This Resource serves to explain and contain the methodology, R codes, and results of the PRISM freshwater supply key indicator analysis for my thesis. For more information, see my thesis at the USU Digital Commons.

Freshwater availability in the state can be summarized using streamflow, reservoir level, precipitation, and temperature data. Climate data for this study have a period of record greater than 30 years, preferably extending beyond 1950, and are representative of natural conditions at the county-level.

Oregon State University, Northwest Alliance for Computational Science and Engineering PRISM precipitation and temperature gridded data are representative of statewide, to county-level, from 1895-2015. These data are available online from the PRISM Climate Group. Using the R ‘prism’ package, monthly PRISM 4km raster grids were downloaded. Boundary shapefiles of Utah state, and each county, were obtained online from the Utah Geospatial Resource Center webpage. Using the R ‘rgdal’ and ‘sp’ packages, these shapefiles were transformed from their native World Geodetic System 1984 coordinate system to match the PRISM BIL raster’s native North American Datum 1983 coordinate system. Using the R ‘raster’ package, medians of PRISM precipitation grids at each spatial area of interest were calculated and summed for water years and seasons. Medians were also calculated for PRISM temperature grids and averaged over water years and seasons. For analysis of single months, the median results were used for all PRISM indicators. Seasons were analyzed for the calendar year which they are in, Winter being the first season of each year. Freshwater availability key indicators were non-parametrically separated per temporal/spatial delineation into quintiles representing Very Wet/Very High/Hot (top 20% of values), Wet/High/Hot (60-80%), Moderate/Mid-level (40-60%), Dry/Low/Cool (20-40%), to Very Dry/Very Low/Cool (bottom 20%). Each quintile bin was assigned a rank value 1-5, with ‘5’ being the value of the top quintile, in preparation for the Kendall Tau-b correlation analysis. These results, along with USGS irrigation withdrawal and acreage data, were loaded into R. State-level quintile results were matched according to USGS report year. County quintile results were matched with corresponding USGS irrigation withdrawal and acreage county-level data per report year for all other areas of interest. Using the base R function cor(), with the “kendall” method selected (which is, by default, the Kendall Tau-b calculation), relationship correlation matrices were produced for all areas of interest. The USGS irrigation withdrawal and acreage data correlation analysis matrices were created using the R ‘corrplot’ package for all areas of interest.

See Word file for an Example PRISM Analysis, made by Alan Butler at the United States Bureau of Reclamation, which was used as a guide for this analysis.

(Adapted from Figure 4. Flint and Flint Ecological Processes 2012 1∶2).

Historical ownership data of R by Prism Advisors Inc

Dataset 6: For each participant, percentage accuracy was calculated within each level of visual field and horizontal shift direction. The group data were subjected to a repeated measures ANOVA. The analyses of percentage accuracy are not reported, as they resulted in similar results with regards to the experimental hypotheses as the analysis of mean RTs. L = leftward-shifting prism group; R = rightward-shifting prism group; N=neutral pointing group; LVF = left visual field; RVF = right visual field. Dataset 7: For each participant, RTs (ms) were averaged within each level of visual field and horizontal shift direction. The group data were subjected to a repeated measures ANOVA, as reported in the text. L = leftward-shifting prism group; R = rightward-shifting prism group; N=neutral pointing group; LVF = left visual field; RVF = right visual field;

Historical holdings data showing quarterly positions, market values, shares held, and portfolio percentages for R held by Prism Advisors Inc from Q1 2018 to Q2 2020

Flight line reflectance measurements from the Portable Remote Imaging Spectrometer (PRISM) instrument aboard the Tempus Applied Solutions Gulfstream-IV (G-IV) aircraft, taken as part of the NASA COral Reef Airborne Laboratory (CORAL) Earth Venture Suborbital-2 (EVS-2) mission designed to provide an extensive, uniform picture of coral reef composition. The CORAL mission surveyed parts of the reefs surrounding the Mariana Islands, Palau, portions of the Great Barrier Reef, the main Hawaiian Islands, and the Florida Keys.

This dataset contains Portable Remote Imaging Spectrometer (PRISM) data for the ORCAS (O2/N2 Ratio and CO2 Airborne Southern Ocean (ORCAS) Study) project. PRISM is an imaging spectrometer that measures reflected radiance at 3nm intervals in the Visible/Near-Infrared (VNIR) spectral range from 380-1050nm. Additional information may be found in the readme file and at http://prism.jpl.nasa.gov.

Pointing errors (º) were measured for each individual participant in sets of 12 trials before (pre-test) and after (post-test) the first set of prism adaptation, and at the end of the experimental session (late-test). For each participant the errors were averaged within each pointing block and are provided here. L = leftward-shifting prism group; R = rightward-shifting prism group; N=neutral pointing group.

This data release contains nucleosynthesis predictions for the r-process abundances presented in Côté, Eichler, Yagüe, Vassh et al. (2021) for compact object merger ejecta based on the publicly available simulation trajectories of Rosswog et al. (2013). All ejecta for the merger scenarios considered here are very neutron-rich (Ye ~ 0.016-0.11). Calculations were performed with the PRISM code (Mumpower et al. 2018) which accounts for nuclear reheating (here with a reheating efficiency of 50%). Results are reported for several different theoretical nuclear physics inputs but all calculations make use of the GEF fission yield prescription (see Vassh et al. 2019). All abundances are given at 1 Myr (10^6 years) post-merger. Please see the README file for more details and references.

When using these nucleosynthesis yields, please cite this Zenodo data release (Vassh et al. 2021), and refer to Vassh et al. (2019) and Côté, Eichler, Yagüe, Vassh et al. (2021) for further details on the nuclear data applied as well as Rosswog et al. (2013), Piran et al. (2013), and Korobkin et al. (2012) for further details on the merger ejecta trajectories.

Monthly totals of precipitation in millimeters (mm), monthly means of daily maximum air temperature in degrees Celsius (C), and monthly means of daily minimum air temperature (C) were developed at the 5 arc minute grid level for the conterminous United States (US). Also, included are computed monthly mean of daily potential evapotranspiration (mm) and mean grid elevation in meters (m). These data were developed from climate scenarios used in the Fourth Assessment of the Intergovernmental Panel on Climate Change, specifically the A1B and the A2 SRES (Special Report on Emissions Scenarios) scenarios as modeled by these climate models: CGCM3.1MR, CSIRO-MK3.5, and MIROC3.2MR. The monthly change factors were developed from global model output and downscaled to the 5 arc minute spatial grid using ANUSPLIN. The 30 year mean climatology (1961-1990) was developed from PRISM (Parameter-elevation Regressions on Independent Slopes Model) data at the 2.5 arc minute scale and aggregated to the 5 arc minute grid scale. The change factors were imposed upon the 30-year period (1961-1990) to develop the projections for each climate scenario.The USDA Forest Service (USFS) produces a periodic assessment of the condition and trends of the Nation's renewable resources as required by the Forest and Rangeland Renewable Resources Planning Act (RPA) of 1974. This RPA Assessment provides a snapshot of current US forest and rangeland conditions and trends on all ownerships, identifies drivers of change, and projects 50 years into the future (//www.fs.fed.us/research/rpa/, accessed 07/06/2015). For 2010 RPA Assessment, an integrated modeling framework is being used in which the potential implications of climate change can be analyzed across some resource areas (Langner et al. 2012). The nature of the climate variables needed to address climate change impacts for these resource analyses in the 2010 RPA Assessment were determined to be monthly precipitation and temperature variables at the 5 arc minute grid level spatial scale.Original metadata dated 08/02/2010. Minor modifications made to Attribute Accuracy section of metadata on 09/17/2010. Metadata modified on 02/22/2012 to adjust citation to include the addition of a DOI (digital object identifier) and update to the cross-reference section. Minor metadata updates on 02/20/2013. Metadata modified on 07/22/2015 to update cross-reference citations and other minor updates. Additional minor metadata updates on 12/13/2016.

Prism adaptation (PA) is both a model for visuomotor learning and a promising treatment for visuospatial neglect after stroke. The task involves reaching for targets while prism glasses horizontally displace the visual field. Adaptation is hypothesized to occur through two processes: strategic recalibration, a rapid self-correction of pointing errors; and spatial realignment, a more gradual adjustment of visuomotor reference frames that produce prism aftereffects (i.e., reaching errors upon glasses removal in the direction opposite to the visual shift). While aftereffects can ameliorate neglect, not all patients respond to PA, and the neural mechanisms underlying successful adaptation are unclear. We investigated the feedback-related negativity (FRN) and the P300 event-related potential (ERP) components as candidate markers of strategic recalibration and spatial realignment, respectively. Healthy young adults wore prism glasses and performed memory-guided reaching toward vertical-line targets. ERPs were recorded in response to three different between-subject error feedback conditions at screen-touch: view of hand and target (Experiment 1), view of hand only (Experiment 2), or view of lines to mark target and hand position (view of hand occluded; Experiment 3). Conditions involving a direct view of the hand-produced stronger aftereffects than indirect hand feedback, and also evoked a P300 that decreased in amplitude as adaptation proceeded. Conversely, the FRN was only seen in conditions involving target feedback, even when aftereffects were smaller. Since conditions producing stronger aftereffects were associated with a phase-sensitive P300, this component may index a “context-updating” realignment process critical for strong aftereffects, whereas the FRN may reflect an error monitoring process related to strategic recalibration.

CERN 2M HBC. PRISM PLOT ANALYSIS. RESONANCE DECAY DISTRIBUTIONS AND DIFFRACTIVE SLOPES STUDIED. FOR THE PI0 DEL++ CHANNEL, SEE ALSO THE LATER RESULTS IN R. HONECKER ET AL., NP B131, 189 (1977).

This is the first release of a sample of the PRISM Coq proof repair dataset. We will update with later versions when repair mining is complete and goes through DARPA's approval process. The initial release (data and limitations) is documented here: https://docs.google.com/document/d/19A6YMm1glkcd7ze8wi87pxmUgogA3Mitny7mDl-192c/edit?usp=sharing. It includes about 200 unique changes. Later versions will include more data and better line number information, and will be deduplicated.

Monthly totals of precipitation in millimeters (mm), monthly means of daily maximum air temperature in degrees Celsius (C), and monthly means of daily minimum air temperature (C) were developed at the county level for the conterminous United States (US). Also, included are computed monthly mean of daily potential evapotranspiration (mm) and mean grid elevation in meters (m). These data were developed from climate scenarios used in the Fourth Assessment of the Intergovernmental Panel on Climate Change, specifically the A1B and the A2 SRES (Special Report on Emissions Scenarios) scenarios as modeled by these climate models: CGCM3.1MR, CSIRO-MK3.5, and MIROC3.2MR. The monthly change factors were developed from global model output and downscaled to the 5 arc minute spatial grid using ANUSPLIN. The 30 year mean climatology (1961-1990) was developed from PRISM (Parameter-elevation Regressions on Independent Slopes Model) data at the 2.5 arc minute scale and aggregated to the 5 arc minute grid scale. The change factors were imposed upon the 30-year period (1961-1990) to develop the projections for each climate scenario. The county means were computed using a weighted mean of the 5 arc minute grids within the county.The USDA Forest Service (USFS) produces a periodic assessment of the condition and trends of the Nation's renewable resources as required by the Forest and Rangeland Renewable Resources Planning Act (RPA) of 1974. This RPA Assessment provides a snapshot of current US forest and rangeland conditions and trends on all ownerships, identifies drivers of change, and projects 50 years into the future (https://www.fs.usda.gov/research/inventory/rpaa). For 2010 RPA Assessment, an integrated modeling framework is being used in which the potential implications of climate change can be analyzed across some resource areas (Langner et al. 2012). The nature of the climate variables needed to address climate change impacts for these resource analyses in the 2010 RPA Assessment were determined to be monthly precipitation and temperature variables at the county level spatial scale, and for some resources, at the 5 arc minute grid scale.Original metadata date was 08/03/2010. Metadata modified on 04/18/2011 to adjust citation to include the addition of a DOI (digital object identifier). Minor metadata updates on 02/19/2013. Metadata modified on 07/22/2015 to update cross-reference citations and other minor updates. Additional minor metadata updates on 12/13/2016, 02/08/2021, and 10/27/2022.

Associated publication: https://www.nature.com/articles/s41586-022-05677-y

OVERVIEW: Transmission spectroscopy of exoplanets has revealed signatures of water vapor, aerosols, and alkali metals in a few dozen exoplanet atmospheres. However, these previous inferences with the Hubble and Spitzer Space Telescopes were hindered by the observations’ relatively narrow wavelength range and spectral resolving power, which precluded the unambiguous identification of other chemical species—in particular the primary carbon-bearing molecules. Here we report a broad-wavelength 0.5–5.5 µm atmospheric transmission spectrum of WASP-39 b, a 1200 K, roughly Saturn-mass, Jupiter-radius exoplanet, measured with JWST NIRSpec’s PRISM mode as part of the JWST Transiting Exoplanet Community Early Release Science Team program. We robustly detect multiple chemical species at high significance, including Na (19σ), H₂O (33σ), CO₂ (28σ), and CO (7σ). The non-detection of CH₄, combined with a strong CO₂ feature, favours atmospheric models with a super-solar atmospheric metallicity. An unanticipated absorption feature at 4 µm is best explained by SO₂ (2.7σ), which could be a tracer of atmospheric photochemistry. These observations demonstrate JWST’s sensitivity to a rich diversity of exoplanet compositions and chemical processes.

Sediment core GeoB21367-1 (GC23) was collected during R/V Poseidon expedition POS499 using a gravity corer. The position is close to the Sartori Mud Volcano located in the Calabrian accretionary prism (Mediterranean Sea). The gravity core was longitudinally split directly after recovery on board of cruise POS499. The archive halve was photographed using the smartCIS1600 line scan technique of the MARUM GeoB Core repository at a 500-dpi resolution in 2019. To investigate lithological changes in more detail, a macroscopic core description is prepared. The core description provides information regarding core length, exact position, water depth, number of core sections, core image, color, lithology, sedimentary structures and a descriptive text. Sediment color was determined qualitatively using Munsell soil color charts.

Direction and rates of change in max, min and mean temperature (±SD) over the past 56 years projected by the RMFR climate stations and interpolated by PRISM.

This data and code archive provides all the files that are necessary to replicate the empirical analyses that are presented in the paper "Climate impacts and adaptation in US dairy systems 1981-2018" authored by Maria Gisbert-Queral, Arne Henningsen, Bo Markussen, Meredith T. Niles, Ermias Kebreab, Angela J. Rigden, and Nathaniel D. Mueller and published in 'Nature Food' (2021, DOI: 10.1038/s43016-021-00372-z). The empirical analyses are entirely conducted with the "R" statistical software using the add-on packages "car", "data.table", "dplyr", "ggplot2", "grid", "gridExtra", "lmtest", "lubridate", "magrittr", "nlme", "OneR", "plyr", "pracma", "quadprog", "readxl", "sandwich", "tidyr", "usfertilizer", and "usmap". The R code was written by Maria Gisbert-Queral and Arne Henningsen with assistance from Bo Markussen. Some parts of the data preparation and the analyses require substantial amounts of memory (RAM) and computational power (CPU). Running the entire analysis (all R scripts consecutively) on a laptop computer with 32 GB physical memory (RAM), 16 GB swap memory, an 8-core Intel Xeon CPU E3-1505M @ 3.00 GHz, and a GNU/Linux/Ubuntu operating system takes around 11 hours. Running some parts in parallel can speed up the computations but bears the risk that the computations terminate when two or more memory-demanding computations are executed at the same time.

This data and code archive contains the following files and folders:

* README
Description: text file with this description

* flowchart.pdf
Description: a PDF file with a flow chart that illustrates how R scripts transform the raw data files to files that contain generated data sets and intermediate results and, finally, to the tables and figures that are presented in the paper.

* runAll.sh
Description: a (bash) shell script that runs all R scripts in this data and code archive sequentially and in a suitable order (on computers with a "bash" shell such as most computers with MacOS, GNU/Linux, or Unix operating systems)

* Folder "DataRaw"
Description: folder for raw data files
This folder contains the following files:

- DataRaw/COWS.xlsx
Description: MS-Excel file with the number of cows per county
Source: USDA NASS Quickstats
Observations: All available counties and years from 2002 to 2012

- DataRaw/milk_state.xlsx
Description: MS-Excel file with average monthly milk yields per cow
Source: USDA NASS Quickstats
Observations: All available states from 1981 to 2018

- DataRaw/TMAX.csv
Description: CSV file with daily maximum temperatures
Source: PRISM Climate Group (spatially averaged)
Observations: All counties from 1981 to 2018

- DataRaw/VPD.csv
Description: CSV file with daily maximum vapor pressure deficits
Source: PRISM Climate Group (spatially averaged)
Observations: All counties from 1981 to 2018

- DataRaw/countynamesandID.csv
Description: CSV file with county names, state FIPS codes, and county FIPS codes
Source: US Census Bureau
Observations: All counties

- DataRaw/statecentroids.csv
Descriptions: CSV file with latitudes and longitudes of state centroids
Source: Generated by Nathan Mueller from Matlab state shapefiles using the Matlab "centroid" function
Observations: All states

* Folder "DataGenerated"
Description: folder for data sets that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these generated data files so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).

* Folder "Results"
Description: folder for intermediate results that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these intermediate results so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).

* Folder "Figures"
Description: folder for the figures that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these figures so that people who replicate our analysis can more easily compare the figures that they get with the figures that are presented in our paper. Additionally, this folder contains CSV files with the data that are required to reproduce the figures.

* Folder "Tables"
Description: folder for the tables that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these tables so that people who replicate our analysis can more easily compare the tables that they get with the tables that are presented in our paper.

* Folder "logFiles"
Description: the shell script runAll.sh writes the output of each R script that it runs into this folder. We provide these log files so that people who replicate our analysis can more easily compare the R output that they get with the R output that we got.

* PrepareCowsData.R
Description: R script that imports the raw data set COWS.xlsx and prepares it for the further analyses

* PrepareWeatherData.R
Description: R script that imports the raw data sets TMAX.csv, VPD.csv, and countynamesandID.csv, merges these three data sets, and prepares the data for the further analyses

* PrepareMilkData.R
Description: R script that imports the raw data set milk_state.xlsx and prepares it for the further analyses

* CalcFrequenciesTHI_Temp.R
Description: R script that calculates the frequencies of days with the different THI bins and the different temperature bins in each month for each state

* CalcAvgTHI.R
Description: R script that calculates the average THI in each state

* PreparePanelTHI.R
Description: R script that creates a state-month panel/longitudinal data set with exposure to the different THI bins

* PreparePanelTemp.R
Description: R script that creates a state-month panel/longitudinal data set with exposure to the different temperature bins

* PreparePanelFinal.R
Description: R script that creates the state-month panel/longitudinal data set with all variables (e.g., THI bins, temperature bins, milk yield) that are used in our statistical analyses

* EstimateTrendsTHI.R
Description: R script that estimates the trends of the frequencies of the different THI bins within our sampling period for each state in our data set

* EstimateModels.R
Description: R script that estimates all model specifications that are used for generating results that are presented in the paper or for comparing or testing different model specifications

* CalcCoefStateYear.R
Description: R script that calculates the effects of each THI bin on the milk yield for all combinations of states and years based on our 'final' model specification

* SearchWeightMonths.R
Description: R script that estimates our 'final' model specification with different values of the weight of the temporal component relative to the weight of the spatial component in the temporally and spatially correlated error term

* TestModelSpec.R
Description: R script that applies Wald tests and Likelihood-Ratio tests to compare different model specifications and creates Table S10

* CreateFigure1a.R
Description: R script that creates subfigure a of Figure 1

* CreateFigure1b.R
Description: R script that creates subfigure b of Figure 1

* CreateFigure2a.R
Description: R script that creates subfigure a of Figure 2

* CreateFigure2b.R
Description: R script that creates subfigure b of Figure 2

* CreateFigure2c.R
Description: R script that creates subfigure c of Figure 2

* CreateFigure3.R
Description: R script that creates the subfigures of Figure 3

* CreateFigure4.R
Description: R script that creates the subfigures of Figure 4

* CreateFigure5_TableS6.R
Description: R script that creates the subfigures of Figure 5 and Table S6

* CreateFigureS1.R
Description: R script that creates Figure S1

* CreateFigureS2.R
Description: R script that creates Figure S2

* CreateTableS2_S3_S7.R
Description: R script that creates Tables S2, S3, and S7

* CreateTableS4_S5.R
Description: R script that creates Tables S4 and S5

* CreateTableS8.R
Description: R script that creates Table S8

* CreateTableS9.R
Description: R script that creates Table S9

Shallow sediment cores were collected with a gravity corer from several sites at the Sartori Mud Volcano in the Calabrian accretionary prism during cruise SO278 with R/V SONNE. In order to determine Corg/Ntot values (orgainc carbon/total nitrogen), as well as contents of total carbon, inorganic carbon and organic carbon, sediment samples were taken from the sediment cores with cut-off syringes and transferred into glass vials. The sediment samples were analyzed with a vario EL III elemental analyzer (Elementar Analysensysteme GmbH). Contents of organic carbon were determined after removal of inorganic carbon through acidification.

Horses receiving antimicrobials may develop diarrhea due to changes in the gastrointestinal microbiome and metabolome. This matched, case-controlled study compared the fecal microbiome and metabolome in hospitalized horses on antibiotics that developed diarrhea (AAD), hospitalized horses on antibiotics that did not develop diarrhea (ABX) and a healthy, non-hospitalized control population (CON). Naturally-voided fecal samples were collected from AAD horses (n=17) the day that diarrhea developed and matched to ABX (n=15) and CON (n=31) horses for diet, antimicrobial agent and duration of antimicrobial therapy (< 5 days or > 5 days). Illumina sequencing of 16S rRNA genes on fecal DNA was performed. Alpha and beta diversity metrics were generated using QIIME 2.0. A Kruskal-Wallis with Dunn’s post-test and ANOSIM testing was used for statistical analysis. Microbiome composition in AAD was significantly different from CON (ANOSIM, R= 0.568, p=0.001) and ABX (ANOSIM, R=0.121, p=0.0012). Fecal samples were lyophilized and extracted using a solvent-based method. Untargeted metabolomics using gas chromatography-mass spectrometry platforms was performed. Metabolomic data was analyzed using Metaboanalyst 4.0 and Graphpad Prism v 7. Principal component analysis plots (PCA) were used to visualize the distribution of metabolites between groups. Heat maps were used to identify the relative concentrations amongst the most abundant 25 metabolites. A one-way ANOVA was used to compare differences in metabolites amongst the three groups of horses. Only named metabolites were included in the analysis. The microbiome of AAD and ABX horses had significantly decreased richness and evenness than CON horses (p<0.05). Actinobacteria (q=0.0192) and Bacteroidetes (q=0.0005) were different between AAD and CON. Verrucomicrobia was markedly decreased in AAD compared to ABX and CON horses (q=0.0005). Horses with AAD have a dysbiosis compared to CON horses, and show minor differences in bacterial community composition to ABX horses. Metabolite profiles of horses with AAD clustered separately from those with AAD or CON. Ten metabolites were found to be significantly different between groups (P<0.05) and are listed according to their metabolic pathway: amino acid metabolism (R-equol, L-tyrosine, kynurenic acid, xanthurenic acid, 5-hydroxyindole-3-acetic acid ) lipid metabolism (docosahexaenoic acid ethyl ester), biosynthesis of secondary metabolites (daidzein, isoquinoline) and two metabolites with unidentified pathways (1,3-divinyl-2-imidazolidinone, N-acetyltyramine).