Monthly Mean Dissolved-Solids Concentration and Monthly Mean Dissolved-Solids Load Data Flow weighted monthly mean dissolved-solids concentrations (mg/L) data and monthly mean dissolved-solids load data from 1928-2016 were computed by USGS using raw data from the Bureau of Reclamation. These data were computed by USGS for all of the seven sites (listed below). Colorado River above Imperial Dam, AZ-CA, 09429490 (1976-2016) Colorado River at Lees Ferry, AZ, 09380000 (1947-2016) Colorado River at Northern International Boundary, above Morelos Dam, AZ, 09522000 (1961-2016) Colorado River below Hoover Dam, AZ-NV, 09421500 (1948-2016) Colorado River near Cisco, UT, 09180500 (1928-2016) Green River at Green River, UT, 09315000 (1928-2016) San Juan River near Bluff, UT, 09379500 (1929-2016) Monthly mean dissolved-solids concentrations and loads were not calculated for several time periods (listed below) because of insufficient discrete dissolved-solids concentration data: Colorado River below Hoover Dam, AZ-NV, 09421500 (October 1962 - September 1953) Colorado River near Cisco, UT, 09180500 (October 1936 - September 1938 and October 1939 - September 1940) Green River at Green River, UT, 09315000 (October 1936 - September 1938, October 1939 - September 1940, and October 1942 - September 1943) Discrete Dissolved-Solids Concentration Data Discrete dissolved-solids concentrations (mg/L) data and specific conductance (microsiemens/cm) from 1990-2016 were computed using raw data from the Bureau of Reclamation. These data were computed for four sites (listed below). Colorado River above Imperial Dam, AZ-CA, 09429490 (1990-2016), dissolved-solids Colorado River above Imperial Dam, AZ-CA, 09429490 (1993-2016), specific conductance Colorado River below Hoover Dam, AZ-NV, 09421500 (1993-2016), dissolved-solids Colorado River at Northern International Boundary, above Morelos Dam, AZ, 09522000 (2001-2016), dissolved-solids

Context

Blood Glucose discrete data set that already interpolated by Spline Method to measure value of MAGE. This data set aim at to find the alternative than using CGM (Continuous Glucose Monitoring) to predict diabetic using discrete data. The discrete data obtained from 27 fluctuations of blood glucose within 3 days that taken by Glucometer. After the data go through Interpolation method, there are 150+ point that can re-present as similar as CGM model.

Content

There are 42 Patients Column A as CLASS means divide the conditions into 3 groups (1 for Pre-Diabet patient, 2 for Diabet patient, 3 for Normal patient)

Acknowledgements

Thank you for 42 volunteers that who are willing to spend time and energy for this study Related article - http://beei.org/index.php/EEI/article/view/2387

Inspiration

Hope with this data can create another study relate with predict Diabetic to personal user, so we can monitor our life-style

A combination of discrete and daily-aligned groundwater levels for the Mississippi River Valley alluvial aquifer clipped to the Mississippi Alluvial Plain, as defined by Painter and Westerman (2018), with corresponding metadata are based on processing of U.S. Geological Survey National Water Information System (NWIS) (U.S. Geological Survey, 2020) data. The processing was made after retrieval using aggregation and filtering through the infoGW2visGWDB software (Asquith and Seanor, 2019). The nomenclature GWmaster mimics that of the output from infoGW2visGWDB. Two separate data retrievals for NWIS were made. First, the discrete data were retrieved, and second, continuous records from recorder sites with daily-mean or other daily statistics codes were retrieved. Each dataset was separately passed through the infoGW2visGWDB software to create a "GWmaster discrete" and "GWmaster continuous" and these tables were combined and then sorted on the site identifier and date to form the data products described herein. A sweep through the combined dataset (the "database") was made to isolate duplicate observations, or observations for the same well and on the same day. If a discrete value was present, it was retained as authoritative for the day and in descending order of priority daily-mean, daily-maximum, and daily minimum. Therefore, only a single record for a well and day are present in the dataset. The duplicate search removed 876 records and 31 wells were involved; in total, this is about 0.3 percent of the database. References: Asquith, W.H., Seanor, R.C., 2019, infoGW2visGWDB—An R groundwater data-processing utility for manipulating, checking the veracity, and converting an "infoGW" object to the "GWmaster" object for the visGWDB software with demonstration for the Mississippi River Valley alluvial aquifer: U.S. Geological Survey software release, Reston, Va., https://doi.org/10.5066/P9MK0B6L. Painter, J.A., and Westerman, D.A., 2018. Mississippi Alluvial Plain extent, November 2017: U.S. Geological Survey data release, https://doi.org/10.5066/F70R9NMJ. U.S. Geological Survey, 2020, USGS water data for the Nation: U.S. Geological Survey National Water Information System database, accessed April 2, 2020, at https://doi.org/10.5066/F7P55KJN.

This dataset is about: (Table 4) Mean values of chemical data from the discrete ash layers of ODP Leg 120 holes. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.757631 for more information.

This data release contains discrete chlorophyll data, specifically corrected chlorophyll a, uncorrected chlorophyll a, and pheophytin pigments, from inland waters in the Illinois River Basin for 1981–2023. These data are discrete samples (collected in the field and analyzed in the laboratory) of plankton (suspended algae) and periphyton (benthic algae) from lakes, streams, rivers, canals, and other aquatic sites. These data support the investigation of harmful algal blooms (HABs) in the Illinois River Basin. The data are multi-source, meaning multiple monitoring organizations collected and analyzed these samples. Data were sourced from the Water Quality Portal (WQP; which contains water quality data from many organizations), Illinois Natural History Survey (INHS), the Fox River Study Group (FRSG; which also contains data from multiple organizations), and previously unpublished data from the US Geological Survey’s National Water Quality Laboratory. Final chlorophyll data are provid ...

Contains data and code for the manuscript 'Mean landscape-scale incidence of species in discrete habitats is patch size dependent'. Raw data consist of 202 published datasets collated from primary and secondary (e.g., government technical reports) sources. These sources summarise metacommunity structure for different taxonomic groups (birds, invertebrates, non-avian vertebrates or plants) in different types of discrete metacommunities including 'true' islands (i.e., inland, continental or oceanic archipelagos), habitat islands (e.g., ponds, wetlands, sky islands) and fragments (e.g., forest/woodland or grass/shrubland habitat remnants). The aim of the study was to test whether the size of a habitat patch influences the mean incidences of species within it, relative to the incidence of all species across the landscape. In other words, whether high-incidence (widespread) or low-incidence (narrow-range) species are found more often than expected in smaller or larger patches. To achieve this, a new standardized effect size metric was developed that quantifies the mean observed incidence of all species present in every patch (the geometric mean of the number of patches in which all species were observed) and compares this with an expectation based on re-sampling the incidences of all species in all patches. Meta-regression of the 202 datasets was used to test the relationship between this metric, the 'mean species landscape-scale incidences per patch' (MSLIP), and the size of habitat patches, and for differences in response among metacommunity types and taxonomic groups. Methods Details regarding keyword and other search strategies used to collate the raw database from published sources were presented in Deane, D. C. & He, F. (2018) Loss of only the smallest patches will reduce species diversity in most discrete habitat networks. Glob Chang Biol, 24, 5802-5814 and in Deane, D.C. (2022) Species accumulation in small-large vs large-small order: more species but not all species? Oecologia, 200, 273-284. Minimum data requirements were presence absence records for all species in all patches and area of each habitat patch. The database consists of 202 published datasets. The first column in each dataset is the area of the patch in question (in hectares), other columns record presence and absence of each species in each patch. In the study, a metric was calculated for every patch that quantifies how the incidence of species in each patch compares with an expectation derived from the occupancy of all species in all patches (called mean species landscape-scale incidences per patch or MSLIP). This value was regressed on patch size and other covariates to determine whether the representation of widespread (or narrowly distributed) species changes with patch size. In summary, the work flow proceeded in three steps. 1. Pre-processing. This stage consisted of calculating a standardized effect size (SES) for the MSLIP metric for every patch and extracting important covariates (taxon, patch type, total number of patches, total number of species, patch-level deviations from fitted island species area relationships, data quality) to be used in model building. 2. Model building. MSLIP SES was then modelled against patch area and other covariates using a multilevel Bayesian (meta-)regression model using Stan and brms in the statistical programming langauge R (Version 4.3.0). 3. Model analysis. The final model was analysed by running different scenarios and the patterns interpreted in light of the hypotheses under test and creating figures to illustrate these.

This data release contains six different datasets that were used in the report SIR 2018-5108. These datasets contain discharge data, discrete dissolved-solids data, quality-control discrete dissolved data, and computed mean dissolved solids data that were collected at various locations between the Hoover Dam and the Imperial Dam. Study Sites: Site 1: Colorado River below Hoover Dam Site 2: Bill Williams River near Parker Site 3: Colorado River below Parker Dam Site 4: CRIR Main Canal Site 5: Palo Verde Canal Site 6: Colorado River at Palo Verde Dam Site 7: CRIR Lower Main Drain Site 8: CRIR Upper Levee Drain Site 9: PVID Outfall Drain Site 10: Colorado River above Imperial Dam Discrete Dissolved-solids Dataset and Replicate Samples for Discrete Dissolved-solids Dataset: The Bureau of Reclamation collected discrete water-quality samples for the parameter of dissolved-solids (sum of constituents). Dissolved-solids, measured in milligrams per liter, are the sum of the following constituents: bicarbonate, calcium, carbonate, chloride, fluoride, magnesium, nitrate, potassium, silicon dioxide, sodium, and sulfate. These samples were collected on a monthly to bimonthly basis at various time periods between 1990 and 2016 at Sites 1-5 and Sites 7-10. No data were collected for Site 6: Colorado River at Palo Verde Dam. The Bureau of Reclamation and the USGS collected discrete quality-control replicate samples for the parameter of dissolved-solids, sum of constituents measured in milligrams per liter. The USGS collected discrete quality-control replicate samples in 2002 and 2003 and the Bureau of Reclamation collected discrete quality-control replicate samples in 2016 and 2017. Listed below are the sites where these samples were collected at and which agency collected the samples. Site 3: Colorado River below Parker Dam: USGS and Reclamation Site 4: CRIR Main Canal: Reclamation Site 5: Palo Verde Canal: Reclamation Site 7: CRIR Lower Main Drain: Reclamation Site 8: CRIR Upper Levee Drain: Reclamation Site 9: PVID Outfall Drain: Reclamation Site 10: Colorado River above Imperial Dam: USGS and Reclamation Monthly Mean Datasets and Mean Monthly Datasets: Monthly mean discharge data (cfs), flow weighted monthly mean dissolved-solids concentrations (mg/L) data and monthly mean dissolved-solids load data from 1990 to 2016 were computed using raw data from the USGS and the Bureau of Reclamation. This data were computed for all 10 sites. Flow weighted monthly mean dissolved-solids concentration and monthly mean dissolved-solids load were not computed for Site 2: Bill Williams River near Parker. The monthly mean datasets that were calculated for each month for the period between 1990 and 2016 were used to compute the mean monthly discharge and the mean monthly dissolved-solids load for each of the 12 months within a year. Each monthly mean was weighted by how many days were in the month and then averaged for each of the twelve months. This was computed for all 10 sites except mean monthly dissolved-solids load were not computed at Site 2: Bill Williams River near Parker. Site 8a: Colorado River between Parker and Palo Verde Valleys was computed by summing the data from sites 6, 7 and 8. Bill Williams Daily Mean Discharge, Instantaneous Dissolved-solids Concentration, and Daily Means Dissolved-solids Load Dataset: Daily mean discharge (cfs), instantaneous solids concentration (mg/L), and daily mean dissolved solids load were calculated using raw data collected by the USGS and the Bureau of Reclamation. This data were calculated for Site 2: Bill Williams River near Parker for the period of January 1990 to February 2016. Palo Verde Irrigation District Outfall Drain Mean Daily Discharge Dataset: The Bureau of Reclamation collected mean daily discharge data for the period of 01/01/2005 to 09/30/2016 at the Palo Verde Irrigation District (PVID) outfall drain using a stage-discharge relationship.

In daily life, two common algorithms are used for collecting medical disease data: data integration of medical institutions and questionnaires. However, these statistical methods require collecting data from the entire research area, which consumes a significant amount of manpower and material resources. Additionally, data integration is difficult and poses privacy protection challenges, resulting in a large number of missing data in the dataset. The presence of incomplete data significantly reduces the quality of the published data, hindering the timely analysis of data and the generation of reliable knowledge by epidemiologists, public health authorities, and researchers. Consequently, this affects the downstream tasks that rely on this data. To address the issue of discrete missing data in cardiac disease, this paper proposes the AGAN (Attribute Generative Adversarial Nets) architecture for missing data filling, based on generative adversarial networks. This algorithm takes advantage of the strong learning ability of generative adversarial networks. Given the ambiguous meaning of filling data in other network structures, the attribute matrix is designed to directly convert it into the corresponding data type, making the actual meaning of the filling data more evident. Furthermore, the distribution deviation between the generated data and the real data is integrated into the loss function of the generative adversarial networks, improving their training stability and ensuring consistency between the generated data and the real data distribution. This approach establishes the missing data filling mechanism based on the generative adversarial networks, which ensures the rationality of the data distribution while filling the missing data samples. The experimental results demonstrate that compared to other filling algorithms, the data matrix filled by the proposed algorithm in this paper has more evident practical significance, fewer errors, and higher accuracy in downstream classification prediction.

One of the primary goals of Integrated Vehicle Health Management (IVHM) is to detect, diagnose, predict, and mitigate adverse events during the flight of an aircraft, regardless of the subsystem(s) from which the adverse event arises. To properly address this problem, it is critical to develop technologies that can integrate large, heterogeneous (meaning that they contain both continuous and discrete signals), asynchronous data streams from multiple subsystems in order to detect a potential adverse event, diagnose its cause, predict the effect of that event on the remaining useful life of the vehicle, and then take appropriate steps to mitigate the event if warranted. These data streams may have highly non-Gaussian distributions and can also contain discrete signals such as caution and warning messages which exhibit non-stationary and obey arbitrary noise models. At the aircraft level, a Vehicle-Level Reasoning System (VLRS) can be developed to provide aircraft with at least two significant capabilities: improvement of aircraft safety due to enhanced monitoring and reasoning about the aircraft’s health state, and also potential cost savings through Condition Based Maintenance (CBM). Along with the achieving the benefits of CBM, an important challenge facing aviation safety today is safeguarding against system- and component-level failures and malfunctions. Citation: A. N. Srivastava, D. Mylaraswamy, R. Mah, and E. Cooper, “Vehicle Level Reasoning Systems: Concept and Future Directions,” Society of Automotive Engineers Integrated Vehicle Health Management Book, Ian Jennions, Ed., 2011.

RMSE value of missing data filling algorithms on different datasets (mean ± std).

RMSE value filled by different algorithms (mean ± std).

This data release includes estimates of annual and monthly mean concentrations and fluxes for nitrate plus nitrite, orthophosphate and suspended sediment for nine sites in the Mississippi River Basin (MRB) produced using the Weighted Regressions on Time, Discharge, and Season (WRTDS) model (Hirsch and De Cicco, 2015). It also includes a model archive (R scripts and readMe file) used to retrieve and format the model input data and run the model. Input data, including discrete concentrations and daily mean streamflow, were retrieved from the National Water Quality Network (https://doi.org/10.5066/P9AEWTB9). Annual and monthly estimates range from water year 1975 through water year 2019 (i.e. October 1, 1974 through September 30, 2019). Annual trends were estimated for three trend periods per parameter. The length of record at some sites required variations in the trend start year. For nitrate plus nitrite, the following trend periods were used at all sites: 1980-2019, 1980-2010 and 2010-2019. For orthophosphate, the same trend periods were used but with 1982 as the start year instead of 1980. For suspended sediment, 1997 was used as the start year for the upper MRB sites and the St. Francisville (MS-STFR) site, but 1980 was used for the rest of the sites. All parameters and sites used 2010 as the start year for the last 10-year trend period. Reference: Hirsch, R.M., and De Cicco, L.A., 2015, User guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R packages for hydrologic data (version 2.0, February 2015): U.S. Geological Survey Techniques and Methods book 4, chap. A10, 93 p., doi:10.3133/tm4A10

Political knowledge has emerged as one of the central variables in political behavior research, with numerous scholars devoting considerable effort to explaining variance in citizens' levels of knowledge and to understanding the consequences of this variance for representation. Although such substantive matters continue to receive exhaustive study, questions of measurement also warrant attention. I demonstrate that conventional measures of political knowledge—constructed by summing a respondent's correct answers on a battery of factual items—are of uncertain validity. Rather than collapsing incorrect and "don't know" responses into a single absence-of-knowledge category, I introduce estimation procedures that allow these effects to vary. Grouped-data multinomial logistic regression results demonstrate that incorrect answers and don't knows perform dissimilarly, a finding that suggests deficiencies in the construct validity of conventional knowledge measures. The likely cause of the problem is traced to two sources: knowledge may not be discrete, meaning that a simple count of correct answers provides an imprecise measure; and, as demonstrated by the wealth of research conducted in the field of educational testing and psychology since the 1930s, measurement procedures used in political science potentially result in "knowledge" scales contaminated by systematic personality effects.

Worker movement dataFile containing ant worker movement data within sample triplets along with distance between nests and nest volumes. For full description of labelling see readmemovement_data.txtMicrosatellite dataData for microsatellite variation across 12 loci. Four columns preceeding mirosatellite data describing sampling (see readme), next 12 columns are variation across microsatellite datamicrosatellite_data.txtResource movement dataA four column file with data on the absorbance of individual ants following and ELISA assay. Arranges as follows: colony - the name of the tested triplet, n=10; nest - B, C or U for base, connected or unconnected (see paper Fig. 1 for details); sample - bl (blank, no ant), ctrl (control, known negative ant), 1-100 individual ants being tested for absorbance; absorb - absorbance valueabsorbance_data.txtSample locationsLocations of nests used within this study along with triplet ID and their distance to forest cover historicallysample_locations.txt

Data and methods can be found within Enochs et al. [38]. Long-term data taken from multi-month deployment of data loggers over the duration of the experiment. Discrete bottle samples (n = 8) taken every 6 hours over a two-day period. Numbers in parentheses are standard deviation for long-term data and standard error of the mean for discrete bottle samples.

This digital GIS dataset and accompanying nonspatial files synthesize model outputs from a regional-scale volumetric 3-D geologic model that portrays the generalized subsurface geology of the Powder River Basin and Williston Basin regions from a wide variety of input data sources. The study area includes the Hartville Uplift, Laramie Range, Bighorn Mountains, Powder River Basin, and Williston Basin. The model data released here consist of the stratigraphic contact elevation of major Phanerozoic sedimentary units that broadly define the geometry of the subsurface, the elevation of Tertiary intrusive and Precambrian basement rocks, and point data that illustrate an estimation of the three-dimensional geometry of fault surfaces. The presence of folds and unconformities are implied by the 3D geometry of the stratigraphic units, but these are not included as discrete features in this data release. The 3D geologic model was constructed from a wide variety of publicly available surface and subsurface geologic data; none of these input data are part of this Data Release, but data sources are thoroughly documented such that a user could obtain these data from other sources if desired. The PowderRiverWilliston3D geodatabase contains 40 subsurface horizons in raster format that represent the tops of modeled subsurface units, and a feature dataset “GeologicModel”. The GeologicModel feature dataset contains a feature class of 30 estimated faults served in elevation grid format (FaultPoints), a feature class illustrating the spatial extent of 22 fault blocks (FaultBlockFootprints), and a feature class containing a polygon delineating the study areas (ModelBoundary). Nonspatial tables define the data sources used (DataSources), define terms used in the dataset (Glossary), and provide a description of the modeled surfaces (DescriptionOfModelUnits). Separate file folders contain the vector data in shapefile format, the raster data in ASCII format, and the tables as comma-separated values. In addition, a tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables (EntityAndAttributes). An included READ_ME file documents the process of manipulating and interpreting publicly available surface and subsurface geologic data to create the model. It additionally contains critical information about model units, and uncertainty regarding their ability to predict true ground conditions. Accompanying this data release is the “PowderRiverWillistonInputSummaryTable.csv”, which tabulates the global settings for each fault block, the stratigraphic horizons modeled in each fault block, the types and quantity of data inputs for each stratigraphic horizon, and then the settings associated with each data input.

This data release is comprised of data tables of input variables for seawaveQ and surrogate models used to predict concentrations of select pesticides at six U.S. Geological Survey National Water Quality Network (NWQN) river sites (Fanno Creek at Durham, Oregon; White River at Hazleton, Indiana; Kansas River at DeSoto, Kansas; Little Arkansas River near Sedgwick, Kansas; Missouri River at Hermann, Missouri; Red River of the North at Grand Forks, North Dakota). Each data table includes discrete concentrations of one select pesticide (Atrazine, Azoxystrobin, Bentazon, Bromacil, Imidacloprid, Simazine, or Triclopyr) at one of the NWQN sites; daily mean streamflow; 30-day and 1-day flow anomalies; daily median values of pH and turbidity; daily mean values of dissolved oxygen, specific conductance, and water temperature; and 30-day and 1-day anomalies for pH, turbidity, dissolved oxygen, specific conductance, and water temperature. Two pesticides were modeled at each site with three types of regression models. Also included is a zip file with outputs from seawaveQ model summary. The processes for retrieving and preparing data for regression models followed those outlined in the SEAWAVE-Q R package documentation (Ryberg and Vecchia, 2013; Ryberg and York, 2020). The R package waterData (Ryberg and Vecchia, 2012) was used to import daily mean values for discharge and either daily mean or daily median values for continuous water-quality constituents directly into R depending on what data were available at each site. Pesticide concentration, streamflow, and surrogate data (continuously measured field parameters) were imported from and are available online from the USGS National Water Information System database (USGS, 2020). The waterData package was used to screen for missing daily mean discharge values (no missing values were found for the sites) and to calculate short-term (1 day) and mid-term (30 day) anomalies for flow and short-term anomalies (1 day) for each water-quality variable. A mid-term streamflow anomaly, for instance, is the deviation of concurrent daily streamflow from average conditions for the previous 30 days (Vecchia and others, 2008). Anomalies were calculated as additional potential model variables. Pesticide concentrations for select constituents from each site were pulled into R using the dataRetrieval package (De Cicco and others, 2018). Three of the six sites (Kansas River at DeSoto, Kansas; Missouri River at Hermann, Missouri; and White River at Hazleton, Indiana) pulled pesticide data for WY 2013–17 whereas the other three sites (Fanno Creek at Durham, Oregon; Little Arkansas River near Sedgwick, Kansas; and Red River of the North at Grand Forks, North Dakota) pulled pesticide data for WY 2013–18. Discrete pesticide data were matched with daily mean discharge and daily mean or median water-quality constituents and the associated calculated short-term (1-day) and mid-term (30-day) anomalies from the date of sampling. Pesticide concentrations were estimated using the SEAWAVE-Q (with surrogates) model using 19 combinations of surrogate variables (table 2 in the associated SIR, "Comparison of Surrogate Models to Estimate Pesticide Concentrations at Six U.S. Geological Survey National Water Quality Network Sites During Water Years 2013–18.") at each of 12 site-pesticide combinations (table 3 in the associated SIR). Three measures of model performance—the generalized coefficient of determination (R2), Akaike’s Information Criteria (AIC), and scale—were included in the output and used to select best-fit models (Table 4 of the associated SIR). The three to four best-fit SEAWAVE-Q (with surrogates) models with sample sizes at least five times the number of variables were selected for each site-pesticide combination based on generalized R2 values—the higher, the better. If generalized R2 values were the same, the model with the lower AIC value was used. The standard surrogate regression and base SEAWAVE-Q models were then applied using the same samples that were used for each of the best-fit SEAWAVE-Q (with surrogates) models so that direct comparisons could be made for each site-pesticide-surrogate instance. The input data used to estimate daily pesticide concentrations for each of the best fit models have been included in this data release. An example of one output file for each model type is included in a .zip file named "output_examples.zip". Each of the output files shows the three measures of model performance. (1) The output file for the standard regression model named "HAZ8_Atrazine_Standard_Regression_Output.txt" includes: Pseudo R-square (Allison) of 0.631, Model AIC of 174.0232, and a Scale of 0.961. (2) The output file for the base SEAWAVE-Q model named "HAZ8_Atrazine_Base_Seawave-Q_Output.txt" includes: Generalized r-squared of 0.82, AIC (Akaike's An Information Criterion) of 36.38, and a Scale of 0.288. (3) The output file for the SEAWAVE-Q w/Surrogates model named "HAZ8_Atrazine_Seawave-Q_w_Surrogates_Output.txt" includes: Generalized r-squared of 0.85, AIC (Akaike's An Information Criterion) of 33.76, and a Scale of 0.268. These values match those for Site ID = HAZ, Pesticide = Atrazine, and Surrogate variable group 8 for each model type in Table 4 of the associated SIR.

Jointly managed by multiple states and the federal government, there are many ongoing efforts to characterize and understand water quality in the Delaware River Basin (DRB). Many State, Federal and non-profit organizations have collected surface-water-quality samples across the DRB for decades and many of these data are available through the National Water Quality Monitoring Council's Water Quality Portal (WQP). In this data release, WQP data in the DRB were harmonized, meaning that they were processed to create a clean and readily usable dataset. This harmonization processing included the synthesis of parameter names and fractions, the condensation of remarks and other data qualifiers, the resolution of duplicate records, an initial quality control check of the data, and other processing steps described in the metadata. This data set provides harmonized discrete multisource surface-water-quality data pulled from the WQP for nutrients, sediment, salinity, major ions, bacteria, temperature, dissolved oxygen, pH, and turbidity in the DRB, for all available years.

NODC Accession 0115682 includes chemical, discrete sample, physical and profile data collected from METEOR in the North Atlantic Ocean from 2003-06-26 to 2003-07-21 and retrieved during cruise CARINA/06MT20030626 and WOCE AR04. These data include CHLOROFLUOROCARBON-11 (CFC-11), CHLOROFLUOROCARBON-12 (CFC-12), HYDROSTATIC PRESSURE, Potential temperature (theta), SALINITY, SULFUR HEXAFLUORIDE (SF6) and WATER TEMPERATURE. The instruments used to collect these data include CTD and bottle. These data were collected by J. Mortensen of Institute of Marine Research - Norway as part of the CARINA/06MT20030626, WOCE AR04 data set.

The CARINA (CARbon dioxide IN the Atlantic Ocean) data synthesis project is an international collaborative effort of the EU IP CARBOOCEAN, and U.S. partners. It has produced a merged internally consistent data set of open ocean subsurface measurements for biogeochemical investigations, in particular, studies involving the carbon system. The original focus area was the North Atlantic Ocean, but over time the geographic extent expanded and CARINA now includes data from the entire Atlantic, the Arctic Ocean, and the Southern Ocean.

This digital GIS dataset and accompanying nonspatial files synthesize the model outputs from a regional-scale volumetric 3-D geologic model that portrays the generalized subsurface geology of western South Dakota from a wide variety of input data sources.The study area includes all of western South Dakota from west of the Missouri River to the Black Hills uplift and Wyoming border. The model data released here consist of the stratigraphic contact elevation of major Phanerozoic sedimentary units that broadly define the geometry of the subsurface, the elevation of Tertiary intrusive and Precambrian basement rocks, and point data representing the three-dimensional geometry of fault surfaces. the presence of folds and unconformities are implied by the 3D geometry of the stratigraphic units, but these are not included as discrete features in this data release. The 3D geologic model was constructed from a wide variety of publicly available surface and subsurface geologic data; none of these input data are part of this Data Release, but data sources are thoroughly documented such that a user could obtain these data from other sources if desired. This model was created as part of the U.S. Geological Survey’s (USGS) National Geologic Synthesis (NGS) project—a part of the National Cooperative Geologic Mapping Program (NCGMP). The WSouthDakota3D geodatabase contains twenty-five (25) subsurface horizons in raster format that represent the tops of modeled subsurface units, and a feature dataset “GeologicModel”. The GeologicModel feature dataset contains a feature class of thirty-five (35) faults served in elevation grid format (FaultPoints). The feature class “ModelBoundary” describes the footprint of the geologic model, and was included to meet the NCGMP’s GeMS data schema. Nonspatial tables define the data sources used (DataSources), define terms used in the dataset (Glossary), and provide a description of the modeled surfaces (DescriptionOfModelUnits). Separate file folders contain the vector data in shapefile format, the raster data in ASCII format, and the nonspatial tables as comma-separated values. In addition, a tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables (EntityAndAttributes). An included READ_ME file documents the process of manipulating and interpreting publicly available surface and subsurface geologic data to create the model. It additionally contains critical information about model units, and uncertainty regarding their ability to predict true ground conditions. Accompanying this data release is the “WSouthDakotaInputSummaryTable.csv”, which tabulates the global settings for each fault block, the stratigraphic horizons modeled in each fault block, the types and quantity of data inputs for each stratigraphic horizon, and then the settings associated with each data input.