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.
Gere, A.R., Editor. Developing Writers in Higher Education: A Longitudinal Study, fulcrum.org. University of Michigan Press. https://doi.org/10.3998/mpub.10079890
There is a directory tree inside this zipped file. The main directory has the Adobe Illustrator plots of the figures in the paper, Space Weather journal manuscript # 2018SW002067, "Model evaluation guidelines for geomagnetic index predictions" by M. W. Liemohn and coauthors. The three subdirectories have the files for the individual models, the data to which they are compared, and the IDL code used to create the figure plots and metrics calculations. and Date coverage is specific to each model. The RAMSCB model covers January 2005, the WINDMI model all of 2014, and the UPOS model 1.5 solar cycles, from 1 October 2001 through 29 July 2013.
Data is collected from research flights based in West Memphis, Arkansas, covering the Mississippi River Valley. The data file contains all merged flight data from each flight day.
Gvakharia, A., Kort, E.A., Smith, M.L., Conley, S., 2018. Testing and evaluation of a new airborne system for continuous N2O, CO2, CO, and H2O measurements: the Frequent Calibration High-performance Airborne Observation System (FCHAOS). Atmospheric Measurement Techniques; Katlenburg-Lindau 11, 6059. https://doi.org/10.5194/amt-11-6059-2018
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.
Lee KH, Gordon A, Shedden K, Kuan G, Ng S, Balmaseda A, Foxman B. The respiratory microbiome and susceptibility to influenza virus infection. PloS One. 2019;14:e0207898. https://doi.org/10.1371/journal.pone.0207898
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.
Lee KH, Gordon A, Shedden K, Kuan G, Ng S, Balmaseda A, Foxman B. The respiratory microbiome and susceptibility to influenza virus infection. PloS One. 2019;14:e0207898. https://doi.org/10.1371/journal.pone.0207898
The eastern coastal basins of Brazil are a series of small and isolated rivers that drain directly into the Atlantic Ocean. During the Pleistocene, sea-level retreat caused by glaciations exposed the continental shelf, resulting in enlarged paleodrainages that connected rivers that are isolated today. Using Geographic Information System (GIS), we infer the distribution of these paleodrainages, and their properties for the east Brazilian coast. Here, we publicly make available the shapefiles that demonstrate the paleodrainage structure along the Brazilian coast during the largest sea-level retreats in the Pleistocene, the riverine vectors during the same period and the coastal line for a drop of -125m in the sea.
Percent Weight Change Data:
The model was run continuously on a daily time step for seasonal intervals (Spring: March thru May; Summer: June thru August; Fall: September thru November) as well as contiguously from Spring to Fall to assess total growth over the likely growing season (March thru November). CSV files represent the simulated weight change (%) of Bighead and Silver Carp for the respective time periods associated with the file name. Initial fish mass for each seasonal interval and growing season was 4350 g for Silver Carp and 5480 g for Bighead Carp. Maximum and mean total weight change (%) was determined for three depth ranges (near surface depths [NS]: 0 – 10 m; deep chlorophyll layer depths [DCL]: 10 - 50 m; and whole water column [WC]). Coordinates are in decimal degrees.
File naming convention: speciesSeasonWtChange (e.g. bigheadFallWtChange = % weight change of Bighead Carp from September through November)
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Monthly Habitat Quality Data:
Rdata files contain matrices of Bighead or Silver carp growth rate potential as represented as a mass-proportional growth rate (gram of carp/gram of carp/day [g/g/d]) for the 15th day of each month. Habitats with growth rate potential >= 0 g/g/d were deemed suitable.
Matrix attributes:
Rows: Row numbers refer to the spatial node with 20 equally-spaced vertical layers.
Columns: Columns 1-20 refer to the growth rate potential value for each vertical layer of each node. Vertical layers are evenly spaced based on the total depth of the water column for each node. Depth for each node can be found in the grid attributes data file. Columns 21 ("meanG") and 22 ("Gmax") represent the average and maximum growth rate potential, respectively, of the fish across the whole water column for the corresponding node.
File naming convention: species_MonthNumber (e.g. silver_06 = Silver carp growth rate potential in June)
Spatial coordinates for each node can be found in the grid attributes data files.,
Grid attributes data:
This Rdata file provides the spatial reference data and other grid attributes. Coordinates are provided in UTM (x & y) and latitude and longitude (decimal degrees). Depth (meters) for each node is listed in this file.
, GRP Model code:
Details bioenergetics equations, foraging equation, functions for running the model on a monthly time-step and daily time step, and functions for basic analyses. Model is coded in R., and
The simulated input data (prey and temperature) used to run our model is not included in this data set. Instead we provide the model code, grid attributes, and outputs of the model.
The readRDS() function (R Base Package v.3.5.1) is required to read in .Rdata files in R.
This work contains the experimental data and associated analysis that are described in the research publication entitled "Ultra-specific and Amplification-free Quantification of Mutant DNA by Single-molecule Kinetic Fingerprinting". This work contains multiple zip files, each of which represents one of the principal experiment groups presented in the publication. Each experiment group contains movie and analysis files corresponding to various experimental conditions related to that experiment group.
This dataset contains all data used to generate the figures in The Cryosphere manuscript “Measuring Snow Specific Surface Area with 1.30 and 1.55 micro-meter Bidirectional Reflectance Factors,” by Adam Schneider, Mark Flanner, and Roger De Roo. These data support the theory, calibration, and application of the Near-Infrared Emitting and Reflectance Monitoring Dome (NERD), an instrument engineered to rapidly retrieve surface snow specific surface area in the field. Note that this deposit includes a microCT scan database for natural snowfall samples collected in New Hampshire during 2015-2017, comprised of raw tiff files as well as reconstructions, binarized reconstructions, and some 3D model reconstructions. and Running python scripts generally require that the following packages are installed: NumPy, SciPy, Matplotlib, Pandas, and ipdb (for debugging).