Datasets

This dataset is the field measurements of water depth at the Wax Lake delta conducted in late 2012.

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.

This dataset is the field measurements of water depth at the Wax Lake delta on the date 2012-12-01.

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.

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.

This dataset consists of all the figure files that are part of the main text of the manuscript titled "Magnetic-field sensitive charge density waves in the superconductor UTe2". For detailed information on the individual files refer to the readme file.

Agriculture is the largest user of water in the United States. Yet, we do not understand the spatially resolved sources of irrigation water use by crop. The goal of this study is to estimate crop-specific irrigation water use from surface water withdrawals, total groundwater withdrawals, and nonrenewable groundwater depletion for the Continental United States. Water use by source is provided for 20 crops and crop groups from 2008 to 2020 at the county spatial resolution. These results present the first national-scale assessment of irrigation by crop, county, water source, and year. In total, there are nearly 2.5 million data points in this dataset (3,142 counties; 13 years; 3 water sources; and 20 crops). This dataset supports the paper by Ruess et al (2023) in Water Resources Research, https://doi.org/10.1029/2022WR032804. When using, please cite as: Ruess, P.J., Konar, M., Wanders, N. , & Bierkens, M. (2023). Irrigation by crop in the Continental United States from 2008 to 2020, Water Resources Research, 59, e2022WR032804. https://doi.org/10.1029/2022WR032804

This data set contains all the map data used for "Quantifying transportation energy vulnerability and its spatial patterns in the United States". The multiple dimensions (i.e., exposure, sensitivity, adaptive capacity) of transportation energy vulnerability (TEV) at the census tract level in the United States, the changes in TEV with electric vehicles adoption, and the detailed data for Chicago, Los Angeles, and New York are in the dataset.

This dataset contains data derived from large-scale particle velocimetry measurements obtained at the confluence of the Saline Branch and an unnamed tributary in Illinois. The data were collected using two cameras positioned about the confluence, one mounted on a cable and the other mounted on a tripod. A description of the content of the files can be found in Description of Files.rtf.

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.

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).

This dataset provides the raw data, code and related figures for the paper, "Channel Activation of CHSH Nonlocality"

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 %.

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>

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.

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.