Wyoming Data Repository
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Digital data for the geologic map of the Muddy Gap quadrangle, Carbon County, Wyoming
No full textThis data release contains a digitized version of the original 1:24,000-scale analog geologic map titled "Geologic map of the Muddy Gap quadrangle, Carbon County, Wyoming," published by the U.S. Geological Survey in 1968. The database includes geospatial features (points, lines, and polygons) with matching attribute tables, nonspatial descriptive and reference tables, and ancillary resource files for correct symbolization, in formats that conform to the Geologic Map Schema (GeMS)--a standard format for the digital publication of geologic maps, available at http://ngmdb.usgs.gov/Info/standards/GeMS/
Digital data for the geologic map of the Whiskey Peak quadrangle, Carbon, Fremont, and Sweetwater counties, Wyoming
No full textThis data release contains a digitized version of the original 1:24,000-scale analog geologic map titled "Geologic map of the Whiskey Peak quadrangle, Carbon, Fremont, and Sweetwater Counties, Wyoming," published by the U.S. Geological Survey in 1968. The database includes geospatial features (points, lines, and polygons) with matching attribute tables, nonspatial descriptive and reference tables, and ancillary resource files for correct symbolization, in formats that conform to the Geologic Map Schema (GeMS)--a standard format for the digital publication of geologic maps, available at http://ngmdb.usgs.gov/Info/standards/GeMS/
Field-grown B.stricta rhizosphere microbiomes exhibit minimal diel changes in microbial membership and potential function
No full textFasta file of raw data, csv metadata for fasta reads, and cleaned phyloseq object containing bacterial 16S rRNA transcript and 16S rRNA gene reads.
Abstract for paper is as follows: The rhizosphere microbiome greatly affects plant health and fitness. Quantifying bacterial responses to fine-scale plant-mediated changes in the rhizosphere, such as those associated with diel cycling of host plant physiology, will increase our understanding of microbial community assembly patterns. Here, we used 16S rRNA biomarker gene (DNA) and transcript (RNA) sequencing to characterize changes in the rhizosphere community membership and activity over short timescales in field-grown Boechera stricta plants. Microbial communities characterized by 16S-rRNA-transcripts, which serve as a proxy for microbial activity, showed greater sensitivity to fine-scale environmental changes than did communities characterized by 16S-rRNA biomarker gene sequencing, which reflects microbial presence/absence. Significant differences were seen between overall communities characterized by RNA vs. DNA, with RNA-derived communities showing more significant differences between the rhizosphere and control soil communities within phyla and in differential abundance analysis of genera. Communities reconstructed from RNA were also more sensitive to the effects of field block and collection timepoint. Using differential abundance analysis showed five genera to have significantly (p<0.05) differential abundance in the rhizosphere soil between the pre-dawn (AM) and early afternoon (PM) timepoints based on 16S rRNA transcripts, including the close plant associated genus Curtobacterium. However, when variance was partitioned between day of collection, the amplitude of the signal between timepoints was not significant. In sum, community composition and activity was highly sensitive to abiotic factors expressed over the small spatial scale of field blocks and short 24-hr periods between collection days, but showed minimal to no diel patterning
Rock-on-a-chip: A novel method for designing representative microfluidic platforms based on real rock structure and pore network modelling
No full text1. Data for Figures 5, 6, 10, 14, and 15 (Excel file) This dataset contains the numerical values used to generate Figures 5, 6, 10, 14, and 15 of the associated manuscript. The data are organized in a Microsoft Excel (.xls) file with separate sheets corresponding to each figure. These figures represent key results from permeability measurements and flow simulations comparing experimental and modeled pore networks in quasi-2D microfluidic chips derived from CT-scanned rock samples.
2. MATLAB Script for 2D Mosaic Generation from 3D Pore Networks (MATLAB .m file) This MATLAB script implements the multi-step image processing workflow described in the manuscript for generating realistic 2D pore geometries from 3D CT-based pore network data. Starting from a binarized image of unconnected pores, the script includes the following key functions: connect_mosaic: Connects isolated pores with uniform-width throats and outputs an image of separated throats. distrib_throats: Edits throat widths based on a target throat size distribution and specified number of categories (Nmax), allowing control over heterogeneity in sparse mosaics. smooth_image: Applies smoothing to pore-grain boundaries to enhance fabrication compatibility. grain_fix: Removes small dead-end pores and fixes partially connected throats in large grains, with the minimum grain size (L_min) set by the user. All parameters can be adjusted by the user to match the pore and throat size distributions (PSD and TSD) from the 3D subvolume data. This script was used to produce the microfluidic patterns described in the study, ensuring morphological consistency between 3D and 2D representations
Microbial and metabolomic rhizosphere effect of B. stricta genotypes are often suppressed when in local soil environment
No full textRaw data (OUT_Tax_16S.sintax and metafinal.csv) used to generate phyloseq object (genoXsoil_phyloseq.rds) used for paper analysis of 16S rRNA DNA gene bacterial data. Two xls files of chemical data sampled from bulk and rhizosphere soils where bacteria were sampled, generated using MetaboAnalyst.
The abstract for the paper generated using this data is as follows:
The rhizosphere microbial and chemical environments vary depending on the species and genotype of the plant host. In addition to the variation of plasticity inherent between genotypes, allelic changes at even a few plant genes can have far-reaching consequences on genotype by environment interactions, and little is known about genotypic sensitivity of the rhizosphere microbial and chemical environments to local vs. foreign soil environments. Our study investigated the rhizosphere microbiome and metabolome of three distinct B. stricta clonal lines in and out of their local soil microbial environment. We found that while soil matrix was the greatest predictor of plant health and rhizosphere microbial and chemical composition, plant genotype was a significant, though not consistent, predictor of those biotic and abiotic factors as well. While some differences could be explained by genotype-specific life histories, others were driven by genotype by environment interactions of plants with their local soil-microbiome environment. Plants in their local soils by and large showed decreased differentiation of the rhizosphere from the bulk soil environment both via differential abundance of bacteria and total number of significantly different chemical features, which could indicate a tradeoff in carbon allocation to convey a fitness advantage to the plant. Further research into the magnitude of host genotypes’ effects on the rhizosphere and into disentangling biotic origins of chemical features will further our understanding of the complicated top-down feedback in the plant-microbial-soil microenvironment of the rhizosphere
Replication Data for UWYO Grad Outcomes
No full textThis dataset is for replication purposes for an unpublished manuscript analyzing graduate outcomes for University of Wyoming students
Replication Data for Evaluation of Harmonized Landsat-8 and Sentinel-2 Data for Rapid Flood Mapping Applications
No full textThis collection includes both raw and processed satellite data sourced from NASA. It contains satellite imagery saved as individual granules, which can be viewed and analyzed using specialized GIS software such as ERDAS Imagine or ArcGIS. The dataset also includes Excel spreadsheets used for spectral band comparisons, threshold calculations, and accuracy assessments. This resource supports remote sensing analysis related to water detection and environmental monitoring across multiple flood-impacted study sites
The Inclusion of Cover Crop Mixes During Dryland Winter Wheat (Triticum aestivum, L.) - Fallow Rotation Under Semi-Arid Conditions Short Communications Paper Data Set
No full textAbstract:
Winter wheat (Triticum aestivum, L.) (WW) is the primary dryland crop in the semi-arid US Great Plains. Local producers in extremely low precipitation areas are interested in incorporating cover crops (CC) into a WW-fallow rotation. Information on suitable CC mixes and their impact on soil and competition with weeds is limited. A producer designed and planted two CC mixes: (1) legume dominated three species mix (69-17-14) (3 species legume-dominated): forage pea (Pisum sativum, L.), red clover (Trifolium pratense, L.), and daikon radish (Raphanus sativus, L.) and (2) grass dominated four species mix (55-35-7-3) (4 species grass-dominated): oat (Avena sativa, L.), forage pea, daikon radish, and purple top turnip (Brassica rapa, L.). The CC performance was compared to a weedy fallow (WF) (tilled once and allowed to fallow without weed control) and a cultivated fallow (CF) (tilled five times for weed control). Soil and vegetation sampling took place at 11 weeks and 26 weeks after CC planting. Soil was analyzed for water content and inorganic nitrogen (IN) concentrations and CC and weedy species vegetation biomass was analyzed. Results suggest that soil moisture at the time of WW planting was not negatively affected by CC. The 4 species grass-dominated mix resulted in 97% higher soil IN than weedy fallow and outperformed the 3 species legume-dominated mix in competing with weedy species (73% reduction in weedy biomass). In conclusion, for areas of extremely low precipitation, fallow could be replaced by planting the 4 species grass-dominated CC mix tested in this experiment. </p
ICAP and SIS services prediction
No full textAnalytic dataset that accompanies the paper titled "Support needs and adaptive behavior surveys: services prediction and relationship," to be published in PLOS One
A Comparison of 16S rRNA-gene and 16S rRNA-transcript Derived Microbial Communities in Bulk and Rhizosphere Soils
No full text16S ribosomal RNA rRNA transcripts and 16S ribosomal DNA rRNA gene reads for soil bacterial.
Data availability files related to publication: Ceretto A and Weinig C (2025) A comparison of 16S rRNA-gene and 16S rRNA-transcript derived microbial communities in bulk and rhizosphere soils. Front. Microbiol. 16:1608399. doi: 10.3389/fmicb.2025.1608399