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This dataset contains the emulated global multi-model urban daily temperature projections under RCP 8.5 scenario. The dataset is derived from the study "Large model structural uncertainty in global projections of urban heat waves" (XXXX). Details about this dataset and the local urban climate emulator are described in the article. This dataset documents the global urban daily temperatures of 17 CMIP5 Earth system models for 2006-2015 and 2061-2070. This dataset may be useful for multiple communities regarding urban climate change, heat waves, impacts, vulnerability, risks, and adaptation applications.

This dataset contains the raw nuclear background radiation data collected in the engineering campus of University of Illinois at Urbana-Champaign. It contains three columns, x, y, and counts, which corresponds to longitude, latitude, and radiation count rate (counts per second). In addition to the original background radiation data, there are several separate files that contain the simulated radioactive sources. For more detailed README file, please refer to this documentation: <a href= "https://www.dropbox.com/s/xjhmeog7fvijml7/README.pdf?dl=0">https://www.dropbox.com/s/xjhmeog7fvijml7/README.pdf?dl=0</a>

This dataset contains the emulated global multi-model urban climate projections under RCP 8.5 and RCP 4.5 used in the article "Global multi-model projections of local urban climates" (https://www.nature.com/articles/s41558-020-00958-8). Details about this dataset and the local urban climate emulator are described in the article. This dataset documents the monthly mean projections of urban temperatures and urban relative humidity of 26 CMIP5 Earth system models (ESMs) from 2006 to 2100 across the globe. This dataset may be useful for multiple communities regarding urban climate change, impacts, vulnerability, risks, and adaptation applications.

This dataset contains scripts and data developed as a part of the research manuscript titled “Spatial and Temporal Allocation of Ammonia Emissions from Fertilizer Application Important for Air Quality Predictions in U.S. Corn Belt”. This includes (1) Spatial and temporal factors for ammonia emissions from agricultural fertilizer usage developed using the hybrid ISS-DNDC method for the Midwest U.S., (2) CAMx job scripts and outputs of predictions of ambient ammonia and total and speciated PM2.5, (3) Observation data used to statistically evaluate CAMx predictions, and (4) MATLAB programs developed to pair CAMx predictions with ground-based observation data in space and time.

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.

Data and code supporting the paper titled "Estimating the Electric Vehicle Charging Demand of Multi-Unit Dwelling Residents in the United States" by Xi Cheng and Eleftheria Kontou at the University of Illinois Urbana-Champaign. The data and the code enable analytics and assessment of multi-unit dwelling residents travel patterns and their electric vehicle charging demand.

This data set includes information on mixing metric values and distances to determine the average length scale, rates and variability of mixing downstream of 43 river confluences for 150 mixing events. The file "pmx_all data.csv" contains confluence names, the number of events per confluence site, and Pmx values measured at various actual and dimensionless downstream distances. The file "pmx_binned data.csv" provides mean Pmx values within 0.5-unit dimensionless distance bins.

This dataset contains all the necessary information to recreate the study presented in the paper entitled "Learning coagulation processes with combinatorially-invariant neural networks". This consists of (1) the aggregated output files used for machine learning, (2) the machine learning codes used to learn the presented models, (3) the PartMC model source code that was used to generate the simulation data and (4) the Python scripts used construct the scenario library for training and testing simulations. This data was used to investigate a method (combinatorally-invariant neural network) for learning the aerosol process of coagulation. This data may be useful for application of other methods.

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).

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 data repository includes the features and the trained backbone parameters used in the ICLR 2022 Paper "On the Importance of Firth Bias Reduction in Few-Shot Classification". The code accompanying this data is open-source and available at https://github.com/ehsansaleh/firth_bias_reduction The code and the data have three modules: 1. The "code_firth" module (10 files) relates to the basic ResNet backbones and logistic classifiers (e.g., Figures 2 and 3 in the main paper). 2. The "code_s2m2rf" module (2 files) relates to the S2M2R feature backbones and cosine classifiers (e.g., Figure 4 in the main paper). 3. The "code_dcf" module (3 files) relates to the few-shot Distribution Calibration (DC) method (e.g., Table 1 in the main paper). The relevant files for each module have the module name as a prefix in their name. 1. For instance, the "code_dcf_features.tar" file should be placed at the "features" directory of the "code_dcf" module. 2. As another example, "code_firth_features_cifarfs_novel.tar" should be placed in the "features" directory of the "code_firth" module, and it includes the features extracted from the novel split of mini-ImageNet dataset. Each tar-ball should be extracted in its relevant directory, and the md5 check-sums of the extracted files are also provided in the open-source code repository for verification. Please note that the actual datasets of images are not included here (since we do not own those datasets). However, helper scripts for automatically downloading the original datasets are also provided in the every module and sub-directory of the GitHub code repository.

This dataset contains model output from the Community Earth System Model, Version 2 (CESM2; Danabasoglu et al. 2020). These data were used for analysis in Impacts of Large-Scale Soil Moisture Anomalies in Southeastern South America, published in the Journal of Hydrometeorology (DOI: 10.1175/JHM-D-20-0116.1). See this publication for details of the model simulations that created these data. Four NetCDF (.nc) files are included in this dataset. Two files correspond to the control simulation (FHIST_SP_control) and two files correspond to a simulation with a dry soil moisture anomaly imposed in southeastern South America (FHIST_SP_dry; see the publication mentioned in the preceding paragraph for details on the spatial extent of the imposed anomaly). For each simulation, one file corresponds to output from the atmospheric model (file names with "cam") of CESM2 and the other to the land model (file names with "clm2"). These files are raw CESM output concatenated into a single file for each simulation. All files include data from 1979-01-02 to 2003-12-31 at a daily resolution. The spatial resolution of all files is about 1 degree longitude x 1 degree latitude. Variables included in these files are listed or linked below. Variables in atmosphere model output: Vertical velocity (omega) Convective precipitation Large-scale precipitation Surface pressure Specific humidity Temperature (atmospheric profile) Reference temperature (temp. at reference height, 2 meters in this case) Zonal wind Meridional wind Geopotential height Variables in land model output: See https://www.cesm.ucar.edu/models/cesm1.2/clm/models/lnd/clm/doc/UsersGuide/history_fields_table_40.xhtml Note that not all of the variables listed at the above link are included in the land model output files in this dataset. This material is based upon work supported by the National Science Foundation under Grant No. 1454089. We acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. The CESM project is supported primarily by the National Science Foundation. We thank all the scientists, software engineers, and administrators who contributed to the development of CESM2. References Danabasoglu, G., and Coauthors, 2020: The Community Earth System Model Version 2 (CESM2). Journal of Advances in Modeling Earth Systems, 12, e2019MS001916, https://doi.org/10.1029/2019MS001916.

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).

The data provided herein is accelerometer and strain data taken from free vibration response of pre-tensioned, partially submerged steel beam specimens (modulus of elasticity assumed = 29,000 ksi). The specimens were subjected to various levels of pre-tension, and various levels of submersion in water. The purpose of the testing was to quantify the effects of partial submersion on the vibrating frequencies of pretensioned beams. Three specimens were tested, each with different cross section (but identical cross-sectional area). The different cross sections allow investigation of the effects of specimen width as the specimen vibrates through water. The testing procedure was as follows: 1) Apply a specified level of tension in the beam. Measure tension via 3 strain gages. 2) Submerge the specimens to a specified depth of water 3) Excite the beams with either a hammer impact or a pull-and-release method (physically pull the middle of the bar and quickly release) 4) Measure the free vibration of the beam with 2 accelerometers. Schematic drawings of the test setup and the test specimens are provided, as is a picture of the test setup.

¹²CO and ¹³CO maps for six molecular clouds in the Large Magellanic Cloud, obtained with the Atacama Large Millimeter/submillimeter Array (ALMA). See the associated article in the Astrophysical Journal, and README files within each ZIP archive. Please cite the article if you use these data.

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 database provides estimates of agricultural and food commodity flows [in both tons and $US] between the US and China for the year 2017. Pairwise information is provided between US states and Chinese provinces, and US counties and Chinese provinces for 7 Standardized Classification of Transported Goods (SCTG) commodity categories. Additionally, crosswalks are provided to match Harmonized System (HS) codes and China's Multi-Regional Input Output (MRIO) commodity sectors to their corresponding SCTG commodity codes. The included SCTG commodities are: - SCTG 01: Iive animals and fish - SCTG 02: cereal grains - SCTG 03: agricultural products (except for animal feed, cereal grains, and forage products) - SCTG 04: animal feed, eggs, honey, and other products of animal origin - SCTG 05: meat, poultry, fish, seafood, and their preparations - SCTG 06: milled grain products and preparations, and bakery products - SCTG 07: other prepared foodstuffs, fats and oils For additional information, please see the related paper by Pandit et al. (2022) in Environmental Research Letters. ADD DOI WHEN RECEIVED

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

Read me file for the data repository ******************************************************************************* This repository has raw data for the publication "Enhancing Carrier Mobility In Monolayer MoS2 Transistors With Process Induced Strain". We arrange the data following the figure in which it first appeared. For all electrical transfer measurement, we provide the up-sweep and down-sweep data, with voltage units in V and conductance unit in S. All Raman modes have unit of cm^-1. ******************************************************************************* How to use this dataset All data in this dataset is stored in binary Numpy array format as .npy file. To read a .npy file: use the Numpy module of the python language, and use np.load() command. Example: suppose the filename is example_data.npy. To load it into a python program, open a Jupyter notebook, or in the python program, run: import numpy as np data = np.load("example_data.npy") Then the example file is stored in the data object. *******************************************************************************

Data from manuscript Atomic-Scale Visualization of a Cascade of Magnetic Orders in the Layered Antiferromagnet GdTe3, to be published in npj Quantum Materials. Powerpoint file has details on how the data can be opened and how the data are labeled.

This datasets provide basis of our analysis in the paper - Potential Impacts of Supersonic Aircraft on Stratospheric Ozone and Climate. All datasets here can be categorized into emission data and model output data (WACCM). All the model simulations (background and perturbation) were run to steady-state and only the datasets used in analysis are archived here.

This dataset contains all the data for the results section in the study presented in the paper entitled "Chemistry Across Multiple Phases (CAMP) version 1.0: An integrated multi-phase chemistry mode" submitted to Geoscientific Model Development (GMD). In this paper, two sets of simulations were run to test CAMP with this results included here. This consists of (1) box model inputs and outputs presented in Section 4.2 for modal, binned and particle-resolved simulations to compare the application of identical chemical mechanisms to different aerosol representations and (2) the 3D Eulerian output presented in Section 4.3.

Simulation trajectory data and scripts for Nature Nanotechnology manuscript "A DNA turbine powered by a transmembrane potential across a nanopore" that demonstrates a rationally designed nanoscale DNA-origami turbine with three chiral blades that uses a transmembrane electrochemical potential across a nanopore to drive a DNA bundle into sustained unidirectional rotations of up to 10 revolutions/s. Driven by the asymmetric mobility of a DNA duplex, the rotation direction of the turbine is set by its designed chirality and the salinity of the solvent.

Rising global temperatures and urban heat island effects challenge environmental health and energy systems at the city level, particularly in summer. Increased heatwaves raise energy demand for cooling, stressing power facilities, increasing costs, and risking blackouts. Heat impacts vary across cities due to differences in urban morphology, geography, land use, and land cover, highlighting vulnerable areas needing targeted heat mitigation. Urban tree canopies, a nature-based solution, effectively mitigate heat. Trees provide shade and cooling through evaporation, improving thermal comfort, reducing air conditioning energy consumption, and enhancing climate resilience. This report focused on the ComEd service area in the Chicago Metropolitan Region and assessed the impacts of population growth, urbanization, climate change, and an ambitious plan to plant 1 million trees. The report evaluated planting 1 million trees to quantify regional cooling effects projected for the 2030s. Afforestation locations were selected to avoid interference with existing infrastructure. Key findings include (i) extreme hot hours (>95°F) will increase from 30 to 200 per year, adding 420 Cooling Degree Days (CCD) by the 2030s, (ii) greener areas can be up to 10°F cooler than less vegetated neighborhoods in summer, (iii) tree canopies can create localized cooling, reducing temperatures by 0.7°F and lowering annual CCD by 60 to 65, and (iv) afforestation can reduce the region’s temperature by 0.7°F, saving 400 to 1100 Megawatt hours of daily power usage during summer. <b>Note: The data is available upon request from <a href="mailto:dpiclimate@uilliois.edu">dpiclimate@uilliois.edu.