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- Description:
- 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.
- Keyword:
- protozoa, Kraken2 database, plant, bacteria, genomes, fungi, human, archaea, plasmid, and virus
- Subject:
- Mycology and Genomics
- Creator:
- Dentinger, Bryn T. M.
- Owner:
- Bryn Dentinger
- Language:
- English
- Date Uploaded:
- 06/08/2022
- Date Modified:
- 06/03/2024
- Date Created:
- 2020-03-29
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Software or Program Code and Dataset
- Identifier:
- https://doi.org/10.7278/S50d-154b-fppf
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- Description:
- This SAS program can be used to calculate Grocery Purchase Quality Index-2016 (GPQI-2016) total and component scores from food purchase data (dollars and cents) that have been summarized into the 29 categories of the USDA Food Plans. The code can be adapted to calculate GPQI-2016 scores for data that use a smaller number of categories.
- Keyword:
- nutrition informatics, food expenditures, Healthy Eating Index, grocery shopping, grocery quality, nutrition, and USDA
- Subject:
- human nutrition and food purchasing
- Creator:
- Brewster, Philip J., Guenther, Patricia M., and Hurdle, John F.
- Contributor:
- Tran, Lethuy and Chidambaram, Valli
- Owner:
- BRIAN MCBRIDE
- Based Near Label Tesim:
- University of Utah, Utah, United States
- Language:
- English
- Date Uploaded:
- 07/10/2019
- Date Modified:
- 06/03/2024
- Date Created:
- 2018-01-01 to 2018-12-31
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Software or Program Code
- Identifier:
- https://doi.org/10.7278/S5HD7ST1
-
- Description:
- This dataset encompasses the valid, completed, and qualitative data collected during the 2021 “Survey of Anime Convention Attendance in Response to Covid-19.” This survey was distributed online through social media platforms, community spaces, and industry listservs/resources in order to reach organizers, attendees, and fans of anime conventions (i.e., “cons”). The survey was intended to discover how those who attend anime conventions (i.e., "con-goers") have been experiencing changes in the anime convention scene during the COVID-19 pandemic, particularly in 2020-2021. Traditionally, anime cons and con-related activities such as cosplay (dressing up as a favorite character) are held in person. However, in 2020-2021, most cons have been cancelled or moved online; this is the first time in over 40 years, in the US and worldwide, that the anime convention scene has been so quiet. With this survey, investigators sought to capture firsthand impressions of this unprecedented moment, learning how con-goers were experiencing these changes and whether they had safety or other concerns about anime cons returning in late 2021 and early 2022.
- Keyword:
- anime, COVID-19, consumer studies, anime conventions, popular culture, pandemic, consumers, conventions, and fans
- Subject:
- Popular Culture
- Creator:
- Alberto, Maria and Tringali, Billy
- Owner:
- Language:
- English
- Date Uploaded:
- 09/16/2021
- Date Modified:
- 06/03/2024
- Date Created:
- 2021-05-01 to 2021-09-01
- License:
- CC BY – Allows others to use and share your data, even commercially, with attribution.
- Resource Type:
- Dataset
- Identifier:
- https://doi.org/10.7278/S50D-8FF8-NT67
-
- Description:
- Data were collected from the free online available International Ionosphere Reference (IRI) database ( https://kauai.ccmc.gsfc.nasa.gov/instantrun/iri) for January 1, 2020. The data were then interpolated from 1km to our desired resolution of 0.1km. This is is profile used to simulate the 3D FDTD models to observe the propagation of power line harmonic radiation through the ionosphere.
- Keyword:
- Ionosphere and Inhomogeneous Ionosphere
- Subject:
- FDTD (mathematics), plasmas (physics) , and ionospheric electron density
- Creator:
- Pedgaonkar, Apoorva
- Owner:
- Based Near Label Tesim:
- Salt Lake City, Utah, United States
- Language:
- English
- Date Uploaded:
- 07/12/2024
- Date Modified:
- 09/06/2024
- Date Created:
- 2020-01-20
- License:
- Public Domain – This data is free of copyright restrictions (e.g. government sponsored data).
- Resource Type:
- Dataset
- Identifier:
- http://doi.org/10.7278/S5d-hpp0-g1wy
-
- Description:
- 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.
- Keyword:
- transmission, infection prevention, multidrug-resistant organism (MDRO), long-term care facility, care delivery patterns, and nursing home
- Subject:
- infection prevention, nursing homes, and healthcare-associated infection
- Creator:
- University of Utah Epicenter
- Owner:
- Date Uploaded:
- 04/15/2024
- Date Modified:
- 04/16/2024
- Date Created:
- 2019-01-01 to 2020-06-01
- License:
- Public Domain – This data is free of copyright restrictions (e.g. government sponsored data).
-
- Description:
- Environmental noise may affect hearing and a variety of non-auditory disease processes. There is some evidence that, like other environmental hazards, noise may be differentially distributed across communities based on socioeconomic status. We aimed to a) predict daytime noise pollution levels and b) assess disparities in daytime noise exposure in Chicago, Illinois. We measured 5-minute daytime noise levels (Leq, 5-min) at 75 randomly selected sites in Chicago in March 2019. Geographically based variables thought to be associated with noise were obtained and used to fit a noise land-use regression model to estimate the daytime environmental noise level at the centroid of the census blocks. Demographic and socioeconomic data were obtained from the City of Chicago for the 77 community areas, and associations with daytime noise levels were assessed using spatial autoregressive models. Mean sampled noise level (Leq, 5-min) was 60.6 dBA. The adjusted R2 and root mean square error of the noise land use regression model and the validation model were 0.60 and 4.67 dBA and 0.51 and 5.90 dBA, respectively. Nearly 75% of city blocks and 85% of city communities have predicted daytime noise level higher than 55 dBA. Of the socioeconomic variables explored, only community per capita income was associated with mean community predicted noise levels and was highest for communities with incomes in the 2nd quartile. Both the noise measurements and land-use regression modeling demonstrate that Chicago has levels of environmental noise likely contributing to the total burden of environmental stressors. Noise is not uniformly distributed across Chicago; it is associated with proximity to roads and public transportation and is higher among communities with mid-to-low incomes per capita, which highlights how socially and economically disadvantaged communities may be disproportionately impacted by this environmental exposure.
- Keyword:
- noise, land use regression, health disparities, geospatial data, and environmental exposure
- Subject:
- Environmental Sciences
- Creator:
- Conroy, Lorraine M., Huang, Yu-Kai, Mitchell, Uchechi A., and Jones, Rachael M.
- Owner:
- Rachael Jones
- Based Near Label Tesim:
- Chicago, Illinois, United States
- Language:
- English
- Date Uploaded:
- 05/19/2021
- Date Modified:
- 05/01/2024
- Date Created:
- 2019-03-01 to 2019-03-31
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Dataset
- Identifier:
- https://doi.org/10.7278/S50D-SPT1-FNHH
-
- Description:
- This study of the role and impact of the subject selector in academic libraries is unique and long overdue. We focused on the Pac-12 university libraries, a representative sample of nationwide academic libraries. The strength of our investigation is this small, focused sample size and unique statistical analysis of subject specialists. There is a wide variety among these libraries with respect to the hiring requirements for MLIS, the MLIS with an additional advanced-subject master’s degree, and those libraries who hire non-MLIS librarians. This investigation has the possibility of promoting greater awareness for the future of subject specialists in academic libraries.
- Keyword:
- advanced subject degrees, academic libraries, academic librarianship, subject librarianship, collection development, subject specialist, and salary compensation
- Subject:
- Library science
- Creator:
- Love, April and Mirfakhrai, Mohammad
- Owner:
- April Love
- Based Near Label Tesim:
- United States, , United States
- Language:
- English
- Date Uploaded:
- 06/14/2021
- Date Modified:
- 05/01/2024
- Date Created:
- 2018-03-31 to 2018-07-31
- License:
- CC BY – Allows others to use and share your data, even commercially, with attribution.
- Resource Type:
- Dataset
- Identifier:
- https://doi.org/10.7278/S50D-F4CC-ZMV2
-
- Description:
- 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.
- Keyword:
- invasive species, wetlands, phragmites australis, Howard Slough Waterfowl Management Area, multispectral imagery, remote sensing, and uncrewed aerial systems
- Subject:
- geography
- Creator:
- Saltiel, Troy M., Campbell, Michael J., Thompson, Thomas R., Hambrecht, Keith R., and Dennison, Philip E.
- Contributor:
- Christian Hardwick (Utah Geological Survey)
- Owner:
- Based Near Label Tesim:
- Howard Slough Waterfowl Management Area, Utah, United States
- Language:
- English
- Date Uploaded:
- 04/21/2022
- Date Modified:
- 12/05/2023
- Date Created:
- 2020-08-11
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Dataset
- Identifier:
- https://doi.org/10.7278/S50d-h9z0-5ft8
-
- Description:
- We apply Bayesian inference to instrument calibration and experimental-data uncertainty analysis for the specific application of measuring radiative intensity with a narrow-angle radiometer. We develop a physics-based instrument model that describes temporally varying radiative intensity, the indirectly measured quantity of interest, as a function of scenario and model parameters. We identify a set of five uncertain parameters, find their probability distributions (the posterior or inverse problem) given the calibration data by applying Bayes’ Theorem, and employ a local linearization to marginalize the nuisance parameters resulting from errors-in-variables. We then apply the instrument model to a new scenario that is the intended use of the instrument, a 1.5 MW coal-fired furnace. Unlike standard error propagation, this Bayesian method infers values for the five uncertain parameters by sampling from the posterior distribution and then computing the intensity with quantifiable uncertainty at the point of a new, in-situ furnace measurement (the posterior predictive or forward problem). Given the instrument-model context of this analysis, the propagated uncertainty provides a significant proportion of the measurement error for each in-situ furnace measurement. With this approach, we produce uncertainties at each temporal measurement of the radiative intensity in the furnace, successfully identifying temporal variations that were otherwise indistinguishable from measurement uncertainty.
- Subject:
- Validation and Simulation
- Creator:
- Scheib, Kaitlyn, Spinti, Jennifer P., Smith, Sean T., Harding, N. Stanley, Smith, Philip J., and Draper, Teri S.
- Owner:
- Philip Smith
- Based Near Label Tesim:
- Salt Lake City, Utah, United States
- Language:
- English
- Date Uploaded:
- 12/01/2020
- Date Modified:
- 01/28/2022
- Date Created:
- November 2020
- Resource Type:
- Software or Program Code
- Identifier:
- https://doi.org/10.7278/S50D6AFQ84VP
-
- Description:
- 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.
- Keyword:
- Paris, France, funerary monuments, nineteenth centruy, cemeteries, and business history
- Subject:
- funerary structures, cemeteries, and nineteenth century (dates CE)
- Creator:
- Alexander, Kaylee P.
- Owner:
- Kaylee Alexander
- Based Near Label Tesim:
- Paris, Île-de-France, France
- Language:
- English
- Date Uploaded:
- 02/09/2023
- Date Modified:
- 06/03/2024
- Date Created:
- 2020-01-01 to 2020-01-31
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Dataset
- Identifier:
- https://doi.org/10.7278/S50d-t4sn-67e3