AndreaBozzo/ceres-open-data-index huggingface dataset:
Data from: A checklist of South Dakota bumble bees (Hymenoptera: Apidae)

<p>Data files for manuscript titled "A checklist of South Dakota bumble bees (Hymenoptera: Apidae) ", <a href="https://doi.org/10.3897/jhr.94.94584">https://doi.org/10.3897/jhr.94.94584</a>. </p>
<p>Excel file with 4 tabs: Metadata, county maps, species data, and species matrix. Metadata is contained within Excel file that describes all variables for each tab.</p>
<p>Abstract from paper:
Several bumble bee species (<em>Bombus</em> Latreille) are declining and efforts to conserve populations will be strengthened by an improved knowledge of their geographic distribution. Knowledge gaps exist, however, especially in central portions of North America. Here we report 29 species of bumble bees from South Dakota in the north-central USA, based on 130 years of records from 1891 to 2021. Specimens or observations were available for >90% of the 66 counties, though they were not distributed evenly as most records came from Pennington, Lawrence, Custer, Brookings, and Day Counties. The five most commonly collected or reported bumble bee species were <em>B. griseocollis</em> (54 counties), <em>B. pensylvanicus</em> (41 counties), <em>B. fervidus</em> (39 counties), <em>B. huntii</em> (27 counties), and <em>B. bimaculatus</em> (25 counties). Twenty species were recorded from 10 or fewer counties. Despite differences in occurrence, 66% of the <em>Bombus</em> species in South Dakota were collected or observed since 2020, including six of the nine species of conservation concern (<em>B. fraternus, B. pensylvanicus, B. fervidus, B. occidentalis, B. terricola</em>, and <em>B. morrisoni</em>). However, the critically endangered <em>B. affinis, B. variabilis</em>, and <em>B. suckleyi</em> have not been collected or observed for over 50 years. While this checklist is the first for South Dakota bumble bees in nearly 100 years, data are still lacking as ~55% of counties had fewer than five species reported. We suggest future efforts should focus on these under-sampled areas to fill in baseline knowledge of the wild bee fauna towards completing a more holistic view of bumble bee distributions across the Great Plains. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Data from: A checklist of South Dakota bumble bees (Hymenoptera: Apidae).</p> <p>File Name: South Dakota Bumble Bees - Final Data File.xlsx</p><p>Resource Description: Excel file with 4 tabs: Metadata, county maps, species data, and species matrix.</p>
<p></p></li></ul><p></p>

Tags: ars, bombus, community-science, conservation, data-gov, faunal-inventory, iucn-red-list, museum-collections, natural-history, np304, pollinator
Organization: Department of Agriculture
The Bronson Files, Dataset 10, Field 113, 2018 Cotton

<p>Dr. Kevin Bronson provides a dataset representing the third of three consecutive years of cotton and nitrogen management experimentation in Field 113 of the Maricopa Agricultural Center, Arizona USA. Included is an intermediate analysis mega-table of correlated and calculated parameters, laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs.</p>
<p>See included README file for operational details and further description of the measured data signals.</p>
<p>Summary -
Active optical proximal cotton canopy sensing spatial data and including additional related metrics are presented.
Agronomic nitrogen and irrigation management related field operations are listed.
Unique research experimentation intermediate analysis table is made available, along with raw data.
The raw data recordings, and annotated table outputs with calculated VIs are made available.
Plot polygon coordinate designations allow a re-intersection spatial analysis.
Data was collected in the 2018 cotton season at Maricopa Agricultural Center, Arizona, USA.
High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled using a modified high-clearance Hamby spray-rig.
Acquired data conforms to location standard methodologies of high-throughput plant phenotyping.
The weekly proximal sensing data collected include the primary canopy reflectance at six wavelengths. Lint and seed yields, first open boll biomass, and nitrogen uptake was also determined. Soil profile nitrate to 1.8 m depth was determined in 30-cm increments, before planting and after harvest. Nitrous oxide emissions were determined with 1-L vented chambers (samples taken at 0, 12, and 24 minutes). Nitrous oxide was determined by gas chromatography (electron detection detector).</p>

Tags: agroecosystems-environment-weather-and-climate, ars, data-gov, earth-science-agriculture-soils-nitrogen, earth-science-biosphere-vegetation-plant-phenology, field-based-high-throughput-phenotyping, np211, plant-phenotyping
Organization: Department of Agriculture
Soil Water Content Data for The Bushland, Texas Large Weighing Lysimeter Experiments

<p>This dataset contains soil water content data developed from neutron probe readings taken in access tubes in each of the four large, precision weighing lysimeters and in the fields surrounding each lysimeter at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL) beginning in 1989. Readings were taken periodically with a field-calibrated neutron probe at depths from 10 cm to 230 cm (maximum of 190 cm depth in the lysimeters) in 20-cm depth increments. Periods between readings were typically one to two weeks, sometimes longer according to experimental design and need for data. Field calibrations in the Pullman soil series were done every few years. Calibrations typically produced a regression equation with RMSE <= 0.01 m<sup>3</sup> m<sup>-3</sup>. Data were used to guide irrigation scheduling to achieve full or deficit irrigation as required by the experimental design. Data may be used to calculate the soil profile water content in mm of water from the surface to the maximum depth of reading. Profile water content differences between reading times in the same access tube are considered the change in soil water storage during the period in question and may be used to compute evapotranspiration (ET) using the soil water balance equation: ET = (change in storage + P + I + F + R, where P is precipitation during the period, I is irrigation during the period, F is soil water flux (drainage) out of the bottom of the soil profile during the period, and R is the sum of runon and runoff during the period. Typically, R is taken as zero because the fields were furrow diked to prevent runon and runoff during most of each growing season.</p>
<p>See the README for descriptions of each data file.</p>

Tags: agroecosystems-environment-soil, agroecosystems-environment-water, ars, data-gov, evapotranspiration, np211
Organization: Department of Agriculture
Gridded 20-Year Parameterization of a Stochastic Weather Generator (CLIGEN) for South American and African Continents at 0.25 Arc Degree Resolution

<p>CLImate GENerator (CLIGEN) is a stochastic weather generator that produces daily and sub-daily timeseries of weather variables. The resulting timeseries are statistically similar to observed timeseries considering various temporal scales and climate factors. This dataset consisting of CLIGEN inputs may be used to generate timeseries at any point in a 0.25 arc degree resolution grid covering South American and African continents. Estimated parameter values at each grid point are based on 20-year records taken from global climate datasets. Precipitation parameters are statistically downscaled from grid-scale to point-scale based on observations from globally distributed ground networks representing >10,000 stations. This dataset is intended for use in climate-related research in ungauged areas where observed climate records are unavailable.</p>
<p>The data are formatted as CLIGEN <em>.par files, which are the only required input for CLIGEN. The files are separated into Africa and South America folders containing n=40936 and n=24588 files, respectively. The files are labeled according to grid point lat/lon coordinates (WGS84) in decimal degrees. The labeling convention uses 'N' and 'E' (north, east) to represent coordinates with a positive sign and 'S' and 'W' (south, west) to represent coordinates with a negative sign. </em></p><div><em><br>Resources in this dataset:</em></div><em><br></em><ul><em></em><li><em><p>Resource Title: Grid Files.</p> <p>File Name: Grid Files.zip</p></em><p><em>Resource Description: CLIGEN input files (</em>.par) for the South America and Africa grid.</p></li><br><li><p>Resource Title: Summary Table.</p> <p>File Name: Summary Table.docx</p><p>Resource Description: Summary table that lists CLIGEN parameters and basic dataset characteristics of the gridded parameterization.</p></li></ul><p></p>

Tags: ars, climate-change, climate-models, climatologymeteorologyatmosphere, data-gov, hydrology, soil-erosion
Organization: Department of Agriculture
LTAR Phosphorus Budget Summary

<p>Surface agronomic P budgets for 61 cropping systems using field-scale P flux data across 24 research sites in the United States and Canada.  Data are representative of P inputs and outputs associated with the production of each crop in a respective rotation year, ranging from 1 to 10 rotation years.  This dataset provides a comparison of field-scale soil surface P fluxes and phosphorus budgets across sites and cropping systems. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: LTAR Phosphorus Budget Summary Data Sources and References.</p> <p>File Name: DataSourcesAndReferences.csv</p><p>Resource Description: This file includes data sources and references relevant to calculated P budgets.  Affiliated numerical data can be found in the LTAR Phosphorus Budget Summary Data file.</p></li><br><li><p>Resource Title: LTAR Phosphorus Budget Summary Data.</p> <p>File Name: PBudgetData.csv</p><p>Resource Description: Agronomic annual and system data for calculated P budgets for cropping systems throughout the United States and Canada. </p></li></ul><p></p>

Tags: aafc, abs-uf, agrifoods-canada, archbold-biological-station, ars, caf, central-mississippi-river-basin, central-plains-experimental-range, cmrb, cper, cropping-systems, data-gov, eastern-corn-belt, ecb, field-scale, gacp, gulf-atlantic-coastal-plain-ltar, jer, jornada-experimental-range, kansas-state-university, kbs, kellogg-biological-station, ksu, lcb, lmrb, lower-chesapeake-bay, lower-mississippi-river-basin, ltar, ncsu, nisr, north-carolina-state-university, northern-plains, northwest-irrigation-and-soils-research, np, np211, np216, nutrient-cycling, nutrient-management, p-flux, phosphorus, phosphorus-flux, r-j-cook-agronomy-farm, soil-and-water-management-research, southern-plains, sp, swmr, texas-gulf, tg, u-of-a, ucb, uf, uk, umrb, university-of-arkansas, university-of-florida, university-of-kentucky, university-of-vermont, university-of-waterloo, upper-chesapeake-bay-ltar, upper-mississippi-river-basin, usda, uvm, uw
Organization: Department of Agriculture
Data from: Genomic divergence of zebu and taurine cattle identified through high-density SNP genotyping

<p>The following is taken directly from the introduction of the paper.  In this study, we used a pure drift FST model [11] which assumes all animals originated from the same ancestral population. This model was applied to taurine and zebu animals to identify loci under selection. These two groups correspond to the main (and most ancestral) separation of domestic cattle, which in most but not all cases corresponds to animals adapted to tropical and temperate environments. The identification of such loci can aid in the identification of genes and genomic variants that are related to environmental adaptation and/or selection derived from human agro-pastoral activities. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Copies of B allele for BovineHD 770k SNP array for multiple cattle breeds spanning broad diversity.</p> <p>File Name: g.csv.gz</p><p>Resource Description: Table contains 777,962 rows, one for each SNP and 528 columns, 1 for each animal.  A few animals are duplicated.  Most animals have breed identified by a 3 letter code but a few do not.  Breed abbreviations follow.
ANG – Angus
BMA – Beefmaster
BRM – Brahman
BSW – Brown Swiss
CHL – Charolais
GIR – Gir
GNS – Guernsey
HFD – Hereford
HOL – Holstein
JER – Jersey
LMS – Limousin
NEL – Nelore
NRC – Norwegian Red Cow
PMT – Piedmontese
RGU – Red Angus
RMG – Romagnola
SGT – Santa Gertrudis
SHK – Sheko
</p></li></ul><p></p>

Tags: ars, data-gov, genotypes, np101, number-of-copies-of-b-allele
Organization: Department of Agriculture
Semiarid grasslands and extreme precipitation events: Do experimental results scale to the landscape?

<p>This dataset contains growing season precipitation data collected from an extensive rain gauge network with thirty catch cans distributed on the USDA-Central Plains Experimental Range (CPER) in Nunn, Colorado from 1982-2013. The CPER is a site in the USDA Long-term Agroecosystem Research Network.  These data provide records of long-term observations, which can be applied to assess responses to naturally occurring deluges across the 62.7 km2 CPER in NE Colorado, a SGS ecosystem with significant spatial variation in precipitation received during the growing season, soils and grazing management, and where several small-scale deluge experiments have been conducted.   </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Raw Precipitation Data .</p> <p>File Name: PrecipitationData_1982_2013_Raw.csv</p><p>Resource Description: Raw Precipitation Data from thirty catch can gauges on the Central Plains Experimental Range</p></li><br><li><p>Resource Title: Processed precipitation data to identify deluges.</p> <p>File Name: PrecipitationData_Processed.csv</p><p>Resource Description:  Processed precipitation data to identify deluges on the Central Plain Experimental Range</p></li><br><li><p>Resource Title: Data Dictionary for raw and processed data.</p> <p>File Name: DataDictionary_PrecipitationData_Raw_Processed_CPER.csv</p><p>Resource Description: Data dictionary for processed precipitation data to identify deluges and for raw precipitation data from thirty catch can gauges on the Central Plains Experimental Range.
</p></li></ul><p></p>

Tags: annual-precipitation, ars, data-gov, deluge, earth-science-biosphere-terrestrial-ecosystems-grasslands, grasslands, np215, precipitation
Organization: Department of Agriculture
The Bronson Files, Dataset 5, Field 105, 2014

<p>Dr. Kevin Bronson provides a second year of nitrogen and water management in wheat agricultural research dataset for compute. Ten irrigation treatments from a linear sprinkler were combined with nitrogen treatments. This dataset includes notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, including laboratory analysis results generated during the experimentation, plus high resolution plot level intermediate data tables of SAS process output, as well as the complete raw data sensor records and logger outputs. </p>
<p>This proximal terrestrial high-throughput plant phenotyping data examples our early tri-metric field method, where a geo-referenced 5Hz crop canopy height, temperature and spectral signature are recorded coincident to indicate a plant health status. In this development period, our Proximal Sensing Cart Mark1 (PSCM1) platform suspends a single cluster of sensors on a dual sliding vertical placement armature.</p>
<p>See included README file for operational details and further description of the measured data signals.</p>
<p>Summary:
Active optical proximal wheat canopy sensing spatial data and including additional related metrics such as thermal are presented.
Agronomic nitrogen and irrigation management related field operations are listed.
Unique research experimentation intermediate analysis table is made available, along with raw data.
The raw data recordings, and annotated table outputs with calculated VIs are made available.
Plot polygon coordinate designations allow a re-intersection spatial analysis.
Data was collected in the 2014 season at Maricopa Agricultural Center, Arizona, USA.
High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled.
Acquired data using USDA Maricopa first mobile platforms, such as the Proximal Sensing Cart Mark 1, where the first cluster sensor bracket design and rickshaw inspired operator's handle were successfully employed.
SAS and GIS compute processing output tables, including Excel formatted examples are presented, where intermediate data tabulation and analysis is available.
The weekly proximal sensing data collected include canopy reflectance at six wavelengths, ultrasonic distance sensing of canopy height, and infrared thermometry.<br>
Ten levels gradient irrigation application from linear move sprinkler system were applied.
Soil physical texture and fertility chemistry results are available.
Durum wheat data includes in-season biomass and plant N content, final total biomass, grain yield, grain nitrogen, and yellow berry assessment.</p>

Tags: agroecosystems-environment-weather-and-climate, ars, data-gov, earth-science-agriculture-soils-nitrogen, earth-science-biosphere-vegetation-plant-phenology, field-based-high-throughput-phenotyping, np211, plant-phenotyping
Organization: Department of Agriculture
The Bronson Files, Dataset 4, Field 105, 2013

<p>Dr. Kevin Bronson provides this unique nitrogen and water management in wheat agricultural research dataset for compute. Ten irrigation treatments from a linear sprinkler were combined with nitrogen treatments. This dataset includes notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, including laboratory analysis results generated during the experimentation, plus high resolution plot level intermediate data tables of SAS process output, as well as the complete raw sensors records and logger outputs. </p>
<p>This data was collected during the beginning time period of our USDA Maricopa terrestrial proximal high-throughput plant phenotyping tri-metric method generation, where a 5Hz crop canopy height, temperature and spectral signature are recorded coincident to indicate a plant health status. In this early development period, our Proximal Sensing Cart Mark1 (PSCM1) platform supplants people carrying the CropCircle (CC) sensors, and with an improved view mechanical performance result.</p>
<p>See included README file for operational details and further description of the measured data signals.</p>
<p>Summary:
Active optical proximal wheat canopy sensing spatial data and including additional related metrics such as thermal are presented.
Agronomic nitrogen and irrigation management related field operations are listed.
Unique research experimentation intermediate analysis table is made available, along with raw data.
The raw data recordings, and annotated table outputs with calculated VIs are made available.
Plot polygon coordinate designations allow a re-intersection spatial analysis.
Data was collected in the 2013 season at Maricopa Agricultural Center, Arizona, USA.
High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled.
Acquired data using USDA Maricopa first mobile platforms, such as the Proximal Sensing Cart Mark 1, where the first cluster sensor bracket design and rickshaw inspired operator's handle were successfully employed.
SAS and GIS compute processing output tables, including Excel formatted examples are presented, where intermediate data tabulation and analysis is available.
The weekly proximal sensing data collected include canopy reflectance at six wavelengths, ultrasonic distance sensing of canopy height, and infrared thermometry.<br>
Ten levels gradient irrigation application from linear move sprinkler system were applied.
Soil physical texture and fertility chemistry results are available.
Yield and seed information is presented.</p>

Tags: agroecosystems-environment-weather-and-climate, ars, data-gov, earth-science-agriculture-soils-nitrogen, earth-science-biosphere-vegetation-plant-phenology, field-based-high-throughput-phenotyping, np211, plant-phenotyping
Organization: Department of Agriculture
Data from: Retention of Pantoea agglomerans Sc1R across stadia of the southern green stink bug, Nezara viridula (L.) (Hemiptera: Pentatomidae)

<p>Southern green stink bug [<em>Nezara viridula</em> (L.)] adults and other pentatomid pests can transmit pathogens (e.g., the bacterium <em>Pantoea agglomerans</em>) that cause disease in cotton (<em>Gossypium hirsutum</em> L.) and other high-value cash crops worldwide. First instars of <em>N. viridula</em> were recently shown to ingest <em>P. agglomerans</em> strain Sc1R with rifampicin-resistance, and to retain the pathogen to the 2nd instar. The objective of this study was to determine the acquisition of <em>P. agglomerans</em> Sc1R by early instars of <em>N. viridula</em> and determine persistence of <em>P. agglomerans</em> Sc1R across subsequent stadia. In three trials, early instars (1st and 2nd) were exposed to <em>P. agglomerans</em> Sc1R and subsequently maintained to adulthood; cohorts were sampled at 3rd and 5th instars, as well as adults. In every trial, <em>P. agglomerans</em> Sc1R was detected in all stadia, including adults, but significantly higher frequencies of infection than expected were observed at the initial stage of infection (either 1st or 2nd instar). Higher densities of <em>P. agglomerans</em> Sc1R were detected in 1st and 2nd instars, and lower densities were observed in subsequent stadia. Densities of innate bacterial flora were generally lower when the initial stage of exposure was at 1st instar than when the initial stage of exposure was at the 2nd instar. Overall, half of the adults possessed <em>P. agglomerans</em> Sc1R. These findings demonstrated that <em>N. viridula</em> nymphs can acquire <em>P. agglomerans</em> Sc1R and retain the pathogen to adulthood. Potential avenues of research to further elucidate the implications of nymphs harboring pathogens to adulthood are discussed. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Raw data for interstadial retention of Pantoea agglomerans Sc1R by southern green stink bug stadia.</p> <p>File Name: Interstadial pathogen retention_final data.csv</p><p>Resource Description: Raw data for Retention of Sc1R across interstadial molts of the southern green stink bug, 2019; CFU, colony-forming units; CFUs_count, number of colony-forming units; LBA, Luria Bertani agar; LBA_Rif, Luria Bertani agar amended with rifampicin; Sc1R, <em>Pantoea agglomerans</em> Sc1R</p></li><br><li><p>Resource Title: Summary tables of LSMeans and back-transformed means for densities of Pantoea agglomerans and general bacteria across southern green stink bug stadia.</p> <p>File Name: Log-LSMeans and back_transformed means for Pantoea agglomerans Sc1R densities in southern green stink bug stadia.csv</p><p>Resource Description: Summary tables of densities of <em>P. agglomerans</em> Sc1R and general bacteria across stadia of the southern green stink bug.</p></li></ul><p></p>

Tags: ars, data-gov, interstadial, nezara-viridula, np303, np304, pantoea-agglomerans-sc1r, pathogen-retention, southern-green-stink-bug, stadium
Organization: Department of Agriculture