cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15482/usda.adc/25114670.v1
title: Weighing Lysimeter Data for The Bushland, Texas, Cotton Datasets

description: This dataset consists of weighing lysimeter data for upland cotton [<i>Gossypium hirsutum</i> (L.)] grown for lint and seed at the USDA-ARS Conservation and Production Research Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL) in 2000 through 2004, 2008, 2010, 2012, 2020, and 2021 on from one to four large, precision weighing lysimeters, each in the center of a 4.44 ha square field similarly cropped. In 2019, cotton was grown on four large, precision weighing lysimeters, each in the center of a 4.4-ha square field. The weighing lysimeters were used to measure mass, which was converted to relative soil water storage with 0.05 mm accuracy at 5-minute intervals, and the 5-minute change in soil water storage was used along with precipitation and irrigation amounts to calculate crop evapotranspiration (ET), which is reported at 15-minute intervals. Although a quality control process was used, the ET data in this dataset are considered raw data. Advanced algorithms for detection of precipitation, dew and frost were applied in a separate process to determine ET values that are reported in files in a dataset entitled "Evapotranspiration and Water Balance Data for The Bushland, Texas Cotton Datasets". Those files have "water-balance" in their names. Each lysimeter was equipped with a suite of instruments to sense wind speed, air temperature and relative humidity, components of the radiation balance (e.g., net radiation, incoming and reflected shortwave, photosynthetically active radiation (PAR), incoming and reflected longwave, thermal infrared emitted by the plant/soil surface), soil heat flux, soil temperature, and soil volumetric water content at certain depths. Not all properties were always sensed in any one year; and instruments used changed from season to season, which are reasons that subsidiary datasets and data dictionaries for each season are required.<br>These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), lack of irrigation (dryland production), agronomic practices, cultivar, and weather. Prior publications have focused on cotton ET, crop coefficients, crop water productivity, and simulation modeling of crop growth, water use, and yield. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used by both USDA and university researchers.<br>See the README file (README_Bushland_Cotton_Lys.txt) for descriptions of each data file. Descriptions are different for each year because experimental protocols changed yearly.
DOI: 10.15482/usda.adc/25114610
title: Weighing Lysimeter Data for The Bushland, Texas, Sorghum Datasets

description: This dataset consists of weighing lysimeter data for sorghum [<i>Sorghum bicolor</i> (L.)] grown for grain or forage at the USDA-ARS Conservation and Production Research Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL) in 1987, 1988, 1991, 1993, 1997, 1998, 1999, 2003 through 2007, 2014, and 2015 (13 years) on from one to four large, precision weighing lysimeters, each in the center of a 4.44 ha square field similarly cropped. In 2006 and 2007, sorghum was also grown for forage. The weighing lysimeters were used to measure mass, which was converted to relative soil water storage with 0.05 mm accuracy at 5-minute intervals, and the 5-minute change in soil water storage was used along with precipitation and irrigation amounts to calculate crop evapotranspiration (ET), which is reported at 15-minute intervals. Although a quality control process was used, the ET data in this dataset are considered raw data. Advanced algorithms for detection of precipitation, dew and frost were applied in a separate process to determine ET values that are reported in files in a dataset entitled "Evapotranspiration and Water Balance Data for The Bushland, Texas Sorghum Datasets". Those files have "water-balance" in their names. Each lysimeter was equipped with a suite of instruments to sense wind speed, air temperature and relative humidity, components of the radiation balance (e.g., net radiation, incoming and reflected shortwave, photosynthetically active radiation (PAR), incoming and reflected longwave, thermal infrared emitted by the plant/soil surface), soil heat flux, soil temperature, and soil volumetric water content at certain depths. Not all properties were always sensed in any one year; and instruments used changed from season to season, which are reasons that subsidiary datasets and data dictionaries for each season are required.These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on sorghum ET, crop coefficients, crop water productivity, and simulation modeling of crop water use, growth, and yield. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used for testing, and calibrating models of ET that use satellite and/or weather data.See the README file (README_Bushland_Sorghum_Weighing_Lysimeter) for descriptions of resources in this dataset. The descriptions vary by year because experimental protocols varied by year.
DOI: 10.15482/usda.adc/25114610.v1
title: Weighing Lysimeter Data for The Bushland, Texas, Sorghum Datasets

description: This dataset consists of weighing lysimeter data for sorghum [<i>Sorghum bicolor</i> (L.)] grown for grain or forage at the USDA-ARS Conservation and Production Research Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL) in 1987, 1988, 1991, 1993, 1997, 1998, 1999, 2003 through 2007, 2014, and 2015 (13 years) on from one to four large, precision weighing lysimeters, each in the center of a 4.44 ha square field similarly cropped. In 2006 and 2007, sorghum was also grown for forage. The weighing lysimeters were used to measure mass, which was converted to relative soil water storage with 0.05 mm accuracy at 5-minute intervals, and the 5-minute change in soil water storage was used along with precipitation and irrigation amounts to calculate crop evapotranspiration (ET), which is reported at 15-minute intervals. Although a quality control process was used, the ET data in this dataset are considered raw data. Advanced algorithms for detection of precipitation, dew and frost were applied in a separate process to determine ET values that are reported in files in a dataset entitled "Evapotranspiration and Water Balance Data for The Bushland, Texas Sorghum Datasets". Those files have "water-balance" in their names. Each lysimeter was equipped with a suite of instruments to sense wind speed, air temperature and relative humidity, components of the radiation balance (e.g., net radiation, incoming and reflected shortwave, photosynthetically active radiation (PAR), incoming and reflected longwave, thermal infrared emitted by the plant/soil surface), soil heat flux, soil temperature, and soil volumetric water content at certain depths. Not all properties were always sensed in any one year; and instruments used changed from season to season, which are reasons that subsidiary datasets and data dictionaries for each season are required.These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on sorghum ET, crop coefficients, crop water productivity, and simulation modeling of crop water use, growth, and yield. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used for testing, and calibrating models of ET that use satellite and/or weather data.See the README file (README_Bushland_Sorghum_Weighing_Lysimeter) for descriptions of resources in this dataset. The descriptions vary by year because experimental protocols varied by year.
DOI: 10.15482/usda.adc/25590717
title: Data from: Establishment of the wasp Tetramesa romana for biological control of Arundo donax in northern California and the role of release plot manipulation

description: <i>Arundo donax</i> is a non-native, invasive large-statured grass of riparian systems in the southwestern U.S.A., including the Sacramento and San Joaquin River watersheds of northern California and the Sacramento-San Joaquin Delta. In 2017, the shoot tip-galling wasp <i>Tetramesa romana</i> was released at nine sites, three in each region. Shoots in some release plots were manipulated prior to release by cutting to ground or pruning to 1 m height, while others were left uncut. One year later, exit holes made by emerging adult wasps were found at two of nine sites. Exit hole density per main shoot length was 16-fold higher on regrowth shoots in ground-cut plots than in uncut plots. An additional plot manipulation study at two other sites found that exit hole density per shoot length was 19-fold higher in plots that were double-cut (cut to ground and regrowth pruned) than in single-cut plots. By 2023, <i>T. romana</i> was established at eight sites spanning both river watersheds and their Delta with dispersal up to 6.4 km, based on dissection of shoots, multi-year counts of exit holes and galls, and trapping of adult <i>T. romana</i> with sticky traps. The abundance of <i>T. romana</i> may be limited in northern California by low annual heat unit accumulation. The results show that physical manipulation of host plants improves short-term establishment and demonstrate the importance of using multiple monitoring methods to determine long-term establishment.
DOI: 10.15482/usda.adc/25590717.v1
title: Data from: Establishment of the wasp Tetramesa romana for biological control of Arundo donax in northern California and the role of release plot manipulation

description: <i>Arundo donax</i> is a non-native, invasive large-statured grass of riparian systems in the southwestern U.S.A., including the Sacramento and San Joaquin River watersheds of northern California and the Sacramento-San Joaquin Delta. In 2017, the shoot tip-galling wasp <i>Tetramesa romana</i> was released at nine sites, three in each region. Shoots in some release plots were manipulated prior to release by cutting to ground or pruning to 1 m height, while others were left uncut. One year later, exit holes made by emerging adult wasps were found at two of nine sites. Exit hole density per main shoot length was 16-fold higher on regrowth shoots in ground-cut plots than in uncut plots. An additional plot manipulation study at two other sites found that exit hole density per shoot length was 19-fold higher in plots that were double-cut (cut to ground and regrowth pruned) than in single-cut plots. By 2023, <i>T. romana</i> was established at eight sites spanning both river watersheds and their Delta with dispersal up to 6.4 km, based on dissection of shoots, multi-year counts of exit holes and galls, and trapping of adult <i>T. romana</i> with sticky traps. The abundance of <i>T. romana</i> may be limited in northern California by low annual heat unit accumulation. The results show that physical manipulation of host plants improves short-term establishment and demonstrate the importance of using multiple monitoring methods to determine long-term establishment.
DOI: 10.15468/dl.afe8ba
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Proneura prolongata",
 "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/0002992-251120083545085/datasets/export for details.
DOI: 10.15482/usda.adc/28232858
title: Data from: On-farm wildflower plantings generate opposing reproductive outcomes for solitary and bumble bee species

description: Pollinator habitat can be planted on farms to enhance floral and nesting resources, and subsequently, pollinator populations. There is ample evidence linking such plantings to greater pollinator abundance on farms, but less is known about their effects on pollinator reproduction. We placed <i>Bombus impatiens</i> Cresson (Hymenoptera: Apidae) and <i>Megachile rotundata</i> (F.) (Hymenoptera: Megachilidae) nests out on 19 Mid-Atlantic farms in 2018, where half (n=10) the farms had established wildflower plantings and half (n=9) did not. <i>Bombus impatiens</i> nests were placed at each farm in spring and mid-summer and repeatedly weighed to capture colony growth. We quantified the relative production of reproductive castes and assessed parasitism rates by screening for conopid fly parasitism and <i>Nosema</i> spores within female workers. We also released <i>M. rotundata</i> cocoons at each farm in spring and collected new nests and emergent adult offspring over the next year, recording female weight as an indicator of reproductive potential and quantifying <i>Nosema</i> parasitism and parasitoid infection rates. <i>Bombus impatiens</i> nests gained less weight and contained female workers with <i>Nosema</i> spore loads over 150x greater on farms with wildflower plantings. In contrast, <i>M. rotundata</i> female offspring weighed more on farms with wildflower plantings and marginally less on farms with honey bee hives. We conclude that wildflower plantings likely enhance reproduction in some species, but that they could also enhance microsporidian parasitism rates in susceptible bee species. It will be important to determine how wildflower planting benefits can be harnessed while minimizing parasitism in wild and managed bee species.
DOI: 10.15482/usda.adc/28232858.v1
title: Data from: On-farm wildflower plantings generate opposing reproductive outcomes for solitary and bumble bee species

description: Pollinator habitat can be planted on farms to enhance floral and nesting resources, and subsequently, pollinator populations. There is ample evidence linking such plantings to greater pollinator abundance on farms, but less is known about their effects on pollinator reproduction. We placed <i>Bombus impatiens</i> Cresson (Hymenoptera: Apidae) and <i>Megachile rotundata</i> (F.) (Hymenoptera: Megachilidae) nests out on 19 Mid-Atlantic farms in 2018, where half (n=10) the farms had established wildflower plantings and half (n=9) did not. <i>Bombus impatiens</i> nests were placed at each farm in spring and mid-summer and repeatedly weighed to capture colony growth. We quantified the relative production of reproductive castes and assessed parasitism rates by screening for conopid fly parasitism and <i>Nosema</i> spores within female workers. We also released <i>M. rotundata</i> cocoons at each farm in spring and collected new nests and emergent adult offspring over the next year, recording female weight as an indicator of reproductive potential and quantifying <i>Nosema</i> parasitism and parasitoid infection rates. <i>Bombus impatiens</i> nests gained less weight and contained female workers with <i>Nosema</i> spore loads over 150x greater on farms with wildflower plantings. In contrast, <i>M. rotundata</i> female offspring weighed more on farms with wildflower plantings and marginally less on farms with honey bee hives. We conclude that wildflower plantings likely enhance reproduction in some species, but that they could also enhance microsporidian parasitism rates in susceptible bee species. It will be important to determine how wildflower planting benefits can be harnessed while minimizing parasitism in wild and managed bee species.
DOI: 10.15468/dl.xs66zk
title: Occurrence Download

description: A dataset containing 1058 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cryptocarya alba",
 "Country is Chile",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 1058 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002994-251120083545085/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/25033856
title: Data from: Soil Landscapes of the United States 100-meter (SOLUS100) soil property maps project repository

description: This repository provides extended documentation, code, and updated links to access the Soil Landscapes of the United States (SOLUS) 100-meter soil property maps. It provides supporting materials for a peer reviewed paper (Nauman et al., <em>Soil Science Society of America Journal</em>, 1–20. https://doi.org/10.1002/saj2.20769) documenting the theory and novel application of hybridized legacy training datasets used to inform the machine learning models used to create the new soil property maps presented here. The SOLUS dataset includes 20 different soil properties (listed below) with most properties predicted for seven standard depths (0, 5, 15, 30, 60, 100, and 150 cm). Further details on these properties and all included files are available in the README.docx document. Also included is a git repository formatted as a hybrid R package that includes all code used to create the soil property maps.
All SOLUS100 mapping layers are available as cloud optimized geotiffs at: https://storage.googleapis.com/solus100pub/index.html
Metadata: https://storage.googleapis.com/solus100pub/SOLUS100_metadata_pub.html
List of files at this URL are listed at: https://storage.googleapis.com/solus100pub/Final_Layer_Table_20231215.csv
Note that many of the raster files are scaled by multipliers of 10, 100, or 1000 to store the values as integers to decrease file size. The ‘scalar’ field of the file list table (Final_Layer_Table_20231215.csv) files provide those values. The actual rasters must be divided by the scalars to get the actual units of the properties. To download files, simply concatenate the google API URL with a forward slash and the file name listed in the table into a browser (e.g. EC at 0 cm would be https://storage.googleapis.com/solus100pub/ec_15_cm_p.tif). To automate downloads, a loop in python, R or your language of choice that builds file download urls from the file list in the csv can be implemented. Alternatively, some GIS programs (e.g. QGIS) will let you visualize and interact with the files without downloading the files by entering the URL.
All raster environmental covariates used in mapping are available here: https://storage.googleapis.com/cov100m/index.html
Properties included in SOLUS100:

 Bulk density (oven dry)
 Calcium carbonate
 Cation Exchange Capacity (pH 7)
 Clay
 Coarse sand
 Electrical Conductivity (sat. paste)
 Effective cation exchange capacity
 Fine sand
 Gypsum (in &lt;20 mm fraction)
 Medium sand
 pH (1:1 method)
 Rock content
 Sand
 Sodium adsorption ratio
 Silt
 Soil organic carbon
 Very coarse sand
 Very fine sand
 Depth to bedrock
 Depth to restriction

<br>
DOI: 10.15482/usda.adc/25033856.v1
title: Data from: Soil Landscapes of the United States 100-meter (SOLUS100) soil property maps project repository

description: This repository provides extended documentation, code, and updated links to access the Soil Landscapes of the United States (SOLUS) 100-meter soil property maps. It provides supporting materials for a peer reviewed paper (Nauman et al., <em>Soil Science Society of America Journal</em>, 1–20. https://doi.org/10.1002/saj2.20769) documenting the theory and novel application of hybridized legacy training datasets used to inform the machine learning models used to create the new soil property maps presented here. The SOLUS dataset includes 20 different soil properties (listed below) with most properties predicted for seven standard depths (0, 5, 15, 30, 60, 100, and 150 cm). Further details on these properties and all included files are available in the README.docx document. Also included is a git repository formatted as a hybrid R package that includes all code used to create the soil property maps.
All SOLUS100 mapping layers are available as cloud optimized geotiffs at: https://storage.googleapis.com/solus100pub/index.html
Metadata: https://storage.googleapis.com/solus100pub/SOLUS100_metadata_pub.html
List of files at this URL are listed at: https://storage.googleapis.com/solus100pub/Final_Layer_Table_20231215.csv
Note that many of the raster files are scaled by multipliers of 10, 100, or 1000 to store the values as integers to decrease file size. The ‘scalar’ field of the file list table (Final_Layer_Table_20231215.csv) files provide those values. The actual rasters must be divided by the scalars to get the actual units of the properties. To download files, simply concatenate the google API URL with a forward slash and the file name listed in the table into a browser (e.g. EC at 0 cm would be https://storage.googleapis.com/solus100pub/ec_15_cm_p.tif). To automate downloads, a loop in python, R or your language of choice that builds file download urls from the file list in the csv can be implemented. Alternatively, some GIS programs (e.g. QGIS) will let you visualize and interact with the files without downloading the files by entering the URL.
All raster environmental covariates used in mapping are available here: https://storage.googleapis.com/cov100m/index.html
Properties included in SOLUS100:

 Bulk density (oven dry)
 Calcium carbonate
 Cation Exchange Capacity (pH 7)
 Clay
 Coarse sand
 Electrical Conductivity (sat. paste)
 Effective cation exchange capacity
 Fine sand
 Gypsum (in &lt;20 mm fraction)
 Medium sand
 pH (1:1 method)
 Rock content
 Sand
 Sodium adsorption ratio
 Silt
 Soil organic carbon
 Very coarse sand
 Very fine sand
 Depth to bedrock
 Depth to restriction

<br>