Data include variables used to run accelerated failure time models examining the association between the nose/throat microbiome and 1) symptom duration, 2) shedding duration, and 3) time to infection. Certain individual participant data have been excluded due to identifiability concerns. Data also include the oligotype count table and taxonomic classifications.
Data include variables used to run mixed effects models examining the association between the nose/throat microbiome and influenza virus infection. Certain individual participant data have been excluded due to identifiability concerns. Data also include the oligotype count table and taxonomic classifications. and Curation Notes: Readme updated Nov. 29, 2018 with context for oligotype and taxonomy files, and citation to associated article.
These files contain the raw data and processing parameters to go with the paper "Hierarchical structure guides rapid linguistic predictions during naturalistic listening" by Jonathan R. Brennan and John T. Hale. These files include the stimulus (wav files), raw data (matlab format for the Fieldtrip toolbox), data processing paramters (matlab), and variables used to align the stimuli with the EEG data and for the statistical analyses reported in the paper.
This eportfolio was created for the Gateway course of the Sweetland Minor in Writing to provide an opportunity for students to reflect on their growing identities as writers, as captured in their text-based and multimodal compositions produced over the Gateway semester. The title of the work contains the pseudonym created for the study while the creator field lists the student's given name to allow proper attribution for their work. The eportfolio is collected here as an artifact in the Sweetland Writing Development Study, which has been published as Developing Writers in Higher Education: A Longitudinal Study (University of Michigan Press, 2019). To learn more about this study, please see the epublication: https://doi.org/10.3998/mpub.10079890), and to learn more about the Minor in Writing program and the eportfolio prompts, please see Appendix 2a ( https://doi.org/10.3998/mpub.10079890.cmp.1) to the publication.
This eportfolio was created for the Gateway course of the Sweetland Minor in Writing to provide an opportunity for students to reflect on their growing identities as writers, as captured in their text-based and multimodal compositions produced over the Gateway semester. The title of the work contains the pseudonym created for the study while the creator field lists the student's given name to allow proper attribution for their work. The eportfolio is collected here as an artifact in the Sweetland Writing Development Study, which has been published as Developing Writers in Higher Education: A Longitudinal Study (University of Michigan Press, 2019). To learn more about this study, please see the epublication https://doi.org/10.3998/mpub.10079890, and to learn more about the Minor in Writing program and the eportfolio prompts, please see Appendix 2a ( https://doi.org/10.3998/mpub.10079890.cmp.1) to the publication.
The dataset includes the reproducible search strategies for all literature databases searched during the review, the key articles used to generate relevant search terms and test the effectiveness of the searches, the Endnote library that has all citations considered for inclusion, a flow chart describing the screening process, and the screening forms used for inclusion and exclusion.
This collection was produced as part of the project, “A ‘Big Data’ Approach to Understanding Neighborhood Effects in Chronic Illness Disparities.” The Investigators for the project are Tiffany Veinot, Veronica Berrocal, Phillipa Clarke, Robert Goodspeed, Daniel Romero, and VG Vinod Vydiswaran from the University of Michigan. The study took place from 2015-2016, with funding from the University of Michigan’s Social Sciences Annual Institute, MCubed, and the Sloan and Moore Foundations.
Contact: Tiffany Veinot, MLS, PhD
Office: 3443 North Quad
Phone: 734/615-8281
Email: tveinot@umich.edu
Data reflect the knowledge, attitudes, and beliefs of health care providers regarding neonatal near-misses in Neonatal Intensive Care Units (NICUs) in southern Ghana.
Wind exposure is a key physical driver of coastal systems in aquatic environments influencing circulation and wave dynamics. A measure of wind exposure is fetch, the distance over which wind can travel across open water. In large lake systems, such as the Laurentian Great Lakes, estimating fetch has proved to be difficult due to their vast size and complex topobathymetry. Here we describe the development of two spatially discrete indicators of exposure to provide a more accurate indicator of influence of wind exposure in the nearshore of the Laurentian Great Lakes. We summarized wind data from offshore buoys and leveraged existing tools to calculate effective fetch and relative exposure index (effective fetch scaled by mean wind speed) at a 30 m grid cell resolution. We validated these models by comparing our exposure maps to the U.S. Army Corps of Engineers Wave Information Studies models and found general agreement. These exposure maps are available for public download for the years 2004-2014.
