This dataset accompanies the research article entitled, "Ambient vibration modal analysis of natural rock towers and fins," where we investigate the ambient vibrations of 14 rock rowers and perform modal analysis on 3D models of the landforms. Included are the vibration data and 3D models.
The similar orbital distances and incidence rates of debris disks and the prominent rings observed in protoplanetary disks suggest a potential connection between these structures. We explore this connection with new calculations that follow the evolution of rings of pebbles and planetesimals as they grow into planets and generate dusty debris. Depending on the initial solid mass and planetesimal formation efficiency, the calculations predict diverse outcomes for the resulting planet masses and accompanying debris signature. When compared with debris disk incidence rates as a function of luminosity and time, the model results indicate that the known population of bright cold debris disks can be explained by rings of solids with the (high) initial masses inferred for protoplanetary disk rings and modest planetesimal formation efficiencies that are consistent with current theories of planetesimal formation. These results support the possibility that large protoplanetary disk rings evolve into the known cold debris disks. The inferred strong evolutionary connection between protoplanetary disks with large rings and mature stars with cold debris disks implies that the remaining majority population of low-mass stars with compact protoplanetary disks leave behind only modest masses of residual solids at large radii and evolve primarily into mature stars without detectable debris beyond 30 au. The approach outlined here illustrates how combining observations with detailed evolutionary models of solids strongly constrains the global evolution of disk solids and underlying physical parameters such as the efficiency of planetesimal formation and the possible existence of invisible reservoirs of solids in protoplanetary disks.
This dataset includes a 3-D model of the Courthouse Mesa toppling rock slab instability in Utah. These data were used in conjunction with ambient seismic array data to conduct modal analyses and improve the structural characterization of the rock slope instability. Data include a 3-D model of the rock slope instability (.stl) and a COMSOL Multiphysics project file showing the boundary conditions and solutions of the best model run (.mph). This dataset accompanies the research article entitled "Rock slope instability structural characterization using array-based modal analysis."
This dataset accompanies the research article entitled, "Ground Motion Amplification at Natural Rock Arches in the Colorado Plateau ," where we analyzed 13 sandstone arches in Utah, computing site-to-reference spectral amplitude ratios from continuous ambient seismic data and comparing these to spectral ratios during earthquakes and teleseismic activity. Included in this dataset are the arch vibration data.
This dataset contains the materials necessary to reproduce the study submitted to Remote Sensing: "Tradeoffs Between UAS Spatial Resolution and Accuracy for Deep Learning Semantic Segmentation Applied to Wetland Vegetation Species Mapping". This includes the raw imagery output from the camera aboard the unoccupied aerial vehicle, the Red-Edge MX, captured over the Howard Slough Waterfowl Management Area, Utah, in August of 2020, resampled images, code to resample the images, a link to ground reference data, and the training and testing data used for the convolutional neural network in the study.
This dataset is a custom Kraken2 formatted database for the identification of Fungi from shotgun metagenomic data. Kraken2 is a k-mer based read classifier (Wood et al. 2019; https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1891-0). The dataset was built with the default k-mer length (k=35) from all publicly available fungal genomes at JGI Mycocosm ( https://mycocosm.jgi.doe.gov/mycocosm/home), and all archaea, bacteria, viral, plasmid, human, fungi, plant, and protozoa genomes, as well as the UniVec Core and nt reference database at NCBI ( https://www.ncbi.nlm.nih.gov/). The reference genomes and sequences were downloaded from JGI and NCBI in March 2020.
This file contains experimental data from the Ph.D. thesis “Mechanisms Governing Ash Aerosol Formation and Deposition during Solid Fuel Combustion” at the University of Utah. The data include particle sizes, weights, and compositions of ash aerosols and deposits formed in the combustion of a range of fossil and biomass solid fuels under a wide range of conditions. Operation pressure, fuel composition and combustor scale are changed across these tests. These experimental data can provide information and inputs for further studies, such as modeling the ash deposition process, in the future.
Research background: Concern about global warming has called for new combustion systems to be used in order to reduce CO2 emissions from coal-fired power generation. Pressurized oxy-coal combustion coupled with carbon capture and storage as well as co-firing biomass with coal are gaining more interest in building new power plants and retrofitting existing plants. The combustion conditions of these systems could be significantly changed and thus affect the ash formation and deposition. The experimental work of this thesis consists of combustion tests at various scales and conditions, namely, on a 100 kWth rated oxy-fuel combustor (OFC), a 300 kWth rated entrained flow pressurized reactor (EFPR), a 1.5 MWth rated horizontal multifuel combustor (L1500) and a 500 MWe full-size utility boiler (Hunter). The solid fuels involved in these tests include pulverized coal, torrefied wood, blend fuels of the coal and wood, and coal with K/Cl/S additives. In each test, iso-kinetically sampled ash aerosols are analyzed in terms of particle size distributions and size-segregated compositions. Ash deposition rates are measured using a surface-temperature-controlled probe which simulates the deposition process on superheater tubes.
The dataset contains Gas Chromatography (GC) data pertaining to the bulk electrolytic experiments, biocatalytic, organocatalytic reactions, and standards used in the study. The standard GC files calibrate the sensitivity of the column in the Gas Chromatograph to 1-heptanol, heptanal, and the corresponding alpha-hydrazino aldehyde. This information is used to quantify the peaks of 1-heptanol and heptanal obtained in the bulk electrolytic experiments and the alpha-hydrazino aldehyde obtained in the organocatalytic step.
Abstract: Data for Performance evaluation of the Alphasense OPC-N3 and Plantower PMS5003 sensor in measuring dust events in the Salt Lake Valley, Utah
This data file was used to estimate the performance of the Alphasense OPC-N3 and PMS5003 sensor in measuring ambient PM10, especially during dust events, and to obtain correction factors to correct the PMS5003 data. During April 2022, the OPC-N3 and PMS5003 sensors were collocated with federal equivalent method (FEM)at two Utah Division of Air Quality (UDAQ) sites: Hawthorne (HW) station and Environmental Quality (EQ) station. One residential site (RS)was also tested, with OPC-N3 and PMS5003 collocated with GRIMM portable aerosol spectrophotometer. The FEM data (PM2.5 and PM10 concentrations) and meteorological parameters (wind speed, wind direction, relative humidity, and temperature) for the two UDAQ sites were downloaded from the EPA website. The Excel sheet contained all the raw data and the processed data. The FEM, OPC-N3, and PMS5003 measurements were labeled as FEM-YYY, OPC-YYY, and PMS-YYY, where YYY represents the sites nomenclature, i.e., HW, EQ, and RS. The sheet labeled “HW”, “RS”, and,” EQ” contained the raw measurements (meteorological, PM10, and PM2.5 (whenever applicable)) for the sites. The sheet” PM-ratio-based correlation” provided the data used to get the PM-ratio-based correlation. Briefly, based on the ratio of FEM-HW PM2.5/PM10, the FEM-HW and PMS-HW PM10 measurements were segregated into six bins: PM2.5/PM10: <0.2, 0.2-0.3, 0.3-0.4, 0.4-0.5, 0.5-0.7, and >0.7. For each bin, the co-located PMS-HW PM10 concentrations were linearly regressed against the FEM-HW PM10 concentrations to obtain correction factors (slope and intercept). These correction factors were later used to correct the PMS PM10 concentrations at the other two locations (RS and EQ), presented in the sheets with labels “RS correction using GRIMM ratio”, “RS correction using opc ratio” and “EQ corrected using EQ ratio”. Each sheet also includes the calculation of RMSE and NRMSE of OPC-YYY and PMS-YYY against FEM-YYY, with YYY as the site nomenclature.
Ultralow-velocity zones (ULVZs) have been studied using a variety of seismic phases; however, their physical origin is still poorly understood. Short period ScP (S wave converted to, and reflected as, P wave from the core-mantle boundary) waveforms are extensively used to infer ULVZ properties because they may be sensitive to all ULVZ elastic moduli. However, ScP waveforms are additionally complicated by the effects of path attenuation, coherent noise, and source-time function (STF) complexity. To address these complications, we developed a hierarchical Bayesian inversion method that allows us to invert ScP waveforms from multiple events simultaneously and accounts for path attenuation and correlated noise. The inversion method is tested with synthetic predictions which show that the inclusion of attenuation is imperative to recover ULVZ parameters and that the ULVZ thickness and S-wave velocity decrease (δVS) are most reliably recovered. Utilizing multiple events reduces the effects of coherent noise and STF complexity, which in turns allows for the inclusion of more data to be used in the analyses. We next applied the method to ScP data recorded in Australia for 291 events that sample the CMB beneath the Coral Sea. Our results indicate that S-wave velocity across the region is ~-14% in average, but there is a greater variability in the south than that in the north. P-wave velocity reductions and density perturbations are mostly below 10%. These ScP data show more than one ScP post-cursor in some areas which may indicate complex 3-D ULVZ structures. Seismic data are provided for 291 earthquakes in Northern Territory, Australia.
The data are bed-scale measurements taken from virtual outcrop models (Morris, E.A., Atlas, C.E., Johnson, C.L., 2023, Architectural analysis of the Panther Tongue - virtual outcrop models) and calibrated with measurements taken at outcrop in the field.
This is the IDL code used to create the results published in Mace, G. G., Benson, S., Humphries, R., Gombert P. M., Sterner, E.: Natural marine cloud brightening in the Southern Ocean, Atmospheric Chemistry and Physics. The IDL code processes MOD03 geolocation fields, MOD06_L2 cloud retrievals, MODIS ocean color chlorophyll-a concentrations and CERES shortwave albedo data that is distributed by NASA data archives. It creates statistical results for non-precipitating or weakly precipitating warm, liquid, shallow, marine boundary layer clouds.
We determined whether a large, multi-analyte panel of circulating biomarkers can improve detection of early-stage pancreatic ductal adenocarcinoma (PDAC). We defined a biologically relevant subspace of blood analytes based on previous identification in premalignant lesions or early-stage PDAC and evaluated each in pilot studies. The 31 analytes that met minimum diagnostic accuracy were measured in serum of 837 subjects (461 healthy, 194 benign pancreatic disease, 182 early stage PDAC). We used machine learning to develop classification algorithms using the relationship between subjects based on their changes across the predictors. Model performance was subsequently evaluated in an independent validation data set from 186 additional subjects.
The dataset was collected in the process of carrying out a research on the effects of photochemical aging and interactions with secondary organic aerosols on cellular toxicity of combustion particles between the year 2021 to 2022
This dataset accounts for all jobs undertaken by the Société Le Roy Bouillon, a funerary monuments company in Paris, from 1890 to 1902. The first sheet, “Activity Data” accounts for each job and the fee charged to the client for that job. It also categories each job as either a new cemetery construction, maintenance to existing cemetery structures, or other jobs unrelated to cemetery construction. The second sheet, “Outside Paris,” summarizes the annual activity, recording the number of projects undertaken within Paris versus outside of the city, new constructions versus maintenance work, and revenue coming in from each type of job. The original records are currently housed in a private collection in Paris and were manually transcribed by the author.
This dataset summarizes burial counts according to burial type (free, temporary, or perpetual) for the cemeteries of Père-Lachaise, Montmartre, and Montparnasse in Paris. The data covers the period of 1804 to 1840 and was derived from the digitized daily records of burial for the city of Paris, which are currently held in the Archives de Paris. See Registres journaliers d'inhumation https://archives.paris.fr/r/216/cimetieres). These data are organized by the number of each burial type recorded per page of the digitized records.
The data was obtained from the FDTD simulations. For one of the FDTD simulations, the conductivity data for British Columbia was used in order to obtain the simulated data. The data obtained from simulations are post-processed using MATLAB for plotting the figures in the paper.
The objective of using the wireless sensors was to improve understanding of the heterogeneity of healthcare worker (HCW) contact with patients and the physical environment in patients’ rooms. The framework and design were based on contact networks with a) nodes defined by HCW’s, rooms, and items in the room and b) edges defined by HCW’s in the room, near the bed, and touching items. Nodes had characteristics of HCW role and room number. Edges had characteristics of day, start time, and duration. Thus, patterns and heterogeneity could be understood within contexts of time, space, roles, and patient characteristics. At the University of Utah Hospital Cardiovascular ICU (CVICU), a 20-bed unit, we collected data for 54 days. HCW contact with patients was measured using wireless sensors to capture time spent in patient rooms as well as time spent near the patient bed. HCW contact with the physical environment was measured using wireless sensors on the following items in patient rooms: door, sink, toilet, over-bed table, keyboard, vital signs monitor touchscreen, and cart. HCW’s clipped a sensor to their clothing or lanyard.
Abstract from Paper (Lange et. al, 2022): Atypical atrial flutter is seen post-ablation in patients, and it can be challenging to map. These flutters are typically set up around areas of scar in the left atrium. MRI can reliably identify left atrial scar. We propose a personalized computational model using patient specific scar information, to generate a monodomain model. In the model conductivities are adjusted for different tissue regions and flutter was induced with a premature pacing protocol. The model was tested prospectively in patients undergoing atypical flutter ablation. The simulation-predicted flutters were visualized and presented to clinicians. Validation of the computational model was motivated by recording from electroanatomical mapping. These personalized models successfully predicted clinically observed atypical flutter circuits and at times even better than invasive maps leading to flutter termination at isthmus sites predicted by the model.
In the element database, major elements are reported in weight percent oxide (wt%). Trace element concentrations are reported in parts per million (ppm). Available lithologic information (“lithology” column) and the type of igneous sample (intrusive or extrusive in the “Sample-Type” column) were included. The name of the area or of the corresponding igneous body were included when available (“Location/Body-Name” column). The location of the samples is reported in decimal degrees (WGS84), however, uncertainties explained below must be considered. Coordinates were obtained from three different ways of presenting the information about the location. The three scenarios are distinguished as “GPS”, “Figure-Point”, and “Figure-Polygon” in the “Location-Type” column. Samples with a location in a coordinate system were transformed to decimal degrees (WGS84) and classified as “GPS”. Samples individually identified in a georeferenced geologic map were approximately located after georeferencing the map in Google Earth or ArcGis (“Figure-Point”). Samples identified with a polygon in a georeferenced map (through age, body name or unidentified sample locations), but without more detailed information were approximately located in the middle of the corresponding polygon after georeferencing the map in Google Earth or ArcGis (“Figure-Polygon”). Precise “GPS” locations were obtained for 358 analyses, and approximate locations were obtained for 428 analyses. The age information was organized using three categories: “Age-Approximation”, “Age-number”, and “Age-Error”. “Age-approximation” corresponds to the age information from original paper or from an additional reference detailed in the “Reference-Age” column. “Age-number” corresponds to the age reported in the original paper or previous compilation, or to the average age calculated from a given age range. “Age-Error” corresponds to the error presented in the original paper or previous compilation, or to half of the age range. Information about the methods, analyzed material and laboratory name was included when available. Lastly, the original data sources are available in the “Reference” column. References from previous compilations incorporated in this database are specified as “Compilation-Reference”. Additional references used for constraining the age are detailed in “Reference-Age” column.
Data that were incorrectly reported (e.g., reporting average compositions instead of sample composition) or with anomalous trace element concentrations were filtered-out from the element database. Analyses from weathered or altered samples producing high total volatile content (LOI> 5 wt%) were removed. Samples with no available information to approximately locate them or to constrain their age were eliminated. Despite this screening process, the database suffers from uncertainties related to approximated ages and locations and variable information regarding the lithology, and availability of trace elements The inhomogeneity in this database is explicit and uncertainties related to the age and location should be carefully considered in any interpretation. The final compilation contains 787 geochemical analyses (major, minor and trace elements) and includes data from 36 studies.