Dataset Search Results

References - Jeong, Gangwon, Umberto Villa, and Mark A. Anastasio. "Revisiting the joint estimation of initial pressure and speed-of-sound distributions in photoacoustic computed tomography with consideration of canonical object constraints." Photoacoustics (2025): 100700. - Park, Seonyeong, et al. "Stochastic three-dimensional numerical phantoms to enable computational studies in quantitative optoacoustic computed tomography of breast cancer." Journal of biomedical optics 28.6 (2023): 066002-066002. Overview - This dataset includes 80 two-dimensional slices extracted from 3D numerical breast phantoms (NBPs) for photoacoustic computed tomography (PACT) studies. The anatomical structures of these NBPs were obtained using tools from the Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE) project. The methods used to modify and extend the VICTRE NBPs for use in PACT studies are described in the publication cited above. - The NBPs in this dataset represent the following four ACR BI-RADS breast composition categories: > Type A - The breast is almost entirely fatty > Type B - There are scattered areas of fibroglandular density in the breast > Type C - The breast is heterogeneously dense > Type D - The breast is extremely dense - Each 2D slice is taken from a different 3D NBP, ensuring that no more than one slice comes from any single phantom. File Name Format - Each data file is stored as a .mat file. The filenames follow this format: {type}{subject_id}.mat where{type} indicates the breast type (A, B, C, or D), and {subject_id} is a unique identifier assigned to each sample. For example, in the filename D510022534.mat, "D" represents the breast type, and "510022534" is the sample ID. File Contents - Each file contains the following variables: > "type": Breast type > "p0": Initial pressure distribution [Pa] > "sos": Speed-of-sound map [mm/μs] > "att": Acoustic attenuation (power-law prefactor) map [dB/ MHzʸ mm] > "y": power-law exponent > "pressure_lossless": Simulated noiseless pressure data obtained by numerically solving the first-order acoustic wave equation using the k-space pseudospectral method, under the assumption of a lossless medium (corresponding to Studies I, II, and III). > "pressure_lossy": Simulated noiseless pressure data obtained by numerically solving the first-order acoustic wave equation using the k-space pseudospectral method, incorporating a power-law acoustic absorption model to account for medium losses (corresponding to Study IV). * The pressure data were simulated using a ring-array transducer that consists of 512 receiving elements uniformly distributed along a ring with a radius of 72 mm. * Note: These pressure data are noiseless simulations. In Studies II–IV of the referenced paper, additive Gaussian i.i.d. noise were added to the measurement data. Users may add similar noise to the provided data as needed for their own studies. - In Study I, all spatial maps (e.g., sos) have dimensions of 512 × 512 pixels, with a pixel size of 0.32 mm × 0.32 mm. - In Study II and Study III, all spatial maps (sos) have dimensions of 1024 × 1024 pixels, with a pixel size of 0.16 mm × 0.16 mm. - In Study IV, both the sos and att maps have dimensions of 1024 × 1024 pixels, with a pixel size of 0.16 mm × 0.16 mm.

This is an Excel file containing data about the physical environments of four Brazilian schools and the average daily minutes/day of physical activity and sedentary behavior exhibited by schoolchildren during school hours. The Following Key describes the basic variables: Subject IDs and Characteristics Subject_ID: ID of Subject total_days: Total number of days subject participated in experiment Gender : Gender of subject Age: Age of subject School IDs and Characteristics ID_School = ID of School school1 = 1 if ID_School = 1, else = 0 school2 = 1 if ID_School = 2, else = 0 school3 = 1 if ID_School = 3, else = 0 school4 = 1 if ID_School = 4, else = 0 TotalSiteArea: Total Site Area on School Campus PatioArea: Area of Patio(s) CourtyardArea: Area of Courtyard(s) TotalOpenArea: Total Area of Open Spaces on Campus Class: Number of Sections in the School Population: Total Number of Students Enrolled in the School

Data and code supporting the paper titled "Leveraging electric vehicles as a resiliency solution for residential backup power during outages" by Shanshan Liu, Alex Vlachokostas, and Eleftheria Kontou. The data and the code enable spatiotemporal analytics and assessment of electric vehicle charging demand, remaining driving range, residential energy use, and vehicle-to-home (V2H) energy system resilience metrics.

Data at the lake summary and individual crayfish level that supports the manuscript Sawyer, E.K., Kreps, T. A., Lodge, D. M. and E.R. Larson. “Long-term declines in body size of the invasive rusty crayfish (Faxonius rusticus) in temperate lakes." Includes size measurements of 69,303 individual rusty crayfish (Faxonius rusticus) for 17 lakes of Vilas County, Wisconsin, United States collected between 1980 and 2020.

Simulation data used to generate plots in the associated paper ("Strain rate controls alignment in growing bacterial monolayers").

Dataset used for the paper entitled "Morphological differences between wild and game-farm Mallards in North America". Large-scale releases of domesticated, game-farm Mallards to supplement wild populations have resulted in wide-spread introgressive hybridization that changed the genetic constitution of wild populations in eastern North America. The resulting gene flow is well-documented between game-farm and wild Mallards, but the mechanistic consequences from such interactions remain unknown in North America. We provide the first study to characterize and investigate potential differences in morphology between genetically known, wild and game-farm Mallards in North America. We used nine morphological measurements to discriminate between wild and game-farm Mallards with 96% accuracy. Compared to their wild counterparts, game-farm Mallards had longer bodies and tarsi, shorter heads and wings, and shorter, wider, and taller bills. The nail on the end of the bill of game-farm Mallards was longer, and game-farm Mallard bills had a greater lamellae:bill length ratio than wild Mallards. Differences in body morphologies between wild and game-farm Mallards are consistent with an artificial, terrestrial life whereby game-farm Mallards are fed pelleted foods resulting in artificial selection for a more “goose-like” bill. We posit that 1) game-farm Mallards have diverged from their wild ancestral traits of flying and filter feeding towards becoming optimized to run and peck for food; 2) game-farm morphological traits optimized over the last 400 years in domestic environments are likely to be maladaptive in the wild; and 3) the introgression of such traits into wild populations is likely to reduce fitness. Understanding effects of game-farm Mallard introgression requires analysis of various game-farm × wild hybrid generations to determine how domestically-derived traits persist or diminish with each generation.

Raw data associated with PMID: 38925247

The TrackDLO data release supports the paper, "TrackDLO: Tracking Deformable Linear Objects Under Occlusion with Motion Coherence," published in Robotics and Automation: Letters. The TrackDLO data release includes the raw image and depth data for tracking Deformable Linear Objects (DLOs) under tip occlusion, large-scale mid-section occlusion, and self-occlusion. The released data are Robot Operating System (ROS1) bag files containing raw color images and point clouds. The data were collected using a static Intel Realsense d-435 RGB-D camera while DLOs in the field of view of the camera were manipulated. The data can be used to benchmark the performance of future vision-only DLO tracking algorithms in several manipulation scenarios relevant to DLOs and to verify existing vision-only DLO tracking algorithms. Please see the RA-L paper, the code repository on GitHub, the conference presentation, and the supplementary demonstration video for more information.

The MultiDLO data release supports the paper, "MultiDLO: Simultaneous Shape Tracking of Multiple Deformable Linear Objects with Global-Local Topology Preservation," presented in the IEEE International Conference on Robotics and Automation Workshop on Representing and Manipulating Deformable Objects in May 2023. The data release includes the raw image and depth data for simultaneously tracking multiple Deformable Linear Objects (DLOs). The released data are Robot Operating System (ROS1) bag files containing raw color images and point clouds. The data were collected using a static Intel Realsense d-435 RGB-D camera while DLOs in the field of view of the camera were manipulated. The data can be used to benchmark the performance of future DLO tracking or prediction algorithms in two manipulation scenarios relevant to DLOs and to verify existing DLO tracking algorithms. Please see the accompanying extended abstract, the code repository on GitHub, and the conference presentation video referenced in the `multidlo_data_release.pdf` document for more information.

This dataset includes experimental data supporting the findings in the manuscript "Magnetostriction and Temperature Dependent Gilbert Damping in Boron Doped Fe80Ga20 Thin Films". It contains raw data for X-Ray diffraction, high resolution transmission electron microscopy, magnetic hysteresis loop measurement, magnetostriction measurement, and temperature dependent magnetic damping measurement.

This dataset contains the raw transmission electron microscopy (TEM) and scanning electron microscopy (SEM) images used to calculate the synthesis yield of patchy nanoparticles (NPs), as described in Supplementary Table 1 of the paper “Patchy Nanoparticles by Atomic “Stencilling” (2025).” All the images were taken at the Materials Research Laboratory, University of Illinois at Urbana-Champaign by Qian Chen group. 1. We have 21 subfolders, each with a name corresponding to one of the 21 patchy NPs listed in Supplementary Table 1 of the paper “Patchy Nanoparticles by Atomic “Stencilling” (2025)." 2. In TEM images, the bright and dark regions indicate the polymer patches and NP cores, respectively. 3. In SEM images, the bright and dark regions indicate the NP cores and polymer patches, respectively. 4. Each subfolder contains a “readme (subfolder name).txt” file with more detailed information about each sample.

This page contains the data for the publication "The pioneer transcription factor Zelda controls the exit from regeneration and restoration of patterning in Drosophila" published in the journal Science Advances.

This dataset provides instructions for procedures to use heat transfer analyses to estimate thermal conditions in artificial roosts for bats. The dataset contains scripts to employ in the program GNU Octave, example meteorology data, and example text files specifying roost dimensions and material properties.

Data for the publication of Magnetic Fields in the Pillars of Creation (Sarkar et al.). Contains the fits files and python scripts.

This dataset accompanies the paper "STREETS: A Novel Camera Network Dataset for Traffic Flow" at Neural Information Processing Systems (NeurIPS) 2019. Included are: *Over four million still images form publicly accessible cameras in Lake County, IL. The images were collected across 2.5 months in 2018 and 2019. *Directed graphs describing the camera network structure in two communities in Lake County. *Documented non-recurring traffic incidents in Lake County coinciding with the 2018 data. *Traffic counts for each day of images in the dataset. These counts track the volume of traffic in each community. *Other annotations and files useful for computer vision systems. Refer to the accompanying "readme.txt" or "readme.pdf" for further details.

This dataset includes analysis code used to analyze the data involved with swapping photons between superconducting qubits in separate modules though a superconducting coaxial cable bus. The dataset includes Python code to model and plot the data, CAD designs of the modules that hold the superconducting qubits, high frequency simulation software files to model the electric fields of the superconducting circuits

This dataset contains measurements of water loss as white-tailed deer (Odocoileus virginianus) retroypharyngeal lymph nodes air-dried in a refrigerator for 31 days. Daily weights for lymph nodes are recorded every 24 hours, as are the variables "firmness" and "surface wetness". "Firmness" is a categorical variable measuring how much the tissue deforms to the touch (soft, medium, or hard). "Surface wetness" is the amount of visible moisture on the outside of the lymph node (all, some, or none). Lymph node weights were measured until their weights stabilized for 3 consecutive days at two decimal places (ex. 3.02, 3.02, 3.02) or until the weights fluctuated only by 0.01 (ex. 3.02, 3.03, 3.02). Lymph nodes were from northern Illinois white-tailed deer collected as part of the Illinois Department of Natural Resources' ongoing chronic wasting disease (CWD) management efforts.

This dataset is associated with the manuscript "Residual tau-fluvalinate, a beehive acaricide, disrupts growth and metabolism in the greater wax moth, Galleria mellonella" This dataset includes 2 Excel files: 1) raw_data_bioassay.xlsx: this file contains the raw data for waxworm bioassay. There are 2 worksheets within this file: - LC50: raw data for measuring the LC50 of Galleria mellonella (greater wax moth) in laboratory and field strains exposed to tau-fluvalinate. - RGR: Relative Growth Rate, raw data for measuring body weight of field strain of Galleria mellonella exposed to tau-fluvalinate. 2) raw-data_RT-qPCR.xlsx: this file contains raw data (Ct value) of RT-qPCR.

This dataset encompasses experimental results supporting the upcoming journal paper, "Laboratory-scale assessment of CO2 sealing potential for heterogeneous caprock", which investigates the sealing potential of heterogeneous caprock. The dataset includes the measurements and analyses conducted under controlled laboratory conditions, capturing sealing potential such as permeability and breakthrough pressure.

The dataset includes responses from approximately 550 participants to survey questions about trust in images labeled with AI-related tags, compared to other images found online. The questions also explore how the type of label influences their trust.

There are two files in this dataset. File1: AffiNorm AffiNorm contains 1,001 rows, including one header row, randomly sampled from MapAffil 2018 Dataset ([**https://doi.org/10.13012/B2IDB-2556310_V1**](https://databank.illinois.edu/datasets/IDB-2556310)). Each row in the file corresponds to a particular author on a particular PubMed record, and contains the following 26 columns, comma-delimited. All columns are ASCII, except city which contains Latin-1. COLUMN DESCRIPTION 1. PMID: the PubMed identifier. int. 2. ORDER: the position of the author. int. 3. YEAR - The year of publication. int(4), eg: 1975. 4. affiliation - affiliation string of the author. eg: Department of Pathology, University of Chicago, Illinois 60637. 5. annotation_type: the number of institutions annotated, denoted by S, M, O, or Z, where "S" (single) indicates 1 institution was annotated; "M" (Multiple) indicates more than one institutions were annotated; "O" (Out of Vocabulary or None) indicates no institution was annotated, but an institution was apparently mentioned; "Z" indicates no institution was mentioned. 6. Institution: the standard name(s) of the annotated institution(s), according to ROR. if "S" (single institution), it is saved as a string, eg: University of Chicago; if "M", it is saved as a string that looks like a python list, eg: ['Public Health Laboratory Service'; 'Centre for Applied Microbiology and Research']; if "O" or "Z", then blank. 7. inst_type: the type of institution, according to ROR. the potential values are: education, funder, healthcare, company, archive, nonprofit, government, facility, other. An institution may have more than one type, eg: ['Education', 'Funder'] 8. type_edu: TRUE if the inst_type contains "Education"; FALSE otherwise. 9. RORid: ROR identifier(s), eg: https://ror.org/05hs6h993. when multiple, the order corresponds to institution (column 6) 10. RORid_label. the standard name(s) of the annotated institution(s) according to ROR.same as institution (column 6) 11. GRIDid: GRID identifier(s). eg: grid.170205.1 12. GRIDid_label: the standard name(s) of the annotated institution(s) according to GRID. eg: University of Chicago. 13. WikiDataid: WikiData identifier(s). eg: Q131252 14. WikiDataid_label: the standard name(s) of the annotated institution(s) according to WikiData. eg: University of Chicago 15. synonyms: a comma separated list of variant names from InsVar (file 2) . format of string. eg: University of Chicago, Chicago University, U of C, UChicago, uchicago.edu, U Chicago, ... 16. MapAffil-grid: GRID from the MapAffil 2018 Dataset. 17. MapAffil-grid_label: The standard name of institution from MapAffil 2018 Dataset. 18. judge_mapA: TRUE if GRIDid (column 11) contains MapAffil-grid (column 16); FALSE otherwise. 19. MapAffiltemporal-grid: GRID from the temporal version of MapAffil, http://abel.ischool.illinois.edu/data/MapAffilTempo2018.tsv.gz 20. MapAffiltemporal-grid_label: The standard name of institution from MapAffilTemporal 2018 Dataset. 21. judge_mapT: TRUE if GRIDid (column 11) contains MapAffiltemporal-grid (column 19); FALSE otherwise. 22. RORapi_query_id: ROR from ROR api tool (query endpoint) 23. RORapi_query_id_label: The standard name of institution from ROR api tool (query endpoint). format in string. 24. judge_rorapi_affiliation: TRUE if RORid (column 9) contains RORapi_query_id (column 22); FALSE otherwise. 25. rorapi_affiliation_id: ROR from ROR api tool (affiliation endpoint). 26. judge_rorapi_affiliation: TRUE if RORid (column 9) contains RORapi_affiliation (column 25); FALSE otherwise. File 2: insVar.json InsVar is a supplementary dataset for AffiNorm, which includes the institution ID and its redirected aliases from wikidata. The institution ID list is from GRID, the redirected aliases are from wiki api, for example: https://en.wikipedia.org/wiki/Special:WhatLinksHere?target=University+of+Illinois+Urbana-Champaign&namespace=&hidetrans=1&hidelinks=1&limit=100 In InsVar, the data is saved in a python dictionary format. the key is the GRID identifier, for example: "grid.1001.0" (Australian National University), and the value is a list of redirected aliases strings. {"grid.1001.0": ["ANU", "ANU College", "ANU College of Arts and Social Sciences", "ANU College of Asia and the Pacific", "ANU Union", "ANUSA", "Asia Pacific Week",    "Australia National University", "Australian Forestry School", "the Australian National University", ...], "grid.1002.3": ...}

The dataset shows all poison frogs (superfamily Dendrobatoidea) in private U.S. collections during 1990–2020. For each species and color morph, there is a date of arrival, the way it arrived in U.S. collections, and detailed notes related to its presence in the pet trade.

The materials used to provide Continuing Medical Education on ticks and tick-borne diseases in Illinois on February 1, 2023 at Carle Hospital, along with the pre- and post-quiz and deidentified data of the quiz takers. Files: "Ticks and Tick-borne Diseases of Illinois_Final_w_speaker_notes.pptx": Presentation slides used for CME course, with notes to indicate verbal commentary "CME assessment_final.docx": Pre- and post-CME quiz questions and answers, annotated to indicate correct answers and reasoning for incorrect answers "CME_prequiz_data_for_sharing.csv": De-identified data from pre-CME quiz "CME_postquiz_data_for_sharing.csv": De-identified data from post-CME quiz, including demographics "DataCleaning_forSharing.R": R file used to clean the raw data and calculate the scores "ReadMe.txt":

GIS data and geoprocessing tools associated with White and Lambert (2025) modeling paper that assesses the potential impact of development on the archaeological resources of Illinois.

These datasets contain the complete output from a Monte Carlo simulation of the number of wild cervids to test for chronic wasting disease (CWD) depending on true prevalence. Five CSVs of the simulation results are provided, split due to limitations in file size. The R code used to run the simulation and process the data is included. The data to replicated Table 1 and the data used to compare the simulation results to the CWD surveillance efforts of the Illinois Department of Natural Resources (IDNR) are also provided.