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CC BY (43)
Lao, Yuyang; Caravelli, Francesco; Sheikh, Mohammed; Sklenar, Joseph; Gardeazabal, Daniel; Watts, Justin D. ; Albrecht, Alan M. ; Scholl, Andreas; Dahmen, Karin; Nisoli, Cristiano; Schiffer, Peter (2018): Data from: Classical Topological Order in the Kinetics of Artificial Spin Ice. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0598724_V1
The dataset includes the data used in the study of Classical Topological Order in the Kinetics of Artificial Spin Ice. This includes the photoemission electron microscopy intensity measurement of artificial spin ice at different temperatures as a function of time. The data includes the raw data, the metadata, and the data cookbook. Please refer to the data cookbook for more information. Note: vertex_population.xlsx file in the meta_data_code folder can be disregarded.
artificial spin ice; PEEM; topological order
Zhao, Lei; Oleson, Keith; Bou-Zeid, Elie; Krayenhoff, Eric Scott; Bray, Andrew; Zhu, Qing; Zheng, Zhonghua; Chen, Chen; Oppenheimer, Michael (2021): Multi-model urban climate projections data from: Global multi-model projections of local urban climates. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4585244_V1
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.
Urban climate; multi-model climate projections; CMIP; urban warming; heat stress
Chase, Randy (2020): Dataset for: "A Dual-Frequency Radar Retrieval of Snowfall Properties Using a Neural Network". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0791318_V2
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 (firstname.lastname@example.org) 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>
Horna Munoz, Daniel; Constantinescu, George; Rhoads, Bruce ; Lewis, Quinn; Sukhodolov, Alexander (2020): Confluence Density Effects Simulation Database. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-6257171_V1
This data set shows how density effects have an important influence on mixing at a small river confluence. The data consist of results of simulations using a detached eddy simulation model.
confluence; flow dynamics; density effects
Rhoads, Bruce ; Lewis, Quinn; Sukhodolov, Alexander; Constantinescu, George (2020): Mixing Data for Three Small Confluences in East Central Illinois. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1255710_V1
This data set includes information used to determine patterns of mixing at three small confluences in East Central Illinois based on differences in the temperature or turbidity of the two confluent flows.
mixing; confluences; flow structure
Gasparik, Jessica T.; Ye, Qing; Curtis, Jeffrey H.; Presto, Albert A.; Donahue, Neil M.; Sullivan, Ryan C.; West, Matthew; Riemer, Nicole (2020): Data from: Quantifying Errors in the Aerosol Mixing-State Index Based on Limited Particle Sample Size. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2774261_V1
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.
Atmospheric aerosols; single-particle measurements; sampling uncertainty; NetCDF
Zhang, Jun; Wuebbles, Donald; Kinnison, Douglas; Baughcum, Steven (2020): Potential Impacts of Supersonic Aircraft on Stratospheric Ozone and Climate. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9081595_V1
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.
NetCDF; Supersonic aircraft; Stratospheric ozone; Climate
Eick, Brian (2020): Acceleration and strain data for free vibration of pre-tensioned, partially-submerged beams. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7897650_V1
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.
free vibration; beam; partially-submerged; prestressed;
Zhang, Jun; Wuebbles, Donald; Kinnison, Douglas; Saiz López, Alfonso (2020): Data for: Revising the Ozone Depletion Potentials for Short-Lived Chemicals such as CF3I and CH3I. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-5952573_V1
This datasets provide basis of our analysis in the paper - Revising the Ozone Depletion Potentials for Short-Lived Chemicals such as CF3I and CH3I. All datasets here are from the model output (CAM4-chem). All the simulations (background and perturbation) were run to steady-state and only the last year outputs used in analysis are archived here.
Illinois Data Bank; NetCDF; Ozone Depletion Potential; CF3I and CH3I
Cisneros, Julia (2020): Data for: Dunes in the world’s big rivers are characterised by low-angle leeside slopes and a complex shape. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7525764_V2
Morphologic data of dunes in the World's big rivers. Morphologic descriptors for large dunes include: dune height, dune mean leeside angle, dune maximum leeside angle, dune wavelength, dune flow depth (at the crest), and the fractional height of the maximum slope on the leeside for each dune. Morphologic descriptors for small dunes include: dune height, dune mean leeside angle, dune maximum leeside angle, dune wavelength, and dune flow depth (at the crest).
dune; bedform; rivers; morphology;
Zhang, Yujie; Araiza Bravo, Rodrigo; Chitambar, Eric; Lorenz, Virginia (2019): Dataset for "Channel Activation of CHSH Nonlocality". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3686727_V1
This dataset provides the raw data, code and related figures for the paper, "Channel Activation of CHSH Nonlocality"
Super-activation; Non-locality breaking channel
Kamuda, Mark; Huff, Kathryn (2019): Automated Isotope Identification and Quantification Using Artificial Neural Networks. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4860767_V1
This dataset contains gamma-ray spectra templates for a source interdiction and uranium enrichment measurement task. This dataset also contains Keras machine learning models trained using datasets created using these templates.
gamma-ray spectroscopy; neural networks; machine learning; isotope identification; uranium enrichment; sodium iodide; NaI(Tl)
Wong, Tony; Hughes, A; Tokuda, K; Indebetouw, R; Onishi, T; Bandurski, J. B.; Chen, C. H. R.; Fukui, Y; Glover, S. C. O.; Klessen, R. S.; Pineda, J. L.; Roman-Duval, J.; Sewilo, M.; Wojciechowski, E.; Zahorecz, S. (2019): Data for: Relations Between Molecular Cloud Structure Sizes and Line Widths in the Large Magellanic Cloud. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7090706_V1
<sup>12</sup>CO and <sup>13</sup>CO 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.
Soliman, Aiman; Mackay, Andrew; Schmidt , Arthur; Allan, Brian; Wang, Shaowen (2018): Dataset for: Quantifying the geographic distribution of building coverage across the US for urban sustainability studies. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4137411_V1
A complete building coverage area dataset (i.e. area occupied by building structures, excluding other built surfaces such as roads, parking lots, and public parks) at the level of census block groups for the contiguous United States (CONUS). The dataset was assembled based on an ensemble prediction of nonlinear hierarchical models to account for spatial heterogeneities in the distribution of built surfaces across different urban communities. Percentage of impervious land and housing density were used as predictors of the estimated area of buildings and cross-validation results showed that the product estimated area represented by buildings with a mean error of 0.049 %.
Building Coverage Area; Urban Geography; Regional; Sustainability; US Census Block Groups; CONUS Data
Wang, Wenrui; Wang, Tao; Amin, Vivek P.; Wang, Yang; Radhakrishnan, Anil; Davidson, Angie; Allen, Shane R.; Silva, T. J.; Ohldag, Hendrik; Balzar, Davor; Zink, Barry L.; Haney, Paul M.; Xiao, John Q.; Cahill, David G.; Lorenz, Virginia O.; Fan, Xin (2019): Dataset for "Anomalous Spin-Orbit Torques in Magnetic Single-Layer Films". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7281207_V1
This dataset provides the raw data, code and related figures for the paper, "Anomalous Spin-Orbit Torques in Magnetic Single-Layer Films."
spintronics; spin-orbit torques; magnetic materials
Lao, Yuyang; Schiffer, Peter (2019): Isolated artificial spin ice kinetics. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0214000_V1
This is the experimental data of isolated nanomagnet islands with or without the presence of large nanomagnet islands. The small islands are made of Permalloy materials with size of 170 nm by 470 nm by 2.5 nm. The systems are measured at a temperature where the small islands are fluctuating around room temperature. The data is recorded as photoemission electron microscopy intensity. More details about the data can be found in the note.txt and Spe_2016.xlsx file. Note: The raw data folders are stored in five volumes during the compression. All five volumes are needed in order to recover the original folder.
artificial spin ice; magnetism
Lao, Yuyang; Schiffer, Peter (2019): Tetris artificial spin ice kinetics . University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0779814_V1
This is the experimental data of tetris artificial spin ice. The islands are made of Permalloy materials with size of 170 nm by 470 nm by 2.5 nm. The systems are measured at a temperature where the islands are fluctuating around room temperature. The data is recorded as photoemission electron microscopy intensity. More details about the dataset can be found in the file Note.txt and Tetris_data_list.xlsx Note: 2 files name bl11_teris600_033 and bl11_tetris600_2_135 are not recorded in the excel sheet because they are corrupted during the measurement. Any data that is not recorded in the excel sheet is either corrupted or of low quality. From files *_028 to *_049, tetris is spelled with “t” while in the raw data folder without “t”. This is a typo. Throughout the dataset, tetris and teris are supposed to have the same meaning.
artificial spin ice
Balasubramanian, Srinidhi; Koloutsou-Vakakis, Sotiria; Rood, Mark (2019): Spatial and Temporal Allocation of Ammonia Emissions from Fertilizer Application Important for Air Quality Predictions in U.S. Corn Belt. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4085385_V1
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.
Air quality; Ammonia; Emissions; PM2.5; CAMx; DNDC; spatial resolution; Midwest U.S.
Zhao, Jifu (2019): UIUC Campus Gamma-Ray Radiation Data. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9119873_V1
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>
Fernandez, Roberto; Parker, Gary; Stark, Colin P. (2019): Meltwater Meandering Channels on Ice: Centerlines and Images. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4384362_V1
This dataset includes images and extracted centerlines from experiments looking at the formation and evolution of meltwater meandering channels on ice. The laboratory data includes centimeter- and millimeter-scale rivulets. Dataset also includes an image and corresponding centerlines from the Peterman Ice Island. All centerlines were manually digitized in Matlab but no distributable code was developed for the process. Once digitized, centerlines were smoothed and standardized following methods and routines developed by other authors (Zolezzi and Guneralp, 2016; Guneralp and Rhoads, 2008). Details about the preparation of the centerlines and processing with these methods is included in the dissertation by Fernández (2018) linked to this dataset. "Millimeter scale and Peterman Ice Island centerlines.pdf": This file includes the images of two mm-scale experimetns and the Peterman Ice Island image. Seventeen centerlines were digitized from the former and seven were digitized from the latter. Those centerlines are shown above the images themselves. "Centimeter scale rivulet images.pdf": This file includes images corresponding to all cm-scale centerlines used for the analysis presented in the dissertation by Fernandez (2018). Each image has a short caption indicating the run ID and the time at which it was captured. The images were used to extract centerlines to look at the planform evolution of cm-scale meltwater meandering rivulets on ice. Images include 26 centerlines from four different runs. "Meltwater meandering channel centerlines.xlsx": This spreadsheet contains the centerline data for all fifty centerlines. The workbook includes 51 sheets. The first 50 are related to each one of the channels. The mm scale and Peterman Ice Island ones are identified using the same IDs shown in "Millimeter scale and Peterman Ice Island centerlines.pdf". The cm-scale centerlines are identified by run ID and a number indicating the time in minutes (with t = 0 min being the time at which water started flowing over the ice block). The naming convention is also associated to the images in "Centimeter scale rivulet images.pdf". The last sheet in the workbook includes a summary of the channel widths measured from every image for each centerline. The 50 sheets with the centerline information have four columns each. The titles of the columns are X, Y, S, and C. X,Y are dimensionless coordinates of the centerline. S is dimensionless streamwise coordinate (location along the centerline). C is dimensionless curvature value. All these values were non-dimensionalized with the channel width. See Fernandez (2018), Zolezzi and Guneralp (2016), and Guneralp and Rhoads (2008) for more details regarding the process of smoothing, standardizing and non-dimensionalization of the centerline coordinates.
Meltwater, Meandering, Ice, Supraglacial, Experiments
Xu, Zewei; Wang, Shaowen (2018): A 3DCNN-based method to land cover classification. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0024113_V1
A 3D CNN method to land cover classification using LiDAR and multitemporal imagery
3DCNN; land cover classification; LiDAR; multitemporal imagery
Schiffer, Peter; Le, Brian L. (2017): Magnetotransport measurements of connected kagome artificial spin ice in armchair and zigzag configurations. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1859347_V1
Enclosed in this dataset are transport data of kagome connected artificial spin ice networks composed of permalloy nanowires. The data herein are reproductions of the data seen in Appendix B of the dissertation titled "Magnetotransport of Connected Artificial Spin Ice". Field sweeps with the magnetic field applied in-plane were performed in 5 degree increments for armchair orientation kagome artificial spin ice and zigzag orientation kagome artificial spin ice.
Magnetotransport; artificial spin ice; nanowires
Finlon, Joseph (2018): Matched Radar and Microphysical Properties During MC3E. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-6396968_V1
This dataset contains best estimates of the particle size distribution and measurements of the radar reflectivity factor and total water content for instances where ground-based radar and airborne microphysical observations were considered collocated with each other.
MC3E; MCS; GPM; microphysics; radar; aircraft; ice
Lewis, Quinn; Bruce, Rhoads (2018): Lewis, Quinn; Bruce, Rhoads (2018): Data from: LSPIV Measurements of Two-dimensional Flow Structure in Streams using Small Unmanned Aerial Systems: Parts 1 and 2. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0360762_V1
These data are for two companion papers on use of LSPIV obtained from UAS (i.e. drones) to measure flow structure in streams. The LSPIV1 folder contains spreadsheet data used in each case referred to in Table 1 in the manuscript. In the spreadsheets, there is a cell that denotes which figure was constructed with which data. The LSPIV2 folder contains spreadsheets with data used for the constructed figures, and are labeled by figure.
LSPIV; drone; UAS; flow structure; rivers
Karigerasi, Manohar H.; Wagner, Lucas K.; Shoemaker, Daniel P. (2018): Geometric analysis of magnetic dimensionality. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3897093_V1
This dataset contains bonding networks and tolerance ranges for geometric magnetic dimensionality. The data can be searched in the html frontend above, code obtained at the GitHub repository, or the raw data can be downloaded as csv below. The csv data contains the results of 42520 compounds (unique icsd_code) from ICSD FindIt v3.5.0. The csv is semicolon-delimited since some fields contain multiple comma-separated values.
materials science; physics; magnetism; crystallography