cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15482/usda.adc/25653201.v1
title: Data from: Woody perennial polycultures increase ant diversity and ant-mediated ecosystem services compared to conventional corn-soybean rotations

description: Data files for manuscript titled "Woody perennial polycultures increase ant diversity and ant-mediated ecosystem services compared to conventional corn-soybean rotations".Excel file with 4 tabs: Metadata, abundance and richness, community composition, and sentinel prey consumption.Abstract from paper: The role plant diversity has played in regulating insect communities has been of interest for decades. Recent syntheses from agroecosystems suggest increasing plant diversity can positively affect beneficial insects like predators, reducing pest pressure and increasing yield. However, the agricultural landscape of the Midwestern United States is dominated by just two crops - corn and soybean - which cover approximately 180 million acres of arable land yearly. New ideas to conserve wildlife that additionally provide economic opportunities for farmers must be developed in order to promote sustainable and resilient ecosystems. Here we tested the capacity of an alternative cropping system to support more diverse insect populations than conventional cropping systems. We quantified differences in the diversity of an insect taxon, ants (Hymenoptera: Formicidae), over an annual cycle using pitfall traps in thirty-two 2-m<sup>2</sup> plots of either woody perennial polycultures that contained apples, chestnuts, currants, hazelnuts, and raspberries or conventional corn-soybean rotations. In doing so, we found that woody perennial polycultures supported 2.4-fold more ant species and maintained a unique fauna of specialist and predatory ants. The observed differences in diversity were linked to higher levels of predation as 18.2-fold more sentinel prey were consumed during each month of the growing season. Combined, our results suggest that agricultural landscapes in the Midwestern United States can be modified to support important beneficial insects like ants while still producing commodities that can be economically beneficial to farmers.
DOI: 10.15468/dl.hpk8kx
title: Occurrence Download

description: A dataset containing 46828 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Haematopus palliatus",
 "Year is 2020"
 ]
}

 The dataset includes 46828 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003006-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15482/usda.adc/26514616
title: Data From: Do diet and Fumagillin treatment impact Vairimorpha (Nosema) spp. (Microspora: Nosematidae) infections in honey bees (Hymenoptera: Apidae) and improve survival and growth of colonies overwintered in cold storage?

description: The data set contains two years of data on honey bee colony sizes expressed as frames (combs) of adult bees and brood, Varroa mites counted per 100 bees and <i>Vairimorpha </i>(<i>Nosema</i>) spp. spores in worker bees in each colony. The data set also contains measurement of fat body weights, protein and lipid concentrations made prior to and after colonies were overwintered in cold storage. In the year-2 data set, colony measurements, <i>Nosema</i> and Varroa mite counts and fat body metrics were separated by treatment group. The treatment groups were: colonies fed pollen or protein supplement and treated with Fumagillin for <i>Nosema</i> and colonies fed the same diets but not treated with Fumagillin for <i>Nosema</i>.
DOI: 10.15482/usda.adc/26514616.v1
title: Data From: Do diet and Fumagillin treatment impact Vairimorpha (Nosema) spp. (Microspora: Nosematidae) infections in honey bees (Hymenoptera: Apidae) and improve survival and growth of colonies overwintered in cold storage?

description: The data set contains two years of data on honey bee colony sizes expressed as frames (combs) of adult bees and brood, Varroa mites counted per 100 bees and <i>Vairimorpha </i>(<i>Nosema</i>) spp. spores in worker bees in each colony. The data set also contains measurement of fat body weights, protein and lipid concentrations made prior to and after colonies were overwintered in cold storage. In the year-2 data set, colony measurements, <i>Nosema</i> and Varroa mite counts and fat body metrics were separated by treatment group. The treatment groups were: colonies fed pollen or protein supplement and treated with Fumagillin for <i>Nosema</i> and colonies fed the same diets but not treated with Fumagillin for <i>Nosema</i>.
DOI: 10.15468/dl.8ectuh
title: Occurrence Download

description: A dataset containing 170840 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Thalurania colombica",
 "HasCoordinate is true"
 ]
}

 The dataset includes 170840 records from 143 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002971-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7dkbfy
title: Occurrence Download

description: A dataset containing 131239 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Argiope bruennichi",
 "Year 1980-2025"
 ]
}

 The dataset includes 131239 records from 1187 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002984-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15482/usda.adc/28347167
title: Data and code from: Severity of charcoal rot disease in soybean genotypes inoculated with <i>Macrophomina phaseolina</i> isolates differs among growth environments

description: This dataset includes all the raw data and all the R statistical software code that we used to analyze the data and produce all the outputs that are in the figures, tables, and text of the associated manuscript:Mengistu, A., Q. D. Read, C. R. Little, H. M. Kelly, P. M. Henry, and N. Bellaloui. 2025. Severity of charcoal rot disease in soybean genotypes inoculated with <i>Macrophomina phaseolina</i> isolates differs among growth environments. <i>Plant Disease</i>. DOI: 10.1094/PDIS-10-24-2230-RE.<br>The data included here come from a series of tests designed to evaluate methods for identifying soybean genotypes that are resistant or susceptible to charcoal rot, a widespread and economically significant disease. Four independent experiments were performed to determine the variability in disease severity by soybean genotype and by isolated variant of the charcoal rot fungus: two field tests, a greenhouse test, and a growth chamber test. The tests differed in the number of genotypes and isolates used, as well as the method of inoculation. The accuracy of identifying resistant and susceptible genotypes varied by study, and the same isolate tested across different studies often had highly variable disease severity. Our results indicate that the non-field methods are not reliable ways to identify sources of charcoal rot resistance in soybean.The models fit in the R script archived here are Bayesian general linear mixed models with AUDPC (area under the disease progress curve) as the response variable. One-dimensional clustering is used to divide the genotypes into resistant and susceptible based on their model-predicted AUDPC values, and this result is compared with the preexisting resistance classification. Posterior distributions of the marginal means for different combinations of genotype, isolate, and other covariates are estimated and compared. Code to reproduce the tables and figures of the manuscript is also included.The following files are included:<b>README.pdf</b>: Full description, with column metadata for the data spreadsheets and text description of each R script<b>data2023-04-18.xlsx</b>: Excel sheet with data from three of the four trials<b>cleaned_data.RData</b>: all data in analysis-ready format; generates a set of data frames when imported into an R environment<b>Modified Cut-Tip Inoculation on DT974290 and LS980358 on first 32 isolates.xlsx</b>: Excel spreadsheet with data from the fourth trial<b>data_cleaning.R</b>: Script required to format data from .xlsx files into analysis-ready format (running this script is not necessary to reproduce the analysis; instead you may begin with the following script importing the cleaned .RData object)<b>AUDPC_fits.R</b>: Script containing code for all model fitting, model predictions and comparisons, and figure and table generation<br>
DOI: 10.15482/usda.adc/28347167.v1
title: Data and code from: Severity of charcoal rot disease in soybean genotypes inoculated with <i>Macrophomina phaseolina</i> isolates differs among growth environments

description: This dataset includes all the raw data and all the R statistical software code that we used to analyze the data and produce all the outputs that are in the figures, tables, and text of the associated manuscript:Mengistu, A., Q. D. Read, C. R. Little, H. M. Kelly, P. M. Henry, and N. Bellaloui. 2025. Severity of charcoal rot disease in soybean genotypes inoculated with <i>Macrophomina phaseolina</i> isolates differs among growth environments. <i>Plant Disease</i>. DOI: 10.1094/PDIS-10-24-2230-RE.<br>The data included here come from a series of tests designed to evaluate methods for identifying soybean genotypes that are resistant or susceptible to charcoal rot, a widespread and economically significant disease. Four independent experiments were performed to determine the variability in disease severity by soybean genotype and by isolated variant of the charcoal rot fungus: two field tests, a greenhouse test, and a growth chamber test. The tests differed in the number of genotypes and isolates used, as well as the method of inoculation. The accuracy of identifying resistant and susceptible genotypes varied by study, and the same isolate tested across different studies often had highly variable disease severity. Our results indicate that the non-field methods are not reliable ways to identify sources of charcoal rot resistance in soybean.The models fit in the R script archived here are Bayesian general linear mixed models with AUDPC (area under the disease progress curve) as the response variable. One-dimensional clustering is used to divide the genotypes into resistant and susceptible based on their model-predicted AUDPC values, and this result is compared with the preexisting resistance classification. Posterior distributions of the marginal means for different combinations of genotype, isolate, and other covariates are estimated and compared. Code to reproduce the tables and figures of the manuscript is also included.The following files are included:<b>README.pdf</b>: Full description, with column metadata for the data spreadsheets and text description of each R script<b>data2023-04-18.xlsx</b>: Excel sheet with data from three of the four trials<b>cleaned_data.RData</b>: all data in analysis-ready format; generates a set of data frames when imported into an R environment<b>Modified Cut-Tip Inoculation on DT974290 and LS980358 on first 32 isolates.xlsx</b>: Excel spreadsheet with data from the fourth trial<b>data_cleaning.R</b>: Script required to format data from .xlsx files into analysis-ready format (running this script is not necessary to reproduce the analysis; instead you may begin with the following script importing the cleaned .RData object)<b>AUDPC_fits.R</b>: Script containing code for all model fitting, model predictions and comparisons, and figure and table generation<br>
DOI: 10.15482/usda.adc/27919398
title: Data and code from: Application variables that affect efficacy of disinfestants sprayed on different substrate materials to control <i>Colletotrichum siamense</i>

description: SummaryThis dataset includes all data and code required to reproduce the analyses presented in the manuscript Application variables that affect efficacy of disinfestants sprayed on different substrate materials to control <i>Colletotrichum siamense</i>.Disinfestants are an important sanitation tool used to eliminate plant pathogens, but it is unknown how effective they are even when applied following recommended practices. The efficacy of a selection of hypochlorite, isopropyl alcohol, quaternary ammonium and peroxygen compounds against <i>Colletotrichum siamense</i> was evaluated relative to substrate porosity, contact time and disinfestant wettability properties in seven studies where disinfestants were sprayed on six substrate materials (concrete, galvanized metal, polypropylene ground fabric, polyethylene plastic sheet, pressure-treated pine, and twin-wall clear polycarbonate) commonly found in ornamental plant production surfaces.This dataset includes the raw data and R statistical software code required to reproduce the data processing, statistical model fitting, and production of figures and tables in the associated manuscript. For each of the seven studies, we fit a Bayesian generalized linear mixed model to the data. We graphically assessed model fit, then used posterior distributions of the expected values of the posterior predictive to make predictions. From each of these distributions, we used the median to represent the point estimate and 95% equal-tailed credible intervals to represent uncertainty about the means.ContentsThe following files are included:data_readme.md: Markdown file with column metadata for all CSV files and an explanation of the treatment codes.disinfestant_effectiveness_analysis.Rmd: RMarkdown file with all R code required for reproducing the content of the manuscriptdisinfestant_effectiveness_analysis.html: Rendered output of RMarkdown notebookRaw data<br>Study1 aDsnfSbstAllData.csv: Study 1 data (comparison of disinfestant by substrate by contact time)Study2 aSbstDsnfTimeBGFMdata reduced.csv (comparison of disinfestant by contact time on fabric substrate)Study3 ConcreteEffccy.csv (comparison of disinfestant by treatment schedule on concrete substrate)Study4 FabricEffccy.csv (comparison of disinfestant by treatment schedule on fabric substrate)Study5 WoodEffccy.csv (comparison of disinfestant by treatment schedule on wood substrate)Study6 KZPTdensityData.csv (comparison of disinfestant by substrate by percent coverage)Study7 ZPdensityData.csv (comparison of disinfestant by spray cover level)Cached model objectsSome of the models take several minutes to run and compile, so the cached objects containing the model fits are also archived here to enable rerunning the analysis notebook without refitting all models.exp1zoibfitsd5.rdsexp2zoibfitsd10.rdsexp3zoibfit_concretesd10.rdsexp4zoibfit_fabricsd10.rdsexp5zoibfit_woodsd10.rdsexp6zipfitsd5.rdsexp7zipfitsd5_continuous.rds<br>
DOI: 10.15482/usda.adc/27919398.v1
title: Data and code from: Application variables that affect efficacy of disinfestants sprayed on different substrate materials to control <i>Colletotrichum siamense</i>

description: SummaryThis dataset includes all data and code required to reproduce the analyses presented in the manuscript Application variables that affect efficacy of disinfestants sprayed on different substrate materials to control <i>Colletotrichum siamense</i>.Disinfestants are an important sanitation tool used to eliminate plant pathogens, but it is unknown how effective they are even when applied following recommended practices. The efficacy of a selection of hypochlorite, isopropyl alcohol, quaternary ammonium and peroxygen compounds against <i>Colletotrichum siamense</i> was evaluated relative to substrate porosity, contact time and disinfestant wettability properties in seven studies where disinfestants were sprayed on six substrate materials (concrete, galvanized metal, polypropylene ground fabric, polyethylene plastic sheet, pressure-treated pine, and twin-wall clear polycarbonate) commonly found in ornamental plant production surfaces.This dataset includes the raw data and R statistical software code required to reproduce the data processing, statistical model fitting, and production of figures and tables in the associated manuscript. For each of the seven studies, we fit a Bayesian generalized linear mixed model to the data. We graphically assessed model fit, then used posterior distributions of the expected values of the posterior predictive to make predictions. From each of these distributions, we used the median to represent the point estimate and 95% equal-tailed credible intervals to represent uncertainty about the means.ContentsThe following files are included:data_readme.md: Markdown file with column metadata for all CSV files and an explanation of the treatment codes.disinfestant_effectiveness_analysis.Rmd: RMarkdown file with all R code required for reproducing the content of the manuscriptdisinfestant_effectiveness_analysis.html: Rendered output of RMarkdown notebookRaw data<br>Study1 aDsnfSbstAllData.csv: Study 1 data (comparison of disinfestant by substrate by contact time)Study2 aSbstDsnfTimeBGFMdata reduced.csv (comparison of disinfestant by contact time on fabric substrate)Study3 ConcreteEffccy.csv (comparison of disinfestant by treatment schedule on concrete substrate)Study4 FabricEffccy.csv (comparison of disinfestant by treatment schedule on fabric substrate)Study5 WoodEffccy.csv (comparison of disinfestant by treatment schedule on wood substrate)Study6 KZPTdensityData.csv (comparison of disinfestant by substrate by percent coverage)Study7 ZPdensityData.csv (comparison of disinfestant by spray cover level)Cached model objectsSome of the models take several minutes to run and compile, so the cached objects containing the model fits are also archived here to enable rerunning the analysis notebook without refitting all models.exp1zoibfitsd5.rdsexp2zoibfitsd10.rdsexp3zoibfit_concretesd10.rdsexp4zoibfit_fabricsd10.rdsexp5zoibfit_woodsd10.rdsexp6zipfitsd5.rdsexp7zipfitsd5_continuous.rds<br>
DOI: 10.15482/usda.adc/27697629
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., Stafne, E.T. & Read, Q.D. (2025). Establishment of Southern Highbush Blueberry Cultivars and Suppression of Phytophthora Root Rot Using Cover Crop and Soil Amendment Treatments. <i>PhytoFrontiers</i>. https://doi.org/10.1094/PHYTOFR-09-24-0096-RSummaryPhytophthora 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 notebookVersion historyV2 replaces blueberry_covercrop_analysis.Rmd and blueberry_covercrop_analysis.html with new versions. This corrects an error in the statistical analysis code from the previous version.
DOI: 10.15482/usda.adc/27697629.v2
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., Stafne, E.T. & Read, Q.D. (2025). Establishment of Southern Highbush Blueberry Cultivars and Suppression of Phytophthora Root Rot Using Cover Crop and Soil Amendment Treatments. <i>PhytoFrontiers</i>. https://doi.org/10.1094/PHYTOFR-09-24-0096-RSummaryPhytophthora 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 notebookVersion historyV2 replaces blueberry_covercrop_analysis.Rmd and blueberry_covercrop_analysis.html with new versions. This corrects an error in the statistical analysis code from the previous version.