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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
Pence, Justin; Mohaghegh, Zahra (2018): DT-BASE - Training Quality Causal Model. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3357538_V3
Dataset includes structure and values of a causal model for Training Quality in nuclear power plants. Each entry refers to a piece of evidence supporting causality of the Training Quality causal model. Includes bibliographic information, context-specific text from the reference, and three weighted values; (M1) credibility of reference, (2) causality determined by the author, and (3) analysts confidence level. (M1, M2, and M3) Weight metadata are based on probability language from <a href="https://www.ipcc.ch/ipccreports/tar/vol4/english/index.htm" style="text-decoration: none" >Intergovernmental Panel on Climate Change (IPCC), Climate Change 2001: Synthesis Report</a>. The language can be found in the “Summary for Policymakers” section, in the PDF format. Weight Metadata: LowerBound_Probability, UpperBound_Probability, Qualitative Language 0.99, 1, Virtually Certain 0.9, 0.99, Very Likely 0.66, 0.9, Likely 0.33, 0.66, Medium Likelihood 0.1, 0.33, Unlikely 0.01, 0.1, Very Unlikely 0, 0.01, Extremely Unlikely
Data-Theoretic; Training; Organization; Probabilistic Risk Assessment; Training Quality; Causal Model; DT-BASE; Bayesian Belief Network; Bayesian Network; Theory-Building
Park, Jungsik; Le, Brian; Sklenar, Joseph; Chern, Gia-wei; Watts, Justin; Schiffer, Peter (2017): Data from: Magnetic response of brickwork artificial spin ice. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1528275_V1
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.
Zhang, Qian; Li, Chunyan (2016): Model dataset for the Wax Lake delta. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9511904_V1
This dataset is the numerical simulation data of the computational study of the cold front-related hydrodynamics in the Wax Lake delta. The numerical model used is ECOM-si.
Wax Lake delta; Hydrodynamics; Cold front
Zhang, Qian (2016): Public agency data of the Wax Lake delta. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4871125_V1
This dataset includes data of the the Wax Lake delta from four public agencies: NGDC, USGS, NDBC, and NOAA CO-OPS. Besides the original data, the processed data associated with analyzed figures are also shared.
Wax Lake delta; NOAA CO-OPS; NGDC; USGS; NDBC
Zhang, Qian; Li, Chunyan (2016): Bathymetry data of the Wax Lake delta (2012-12-01). University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4810873_V1
This dataset is the field measurements of water depth at the Wax Lake delta on the date 2012-12-01.
Wax Lake delta; Bathymetry
Zhang, Qian; Chunyan, Li; Braud, Dewitt (2016): LIDAR data for the Wax Lake delta. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3764213_V1
This dataset is about a topographic LIDAR survey (saved in “waxlake-lidar.img”) that was conducted over the Wax Lake delta, between longitudes −91.5848 to −91.292 degrees, and latitudes 29.3647 to 29.6466 degrees. Different from other elevation data, the positive value in the LIDAR data indicates land elevation, while the zero value implies riverbed without identifying specific water depth.
LIDAR; Wax Lake delta
Zhang, Qian; Li, Chunyan (2017): Meterology and ocean data collected at LSU WAVCIS Lab. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2436375_V1
This dataset includes both meteorology and oceanography data collected at stations (CSI03, CSI06, and CSI09) near the Gulf of Mexico from the LSU WAVCIS (Waves-Current-Surge Information System) lab. The associated data analysis visualization is also saved in separate directories.
WAVCIS; Gulf of Mexico; Meteorology; Oceanography