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Datasets

published: 2021-02-10
 
This dataset consists of microclimatic temperature and vegetation structure maps at a 3-meter spatial resolution across the Great Smoky Mountains National Park. Included are raster models for sub-canopy, near-surface, minimum and maximum temperature averaged across the study period, season, and month during the growing season months of March through November from 2006-2010. Also available are the topographic and vegetation inputs developed for the microclimate models, including LiDAR-derived vegetation height, LiDAR-derived vegetation structure within four height strata, solar insolation, distance-to-stream, and topographic convergence index (TCI).
keywords: microclimate buffering; forest vegetation structure; temperature; Appalachian Mountains; climate downscaling; understory; LiDAR
published: 2021-08-15
 
This data set contains mass spectrometry data used for the publication "mspack: efficient lossless and lossy mass spectrometry data compression".
keywords: mass-spectrometry data; compression; proteomics
published: 2022-01-31
 
This dataset contains results from WRF simulations over northern South America. The Orinoco Low-Level Jet (OLLJ) and the Cross-Equatorial Moisture Transport are important circulation structures of the climate of tropical South America. We explore the sensitivity of the OLLJ and cross-equatorial transport to the representation of surface fluxes and turbulence by using two different Land Surface Model (LSM) schemes (Noah and CLM) and three Planetary Boundary Layer (PBL) schemes (YSU, QNSE and MYNN).
keywords: WRF; Orinoco LLJ; preicpitation
published: 2021-02-18
 
Increasingly pervasive location-aware sensors interconnected with rapidly advancing wireless network services are motivating the development of near-real-time urban analytics. This development has revealed both tremendous challenges and opportunities for scientific innovation and discovery. However, state-of-the-art urban discovery and innovation are not well equipped to resolve the challenges of such analytics, which in turn limits new research questions from being asked and answered. Specifically, commonly used urban analytics capabilities are typically designed to handle, process, and analyze static datasets that can be treated as map layers and are consequently ill-equipped in (a) resolving the volume and velocity of urban big data; (b) meeting the computing requirements for processing, analyzing, and visualizing these datasets; and (c) providing concurrent online access to such analytics. To tackle these challenges, we have developed a novel cyberGIS framework that includes computationally reproducible approaches to streaming urban analytics. This framework is based on CyberGIS-Jupyter, through integration of cyberGIS and real-time urban sensing, for achieving capabilities that have previously been unavailable toward helping cities solve challenging urban informatics problems. The files included in this dataset functions as follows: 1) Spatial_interpolation.ipynb is a python based Jupyter notebook that enables users to conduct spatial interpolation with AoT data; 2) Urban_Informatics.ipynb is a Jupyter notebook that helps to explore the AoT dataset; 3) chicago-complete.weekly.2019-09-30-to-2019-10-06.tar includes all the high-frequency urban sensing data from AoT sensors from 2019 September 30th to 2019 October 6th collected in Chicago, US; 4) sensors.csv is a processed dataset including information about the temperature in Chicago, and it is used in Spatial_interpolation.ipynb.
keywords: CyberGIS; Urban informatics; Array of Things
published: 2019-12-12
 
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.
keywords: gamma-ray spectroscopy; neural networks; machine learning; isotope identification; uranium enrichment; sodium iodide; NaI(Tl)
published: 2021-02-24
 
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.
keywords: Climate modeling; atmospheric science; hydrometeorology; hydroclimatology; soil moisture; land-atmosphere interactions
published: 2021-02-01
 
These datasets provide the basis of our analysis in the paper - The Potential Impact of a Clean Energy Society On Air Quality. All datasets here are from the model output (CAM4-chem). All the simulations were run to steady-state and only the outputs used in the analysis are archived here.
keywords: clean energy; ozone; particulates
published: 2022-08-29
 
Example scripts and configuration files needed to perform select simulations described in the manuscript "Percolation transition prescribes protein size-specific barrier to passive transport through the nuclear pore complex."
keywords: Nuclear Pore Complex; simulation setup
published: 2021-10-13
 
Drainage network analysis is fundamental to understanding the characteristics of surface hydrology. Based on elevation data, drainage network analysis is often used to extract key hydrological features like drainage networks and streamlines. Limited by raster-based data models, conventional drainage network algorithms typically allow water to flow in 4 or 8 directions (surrounding grids) from a raster grid. To resolve this limitation, this paper describes a new vector-based method for drainage network analysis that allows water to flow in any direction around each location. The method is enabled by rapid advances in Light Detection and Ranging (LiDAR) remote sensing and high-performance computing. The drainage network analysis is conducted using a high-density point cloud instead of Digital Elevation Models (DEMs) at coarse resolutions. Our computational experiments show that the vector-based method can better capture water flows without limiting the number of directions due to imprecise DEMs. Our case study applies the method to Rowan County watershed, North Carolina in the US. After comparing the drainage networks and streamlines detected with corresponding reference data from US Geological Survey generated from the Geonet software, we find that the new method performs well in capturing the characteristics of water flows on landscape surfaces in order to form an accurate drainage network. This dataset contains all the code, notebooks, datasets used in the study conducted for the research publication titled " A Vector-Based Method for Drainage Network Analysis Based on LiDAR Data ". ## What's Inside A quick explanation of the components * `A Vector Approach to Drainage Network Analysis Based on LiDAR Data.ipynb` is a notebook for finding the drainage network based on LiDAR data *`Picture1.png` is a picture representing the pseudocode of our new algorithm * HPC` folder contains codes for running the algorithm with sbatch in HPC ** `execute.sh` is a bash script file that use sbatch to conduct large scale analysis for the algorithm ** `run.sh` is a bash script file that calls the script file `execute.sh` for large scale calculation for the algorithm ** `run.py` includes the codes implemented for the algorithm * `Rowan Creek Data` includes data that are used in the study ** `3_1.las` and `3_2.las ` are the LiDAR data files that is used in our analysis presented in the paper. Users may use this data file to reproduce our results and may replace it with their own LiDAR file to run this method over different areas ** `reference` folder includes reference data from USGS *** `reference_3_1.tif` and `reference_3_2.tif` are reference data for the drainage system analysis retrieved from USGS.
keywords: CyberGIS; Drainage System Analysis; LiDAR
published: 2022-03-25
 
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.
published: 2022-06-22
 
This dataset helps to investigate the Spatial Accessibility to HIV Testing, Treatment, and Prevention Services in Illinois and Chicago, USA. The main components are: population data, healthcare data, GTFS feeds, and road network data. The core components are: 1) `GTFS` which contains GTFS (<a href="https://gtfs.org/">General Transit Feed Specification</a>) data which is provided by Chicago Transit Authority (CTA) from <a href="https://developers.google.com/transit/gtfs">Google's GTFS feeds</a>. Documentation defines the format and structure of the files that comprise a GTFS dataset: <a href="https://developers.google.com/transit/gtfs/reference?csw=1">https://developers.google.com/transit/gtfs/reference?csw=1</a>. 2) `HealthCare` contains shapefiles describing HIV healthcare providers in Chicago and Illinois respectively. The services come from <a href="https://locator.hiv.gov/">Locator.HIV.gov</a>. 3) `PopData` contains population data for Chicago and Illinois respectively. Data come from The American Community Survey and <a href="https://map.aidsvu.org/map">AIDSVu</a>. AIDSVu (https://map.aidsvu.org/map) provides data on PLWH in Chicago at the census tract level for the year 2017 and in the State of Illinois at the county level for the year 2016. The American Community Survey (ACS) provided the number of people aged 15 to 64 at the census tract level for the year 2017 and at the county level for the year 2016. The ACS provides annually updated information on demographic and socio economic characteristics of people and housing in the U.S. 4) `RoadNetwork` contains the road networks for Chicago and Illinois respectively from <a href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> using the Python <a href="https://osmnx.readthedocs.io/en/stable/">osmnx</a> package. <b>The abstract for our paper is:</b> Accomplishing the goals outlined in “Ending the HIV (Human Immunodeficiency Virus) Epidemic: A Plan for America Initiative” will require properly estimating and increasing access to HIV testing, treatment, and prevention services. In this research, a computational spatial method for estimating access was applied to measure distance to services from all points of a city or state while considering the size of the population in need for services as well as both driving and public transportation. Specifically, this study employed the enhanced two-step floating catchment area (E2SFCA) method to measure spatial accessibility to HIV testing, treatment (i.e., Ryan White HIV/AIDS program), and prevention (i.e., Pre-Exposure Prophylaxis [PrEP]) services. The method considered the spatial location of MSM (Men Who have Sex with Men), PLWH (People Living with HIV), and the general adult population 15-64 depending on what HIV services the U.S. Centers for Disease Control (CDC) recommends for each group. The study delineated service- and population-specific accessibility maps, demonstrating the method’s utility by analyzing data corresponding to the city of Chicago and the state of Illinois. Findings indicated health disparities in the south and the northwest of Chicago and particular areas in Illinois, as well as unique health disparities for public transportation compared to driving. The methodology details and computer code are shared for use in research and public policy.
keywords: HIV;spatial accessibility;spatial analysis;public transportation;GIS
published: 2021-06-17
 
Model output dataset (6-hourly) from the Weather Research and Forecasting (WRF) model simulations over South America with the added capability of water vapor tracers to track the moisture that originates over the Amazon and the La Plata river basins. The simulations were performed for the period 2003-2013 at 20-km horizontal resolution fully coupled with the Noah-MP land surface model. Limited number of original output variables sufficient for reproducing the analyses in papers that cite this dataset are included here. The attached wrfout_southamerica_readme.txt contains detailed information about the file format and variables. For the complete model dataset, contact francina@illinois.edu.
keywords: WRF; Amazon; La Plata; South America; Numerical tracers
published: 2022-03-23
 
This dataset is a estimation of county-to-county commodity delivery through cold chain in 2017. For each county pair, the weight[kg] and value[$] of the cold chain flow between origin and destination for SCTG 5 and SCTG 7 commodities are estimated by our model. - SCTG 5 - Meat, poultry, fish, seafood, and their preparations - SCTG 7 - Other prepared foodstuffs, fats, and oils
keywords: food flows; cold chain; county-scale; United States; carbon footprint
published: 2022-03-20
 
Data for "Generic character of charge and spin density waves in superconducting cuprates". - Neutron scattering data for SDW - RSXS scans of CDW of LESCO x=0.10, 0.125, 0.15, 0.17, 0.20 at various temperatures. - Temperature dependence of CDW peak intensity, correlation length, Qcdw (Lorentzian fit, S(q,T) fit, Landau-Ginzburg fit) - XAS data of LESCO x=0.10, 0.125, 0.15, 0.17, 0.20
published: 2021-02-28
 
This dataset contains the RegCM4 simulations used in the article " Implementation of dynamic ageing of carbonaceous aerosols in regional climate model RegCM". This dataset was used to investigate the impact of a new aging parameterisation scheme implemented in a regional climate model RegCM4. The dataset contains two sets of simulations: Expt_fix and Expt_dyn. It consists of the seasonal mean and daily mean values of the variables that were used to create the visualizations of this study. The Expt_fix and Expt_dyn dataset contain 34 and 38 NetCDF files, respectively. The CERES_vs_2expts_new.mat file is the comparison between CERES shortwave downward flux at the surface and same model outputs from two experiments for clear sky and all sky conditions. -------------------------------------------------- The following information about the dataset was generated on 2021-01-08 by SUDIPTA GHOSH <b>GENERAL INFORMATION</b> <i>1. Date of data collection (single date, range, approximate date):</i> 2019-01-01 to 2019-12-31 <i>2. Geographic location of data collection:</i> Urbana-Champaign,Illinois, USA <i>3. Information about funding sources that supported the collection of the data:</i> This work is supported by the MoEFCC under the NCAP-COALESCE project [Grant No. 14/10/2014-CC]. The first author acknowledges DST-INSPIRE fellowship [IF150055] and Fulbright-Kalam Climate Doctoral fellowship. N. R. acknowledges funding from NSF AGS-1254428 and DOE grant DE-SC0019192. Department of Science and Technology, Funds for Improvement of Science and Technology infrastructure in universities and higher educational institutions (DST-FIST) grant (SR/FST/ESII-016/2014) are acknowledged for the computing support. <b>DATA & FILE OVERVIEW</b> <i>1. File List:</i> Expt_fix and Expt_dyn datasets contain the analysed seasonal means and daily means of the variables that have been used to create the visualizations of this study. Each of the Expt_fix and Expt_dyn datasets contains 34 and 38 NetCDF files, respectively. <i>2. Relationship between files, if important:</i> NA <i>3. Additional related data collected that was not included in the current data package:</i> No <b>METHODOLOGICAL INFORMATION</b> <i>1. Description of methods used for collection/generation of data: </i> The model RegCM4 code is freely available online from <a href="http://gforge.ictp.it/gf/project/regcm/">http://gforge.ictp.it/gf/project/regcm/</a>. The anthropogenic aerosol emissions considered for the simulations are taken from IIASA inventory. The data used can be easily accessed online <a href="http://clima-dods.ictp.it/regcm4/">http://clima-dods.ictp.it/regcm4/</a> website. TRMM observed precipitation data can be assessed from <a href="https://giovanni.gsfc.nasa.gov/giovanni/">https://giovanni.gsfc.nasa.gov/giovanni/</a> website. CRU temperature data is available at <a href="https://crudata.uea.ac.uk/cru/data/hrg/">https://crudata.uea.ac.uk/cru/data/hrg/</a>. CERES satellite surface shortwave downward fluxes are available at <a href="https://ceres.larc.nasa.gov/data/">https://ceres.larc.nasa.gov/data/</a> website. Input files for the RegCM4 model are archived in <a href="http://clima-dods.ictp.it/regcm4/">http://clima-dods.ictp.it/regcm4/</a> website. This dataset contains the RegCM4 simulations used in the article " Implementation of dynamic ageing of carbonaceous aerosols in regional climate model RegCM ". Two sets of simulations: Expt_fix and Expt_dyn consists of the output data . This dataset only contains the analysed seasonal mean and daily mean of the variables that have been used to create the visualizations of this study. Each of Expt_fix and Expt_dyn contains 34 and 38 NetCDF files respectively. This dataset was used to investigate the impact of a new aging parameterisation scheme implemented in a regional climate model RegCM4. <i>2. Methods for processing the data:</i> Seasonal Mean and daily average values were extracted from 6-hourly model output. <i>3. Instrument- or software-specific information needed to interpret the data:</i> CDO-1.7.1, Grads-2.0.a9, Matlab2016b <i>4. Standards and calibration information, if appropriate:</i> NA <i>5. Environmental/experimental conditions:</i> NA <i>6. Describe any quality-assurance procedures performed on the data:</i> NA <i>7. People involved with sample collection, processing, analysis and/or submission:</i> Sudipta Ghosh, Nicole Riemer, Graziano Giuliani, Filippo Giorgi, Dilip Ganguly, Sagnik Dey <b>DATA-SPECIFIC INFORMATION FOR: Expt_fix_data.tar.gz</b> <i>1. Number of variables:</i> 29 <i>2. Number of cases/rows:</i> NA <i>3. Variable List:</i> Mass concentration (Kg m-3) of BC, BC_HB, BC_HL, OC, OC_HB, OC_HL; Columnar burden (mg m-2)] of BC, BC_HL, BC_HB, OC; Dry deposition flux (mg m-2 day-1) of BC_HB, BC_HL, OC_HB, OC_HL; Wet deposition flux due washout (mg m-2 day-1) of BC_HB, BC_HL, OC_HB, OC_HL; Wet deposition flux due to rainout (mg m-2 day-1) of BC_HB, BC_HL OC_HB, OC_HL; AOD (unit less), precipitation (Kg m-2 s-1), temperature (K) , v-wind (m s-1), u-wind (m s-1), Surface shortwave downward flux (W m-2), Shortwave radiative forcing at the surface and top of atmosphere (W m-2) <b>DATA-SPECIFIC INFORMATION FOR: Expt_dyn_data.tar.gz</b> <i>1. Number of variables:</i> 30 <i>2. Number of cases/rows:</i> NA <i>3. Variable List:</i> Mass concentration (Kg m-3) of BC, BC_HB, BC_HL, OC, OC_HB, OC_HL; Columnar burden (mg m-2)] of BC, BC_HL, BC_HB, OC; Dry deposition flux (mg m-2 day-1) of BC_HB, BC_HL OC_HB, OC_HL; Wet deposition flux due washout (mg m-2 day-1) of BC_HB, BC_HL OC_HB, OC_HL; Wet deposition flux due to rainout (mg m-2 day-1) of BC_HB, BC_HL OC_HB, OC_HL; AOD (unit less); precipitation (Kg m-2 s-1); temperature (K); v-wind (m s-1); u-wind (m s-1); Surface shortwave downward flux (W m-2); Shortwave radiative forcing at the surface and top of atmosphere (W m-2); ageingscale (s-1) <b>DATA-SPECIFIC INFORMATION FOR: CERES_vs_2expts_new.mat</b> <i>1. Number of variables:</i> 12 <i>2. Number of cases/rows:</i> NA <i>3. Variable List:</i> Surface shortwave downward flux for clear sky (W/m-2) for CERES, Expt_fix, Expt_dyn (for winter JF and monsoon JJAS seasons); Surface shortwave downward flux for all sky conditions (W/m-2) for CERES, Expt_fix, Expt_dyn (for winter JF and monsoon JJAS seasons). <b>NOTE:</b> The following information applies for all three (3) files: <i> Missing data codes:</i> NA <i>Specialized formats or other abbreviations used:</i> NA
keywords: Carbonaceous aerosols; ageing parameterisation scheme; regional climate model; NetCDF
published: 2024-03-01
 
This dataset contains model output from the Community Earth System Model, Version 1 (CESM1; Hurrell et al., 2013) and variables from the European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis v5 (ERA5; Hersbach et al., 2020). These data were used for analysis in “The location of large-scale soil moisture anomalies affects moisture transport and precipitation over southeastern South America”, published in Geophysical Research Letters. Acknowledgments: This work was supported by NSF Award AGS-1852709. We acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the NSF. We thank Dr. Haiyan Teng for providing guidance on setting up the CESM experiments and offering valuable advice. 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 Hurrell, J. W., and Coauthors, 2013: The Community Earth System Model: A Framework for Collaborative Research. Bull. Amer. Meteor. Soc., 94, 1339–1360, https://doi.org/10.1175/BAMS-D-12-00121.1
keywords: atmospheric sciences; climate modeling; land-atmosphere interactions; soil moisture; regional atmospheric circulation; southeastern South America
published: 2023-03-27
 
This dataset contains the full data used in the paper titled "Enabling High Precision Gradient Index Control in Subsurface Multiphoton Lithography," available at https://doi.org/10.1021/acsphotonics.2c01950 . The data used for Table 1 can be found in the dataset for the related Figure 8. Some supplemental figures' data can be found in the main figures data: Figure S2's data is contained in Figure 6. Figure S4 and Table S1 data is derived from Figure 6. Figure S9 is derived from Figure 7. Figure S10 is contained in Figure 7. Figure S12 is derived from Figure 6 and the Python code prism-fringe-analysis. Figures without a data file named after them do not have any data affiliated with them and are purely graphical representations.
published: 2020-08-01
 
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.
keywords: confluence; flow dynamics; density effects
published: 2021-09-06
 
Airglow images and Meteor radar data used in the paper "Mesospheric gravity wave activity estimated via airglow imagery, multistatic meteor radar, and SABER data taken during the SIMONe–2018 campaign".
keywords: airglow; meteor radar; gravity waves; momentum flux;
published: 2019-09-25
 
<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.
keywords: Radio astronomy
published: 2018-06-20
 
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.
keywords: artificial spin ice; PEEM; topological order
published: 2019-05-20
 
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.
keywords: artificial spin ice
published: 2020-01-20
 
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.
keywords: Illinois Data Bank; NetCDF; Ozone Depletion Potential; CF3I and CH3I
published: 2017-08-11
 
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.
keywords: Magnetotransport; artificial spin ice; nanowires