample_ROOT.tar'. The README.txt file provides information on the Noah-MP output variables in these datasets, among other specifications. Information on HRLDAS Noah-MP and names/definitions of model output variables that are useful in working with these data are available here: http://dx.doi.org/10.5065/ew8g-yr95. Note that some output variables may be listed in this document under a different variable name, so searching for the long name (e.g. 'baseflow' instead of 'QRF') is recommended. Information on additional output variables that were added to the model as part of this study is available here: https://github.com/bieri2/bieri-et-al-2025-EGU-GMD/tree/DynaRoot. Model code, configuration files, and forcing data used to carry out the model simulations are linked in the related resources section.

This dataset describes an analysis of research documents about the debate between hydrogen fuel cells and lithium-ion batteries within the context of electric vehicles. To create this dataset, we first analyzed news articles on the topic of sustainable development. We searched for related science using keywords in Google Scholar. We then identified subtopics and selected one specific subtopic: electric vehicles. We started to identify positions and players about electric vehicles [1]. Within electric vehicles, we started searching in OpenAlex for a topic of reasonable size (about 300 documents) related to a scientific or technical debate. We narrowed to electric vehicles and batteries, then trained a cluster model [2] on OpenAlex’s keywords to develop some possible search queries, and chose one. Our final search query (May 7, 2024) returned 301 document in OpenAlex: Title & abstract includes: Electric Vehicle + Hydrogen + Battery filter is Lithium-ion Battery Management in Electric Vehicle We used a Python script and the Scopus API to find missing abstracts and DOIs [3]. To identify relevant documents, we used a combination of Abstractkr [4] and manual screening. As a starting point for Abstractkr [4], one person manually screened 200 documents by checking the abstracts for “hydrogen fuel cells” and “battery comparisons”. Then we used Abstractkr [4] to predict the relevance of the remaining documents based on the title, abstract, and keywords. The settings we used were single screening, ordered by most likely to be relevant, and 0 pilot size. We set a threshold of 0.6 for the predictions. After screening and predictions, 176 documents remained

This is the raw topographies (without linear background subtraction) related to the publication: https://www.nature.com/articles/s41586-024-07519-5

Dataset with numerical routines and laboratory testing data associated with the manuscript: Bondarenko, N., Podladchikov, Y., Williams‐Stroud, S., & Makhnenko, R. (2025). Stratigraphy‐induced localization of microseismicity during CO2 injection in Illinois Basin. Journal of Geophysical Research: Solid Earth, 130, e2024JB029526. https://doi.org/10.1029/2024JB029526

This is a set of topographies to study the magnetic field response of RbV3Sb5 (related to Fig.4 of https://www.nature.com/articles/s41586-024-07519-5)