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.
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.
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.
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.
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
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.
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.
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.
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.
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.