cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15482/usda.adc/27697629.v1
title: Data and code from: Establishment of southern highbush blueberry cultivars and suppression of Phytophthora root rot using cover crop and soil amendment treatments

description: This dataset contains all data and R statistical software code needed to reproduce the analyses presented in the paper:Smith, B. J., E. T. Stafne, and Q. D. Read. 2024. Establishment of southern highbush blueberry cultivars and suppression of Phytophthora root rot using cover crop and soil amendment treatments. <i>PhytoFrontiers. </i>(Citation to be updated)SummaryPhytophthora root rot is a major disease of blueberry that often relies on cultural practices including the addition of soil amendments for disease management. We evaluated the effect of three cover crops on the establishment and growth of southern highbush blueberry (SHB) plants and their ability to suppress Phytophthora root rot infection of plants. This dataset includes data from soil chemical analyses (Table 1), soil pH and electrical conductivity measurements (Table 2), plant leaf, flower, crop load, and fruit ratings (Table 3), plant vigor rating and survival rate (Table 3), stem count, length, weight, and survival (Table 4), root length, diameter, and weight (Table 5). In addition, seedling weight and count data from a blueberry seedling assay (Table 6) and a lupine seedling assay (Table 7) are included.The associated RMarkdown notebook includes statistical models to analyze all datasets presented here. Linear mixed models, generalized linear mixed models, generalized additive models, and cumulative logistic mixed models are fit where appropriate. A combination of Bayesian and frequentist methods are used; some of the frequentist models include Bayesian-style prior distributions on fixed effects. Models are described in more detail in the notebook.Files includedSmith BB cover crop 7 data sets 10.9.24.xlsx: Excel spreadsheet with seven tabs containing all raw data and explanation of contentsblueberry_covercrop_analysis.Rmd: RMarkdown notebook containing all R code for statistical analysis presented in manuscript, with text explanationsblueberry_covercrop_analysis.html: HTML rendered output of Rmd notebook<br>
DOI: 10.15482/usda.adc/27695847
title: Data and code from: Comparison of infestation rates of fall armyworm (Lepidoptera: Noctuidae) neonates for maize resistance screening

description: This document contains all data and R statistical software code needed to reproduce the analyses presented in the manuscript:Woolfolk, S., G. Matthews, and Q. D. Read. 2024. Comparison of infestation rates of fall armyworm (Lepidoptera: Noctuidae) neonates for maize resistance screening. <i>Journal of Insect Science. </i>(Citation to be updated)SummaryThe data included here come from an experiment to assess a technique for measuring resistance of maize to fall armyworm. An economically important global maize pest, fall armyworm (FAW, <i>Spodoptera frugiperda</i>), causes damage mainly to the above-ground parts of maize plants, primarily the whorl tissues. One of our research unit missions is to identify and develop maize germplasm with resistance to FAW. One method to measure resistance in maize to FAW is visual rating of leaf-feeding damage after infestation with neonates into the whorl. The data presented here are from a replicated experiment crossing eleven maize lines (four susceptible and seven resistant) with five FAW infestation rates; the experiment was repeated in two consecutive years. The measured response was leaf feeding damage on a categorical scale. The statistical model presented here is a cumulative logistic mixed model. It treats the response variable (leaf damage rating) as an ordered categorical response. We include a random effect to account for the split-plot design, and include three-way interactions between fixed effects of genotype, treatment, and year. We present means for each genotype and treatment based on the underlying modeled probabilities of each category (weighted averages of the modeled probabilities).Files<b>FAW_combinedyears.csv</b>: All experimental data, with the following columns:Year: Year of data collection (2023 or 2024)Row: Numeric identifier of rowRep: Numeric identifier of replicate blockEntry: Numeric identifier of genotypePedigree: Text identifier of genotypeTreatment: Number of neonates per whorl (10, 20, 30, 40, 50)Plant: Numeric identifier of individual plant within rowRating: Categorical leaf-feeding damage rating (scale of 0-9)<b>FAW_analysis.Rmd</b>: RMarkdown notebook with all R statistical code to reproduce analyses<b>FAW_analysis.html</b>: HTML rendered output of Rmd notebook
DOI: 10.15482/usda.adc/27695847.v1
title: Data and code from: Comparison of infestation rates of fall armyworm (Lepidoptera: Noctuidae) neonates for maize resistance screening

description: This document contains all data and R statistical software code needed to reproduce the analyses presented in the manuscript:Woolfolk, S., G. Matthews, and Q. D. Read. 2024. Comparison of infestation rates of fall armyworm (Lepidoptera: Noctuidae) neonates for maize resistance screening. <i>Journal of Insect Science. </i>(Citation to be updated)SummaryThe data included here come from an experiment to assess a technique for measuring resistance of maize to fall armyworm. An economically important global maize pest, fall armyworm (FAW, <i>Spodoptera frugiperda</i>), causes damage mainly to the above-ground parts of maize plants, primarily the whorl tissues. One of our research unit missions is to identify and develop maize germplasm with resistance to FAW. One method to measure resistance in maize to FAW is visual rating of leaf-feeding damage after infestation with neonates into the whorl. The data presented here are from a replicated experiment crossing eleven maize lines (four susceptible and seven resistant) with five FAW infestation rates; the experiment was repeated in two consecutive years. The measured response was leaf feeding damage on a categorical scale. The statistical model presented here is a cumulative logistic mixed model. It treats the response variable (leaf damage rating) as an ordered categorical response. We include a random effect to account for the split-plot design, and include three-way interactions between fixed effects of genotype, treatment, and year. We present means for each genotype and treatment based on the underlying modeled probabilities of each category (weighted averages of the modeled probabilities).Files<b>FAW_combinedyears.csv</b>: All experimental data, with the following columns:Year: Year of data collection (2023 or 2024)Row: Numeric identifier of rowRep: Numeric identifier of replicate blockEntry: Numeric identifier of genotypePedigree: Text identifier of genotypeTreatment: Number of neonates per whorl (10, 20, 30, 40, 50)Plant: Numeric identifier of individual plant within rowRating: Categorical leaf-feeding damage rating (scale of 0-9)<b>FAW_analysis.Rmd</b>: RMarkdown notebook with all R statistical code to reproduce analyses<b>FAW_analysis.html</b>: HTML rendered output of Rmd notebook
DOI: 10.15482/usda.adc/25289518
title: Data and code from: Comparison of in-field and laboratory-based phenotyping methods for evaluation of aflatoxin accumulation in maize inbred lines

description: This dataset contains all data and R code, in RMarkdown notebook format, needed to reproduce all statistical analysis, figures, and tables in the manuscript:Jeffers, D., J. S. Smith, E. D. Womack, Q. D. Read, and G. L. Windham. 2024. Comparison of in-field and laboratory-based phenotyping methods for evaluation of aflatoxin accumulation in maize inbred lines. <i>Plant Disease</i>. (citation to be updated upon final acceptance of MS)There is a critical need to quickly and reliably identify corn genotypes that are resistant to accumulating aflatoxin in their kernels. We compared three methods of determining how resistant different corn genotypes are to aflatoxin accumulation: a field-based assay (side-needle inoculation) and two different lab-based assays (wounding and non-wounding kernel screening assays; KSA). In this data object, we present the data from the lab and field assays, statistical models that are fit to the data, procedures for comparing model fit of different variants of the model, and model predictions. This includes how reliably each assay identifies resistant and susceptible check varieties, and how well correlated the assay methods are with one another. Statistical analyses are done using R software, including Bayesian models fit with Stan software.The following files are included:<b>ksa_analysis_revised.Rmd</b>: RMarkdown notebook with all code needed to reproduce analyses and create figures and tables in manuscript<b>ksa_analysis_revised.html</b>: HTML rendered output of notebook<b>step1_ksa.tsv</b>: tab-separated data file with data from the lab assay. Columns include sample ID, genotype ID and entry code, year, treatment (wound or no-wound), subsample ID, replicate ID, aflatoxin concentration (in units of ng/g), logarithm of aflatoxin concentration, and a column indicating genotypes that are susceptible or resistant checks<b>step1_ksa_field.tsv</b>: tab-separated data file with data from the field assay. Columns similar to the lab assay data file with an additional column for row in which the sample was planted.<b>ksa_cov_mod.tsv</b>: tab-separated data file with secondary infection covariate data from the lab assay. Columns similar to the lab assay data file with columns for secondary Asp, Fus, and NI infections and their logarithms.<b>brmfits.zip</b>: zip archive with 12 .rds files. These are model output files for the Bayesian mixed effect models presented in the MS that were fitted using the R function brm(). You may download these to reproduce output without having to compile and run the models yourself.The three .tsv data files should be placed in a subdirectory called "data" in the same directory where the .Rmd notebook is located.
DOI: 10.15482/usda.adc/25289518.v1
title: Data and code from: Comparison of in-field and laboratory-based phenotyping methods for evaluation of aflatoxin accumulation in maize inbred lines

description: This dataset contains all data and R code, in RMarkdown notebook format, needed to reproduce all statistical analysis, figures, and tables in the manuscript:Jeffers, D., J. S. Smith, E. D. Womack, Q. D. Read, and G. L. Windham. 2024. Comparison of in-field and laboratory-based phenotyping methods for evaluation of aflatoxin accumulation in maize inbred lines. <i>Plant Disease</i>. (citation to be updated upon final acceptance of MS)There is a critical need to quickly and reliably identify corn genotypes that are resistant to accumulating aflatoxin in their kernels. We compared three methods of determining how resistant different corn genotypes are to aflatoxin accumulation: a field-based assay (side-needle inoculation) and two different lab-based assays (wounding and non-wounding kernel screening assays; KSA). In this data object, we present the data from the lab and field assays, statistical models that are fit to the data, procedures for comparing model fit of different variants of the model, and model predictions. This includes how reliably each assay identifies resistant and susceptible check varieties, and how well correlated the assay methods are with one another. Statistical analyses are done using R software, including Bayesian models fit with Stan software.The following files are included:<b>ksa_analysis_revised.Rmd</b>: RMarkdown notebook with all code needed to reproduce analyses and create figures and tables in manuscript<b>ksa_analysis_revised.html</b>: HTML rendered output of notebook<b>step1_ksa.tsv</b>: tab-separated data file with data from the lab assay. Columns include sample ID, genotype ID and entry code, year, treatment (wound or no-wound), subsample ID, replicate ID, aflatoxin concentration (in units of ng/g), logarithm of aflatoxin concentration, and a column indicating genotypes that are susceptible or resistant checks<b>step1_ksa_field.tsv</b>: tab-separated data file with data from the field assay. Columns similar to the lab assay data file with an additional column for row in which the sample was planted.<b>ksa_cov_mod.tsv</b>: tab-separated data file with secondary infection covariate data from the lab assay. Columns similar to the lab assay data file with columns for secondary Asp, Fus, and NI infections and their logarithms.<b>brmfits.zip</b>: zip archive with 12 .rds files. These are model output files for the Bayesian mixed effect models presented in the MS that were fitted using the R function brm(). You may download these to reproduce output without having to compile and run the models yourself.The three .tsv data files should be placed in a subdirectory called "data" in the same directory where the .Rmd notebook is located.
DOI: 10.15482/usda.adc/24938556
title: Data and code from: A natural polymer material as a pesticide adjuvant for mitigating off-target drift and protecting pollinator health

description: This dataset contains all data and code required to clean the data, fit the models, and create the figures and tables for the laboratory experiment portion of the manuscript:Kannan, N., Q. D. Read, and W. Zhang. 2024. A natural polymer material as a pesticide adjuvant for mitigating off-target drift and protecting pollinator health. <i>Heliyon</i>, in press. https://doi.org/10.1016/j.heliyon.2024.e35510.In this dataset, we archive results from several laboratory and field trials testing different adjuvants (spray additives) that are intended to reduce particle drift, increase particle size, and slow down the particles from pesticide spray nozzles. We fit statistical models to the droplet size and speed distribution data and statistically compare different metrics between the adjuvants (sodium alginate, polyacrylamide [PAM], and control without any adjuvants). The following files are included:RawDataPAMsodAlgOxfLsr.xlsx: Raw data for primary analysesOrganizedDataPaperRevision20240614.xlsx: Raw data to produce density plots presented in Figs. 8 and 9raw_data_readme.md: Markdown file with description of the raw data filesR_code_supplement.R: All R code required to reproduce primary analysesR_code_supplement2.R: R code required to produce density plots presented in Figs. 8 and 9Intermediate R output files are also included so that tables and figures can be recreated without having to rerun the data preprocessing, model fitting, and posterior estimation steps:pam_cleaned.RData: Data combined into clean R data frames for analysisvelocityscaledlogdiamfit.rds: Fitted **brms** model object for velocitylnormfitreduced.rds: Fitted **brms** model object for diameter distributionemm_con_velo_diam_draws.RData: Posterior distributions of estimated marginal means for velocityemm_con_draws.RData: Posterior distributions of estimated marginal means for diameter distributionThe following software and package versions were used:R version 4.3.1CmdStan version 2.33.1R packages:brms version 2.20.5cmdstanr version 0.5.3fitdistrplus version 1.1-11tidybayes version 3.0.4emmeans version 1.8.9<br>
DOI: 10.15482/usda.adc/24938556.v1
title: Data and code from: A natural polymer material as a pesticide adjuvant for mitigating off-target drift and protecting pollinator health

description: This dataset contains all data and code required to clean the data, fit the models, and create the figures and tables for the laboratory experiment portion of the manuscript:Kannan, N., Q. D. Read, and W. Zhang. 2024. A natural polymer material as a pesticide adjuvant for mitigating off-target drift and protecting pollinator health. <i>Heliyon</i>, in press. https://doi.org/10.1016/j.heliyon.2024.e35510.In this dataset, we archive results from several laboratory and field trials testing different adjuvants (spray additives) that are intended to reduce particle drift, increase particle size, and slow down the particles from pesticide spray nozzles. We fit statistical models to the droplet size and speed distribution data and statistically compare different metrics between the adjuvants (sodium alginate, polyacrylamide [PAM], and control without any adjuvants). The following files are included:RawDataPAMsodAlgOxfLsr.xlsx: Raw data for primary analysesOrganizedDataPaperRevision20240614.xlsx: Raw data to produce density plots presented in Figs. 8 and 9raw_data_readme.md: Markdown file with description of the raw data filesR_code_supplement.R: All R code required to reproduce primary analysesR_code_supplement2.R: R code required to produce density plots presented in Figs. 8 and 9Intermediate R output files are also included so that tables and figures can be recreated without having to rerun the data preprocessing, model fitting, and posterior estimation steps:pam_cleaned.RData: Data combined into clean R data frames for analysisvelocityscaledlogdiamfit.rds: Fitted **brms** model object for velocitylnormfitreduced.rds: Fitted **brms** model object for diameter distributionemm_con_velo_diam_draws.RData: Posterior distributions of estimated marginal means for velocityemm_con_draws.RData: Posterior distributions of estimated marginal means for diameter distributionThe following software and package versions were used:R version 4.3.1CmdStan version 2.33.1R packages:brms version 2.20.5cmdstanr version 0.5.3fitdistrplus version 1.1-11tidybayes version 3.0.4emmeans version 1.8.9<br>
DOI: 10.15482/usda.adc/24659127.v1
title: Data and code from: Extending irrigation reservoir histories for improved groundwater modeling and conjunctive water management in two Arkansas critical groundwater areas

description: This dataset contains all data and code required to reproduce the time-to-event analysis in the associated manuscript. More detail is found in the associated README.md file.Contents of repository<code>Yaeger_ReservoirDataset_Oct2022.csv</code>: comma-separated data file with reservoir characteristics, construction times, and water table depths and percent saturation at 5-year intervals from 1975-2015.<code>CGA_reservoir_analysis.Rmd</code>: RMarkdown notebook with all code required to reproduce the time-to-event analysis in the manuscript and generate the associated plots.<code>CGA_reservoir_analysis.html</code>: HTML file rendered from the .Rmd notebook.<code>README.md</code>: additional details, including column descriptions from the CSV file.Software versions usedR version 4.1.2 (https://cran.r-project.org/bin/windows/base/old/4.1.2)R packages:data.table v1.14.8 (https://rdatatable.gitlab.io/data.table/)ggplot2 v3.4.4 (https://ggplot2.tidyverse.org/)sf v1.0-14 (https://r-spatial.github.io/sf/)survival v3.5-5 (https://cran.r-project.org/package=survival)icenReg v2.0.15 (https://cran.r-project.org/package=icenReg)
DOI: 10.15482/usda.adc/24659127
title: Data and code from: Extending irrigation reservoir histories for improved groundwater modeling and conjunctive water management in two Arkansas critical groundwater areas

description: This dataset contains all data and code required to reproduce the time-to-event analysis in the associated manuscript. More detail is found in the associated README.md file.Contents of repository<code>Yaeger_ReservoirDataset_Oct2022.csv</code>: comma-separated data file with reservoir characteristics, construction times, and water table depths and percent saturation at 5-year intervals from 1975-2015.<code>CGA_reservoir_analysis.Rmd</code>: RMarkdown notebook with all code required to reproduce the time-to-event analysis in the manuscript and generate the associated plots.<code>CGA_reservoir_analysis.html</code>: HTML file rendered from the .Rmd notebook.<code>README.md</code>: additional details, including column descriptions from the CSV file.Software versions usedR version 4.1.2 (https://cran.r-project.org/bin/windows/base/old/4.1.2)R packages:data.table v1.14.8 (https://rdatatable.gitlab.io/data.table/)ggplot2 v3.4.4 (https://ggplot2.tidyverse.org/)sf v1.0-14 (https://r-spatial.github.io/sf/)survival v3.5-5 (https://cran.r-project.org/package=survival)icenReg v2.0.15 (https://cran.r-project.org/package=icenReg)
DOI: 10.3886/e240523
title: HIFLD OPEN US Army Corps of Engineers (USACE) Civil Works Districts

description: Polygons showing USACE Civil Works District boundaries. This dataset was digitized from the NRCS Watershed Boundary Dataset (WBD). Where districts follow administrative boundaries, such as County and State lines, National Atlas and Census datasets were used. USACE District GIS POCs also submitted data to incorporate into this dataset. This dataset has been simplified +/- 30 feet to reduce file size and speed up drawing time. 05/05/20 - Update to show new LRC boundary. Minor change between LRL and LRH. 07/31/24 - Update to show new SAA Caribbean District.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025. (Downloaded 08/26)
DOI: 10.3886/e240523v1
title: HIFLD OPEN US Army Corps of Engineers (USACE) Civil Works Districts

description: Polygons showing USACE Civil Works District boundaries. This dataset was digitized from the NRCS Watershed Boundary Dataset (WBD). Where districts follow administrative boundaries, such as County and State lines, National Atlas and Census datasets were used. USACE District GIS POCs also submitted data to incorporate into this dataset. This dataset has been simplified +/- 30 feet to reduce file size and speed up drawing time. 05/05/20 - Update to show new LRC boundary. Minor change between LRL and LRH. 07/31/24 - Update to show new SAA Caribbean District.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025. (Downloaded 08/26)
DOI: 10.15468/dl.9gax9x
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Denticulobasis ariken",
 "DecimalLongitude is not null",
 "DecimalLatitude is not null"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003007-251120083545085/datasets/export for details.
DOI: 10.5281/zenodo.14632255
title: Data for "Seasonality of the North Pacific Ocean Desert area in the past two decades and a modelling perspective for the 21st century"

description: This is the processed data and codes for BG manuscript Meng_#783869. For the original data, please refer to the supporting information Text S2.
DOI: 10.5281/zenodo.17675565
title: Data for "Seasonality of the North Pacific Ocean Desert area in the past two decades and a modelling perspective for the 21st century"

description: This is the processed data and codes for BG manuscript Meng_#783869. For the original data, please refer to the supporting information Text S2.
DOI: 10.15482/usda.adc/28914434
title: Data from: Topographic position index predicts within-field yield variation in a dryland cereal production system

description: We investigated drivers of sub-field spatial variability in yield for 3 crops (hard red winter wheat, <i>Triticum aestivum</i> L. variety Langin; corn, <i>Zea mays</i> L.; and proso millet, <i>Panicum milaceum</i> L.) usings this multi-year dataset from a dryland research farm in northeastern Colorado, USA. The dataset spanned 18 2.6-4.3 ha management units collected over 4 years (2019-2022). The data includes high resolution topographic data collected via real-time kinematic GPS, densely sampled soil texture and chemical properties, and meteorological data from an on-site weather station.