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1. Data for: Searching for Low-Mass Exoplanets Amid Stellar Variability with a Fixed Effects Linear Model of Line-by-Line Shape Changes

   https://doi.org/10.5281/zenodo.14841436  

   Salzer, Joseph; Cisewski_Kehe, Jessi; Ford, Eric B; Zhao, Lily Ling; Cisewski_Kehe, Jessi; Ford, Eric B; Zhao, Lily Ling (January 2025, Zenodo)

   The following repository contains the data used for the manuscript: "Searching for Low-Mass Exoplanets Amid Stellar Variability with a Fixed Effects Linear Model of Line-by-Line Shape Changes". Within line_property_files.zip there is file "line_property_files_README.md" that contains a description of each column of completeLines.csv. The important columns of this csv are date: time of observation line_order: unique identifier of a line, a combination of that line's central wavelength and order ID rv_template_0.5: the RV measure for a given line on a given day fit_gauss_a,fit_gauss_b,fit_gauss_depth,fit_gauss_sigmasq,proj_hg_coeff_0,proj_hg_coeff_2,proj_hg_coeff_3,proj_hg_coeff_4,proj_hg_coeff_5,proj_hg_coeff_6,proj_hg_coeff_7,proj_hg_coeff_8,proj_hg_coeff_9,proj_hg_coeff_10: the shape-change covariates that we use to control for stellar activity Below is a description of each of the data files completeLines.csv: this csv file contains the time series of every line's shape measurements and RVs, it is used throughout the analysis. line_property_files.zip: this directory contains .h5 files that contain all line-shape information and contaminated RVs for each line used in our analysis. The script clean_data.Rmd uses these as input and combines them all into a single csv file called completeLines.csv. project_template_deriv_onto_gh.h5: this contains the projection vector described in the paper to produce the orthogonal HG coefficients. The script clean_data.Rmd uses this as input and combines them all into a single csv file called completeLines.csv. models.zip: this directory contains the results from each model that was fit for our paper. 

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2. Analysis of Sediment Phosphorus Pools in Shallow Lakes

   https://doi.org/10.6084/m9.figshare.13362971  

   Albright, Ellen; Wilkinson, Grace; Fleck, Rachel; Shingai, Quin (January 2020, figshare)

   {"Abstract":["These data sets summarize the results of a project to quantify and explain spatial variation in sediment phosphorus (P) pools in seven shallow lakes in NW Iowa, USA.1. "Cores_ALLDATA_2018.csv" is the primary data set. It contains sediment core data for all study lakes. Variables include sediment physical characteristics (moisture content, organic matter content, bulk density), sediment total P concentrations, and sediment P speciation (concentrations of loosely-bound, redox-sensitive, aluminum-bound, calcium-bound, and labile organic P). The file "Cores_ALLDATA_2018_README.csv" provides detailed meta-data.2. "Chla_MobileP_Regression.csv" provides additional data on the long-term concentrations of the algal pigment chlorophyll a in the study lakes. We used these data to analyze the relationship between long term lake productivity and mobile sediment P fractions in the sediments. The file "Chla_MobileP_Regression_README.csv" provides detailed meta-data.3. "Swan_macrophytes_2018.csv" summarizes the distribution of aquatic plants (macrophytes) in one study lake (Swan Lake), and "Swan_SedP_2018.csv" summarizes sediment P data for that lake. We used these datasets to analyze spatial relationships between macrophyte beds and sediment P pools. Detailed meta-data can be found in "Swan_macrophytes_2018_README.csv" and "Swan_SedP_2018_README.csv""]} 

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3. Results of the Search for eV-scale sterile neutrinos with 7.5 years of DeepCore data

   https://doi.org/10.7910/dvn/qkl28z  

   Collaboration, IceCube; Collaboration, IceCube (January 2025, Harvard Dataverse)

   {"Abstract":["Results of the Search for eV-scale sterile neutrinos with the DeepCore "golden" sample in machine readable format. Allows reproduction of Figure 5 of the publication.\n\n<ul>\n<li> The file "wilks_contour_90pct.csv" contains the 90% contour line in CSV format. The columns "U\\mu4_sq" and "U\\tau4_sq" correspond to the _squared_ elements of the 3+1 mixing matrix, |U_{&mu;4}|^2 and |U_{&tau;4}|^2, respectively. The contour was drawn assuming that Wilks' theorem holds with two degrees of freedom. The points are ordered by nearest neighbors. </li>\n<li> The file "expected_sensitivity_90pct.csv" contains the expected sensitivity at 90% CL of the analysis for Asimov data produced at the null hypothesis. The format is identical to that of the result. </li>\n<li> The files "marginalized_umu4_sq.csv" and "marginalized_utau4_sq.csv" contain the marginalized scan of the test statistic over the matrix elements. The column "Metric Difference" refers to the delta chi-square metric used in the analysis. Since this metric is a delta chi-square (rather than a likelihood), it should _not_ be multiplied by two when calculating significances. They allow reproduction of the top and right panels of Figure 5 of the publication. </li>\n<li> The file "real_data_wilks_contours_reprod_mar_2025_best_fit_points.tab" contains the best-fit values of all free nuisance parameters in a |U_{&mu;4}|^2 and |U_{&tau;4}|^2 scan. </li>\n<li> The file "real_data_wilks_contours_reprod_mar_2025_metric_diff.tab" contains the \\chi^2 difference between the scan point and the global best-fit in a |U_{&mu;4}|^2 and |U_{&tau;4}|^2 scan. </li>\n</ul>"]} 

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4. Hot Droughts and Forest Tree Dynamics in the Amazon - Statistical Models, Scripts, Data, and Outputs

   https://doi.org/10.15485/2561510  

   Dyer, Lee; Chambers, Jeff; Pastorello, Gilberto; Weber, Anna (January 2025, Next-Generation Ecosystem Experiments (NGEE) Tropics)

   {"Abstract":["This package contains data, outputs, equations, and R scripts for analyses for manuscript entitled "Hot droughts in the Amazon: A window to a future hypertropical climate" by J. Chambers et al., in particular it contains statistical models and analyses for the INPA BIONTE tree mortality study. The Models folder contains details for all statistical models in PDF files. The Scripts folder contains the R scripts for Bayesian Hierarchical Models (two text files) and SEMs (one text file) are separate and reasonably annotated. All data associated with these scripts are in the data folder. The Data folder contains two of the three CSV files used for the analyses and are called by the R scripts. Two of them are part of published datasets (`BIONTE_mortality-rates.csv` from Lima et al. 2024, DOI:10.15486/ngt/1898910 and `SPEI.csv` from Pastorello et al. 2023 DOI:10.15486/ngt/1958257) and also provided in this package for convenience (please see the corresponding datasets for usage and citation terms). The third dataset (`BIONTE_gapfilled_wd.csv`) contains sensitive information and can be obtained by contacting the manuscript lead author. The Outputs folder contains the two output files that provide extra information about the analyses. The file `figuresFeb2025d.pdf` contains all the figures from the manuscript - captions are in the manuscript. The file `ChambersMS.pdf` contains primary results from Bayesian statistical models, regression analyses, and validation steps applied to the tree mortality data from the INPA experiments. The document includes visual summaries, model diagnostics, and leave-one-out (LOO) validation results. A breakdown of file contents can be found in the README file that is part of this package."]} 

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5. Source code: Basins modulate signatures of river salinization

   https://doi.org/10.5281/zenodo.11130548  

   Platt, Lindsay; Dugan, Hilary (May 2024, Zenodo)

   This resource contains source code and select data products behind the following Master's Thesis: Platt, L. (2024). Basins modulate signatures of river salinization (Master's thesis). University of Wisconsin-Madison, Freshwater and Marine Sciences. The source code represents an R-based data processing and modeling pipeline written using the R package "targets". Some of the folders in the source code zipfile are intentionally left empty (except for a hidden file ".placeholder") in order for the code repository to be setup with the required folder structure. To execute this code, download the zip folder, unzip, and open the salt-modeling-data.Rproj file. Then, reference the instructions in the README.md file for installing packages, building the pipeline, and examining the results. Newer versions of this repository may be updated in GitHub at github.com/lindsayplatt/salt-modeling-data. In addition to the source code, this resource contains three data files containing intermediate products of the pipeline. The first two represent data prepared for the random forest modeling. Data download and processing were completed in pipeline phases 1 - 5, and the random forest modeling was completed in phase 6 (see source code).  site_attributes.csv which contains the USGS gage site numbers and their associated basin attributes site_classifications.csv which contains the classification of a site for both episodic signatures ("Episodic" or "Not episodic") and baseflow salinization signatures ("positive", "none", "negative", or NA). Note that an NA in the baseflow classification column means that the site did not meet minimum data requirements for calculating a trend and was not used in the random forest model for baseflow salinization. site_attribute_details.csv contains a table of each attribute shorthand used as column names in site_attributes.csv and their names, units, description, and data source. 

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