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- Creator:
- Bellile, Emily L, Taylor, Jeremy MG, and Wolf, Gregory T
- Description:
- The University of Michigan’s Head and Neck Specialized Program of Research Excellence (SPORE) included an epidemiology project that approached every previously untreated adult head and neck squamous carcinoma (HNSCC) patient evaluated in the multidisciplinary Head and Neck Oncology Program of the University of Michigan (UM; Ann Arbor, MI) Comprehensive Cancer Center for participation in our longitudinal epidemiology study. This analytic dataset includes the most commonly requested covariates and outcome variables for survival analysis of this cohort of HNSCC patients. Data cleaning and creation of this analysis dataset was performed with SAS software v 9.3 (Carey,NC) by a biostatistician supporting multiple projects in the University of Michigan’s Head and Neck Specialized Program of Research Excellence (SPORE) and is available in RedCap for UM investigators to join with discipline specific data collected on the same cohort through a de-identified ID link.
- Keyword:
- Head and Neck Cancer, HNSCC, Squamous Cell Cancer, Epidemiology, Head and Neck Specialized Program of Research Excellence (SPORE). , Cancer, Prognosis, and Survival Analysis
- Citation to related publication:
- Cigarette use, comorbidities, and prognosis in a prospective head and neck squamous cell carcinoma population. Peterson LA, Bellile EL, Wolf GT, Virani S, Shuman AG, Taylor JM, Rozek LS; University of Michigan Head and Neck Specialized Program of Research Excellence Program. Head Neck. 2016 Dec;38(12):1810-1820. doi: 10.1002/hed.24515. Epub 2016 Jul 19. PMID: 27432208. , Development and Assessment of a Model for Predicting Individualized Outcomes in Patients With Oropharyngeal Cancer. Beesley LJ, Shuman AG, Mierzwa ML, Bellile EL, Rosen BS, Casper KA, Ibrahim M, Dermody SM, Wolf GT, Chinn SB, Spector ME, Baatenburg de Jong RJ, Dronkers EAC, Taylor JMG. JAMA Netw Open. 2021 Aug 2;4(8):e2120055. doi: 10.1001/jamanetworkopen.2021.20055. PMID: 34369988., Amlani, L; Bellile, E; Spector, M; Smith, J; Brenner, C; Rozek, L; Nguyen, A; Zarins, K; Thomas, D; McHugh, J; Taylor, J; Wolf, GT. Expression of p53 and prognosis in patients with head and neck squamous cell carcinoma (HNSCC); Int J Cancer Clin Res 2019, 6:122. DOI: 10.23937/2378-3419/1410122., and Spector ME, Bellile E, Amlani L, Zarins K, Smith J, Brenner JC, Rozek L, Nguyen A, Thomas D, McHugh JB, Taylor JMG, Wolf GT; University of Michigan Head and Neck SPORE Program. Prognostic Value of Tumor-Infiltrating Lymphocytes in Head and Neck Squamous Cell Carcinoma. JAMA Otolaryngol Head Neck Surg. 2019 Nov 1;145(11):1012-1019. doi: 10.1001/jamaoto.2019.2427. PMID: 31486841; PMCID: PMC6735419.
- Discipline:
- Science and Health Sciences
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- Creator:
- University of Michigan Museum of Paleontology and CTEES
- Description:
- Reconstructed CT slices for navicular of Cantius trigonodus (University of Michigan Museum of Paleontology catalog number UMMP 87973) as a series of TIFF images. Raw projections are not included in this dataset. The reconstructed slice data from the scan are offered here as a series of unsigned 16-bit integer TIFF images. The upper left corner of the first image (*_0000.tif) is the XYZ origin.
- Keyword:
- Paleontology, Fossil, CT, Notharctidae, UMMP, University of Michigan Museum of Paleontology, Eocene, and a537f0d8-6185-9562-9b9a-a233468bf8e1
- Discipline:
- Science
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- Creator:
- University of Michigan Museum of Paleontology and CTEES
- Description:
- Reconstructed CT slices for L cuboid of Cantius mckennai (University of Michigan Museum of Paleontology catalog number UMMP 81824) as a series of TIFF images. Raw projections are not included in this dataset. The reconstructed slice data from the scan are offered here as a series of unsigned 16-bit integer TIFF images. The upper left corner of the first image (*_0000.tif) is the XYZ origin.
- Keyword:
- Paleontology, Fossil, CT, Notharctidae, UMMP, University of Michigan Museum of Paleontology, Eocene, and e763ae30-4a86-9d02-0b8a-9297ff48cf58
- Discipline:
- Science
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- Creator:
- University of Michigan Museum of Paleontology and CTEES
- Description:
- Reconstructed CT slices for navicular of Cantius trigonodus (University of Michigan Museum of Paleontology catalog number UMMP 73318) as a series of TIFF images. Raw projections are not included in this dataset. The reconstructed slice data from the scan are offered here as a series of unsigned 16-bit integer TIFF images. The upper left corner of the first image (*_0000.tif) is the XYZ origin.
- Keyword:
- Paleontology, Fossil, CT, Notharctidae, Eocene, and 0d607d85-8d27-6be2-dbc5-9cb73f1324ae
- Discipline:
- Science
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- Creator:
- Zhang, Yingxiao MI and Steiner, Allison MI
- Description:
- Atmospheric aerosols are emitted from both natural and anthropogenic sources, and they play an important role in climate, impacting solar radiation and cloud formation. Compared to other types of aerosol particles, primary biological aerosol particles (PBAP, e.g., fungal spores, bacteria, pollen, virus, etc.) are relatively understudied. However, they are linked to adverse health effects and have the potential to influence ice nucleation at higher temperatures. Anemophilous (or wind-driven) pollen is one of the important PBAP, impacts cloud properties under some conditions, and triggers allergic diseases such as allergic rhinitis (also known as hay fever) and asthma. Because pollen emission is closely associated with environmental drivers, the climatic change could influence pollen emission and consequently the incidence of allergic disease. Using CMIP6 model data, our research projects continental-scale changes in pollen emissions at the end of the century, considering the effects of temperature, precipitation, CO2, and future vegetation distribution change. While prior studies have evaluated single types of pollen, we use a mechanistic model to comprehensively simulate total pollen across the United States from all sources. Similar to previous single-source pollen studies, our simulations suggest that pollen season duration will lengthen, and pollen emission will increase in the future, but in addition, we identify new synergies between different pollen types that can influence the maximum daily pollen. Our work highlights that the changes of overlap between pollen seasons of different vegetation taxa can magnify or mitigate the impacts of climate change, which addresses the importance to study all pollen emissions comprehensively. Given pollen is one of the most common triggers of seasonal allergies, our findings also provide information to evaluate global health conditions in the future. In this study, all of the pollen emission data are written in NetCDF files.
- Keyword:
- Pollen emission change, Climate change, Public health, Vegetation land cover change, and CO2 effects
- Citation to related publication:
- Zhang, Y. and Steiner, A. “Projected climate-driven changes in pollen emission season length and magnitude over the continental United States”, under review in Nature Communication, 2022. and yingxz. (2022). steiner-lab/pecm: PECM2.0 (2.0). Zenodo. https://doi.org/10.5281/zenodo.5874177
- Discipline:
- Science
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- Creator:
- Bradshaw, Lisa, Vernon, Julianne, Schmidt, Thomas, James, Timothy, Zhang, Jianzhi, Archbold, Hilary, Cadigan, Ken, Wolfe, John P., and Goldberg, Deborah E.
- Description:
- This is the experimental data referenced in our manuscript entitled "Influence of CUREs on STEM retention depends on demographic identities." The dataset comprises csv files with results from student surveys given to students enrolled in Biology 173 from Fall 2015 through Fall 2019 as well as institutional data of their course grades and cumulative GPA at the time they enrolled in Biology 173, and graduation and major data for student who had graduated by 2021. The survey questions used in the analysis and the IRB consent form are also included as pdfs.
- Keyword:
- undergraduate research, STEM retention, CURE, introductory biology laboratory, and education research
- Citation to related publication:
- Bradshaw, L., Vernon J., Schmidt T., James T., Zhang J., Archbold H., Cadigan K., Wolfe J.P. & Goldberg D. 2023. Research article: Influence of CUREs on STEM retention depends on demographic identities. J Microbiol Biol Educ (accepted)
- Discipline:
- Science
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- Creator:
- Salaree, Amir, Spica, Zack, and Huang, Yihe
- Description:
- The items in this bundle are supporting videos to a study of subsea seismo-acoustics carried out regarding an earthquake in the Persian Gulf. The main data used in the study is a diver's recording of the acoustic waves from the earthquake. The epicenter and topography data used in this study are publicly available as cited in the README.txt file.
- Keyword:
- Seismo-acoustics, Persian Gulf, Divers’ Microphones, Seismic Hazard, Early Warning
- Discipline:
- Science
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- Creator:
- Limon, Garrett C.
- Description:
- The work guides the processing of CAM6 data for use in machine learning applications. We also provide workflow scripts for training both random forests and neural networks to emulate physic s schemes from the data, as well as analysis scripts written in both Python and NCL in order to process our results.
- Keyword:
- Machine Learning, Climate Modeling, and Physics Emulation
- Citation to related publication:
- Limon, G. C., Jablonowski, C. (2022) Probing the Skill of Random Forest Emulators for Physical Parameterizations via a Hierarchy of Simple CAM6 Configurations [Pre Print]. ESSOAr. https://10.1002/essoar.10512353.1
- Discipline:
- Engineering and Science
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Resources for Training Machine Learning Algorithms Using CAM6 Simple Physics Packages
User Collection- Creator:
- Limon, Garrett
- Description:
- The collection contains the code and the data used to train machine learning algorithms to emulate simplified physical parameterizations within the Community Atmosphere Model (CAM6). CAM6 is the atmospheric general circulation model (GCM) within the Community Earth System Model (CESM) framework, developed by the National Center for Atmospheric Research (NCAR). GCMs are made up of a dynamical core, responsible for the geophysical fluid flow calculations, and physical parameterization schemes, which estimate various unresolved processes. Simple physics schemes were used to train both random forests and neural networks in the interest of exploring the feasibility of machine learning techniques being used in conjunction with the dynamical core for improved efficiency of future climate and weather models. The results of the research show that various physical forcing tendencies and precipitation rates can be effectively emulated by the machine learning models.
- Keyword:
- Machine Learning, Climate Modeling, and Physics Emulators
- Discipline:
- Science and Engineering
2Works -
- Creator:
- Limon, Garrett C.
- Description:
- The data represents weekly output from three 60-year CAM6 model runs. The output includes state (.h0. files) and tendency (.h1. files) fields for three difference model configurations of increasing complexity. State fields include temperature, surface pressure, specific humidity, among others; while tendencies include temperature tendencies, specific humidity tendencies, as well as precipitation rates. Using the state variables at a given time step, machine learning techniques can be trained to predict the following tendency field, which can then be applied to the state variables to provide the state at the next physics time step of the model.
- Keyword:
- Machine Learning, Climate Modeling, and Physics Emulation
- Citation to related publication:
- Limon, G. C., Jablonowski, C. (2022) Probing the Skill of Random Forest Emulators for Physical Parameterizations via a Hierarchy of Simple CAM6 Configurations [Preprint]. ESSOAr. https://10.1002/essoar.10512353.1
- Discipline:
- Engineering and Science