This study aims to quantify rare earth element enrichment within coal and coal-adjacent strata in the Uinta Region of Utah and western Colorado. Rare earth elements are a subset of critical minerals used for renewable energy technology in the transition toward carbon-neutral energy. This data contains samples from seven active mines and seven stratigraphically complete cores within the Uinta Region, geochemically evaluated via portable X-ray fluorescence (n=3,113) and inductively coupled plasma-mass spectrometry (n=143) elemental abundance methods. Historical evaluations of geochemical data on Uinta Region coal and coal-adjacent data are sparse, emphasizing the statistical significance of this study’s analyses. These results support the utilization of active mines and coal processing waste piles for the future of domestic rare earth element extraction, offering economic and environmental solutions to pressing global demands.
This dataset contains room occupancy during the study period at University of Utah hospital. Admission, Discharge, and Transfer (ADT) data is captured in participating hospitals to characterize room occupancy and non-occupancy in wards. These data are pulled from multiple sources collected during the study by study staff as well as harvested EHR data. Data were adjudicated and compiled into one comprehensive file. Data manipulation included redaction of dates, replaced with study days 1-n, as well as transformation from long format to wide for ease of use.
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. This dataset contains cleaned sensor pings of RFD reads between healthcare worker worn sensors and environmental sensors placed in facility using methods described in the "Data Cleaning Steps" section.
This data set contains 12-hour manual new snow and liquid precipitation equivalent (LPE) observations collected at the Alta-Collins (CLN) snow-study plot during the 2000–2023 cool seasons (October 1–April 30 with the year defined by the ending calendar year). CLN is located mid-mountain at Alta Ski Area in the Wasatch Range of northern Utah (approximately 111.63889W, 40.57607N) at an elevation of 2945 m.
Datasets include interviews and observations of healthcare staff in 25 long-term care facilities across 7 states and two data collection visits to understand frequency, type, and reason (i.e., types of care activities provided during an interaction) for staff-resident interactions in 2019 and 2020. Staff-resident interactions were studied to examine potential for multidrug-resistant organism (MDRO) transmission within long-term care settings.
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.