cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15482/usda.adc/29980384
title: Water level monitoring data at Lake Carl Blackwell, Payne County, Oklahoma

description: A radar water level sensor (Campbell Scientific ® CS475A-L) was installed on the water intake tower of Lake Carl Blackwell, Payne County, Oklahoma (36.1351 N, 97.2195 W) to automate monitoring of lake water level conducted by USDA Agriculture Research Service staff. Manual monitoring of lake level has been conducted by Hydraulic Engineering Research Unit staff since the laboratory’s inception in 1940. Current lake level monitoring is undertaken by visual observation of a lake level staff gauge. Manual monitoring methods have changed with time and have included visual observations of lake level staff gauges, float and strip chart recorder gauge located on the raw water intake tower, u-tube manometer gauge connected to siphon system, bubble gauge with strip chart recorder and telemetry system located at the downstream toe of the dam, gatehouse staff gauge connected to siphon system, and individual measurements from the auxiliary spillway staff gauge recorded during spillway flow events.This data release provides data collected since installation of the sensor on June 12, 2025. All data are reported with respect to the NGVD of 1929 (EPSG 5702), the datum utilized for manual readings. Time is reported in local time (CST or CDT). Manual readings collected from June 12, 2025, are also provided to quantify variability between past and ongoing monitoring methods (Figure 1). The elevation of the radar transducer face is at 953.85 ft (NGVD, 1929). Spillway crest elevation of 944.17 ft (NGVD 1929) was confirmed by visual observation when lake level indicated by manual staff gauge readings equaled the elevation of spillway crest. Radar water level was initially recorded at 1-minute intervals and then changed to a 10-minute recording interval from June 15, 2025, onwards.Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.
DOI: 10.15482/usda.adc/29980384.v1
title: Water level monitoring data at Lake Carl Blackwell, Payne County, Oklahoma

description: A radar water level sensor (Campbell Scientific ® CS475A-L) was installed on the water intake tower of Lake Carl Blackwell, Payne County, Oklahoma (36.1351 N, 97.2195 W) to automate monitoring of lake water level conducted by USDA Agriculture Research Service staff. Manual monitoring of lake level has been conducted by Hydraulic Engineering Research Unit staff since the laboratory’s inception in 1940. Current lake level monitoring is undertaken by visual observation of a lake level staff gauge. Manual monitoring methods have changed with time and have included visual observations of lake level staff gauges, float and strip chart recorder gauge located on the raw water intake tower, u-tube manometer gauge connected to siphon system, bubble gauge with strip chart recorder and telemetry system located at the downstream toe of the dam, gatehouse staff gauge connected to siphon system, and individual measurements from the auxiliary spillway staff gauge recorded during spillway flow events.This data release provides data collected since installation of the sensor on June 12, 2025. All data are reported with respect to the NGVD of 1929 (EPSG 5702), the datum utilized for manual readings. Time is reported in local time (CST or CDT). Manual readings collected from June 12, 2025, are also provided to quantify variability between past and ongoing monitoring methods (Figure 1). The elevation of the radar transducer face is at 953.85 ft (NGVD, 1929). Spillway crest elevation of 944.17 ft (NGVD 1929) was confirmed by visual observation when lake level indicated by manual staff gauge readings equaled the elevation of spillway crest. Radar water level was initially recorded at 1-minute intervals and then changed to a 10-minute recording interval from June 15, 2025, onwards.Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.
DOI: 10.15482/usda.adc/29640977
title: Data from: Are historical trends in weather consistent with model predictions in the Central United States?

description: This dataset includes the monthly and daily data used for the analysis of historical and future trends in precipitation and temperature at five Long-Term Agroecosystem Research (LTAR) sites: Kellogg Biological Station (KBS) in Michigan, Upper Mississippi River Basin (UMRB) in Iowa, Central Mississippi River Basin (CMRB) in Missouri, Southern Plains (SP) in Oklahoma, and Lower Mississippi River Basin (LMRB) in Mississippi. Historical data include the longest available record of daily precipitation, minimum temperature, and maximum temperature at weather stations from KBS, UMRB, CMRB, and LMRB, and the monthly 1895-2020 data from the National Ocean and Atmospheric Administration for the climate divisions that represent the five LTAR sites. Future data include 2020-2100 monthly predictions for the five sites from 26 Earth System Models and two Shared Socio-economic Pathways (SSP): the middle of the road SSP245 (a continuation of current emission rates and geo-political conditions), and the fossil fueled development scenario SSP 585 (intensification of fossil fuel energy sources and corresponding emissions). In addition, the data includes the trends calculated from historical and future data, snippets of R code used to calculate these trends, and README files that detail the content of each file.Trends in records of 50 years or more showed that temperatures have changed from 1900-2020, more for minimum (0.1 - 0.3 ℃ decade<sup>-1</sup>) than maximum (-0.1 - 0.2 ℃<sup> </sup>decade<sup>-1</sup>), more for winter (-0.1 - 0.3 ℃<sup> </sup>decade<sup>-1</sup>) than summer (-0.1 - 0.1 ℃ decade<sup>-1</sup>), and more often in the north than in the south. Except in Mississippi, annual precipitation has increased at rates of 25 mm decade<sup>-1</sup> or greater over 1950-2020, but monthly trends were inconsistent. Projected trends suggest continued temperature increases, highlighting the need for research on management systems that are resilient to such increases.
DOI: 10.15482/usda.adc/29640977.v1
title: Data from: Are historical trends in weather consistent with model predictions in the Central United States?

description: This dataset includes the monthly and daily data used for the analysis of historical and future trends in precipitation and temperature at five Long-Term Agroecosystem Research (LTAR) sites: Kellogg Biological Station (KBS) in Michigan, Upper Mississippi River Basin (UMRB) in Iowa, Central Mississippi River Basin (CMRB) in Missouri, Southern Plains (SP) in Oklahoma, and Lower Mississippi River Basin (LMRB) in Mississippi. Historical data include the longest available record of daily precipitation, minimum temperature, and maximum temperature at weather stations from KBS, UMRB, CMRB, and LMRB, and the monthly 1895-2020 data from the National Ocean and Atmospheric Administration for the climate divisions that represent the five LTAR sites. Future data include 2020-2100 monthly predictions for the five sites from 26 Earth System Models and two Shared Socio-economic Pathways (SSP): the middle of the road SSP245 (a continuation of current emission rates and geo-political conditions), and the fossil fueled development scenario SSP 585 (intensification of fossil fuel energy sources and corresponding emissions). In addition, the data includes the trends calculated from historical and future data, snippets of R code used to calculate these trends, and README files that detail the content of each file.Trends in records of 50 years or more showed that temperatures have changed from 1900-2020, more for minimum (0.1 - 0.3 ℃ decade<sup>-1</sup>) than maximum (-0.1 - 0.2 ℃<sup> </sup>decade<sup>-1</sup>), more for winter (-0.1 - 0.3 ℃<sup> </sup>decade<sup>-1</sup>) than summer (-0.1 - 0.1 ℃ decade<sup>-1</sup>), and more often in the north than in the south. Except in Mississippi, annual precipitation has increased at rates of 25 mm decade<sup>-1</sup> or greater over 1950-2020, but monthly trends were inconsistent. Projected trends suggest continued temperature increases, highlighting the need for research on management systems that are resilient to such increases.
DOI: 10.15482/usda.adc/30043507
title: Data from: Crop Sequence Complexity of the Major Land Resource Areas (MLRA) in the Contiguous United States (CONUS)

description: Quantifying spatial and temporal dynamics of crop sequences is often accomplished through crop biodiversity metrics. Existing metrics are confounded by crop sequence length, subsequence repetition, and perennialization. Our objective was to formulate a Crop Sequence Complexity Index (CSCI) that accounts for differences in crop sequence lengths, crop sequence compressibility, functional type transitions, and back-to-back perennials. Additionally, we mapped the distribution of crop sequence metrics across the Contiguous United States (CONUS). We joined Crop Sequence Boundary data from the USDA-NASS for two periods: (2008-2015) and (2016-2023) to assemble the 16-year crop sequence of 13.5 million field centroids between 2008 and 2023 and calculated crop sequence metrics aggregated by Major Land Resource Areas (MLRA). We also examined the correlations among crop sequence complexity metrics. We found CSCI was less correlated with both crop sequence length and the number of back-to-back perennials in a crop sequence compared to the Rotational Complexity Index (RCI). Consequently, RCI tended to be lower in MLRA where annual cropping systems dominated, such as the Corn Belt, Mississippi River Basin, and southern Great Plains, and RCI was highest in the irrigated southwestern US. In contrast, CSCI was highest in the northern Great Plains and lowest in the southern Great Plains, with intermediate values throughout most of CONUS. Crop sequences in CONUS usually consist of a very limited number of species. However, crop sequence complexity varies widely because of how sequences ordered functional type transitions, and perennialization. While biophysical constraints are important, socioeconomic factors drive crop sequence complexity.
DOI: 10.15482/usda.adc/30043507.v1
title: Data from: Crop Sequence Complexity of the Major Land Resource Areas (MLRA) in the Contiguous United States (CONUS)

description: Quantifying spatial and temporal dynamics of crop sequences is often accomplished through crop biodiversity metrics. Existing metrics are confounded by crop sequence length, subsequence repetition, and perennialization. Our objective was to formulate a Crop Sequence Complexity Index (CSCI) that accounts for differences in crop sequence lengths, crop sequence compressibility, functional type transitions, and back-to-back perennials. Additionally, we mapped the distribution of crop sequence metrics across the Contiguous United States (CONUS). We joined Crop Sequence Boundary data from the USDA-NASS for two periods: (2008-2015) and (2016-2023) to assemble the 16-year crop sequence of 13.5 million field centroids between 2008 and 2023 and calculated crop sequence metrics aggregated by Major Land Resource Areas (MLRA). We also examined the correlations among crop sequence complexity metrics. We found CSCI was less correlated with both crop sequence length and the number of back-to-back perennials in a crop sequence compared to the Rotational Complexity Index (RCI). Consequently, RCI tended to be lower in MLRA where annual cropping systems dominated, such as the Corn Belt, Mississippi River Basin, and southern Great Plains, and RCI was highest in the irrigated southwestern US. In contrast, CSCI was highest in the northern Great Plains and lowest in the southern Great Plains, with intermediate values throughout most of CONUS. Crop sequences in CONUS usually consist of a very limited number of species. However, crop sequence complexity varies widely because of how sequences ordered functional type transitions, and perennialization. While biophysical constraints are important, socioeconomic factors drive crop sequence complexity.
DOI: 10.15468/dl.yued2y
title: Occurrence Download

description: A dataset containing 8240 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Vaccinium uliginosum",
 "Year 1910-1955"
 ]
}

 The dataset includes 8240 records from 182 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003008-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e240529
title: HIFLD OPEN US Army Corps of Engineers (USACE) Military Divisions

description: USACE Military Division boundaries. Polygons were derived from National Atlas states and/or from data provided by the district.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025.
DOI: 10.3886/e240529v1
title: HIFLD OPEN US Army Corps of Engineers (USACE) Military Divisions

description: USACE Military Division boundaries. Polygons were derived from National Atlas states and/or from data provided by the district.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025.
DOI: 10.3886/e240530
title: HIFLD OPEN US Army Corps of Engineers (USACE) Offices

description: USACE District, Division and Research Lab Locations. This data set does not include field offices.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025.
DOI: 10.3886/e240530v1
title: HIFLD OPEN US Army Corps of Engineers (USACE) Offices

description: USACE District, Division and Research Lab Locations. This data set does not include field offices.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025.
DOI: 10.15482/usda.adc/24851661
title: Data from: Automated Detection of On-Farm Irrigation Reservoirs: A Necessary Precursor for Conjunctive Water Management in Two Critical Groundwater Regions of Arkansas

description: The Inventory from Model (White)Model Reservoirs (Red)Mary and Co's reservoirs (Blue)NHD water (Orange)CDL water (Yellow)<br><br>The Inventory from Model map (1) represents all the water found within the three areas identified the 2023 project; (2) is where the model indicates a reservoir based on the elevation difference based on the Yeager et al. (2017) reservoirs; (3) is based on the Yeager et al. (2017) reservoir boundaries which were plugged into the fifth step of the model (the water identification steps were skipped). Then the model was used to evaluate and classify them into elevation classes. NHD Water map (4) is every waterbody polygon from the National Hydrography Dataset that falls within the three study area areas. The CDL Water map (5) is every open water and aquaculture polygon vectorized from the Cropland Data Layer raster within the three study areas.This Data is a zipped, ArcGIS Pro (V3.1.2) project. The project geodatabase contains the model. In this project there are five maps representing different datasets.NOTE (2024-05-13): ISO xml metadata files added to the root of the zipped folder, describing specific items in the project, saved in ArcGIS Pro V.3.2.2.
DOI: 10.15482/usda.adc/24851661.v2
title: Data from: Automated Detection of On-Farm Irrigation Reservoirs: A Necessary Precursor for Conjunctive Water Management in Two Critical Groundwater Regions of Arkansas

description: The Inventory from Model (White)Model Reservoirs (Red)Mary and Co's reservoirs (Blue)NHD water (Orange)CDL water (Yellow)<br><br>The Inventory from Model map (1) represents all the water found within the three areas identified the 2023 project; (2) is where the model indicates a reservoir based on the elevation difference based on the Yeager et al. (2017) reservoirs; (3) is based on the Yeager et al. (2017) reservoir boundaries which were plugged into the fifth step of the model (the water identification steps were skipped). Then the model was used to evaluate and classify them into elevation classes. NHD Water map (4) is every waterbody polygon from the National Hydrography Dataset that falls within the three study area areas. The CDL Water map (5) is every open water and aquaculture polygon vectorized from the Cropland Data Layer raster within the three study areas.This Data is a zipped, ArcGIS Pro (V3.1.2) project. The project geodatabase contains the model. In this project there are five maps representing different datasets.NOTE (2024-05-13): ISO xml metadata files added to the root of the zipped folder, describing specific items in the project, saved in ArcGIS Pro V.3.2.2.
DOI: 10.15482/usda.adc/24851661.v1
title: Data from: Automated Detection of On-Farm Irrigation Reservoirs: A Necessary Precursor for Conjunctive Water Management in Two Critical Groundwater Regions of Arkansas

description: The Inventory from Model (White)Model Reservoirs (Red)Mary and Co's reservoirs (Blue)NHD water (Orange)CDL water (Yellow)<br>
<br>
The Inventory from Model map (1) represents all the water found within the three areas identified the 2023 project; (2) is where the model indicates a reservoir based on the elevation difference based on the Yeager et al. (2017) reservoirs; (3) is based on the Yeager et al. (2017) reservoir boundaries which were plugged into the fifth step of the model (the water identification steps were skipped). Then the model was used to evaluate and classify them into elevation classes. NHD Water map (4) is every waterbody polygon from the National Hydrography Dataset that falls within the three study area areas. The CDL Water map (5) is every open water and aquaculture polygon vectorized from the Cropland Data Layer raster within the three study areas.
This Data is a zipped, ArcGIS Pro (V3.1.2) project. The project geodatabase contains the model. In this project there are five maps representing different datasets:
DOI: 10.3886/e240531
title: HIFLD OPEN US Army Corps of Engineers (USACE) Owned and Operated Reservoirs

description: This dataset shows maximum conservation pool or is a reasonable representation of the boundaries for reservoirs and lakes owned and operated by USACE. Data is from USACE Districts.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025.
DOI: 10.3886/e240531v1
title: HIFLD OPEN US Army Corps of Engineers (USACE) Owned and Operated Reservoirs

description: This dataset shows maximum conservation pool or is a reasonable representation of the boundaries for reservoirs and lakes owned and operated by USACE. Data is from USACE Districts.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025.
DOI: 10.15468/dl.a7bfa8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Diastatops maxima",
 "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/0003010-251120083545085/datasets/export for details.
DOI: 10.15468/dl.t7actc
title: Occurrence Download

description: A dataset containing 920 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Saxifraga magellanica"
 ]
}

 The dataset includes 920 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003011-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17676563
title: Table 1 in A phylogeny with divergence-time estimation of planthoppers (Hemiptera: Fulgoroidea) based on mitochondrial sequences

description: Table 1. Estimated divergence and diversification times and 95% credibility intervals. NA: not applicable
FamilyEstimated divergence–diversification times95% credibility intervalsDelphacidae173.00 Mya–165.15 Mya (Middle Jurassic)[163.28, 181.60]–[155.24, 174.24]Cixiidae173.00 Mya–164.63 Mya (Middle Jurassic)[163.28, 181.60]–[153.73, 174.54]Kinnaridae148.56 Mya–NA (Upper Jurassic)[131.03, 161.85]–NAMeenoplidae148.56 Mya–117.65 Mya (Lower Cretaceous)[131.03, 161.85]–[99.76, 132.74]Achilidae138.58 Mya–120.75 Mya (Lower Cretaceous)[125.82, 148.98]–[107.00, 132.22]Derbidae138.58 Mya–112.71 Mya (Lower Cretaceous)[125.82, 148.98]–[96.34, 126.12]Fulgoridae130.16 Mya–113.32 Mya (Lower Cretaceous)[118.13, 143.10]–[101.14, 123.13]Dictyopharidae130.16 Mya–97.39 Mya (Upper Cretaceous)[118.13, 143.10]–[79.71, 114.27]Caliscelidae121.12 Mya–98.56 Mya (Upper Cretaceous)[111.00, 130.33]–[84.68, 111.13]Issidae118.72 Mya–110.79 Mya (Lower Cretaceous)[109.52, 126.91]–[101.75, 118.97]Flatidae118.72 Mya–98.84 Mya (Upper Cretaceous)[109.52, 126.91]–[87.21, 109.62]Nogodinidae115.91 Mya–105.34 Mya (Lower Cretaceous)[106.10, 124.55]–[93.86, 115.17]Ricaniidae115.91 Mya–83.54 Mya (Upper Cretaceous)[106.10, 124.55]–[73.00, 94.33]Acanaloniidae115.77 Mya–NA (Lower Cretaceous)[100.65, 127.73]–NATropiduchidae115.77 Mya–105.33 Mya (Lower Cretaceous)[100.65, 127.73]–[89.67, 118.55]Lophopidae101.30 Mya–73.30 Mya (Upper Cretaceous)[88.80, 112.73]–[58.72, 87.41]Eurybrachidae101.30 Mya–69.03 Mya (Upper Cretaceous)[88.80, 112.73]–[52.43, 84.77]
description: Published as part of Wang, Wenqian, Meng, Rui, Huang, Yixin, Fang, Wei, Zhang, Huan, Liu, Huizhen, Stroiński, Adam, Bourgoin, Thierry & Qin, Daozheng, 2024, A phylogeny with divergence-time estimation of planthoppers (Hemiptera: Fulgoroidea) based on mitochondrial sequences, pp. 94 in Zoological Journal of the Linnean Society 201 (1) on page 94, DOI: 10.1093/zoolinnean/zlad110, http://zenodo.org/record/11240887
DOI: 10.5281/zenodo.17676564
title: Table 1 in A phylogeny with divergence-time estimation of planthoppers (Hemiptera: Fulgoroidea) based on mitochondrial sequences

description: Table 1. Estimated divergence and diversification times and 95% credibility intervals. NA: not applicable
FamilyEstimated divergence–diversification times95% credibility intervalsDelphacidae173.00 Mya–165.15 Mya (Middle Jurassic)[163.28, 181.60]–[155.24, 174.24]Cixiidae173.00 Mya–164.63 Mya (Middle Jurassic)[163.28, 181.60]–[153.73, 174.54]Kinnaridae148.56 Mya–NA (Upper Jurassic)[131.03, 161.85]–NAMeenoplidae148.56 Mya–117.65 Mya (Lower Cretaceous)[131.03, 161.85]–[99.76, 132.74]Achilidae138.58 Mya–120.75 Mya (Lower Cretaceous)[125.82, 148.98]–[107.00, 132.22]Derbidae138.58 Mya–112.71 Mya (Lower Cretaceous)[125.82, 148.98]–[96.34, 126.12]Fulgoridae130.16 Mya–113.32 Mya (Lower Cretaceous)[118.13, 143.10]–[101.14, 123.13]Dictyopharidae130.16 Mya–97.39 Mya (Upper Cretaceous)[118.13, 143.10]–[79.71, 114.27]Caliscelidae121.12 Mya–98.56 Mya (Upper Cretaceous)[111.00, 130.33]–[84.68, 111.13]Issidae118.72 Mya–110.79 Mya (Lower Cretaceous)[109.52, 126.91]–[101.75, 118.97]Flatidae118.72 Mya–98.84 Mya (Upper Cretaceous)[109.52, 126.91]–[87.21, 109.62]Nogodinidae115.91 Mya–105.34 Mya (Lower Cretaceous)[106.10, 124.55]–[93.86, 115.17]Ricaniidae115.91 Mya–83.54 Mya (Upper Cretaceous)[106.10, 124.55]–[73.00, 94.33]Acanaloniidae115.77 Mya–NA (Lower Cretaceous)[100.65, 127.73]–NATropiduchidae115.77 Mya–105.33 Mya (Lower Cretaceous)[100.65, 127.73]–[89.67, 118.55]Lophopidae101.30 Mya–73.30 Mya (Upper Cretaceous)[88.80, 112.73]–[58.72, 87.41]Eurybrachidae101.30 Mya–69.03 Mya (Upper Cretaceous)[88.80, 112.73]–[52.43, 84.77]
description: Published as part of Wang, Wenqian, Meng, Rui, Huang, Yixin, Fang, Wei, Zhang, Huan, Liu, Huizhen, Stroiński, Adam, Bourgoin, Thierry & Qin, Daozheng, 2024, A phylogeny with divergence-time estimation of planthoppers (Hemiptera: Fulgoroidea) based on mitochondrial sequences, pp. 94 in Zoological Journal of the Linnean Society 201 (1) on page 94, DOI: 10.1093/zoolinnean/zlad110, http://zenodo.org/record/11240887