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published: 2024-03-01
Chen, Chu-Chun; Dominguez, Francina (2024): Data for The location of large-scale soil moisture anomalies affects moisture transport and precipitation over southeastern South America. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0536017_V1
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: 2023-12-20
Xie, Yuxuan Richard; Castro, Daniel C.; Rubakhin, Stanislav S.; Trinklein, Timothy J.; Sweedler, Jonathan V.; Fan, Lam (2023): Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9740536_V1
Important Note: the raw transient files need to be downloaded through this separate link: https://uofi.box.com/s/oagdxhea1wi8tvfij4robj0z0w8wq7j4. Once downloaded, place the file within the within the .d folder in the unzipped 20210930_ShortTransient_S3_5 folder to perform reconstruction step. The minimal datasets to run the computational pipeline MEISTER introduced in the manuscript titled "Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry". The key steps of our computational pipeline include (1) tissue mass spectrometry imaging (MSI) reconstruction; (2) multimodal image registration and 3D reconstruction; (3) regional analysis; and (4) single-cell and tissue data integration. Detailed protocols to reproduce our results in the manuscript are provided with an example data set shared for learning the protocols. Our computational processing codes are implemented mostly in Python as well as MATLAB (for image registration).
keywords:
deep learning;mass spectrometry;single cells
published: 2023-04-06
Yao, Lehan; Lyu, Zhiheng; Li, Jiahui; Chen, Qian (2023): Data for Unsupervised Sinogram Inpainting for Nanoparticle Electron Tomography (UsiNet) for missing wedge correction. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7963044_V1
Example data for https://github.com/chenlabUIUC/UsiNet The data contains computer simulated and experimental tilting series (or sinograms) of gold nanoparticles. Two training data examples are provided: 1. simulated_data.zip 2. experimental_data.zip In each zip folder, we include an image_data.zip and a training_data.zip. The former is for viewing and only the latter is needed for model training. For more details, please refer to our GitHub repository.
keywords:
electron tomography; deep learning
published: 2019-03-19
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.
keywords:
Meltwater, Meandering, Ice, Supraglacial, Experiments
published: 2019-05-01
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.
keywords:
Air quality; Ammonia; Emissions; PM2.5; CAMx; DNDC; spatial resolution; Midwest U.S.
published: 2018-01-11
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
keywords:
Data-Theoretic; Training; Organization; Probabilistic Risk Assessment; Training Quality; Causal Model; DT-BASE; Bayesian Belief Network; Bayesian Network; Theory-Building
published: 2018-05-21
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.
keywords:
materials science; physics; magnetism; crystallography
published: 2018-08-29
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.
keywords:
MC3E; MCS; GPM; microphysics; radar; aircraft; ice
published: 2018-12-13
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
keywords:
3DCNN; land cover classification; LiDAR; multitemporal imagery
published: 2024-01-30
Aishwarya, Anuva; Madhavan, Vidya (2024): Data for Melting of the charge density wave by generation of pairs of topological defects in UTe2. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-6515700_V1
The data files are for the paper entitled: Melting of the charge density wave by generation of pairs of topological defects in UTe2 to be published in Nature Physics. The data was obtained on a 300 mK custom designed Unisoku scanning tunneling microscope using the Nanonis module. All the data files have been named based on the Figure numbers that they represent.
keywords:
superconductivity; triplet; topology; heavy fermion; Kondo; magnetic field; charge density wave
published: 2016-11-28
Marshak, Stephen; Domrois, Stefanie; Abert, Curtis; Larson, Timothy (2016): DEM of the Great Unconformity, USA cratonic platform. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7546972_V1
These show the topography and relief of the Precambrian surface of the Cratonic Platform of the United States.
keywords:
precambrian; geology; relief; elevation
published: 2016-12-12
Zhang, Qian; Li, Chunyan (2016): Bathymetry data of the Wax Lake delta (late 2012). University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1001307_V1
This dataset is the field measurements of water depth at the Wax Lake delta conducted in late 2012.
keywords:
Wax Lake delta; Bathymetry
published: 2016-12-12
Zhang, Qian; Li, Chunyan (2016): Current data of the Wax Lake delta. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1752285_V1
This dataset is the field measurements of currents at two stations (Big Hogs Bayou and Delta1) in the the Wax Lake delta in November 2012 and February 2013.
keywords:
Wax Lake delta; Currents
published: 2016-12-12
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.
keywords:
LIDAR; Wax Lake delta
published: 2016-12-12
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.
keywords:
Wax Lake delta; Bathymetry
published: 2016-12-12
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.
keywords:
Wax Lake delta; NOAA CO-OPS; NGDC; USGS; NDBC
published: 2016-12-18
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.
keywords:
Wax Lake delta; Hydrodynamics; Cold front
planned publication date: 2025-01-20
Zhang, Yue; Huang, Siyuan; Zhao, Helin; Hossain, Mohammad Abir; van der Zande, Arend (2025): Enhancing Carrier Mobility In Monolayer MoS2 Transistors With Process Induced Strain. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9294852_V1
This dataset is for the preprint paper "Enhancing Carrier Mobility In Monolayer \ce{MoS2} Transistors With Process Induced Strain".
published: 2023-07-31
Zhang, Yue; Hossain, Mohammad Abir; Hwang, Kelly; Ferrari, Paolo; Maduzia, Joe; Pena, Tera; Wu, Stephen; Ertekin, Elif; van der Zande, Arend (2023): Dataset for Design and Pattern Strain in 2D materials. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2595358_V1
published: 2024-01-04
Kim, Hyunchul; Zhao, Helin; van der Zande, Arend (2024): Stretchable thin-film transistors based on wrinkled graphene and MoS2 heterostructures. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7325893_V1
This data set includes all of data related to stretchable TFTs based on 2D heterostructures including optical images of TFTs, Raman and Photoluminescence characteristics data, Transport measurement data, and AFM topography data. Abstract Two-dimensional (2D) materials are outstanding candidates for stretchable electronics, but a significant challenge is their heterogeneous integration into stretchable geometries on soft substrates. Here, we demonstrate a strategy for stretchable thin film transistors (2D S-TFT) based on wrinkled heterostructures on elastomer substrates where 2D materials formed the gate, source, drain, and channel, and characterized them with Raman spectroscopy and transport measurements.
keywords:
2D materials; 2D heterstructures; Stretchable electronics; transistors; buckling engineering
published: 2024-01-04
Blind-Doskocil, Leanne; Trapp, Robert J.; Nesbitt, Stephen W. (2024): Radar analyzed quasi-linear convective system mesovortices during the Propagation, Evolution, and Rotation in Linear Storms (PERiLS) Project. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3906187_V1
This is a collection of 31 quasi-linear convective system (QLCS) mesovortices (MVs) that were 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. 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 within the C-band On Wheels (COW) domain, one of the research radars deployed in the field for the PERiLS project. Further details on how MVs were identified are provided below. This analysis was completed using the Gibson Ridge radar-viewing software (GR2Analyst). 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. ND MVs were ones that usually had a tornado warning placed on them 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 sheet, there are a total of 37 sheets. 32 of the 37 sheets are for each MV that was examined. One of those MVs (sheet titled 'EFU_tor_iop3') was not included in the final count of MVs (31). This MV produced an EFU tornado and only tornadoes that were given ratings were used to calculate MV statistics. 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 pretornadic, predamaging (wind damage), and pre-nondamaging 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.
keywords:
quasi-linear convective system; QLCS; tornado; radar; mesovortex; PERiLS; low-level rotation; tornadic; nontornadic; wind-damaging; Propagation, Evolution, and Rotation in Linear Storms; tornado warning; C-band On Wheels
published: 2023-10-26
Maffeo, Christopher; Aksimentiev, Aleksei (2023): Simulation trajectories for "A DNA turbine powered by a transmembrane potential across a nanopore". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3458097_V1
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.
keywords:
All-atom MD simulation; DNA; nanotechnology; motors and rotors
published: 2023-08-24
Kim, Hyunchul; Zhao, Helin; van der Zande, Arend (2023): Data for Strain-resilient field-effect transistors based on wrinkled graphene/MoS2 heterostructures. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-6434046_V1
This data set includes all of data related to strain-resilient FETs based on 2D heterostructures including optical images of FETs, Raman characteristics data, Transport measurement data, and AFM topography data.
keywords:
2D materials; Stretchable electronics
published: 2022-12-31
Maffeo, Christopher; Wilson, Jim; Quednau, Lauren; Aksimentiev, Aleksei (2022): Simulation Trajectories for "DNA double helix, a tiny electromotor". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-6770800_V1
Trajectory data for Nature Nanotechnology manuscript "DNA double helix, a tiny electromotor" that demonstrates how an electric field applied along the helical axis of a DNA or RNA molecule will generate an electroosmotic flow that causes the duplex to spin about that axis, much like a turbine.
keywords:
All-atom MD simulation; DNA; nanotechnology; motors and rotors
published: 2023-06-29
Pandit, Akshay; Karakoc, Deniz Berfin; Konar, Megan (2023): Data for: Spatially detailed agricultural and food trade between China and the United States. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3649756_V1
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
keywords:
Food flows; High-resolution; County-scale; Bilateral; United States; China