Fluorescence and absorbance spectra were measured in discrete surface water samples collected during three sampling campaigns (Nov 2022, Mar/Apr 2023, Jul 2023) on the Illinois Waterway (IWW) and Chicago Area Waterway System (CAWS), which are the primary drainage of the Illinois River Basin (IRB). Water sampling was conducted concurrently with a boat-based water quality mapping effort using the Fast Limnology Automated Measurement (FLAMe) system (Crawford et al., 2015). Each campaign began in the Chicago metropolitan area, and after having sampled Lake Michigan, entered into the upper extent of the IWW, sampling through the CAWS into the lower reaches of the Des Plaines River and finally the Illinois River. After 8-10 days of traveling downriver through the IWW, sampling ended in the Mississippi River upstream of St Louis, Missouri. Discrete water quality samples were collected from various sites that include main channel, tributaries, and off-channel areas (e.g., backwaters) from a depth of 1 meter (m), typically in the center of the channel or aquatic feature. Between 25 and 40 sites were sampled per campaign dependent upon river conditions and boat accessibility. Data reported here are compiled into three tables: 1) full fluorescence spectra in vectorized format, 2) full absorbance spectra, and 3) summary optical measurements commonly used in statistical analyses.

This data release includes water-quality data collected at up to thirteen locations along the Merrimack River and Merrimack River Estuary in Massachusetts. In this study, conducted by the U.S. Geological Survey (USGS) in cooperation with the Massachusetts Department of Environmental Protection, discrete samples were collected, and continuous monitoring was completed from June to September 2020. The data include results of measured field properties (water temperature, specific conductivity, pH, dissolved oxygen) and laboratory concentrations of nitrogen and phosphorus species, total carbon, pheophytin-a, and chlorophyll-a. These data were collected to assess selected (mainly nutrients) water-quality conditions in the Merrimack River and Merrimack River Estuary at the thirteen locations and identify areas where more water-quality monitoring is needed. The discrete samples and continuous-monitoring data are also available in the USGS National Water Information System at https://waterdata.usgs.gov/nwis. This data release consists of (1) Table of the discrete water-quality data collected (Merrimack_DiscreteWQ_Data.csv); (2) Statistical summaries including the minimum, median, and maximum of the discrete water-quality data collected (Merrimack_DiscreteWQ_Statistical_Data.original.csv); (3) Statistical summaries including the minimum, median, and maximum of the continuous water-quality data collected (Merrimack_ContinuousWQ_Statistical_Data.csv); (4) Table of vertical profile data (Merrimack_VerticalWQ_Profiles_Data.csv); (5) Table of continuous monitor deployment location and dates (Merrimack_ContinuousWQ_Deployment_Dates.csv); (6) Time-series plots of continuous water-quality data (Continuous_QW_Plots_All.zip); (7) Vertical profile plots (Vertical Profiles_QW_Plots.zip).

Context

Data Set Information:

This dataset contains a total of 71 images including 11 types of images with its distorted versions. Each and every image has its own uniqueness of discrete tone image properties.

Content

Attribute Information:

Types of Images 1.System Generated DTI by setting distinct pixel values 2.Discrete Pixel Logo 3.Business Charts 4.Bi-Level 5.Part of Discrete Information from an Continuous Image

Colorspace models 1.RGB 2.Grayscale 3.Binary

Distortion Types 1.JPEG 2.Gaussian White Noise (GWN) 3.Salt and Pepper noise (SP) 4.Multiplicative Speckle Noise (MSN) 5.Poisson Noise (PN)

** Target**

Use this dataset for analysis purpose

Acknowledgements

Source:

Creator:

J.Uthayakumar Research Scholar,Department of Computer Science,Pondicherry University,India. Contact: +91 9677583754 Email Id: uthayresearchscholar '@' gmail.com

Guided By,

Dr.T.Vengattaraman Assistant Professor,Department of Computer Science,Pondicherry University,India. Email Id: vengattaramant '@' gmail.com

Dr.P.Dhavachelvan Professor,Department of Computer Science,Pondicherry University,India. Email Id: dhavachelvan '@' gmail.com

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This data release includes elemental analysis of soil samples collected at breccia-pipe uranium mines, at one undeveloped breccia-pipe uranium deposit, and at a reference site in northern Arizona. Samples were collected near the Arizona 1, Canyon, Kanab North, and Pinenut uranium mines, over the EZ2 breccia-pipe uranium deposit, and at the Little Robinson Tank reference site. Samples were collected around the Arizona 1 mine after active mining had ceased during July 2015; around and within the mine yard at the Canyon mine during mine-development activity and before active mining occurred in June 2013; around and within the mine yard at the Kanab North mine during reclamation and before reclamation was completed in June 2016; around the Pinenut mine during active mining in October 2014; directly over the EZ2 deposit before any development activity occurred during November 2015; and at the Little Robinson Tank reference site during November 2015. This data release includes data for four different types of soil samples: (type 1) incremental soil samples where more than 30 equally-spaced subsamples were collected and composited over a limited areal extent termed a decision unit and depicted generally as a trapezoidal-shaped polygon mapped within a mine yard, or surrounding a mine site; (type 2) incremental soil samples where more than 30 subsamples were collected and composited over a roughly two dimensional linear or sinuous mapped pattern following roads also termed a decision unit; (type 3) discrete integrated soil samples (Bern and others, 2019 use the term “point” for these samples) where more than 30 subsamples were collected within fenced exclosures (generally about 3 meters square) containing Big Springs Number Eight dust sampling equipment; and (type 4) integrated soil samples comprised of at least 10 subsamples collected from underneath plywood cover boards used to collect herpetofauna. Incremental samples (types 1 and 2) were collected in triplicate from the soil surface from 0-5 centimeters (cm) depth using a Multi-Incremental Sampling Tool (MIST) collecting approximately the same volume for each subsample subject to slight variation due to variable soil conditions. The volume of soil represented by each type 1 and 2 sample is termed a decision unit (DU), the areal extent of which is defined by a mapped polygonal or sinuous or linear area, and the depth of which is the 5 cm that is sampled by the MIST. Each subsample of each triplicate incremental sample was passed through a 2-millimeter sieve and composited into a clean 19-liter bucket, with each completed triplicate sample transferred to double zip-top bags for transfer to the laboratory. Integrated samples (types 3 and 4) were collected using a plastic soil scoop to collect soil from 0-5 cm depth and were composited into double zip-top plastic bags for transfer to the laboratory. Data are divided into two different data tables based upon type: types 1 and 2 are in T1_DUSamples.csv; types 3 and 4 are in T2_BSNESamples.csv. The file DataDictionary_v1.csv defines all table headings and abbreviations. Sample preparation and analytical techniques are described in the metadata file. This data release also includes _location information for the approximate center points of the incremental sample polygons and linear features (decision units) and for the discrete integrated samples. Note, locations for incremental samples for decision units (sample types 1 and 2) are the approximate center of the geographical area (polygon, linear, or sinuous feature) over which the sample was collected. As such, the elemental values represent average concentrations for the sample volume collected over the entire geographic area and depth of 0-5 centimeters of each decision unit, and do not represent concentrations that would be measured in a discrete sample collected at that central _location.

These data were complied for the primary analysis underlying the results presented in the manuscript associated with these data (see Larger Work Citation). The data was collected from a 2015 survey of private party Grand Canyon boaters. The open document file contains 3 data sheets: 1) variables used in the Table 2 comparison of samples, 2) the core dichotomous choice contingent valuation questions and responses (used in Table 3), and 3) The Discrete Choice question data used for the models estimated in Table 4.

The data consist of two parts: Time trade-off (TTO) data with one row per TTO question (5 questions), and discrete choice experiment (DCE) data with one row per question (6 questions). The purpose of the data is the calculation of a Swedish value set for the capability-adjusted life years (CALY-SWE) instrument. To protect the privacy of the study participants and to comply with GDPR, access to the data is given upon request.

The data is provided in 4 .csv files with the names:

• tto.csv (252 kB)
• dce.csv (282 kB)
• weights_final_model.csv (30 kB)
• coefs_final_model.csv (1 kB)

The first two files (tto.csv, dce.csv) contain the time trade-off (TTO) answers and discrete choice experiment (DCE) answers of participants. The latter two files (weight_final_model.csv, coefs_final_model.csv) contain the generated value set of CALY-SWE weights, and the pertaining coefficients of the main effects additive model.

Background:

CALY-SWE is a capability-based instrument for studying Quality of Life (QoL). It consists of 6 attributes (health, social relations, financial situation & housing, occupation, security, political & civil rights) and provides the option to gives for attribute answers on 3 levels (Agree, Agree partially, Do not agree). A configuration or state is one of the 3^6 = 729 possible situations that the instrument describes. Here, a config is denoted in the form of xxxxxx, one x for each attribute in order above. X is a digit corresponding to the level of the respective attribute, with 3 being the highest (Agree), and 1 being the lowest (Do not agree). For example, 222222 encodes a configuration with all attributes on level 2 (Partially agree). The purpose of this dataset is to support the publication of the CALY-SWE value set and to enable reproduction of the calculations (due to privacy concerns we abstain from publishing individual level characteristics). A value set consists of values on the 0 to 1 scale for all 729, each of represents a quality weighting where 1 is the highest capability-related QoL, and 0 the lowest capability-related QoL.

The data contains answers to two types of questions: TTO and DCE.

In TTO questions, participants iteratively chose a number of years between 1 to 10. A choice of 10 years is equivalent to living 10 years with full capability (state configuration 333333) in the capability state that the TTO question describes. The answer on the 0 to 1 scale is then calculated as x/10. In the DCE questions, participants were given two states and they chose a state that they found to be better. We used a hybrid model with a linear regression and a logit model component, where the coefficients were linked through a multiplicative factor, to obtain the weights (weights_final_model.csv). Each weight is calculated as constant + the coefficients for the respective configuration. Coefficients for level 3 encode the difference to level 2, and coefficients for level 2 the difference to the constant. For example, for the weight for 123112 is calculated as constant + socrel2 + finhou2 + finhou3 + polciv2 (No coefficients for health, occupation, and security involved as they are on level 1 that is captured in the constant/intercept).

To assess the quality of TTO answers, we calculated a score per participant that takes into account inconsistencies in answering the TTO question. We then excluded 20% of participants with the worst score to improve the TTO data quality and signal strength for the model (this is indicated by the 'included' variable in the TTO dataset). Details of the entire survey are described in the preprint “CALY-SWE value set: An integrated approach for a valuation study based on an online-administered TTO and DCE survey” by Meili et al. (2023). Please check this document for updated versions.

Ids have been randomized with preserved linkage between the DCE and TTO dataset.

Data files and variables:

Below is a description of the variables in each CSV file. - tto.csv:

config: 6 numbers representing the attribute levels. position: The number of the asked TTO question. tto_block: The design block of the TTO question. answer: The equivalence value indicated by the participant, ranging from 0.1 to 1 in steps of 0.1. included: If the answer was included in the data for the model to generate the value set. id: Randomized id of the participant.

• dce.csv:

config1: Configuration of the first state in the question. config2: Configuration of the second state in the question. position: The number of the asked TTO question. answer: Whether state 1 or 2 was preferred. id: Randomized id of the participant.

• weights_final_model.csv

config: 6 numbers representing the attribute levels. weight: The weight calculated with the final model. ciu: The upper 95% credible interval. cil: The lower 95% credible interval.

• coefs_final_model.csv:

name: Name of the coefficient, composed of an abbreviation for the attribute and a level number (abbreviations in the same order as above: health, socrel, finhou, occu, secu, polciv). value: Continuous, weight on the 0 to 1 scale. ciu: The upper 95% credible interval. cil: The lower 95% credible interval.

Experimental data can broadly be divided in discrete or continuous data. Continuous data are obtained from measurements that are performed as a function of another quantitative variable, e.g., time, length, concentration, or wavelength. The results from these types of experiments are often used to generate plots that visualize the measured variable on a continuous, quantitative scale. To simplify state-of-the-art data visualization and annotation of data from such experiments, an open-source tool was created with R/shiny that does not require coding skills to operate it. The freely available web app accepts wide (spreadsheet) and tidy data and offers a range of options to normalize the data. The data from individual objects can be shown in 3 different ways: (1) lines with unique colors, (2) small multiples, and (3) heatmap-style display. Next to this, the mean can be displayed with a 95% confidence interval for the visual comparison of different conditions. Several color-blind-friendly palettes are available to label the data and/or statistics. The plots can be annotated with graphical features and/or text to indicate any perturbations that are relevant. All user-defined settings can be stored for reproducibility of the data visualization. The app is dubbed PlotTwist and runs locally or online: https://huygens.science.uva.nl/PlotTwist

The datasets illustrate the dynamics for the discrete-time predator-prey model when the predator has two stages in its lifetime, namely, juvenile and adult. Figure-1 (datasets associated: fig-1-a-ex-2.txt, fig-1-a-ex-2.txt, and fig-1-a-ex-2.txt) in the associated manuscript (Ackleh et al., 2020) gives the time-series dynamics of the various outcomes (extinction, predator-free, coexistence states) of the model. These situations are described by example-2 in the manuscript. In addition, example-3 illustrates the qualitative change (bifurcation diagrams) in the dynamics of the prey and two stages of the predator densities as a function of adult predator survival probability. The outcome of these dynamics are given in figure-2 (datasets associated: fig-2-abc-ex-3.txt, fig-2-def-ex-3.txt, fig-2-ghi-ex-3.txt, and fig-2-jkl-ex-3.txt) in the manuscript which shows that the model may exhibit chaotic dynamics when the predator has stage-structure in the model while the unstructured predator and prey model did not show such behaviors in the model outcomes. This dataset supports the publication: Ackleh, A. S., Hossain, M. I., Veprauskas, A., & Zhang, A. (2020). Persistence of a Discrete-Time Predator-Prey Model with Stage-Structure in the Predator. Springer Proceedings in Mathematics & Statistics, 145–163. doi:10.1007/978-3-030-60107-2_6.

In this project, I have done exploratory data analysis on the UCI Automobile dataset available at https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data

This dataset consists of data From the 1985 Ward's Automotive Yearbook. Here are the sources

1) 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook. 2) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038 3) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037

Number of Instances: 398 Number of Attributes: 9 including the class attribute

Attribute Information:

mpg: continuous cylinders: multi-valued discrete displacement: continuous horsepower: continuous weight: continuous acceleration: continuous model year: multi-valued discrete origin: multi-valued discrete car name: string (unique for each instance)

This data set consists of three types of entities:

I - The specification of an auto in terms of various characteristics

II - Tts assigned an insurance risk rating. This corresponds to the degree to which the auto is riskier than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is riskier (or less), this symbol is adjusted by moving it up (or down) the scale. Actuaries call this process "symboling".

III - Its normalized losses in use as compared to other cars. This is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/specialty, etc...), and represents the average loss per car per year.

The analysis is divided into two parts:

Data Wrangling

1. Pre-processing data in python
2. Dealing with missing values
3. Data formatting
4. Data normalization
5. Binning

6. Exploratory Data Analysis

7. Descriptive statistics

8. Groupby

9. Analysis of variance

10. Correlation

11. Correlation stats

Acknowledgment Dataset: UCI Machine Learning Repository Data link: https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data

NCEI Accession 0143946 includes discrete sample and profile data collected from MARION DUFRESNE in the Indian Ocean and Savu Sea from 1992-02-17 to 1992-03-23. These data include CHLOROFLUOROCARBON-11 (CFC-11), CHLOROFLUOROCARBON-12 (CFC-12), DELTA HELIUM-3, DISSOLVED OXYGEN, HYDROSTATIC PRESSURE, Potential temperature (theta), SALINITY and WATER TEMPERATURE. The instruments used to collect these data include CTD and bottle.

These data were collected by Michele Fieux of Universite Pierre et Marie Curie; Institut Pierre Simon Laplace; Laboratoire D'Oceanographie et du Climat: Experimentations et Approches Numeriques and Alain Poisson of Universite Pierre et Marie Curie; Laboratoire de Biogeochimie et Chimie Marines as part of the JADE92 data set. CDIAC associated the following cruise ID(s) with this data set: JADE92

NCEI Accession 0108154 includes discrete sample and profile data collected from POLARSTERN in the South Atlantic Ocean and Southern Oceans (> 60 degrees South) from 2008-02-10 to 2008-04-16. These data include CHLOROFLUOROCARBON-11 (CFC-11), CHLOROFLUOROCARBON-12 (CFC-12), DELTA HELIUM-3, DISSOLVED INORGANIC CARBON (DIC), DISSOLVED OXYGEN, HELIUM, HYDROSTATIC PRESSURE, NEON, NITRATE, NITRITE, Potential temperature (theta), SALINITY, TOTAL ALKALINITY (TA), WATER TEMPERATURE, phosphate and silicate. The instruments used to collect these data include Alkalinity titrator, CTD, Coulometer for DIC measurement and bottle.

These data were collected by Eberhard Fahrbach of Alfred Wegener Institute for Polar and Marine Research and Steven van Heuven and Hein de Baar of Royal Netherlands Institute for Sea Research as part of the CLIVAR_A12_ANTXXIV_3 data set. CDIAC associated the following cruise ID(s) with this data set: ANT-XXIV/3

The International CLIVAR Global Ocean Carbon and Repeat Hydrography Program carries out a systematic and global re-occupation of select WOCE/JGOFS hydrographic sections to quantify changes in storage and transport of heat, fresh water, carbon dioxide (CO2), and related parameters.

In cooperation with state and county agencies, including the Chester County Water Resources Authority (CCWRA), the U.S. Geological Survey (USGS) has collected discrete stream samples for analysis of chloride concentrations at three real-time streamflow and water-quality monitoring (specific conductance) stations located in Chester County, Pennsylvania. Data were collected from 2010-2023 at these stations for the application of predicting chloride concentrations using real-time continuous specific conductance and streamflow data. Regression equations were developed by relating discrete-sample chloride and discrete specific conductance data, as well as continuous streamflow data. Regression equations included possible explanatory variables of discrete specific conductance and continuous streamflow and the response variable of chloride concentration with base-10 logarithmic (log) transformations. Data files in .CSV format include the variables of datetime, specific conductance (microsiemens per centimeter at 25 degrees Celsius, uS/cm), streamflow (Q, cubic feet per second), chloride concentrations (milligrams per liter, mg/L), and calculated or transformed variables of log specific conductance, log streamflow, and log chloride concentrations. Data are included for 3 stream sites: Valley Creek at PA Turnpike Bridge near Valley Forge, PA (USGS station 01473169) with discrete Cl and SC data from nearby downstream site Valley Creek at Wilson Road near Valley Forge, PA (USGS station 01473170) White Clay Creek near Strickersville, PA (USGS station 01478245) Brandywine Creek at Chadds Ford, PA (USGS station 01481000) For the model developed for station 01473169, discrete Cl and SC data come from station 01473170, and continuous Q data comes from station 01473169. For the model developed for station 01478245, discrete Cl and SC data and continuous Q data come from station 01478245. For the model developed for station 01481000, discrete Cl and SC data and continuous Q data come from station 01481000.

This dataset includes seasonal water quality and zooplankton data from Portage Lake in Hubbard County, Minnesota, USA, during 2023–2024. The dataset contains three components: (1) vertical profiles of temperature, dissolved oxygen, and chlorophyll a; (2) discrete chlorophyll a samples from 2 depths at lake center; and (3) zooplankton community data containing species-level counts. Water profiles were taken at the lake center using a YSI EXO2 sonde, and discrete water samples were collected from 0.5 m and 3–4 m depths at lake center. Chlorophyll a samples were filtered, frozen, extracted in the dark for 18 hours, and analyzed with a Turner Designs 10-AU fluorometer. Zooplankton were sampled at lake center by pooling three net tows per replicate. Zooplankton were preserved in ethanol and identified under a stereoscopic microscope at 7x-70x magnification using standard identification keys. At least 300 individuals were counted per sample. Abundance is reported as individuals per liter..., , # Seasonal oxygen, temperature, chlorophyll, and zooplankton data – Portage Lake, MN

Dataset DOI: 10.5061/dryad.m63xsj4fs

Description of the data and file structure

Seasonal water quality and zooplankton data for Portage Lake, Hubbard County, MN, USA (2023-2024)

Files and variables

File:

Portage_CTD.csv

Description:Â Complete water column measurements of dissolved oxygen, chlorophyll a, and temperature in Portage Lake, Hubbard County, MN, USA (2023-2024)

Variables:

• "Oxygen_perc_sat" : dissolved oxygen in percent saturation
• "Chlorophyll_a_ug_L" : chlorophyll a in micrograms per liter, μg/L
• "Temperature_C " : temperature in degrees Celcius, °C

Portage_discrete_chlorophyll.csv

Description:Â Discrete chlorophyll a measurements from 2 depths at lake center (surface: 0.5 m below surface; bottom: 3-4 m below surface).

Methodology:Â Whole water, not sieved, filtered through 0.22 cellulose nitra...,

We examine the role of cement on compaction band formation by performing triaxial tests on three sandstones, Bentheim, Castlegate and a synthetic sandstone which possess very similar porosities (~26-29%) and grain sizes (~230-300 µm), but which are cemented differently, with syntaxial quartz overgrowths, clay, and amorphous quartz cement respectively. Each sample was taken to 5% axial strain at a starting effective stress equivalent to 85% of its hydrostatic yield (P*) value, which were identified from yield under hydrostatic loading. These data for the 3 samples are presented as matlab data files. Post-deformation, each of the 3 cores underwent backscatter SEM and subsequent image analysis to examine any localised variations in porosity and grain size. These data are presented as csv files. Discrete bands form in each of the 3 sandstones but are distributed differently across each sample. Our results suggest that cement type plays a significant role in the micromechanics of deformation within each of the sandstones, which in turn, determines where the compaction bands nucleate and develop. These results may provide a starting point to investigate the role of cement on compaction localisation further.

Clear Lake is a 180 km2 freshwater lake located in the California Coast Range, approximately 120 km northwest of Sacramento. The lake supports a wide variety of fish and bird species and is a very popular sport-fishing destination. However, fish consumption advisories associated with mercury (Hg) contamination exist for several popular recreational species. The lake is comprised of three main regions including a large open-water region to the northwest (North Arm), a smaller and narrower region to the southeast (South Arm), and the smallest and narrowest region to the east (Oaks Arm). The Sulfur Bank Mercury Mine (SBMM), located on the south shore of the Oaks Arm, was mined by both open-pit and underground methods (intermittently active from the 1870s until 1957), and is now a U.S. Environmental Protection Agency (USEPA) Superfund site. This former Hg mining area represents a dominant contributor of Hg to the adjacent lake and is the focus of continued clean-up efforts to minimize Hg impacts to Clear Lake. The SBMM area is also host to active hot springs and gas fumaroles. Clear Lake is classified as eutrophic to hypereutrophic, based on high levels of nutrients (various forms of nitrogen, N, and phosphorus, P) that cause excessive algal blooms and periodic fish kills. There are current studies underway (by the Univ. of California, Davis, UCD) to improve the understanding of nutrient cycling within the lake. The way in which nutrient dynamics affect the cycling, transport and bioaccumulation of Hg remains poorly understood. Because of the expense and human resources required to conduct traditional water-quality studies, and the knowledge that water quality may vary significantly on an hourly or daily time scale in some locations, there is a need to develop more spatially and temporally robust monitoring programs to study both mercury and nutrients within Clear Lake and elsewhere. Recent advances in deployable in-situ electrochemical and optical sensors, as well as remote-sensing approaches, offer an opportunity to collect critical water-quality data at high temporal and spatial resolution to a degree previously unobtainable. However, these approaches require a detailed examination of the relationships between the constituent of concern (for example, the concentration of suspended sediment or various Hg species) and the electrochemical or optical properties of water for which the current class of sensors are best suited (for example, turbidity, dissolved organic carbon, or algal concentration). This preliminary study of particulate and filtered Hg species (including total mercury and methylmercury) and non-Hg water-column constituents was designed to lay the groundwork for developing a more robust Hg monitoring program for Clear Lake. This data release documents the results from a single (2-day) sampling event during July 2019 that focused on the collection of two types of surface-water data: a) discrete samples collected with a Van Dorn style sampler for a suite of Hg and non-Hg water constituents collected near the surface (1 m depth) and near bottom (approximately 1 m off of the bottom); and b) continuous vertical profiles collected in-situ with a water-quality sonde (electrochemical / optical probe data). Five of the nine discrete sample locations were co-located with fixed monitoring stations (moorings) being used by UCD for the study of nutrients within Clear Lake. Sampling included all 3 lake regions, including the Upper Arm (3 discrete-sample sites and 9 vertical-profile sites), the Lower Arm (3 discrete-sample sites and 9 vertical-profile sites), and the Oaks Arm (3 discrete-sample sites and 3 vertical-profile sites). This data release includes four data tables given both as Excel (.xlsx) and machine readable 'comma-separated values' format (.csv): 1) ‘T1_Data.Dictionary_CL_07.2019’, the Data Dictionary, which provides definitions and details related to the other three data tables and includes citations of analytical methods; 2) ‘T2_Discrete.SW_CL_07.2019’, the discrete-sample surface-water dataset including concentration data for Hg species (including total mercury and methylmercury in dissolved and particulate forms) and nutrients (including several forms of N and P); 3) ‘T3_Vertical_Profiles.SW_CL_07.2019’, the water-quality sonde vertical-profile dataset; and 4) ‘T4_QA.SW_CL_07.2019’, a quality assurance data summary for the discrete water samples. In addition, file ‘SITES_CL_07.2019’ provides the sampling locations in a machine-readable geospatial file format (*.kmz).

The goal of this study was to develop a suite of inter-related water quality monitoring approaches capable of modeling and estimating the spatial and temporal gradients of particulate and dissolved total mercury (THg) concentration, and particulate and dissolved methyl mercury (MeHg), concentration, in surface waters across the Sacramento / San Joaquin River Delta (SSJRD). This suite of monitoring approaches included: a) data collection at fixed continuous monitoring stations (CMS) outfitted with in-situ sensors, b) spatial mapping using boat-mounted flow-through sensors, and c) satellite-based remote sensing. The focus of this specific Child Page is to present laboratory measured spectral data associated with discrete surface water samples collected as part of both the CMS and boat mapping sampling efforts. All laboratory-based measurement presented herein were conducted by the U.S. Geological Survey (USGS) Organic Matter Research Laboratory (OMRL) in Sacramento, Calif. The machine-readable (comma separated value, *.csv) files presented herein include spectral data collected using two different instruments: 1) Laboratory-based absorbance and fluorescence measurements on filtered water using an Aqualog (Hansen and others, 2018) and 2) Laboratory-based absorption measurements using a Varian Cary spectrophotometer on particulate samples collected on glass fiber filters (Kishino and others, 1985; Roesler, 1998). The reported spectral data includes: 1) fluorescence intensities across a wide range of excitation (240 to 800 nm) and emission (250 to 800 nm) wavelengths expressed as an excitation-emission matrix (EEM), 2) absorbance of light (from 239 nm to 800 nm) due to dissolved and colloidal substances, and 3) absorption coefficients (from 350 nm to 715 nm) for particulates using the quantitative filter technique (QFT).

Pre-processed particle data obtained from DEM simulations. For each particle, the initial position in the twin-screw feeder, the particle radius, and the residence time is given. The data is available for 3 fill levels in the feeder (100%, 66%, 40%). Included in the dataset are example RTDs obtained from the particle data, a minimum working example for a Python script that calculates the RTD of particles in two spatial regions of the feeder, and a video of the complete discharge process rendered from the DEM results.

This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor cruise M130 (EXPOCODE 06MT20160828) in the Tropical Atlantic Ocean from 2016-08-28 to 2016-10-03. These data include water temperature, salinity, dissolved oxygen, nitrate, nitrite, phosphate, silicate, chlorofluorocarbon-12 (CFC-12), sulfur hexafluoride (SF6) and other measurements. R/V Meteor Cruise was aimed at studying biogeochemical and physical processes in the tropical/subtropical Atlantic Ocean. Observations were carried out in the entire water column, from the sea floor to the sea surface.

The file contains 11 worksheets: The "Splice" worksheet provides Anhysteretic Remanent Magnetization (ARM@30mT) data obtained from u-channels collected from core sections of holes U1471A, U1471C and U1471D. The experiments were carried out at the Institute of Astronomy and Geophysics of the University of São Paulo using the triaxial cryogenic magnetometer (2G, mod. 755). The "XRF Scan Data" worksheet contains the results obtained by XRF Scan measurements taken at 1 cm intervals directly on the surface of the divided half-core section (each 1.5 m long) using an Avaatech XRF-Scanning generation at the Ocean Discovery Program (IODP) International Gulf Coast Repository at Texas A&M University. Results are displayed in counts per second. The "Benchtop vs. scanner XRF data" worksheet contains data from discrete measurements in conventional XRF performed on 42 samples collected at each 3 cm interval of the central section U1471C-1H-3W (3 to 4.5 mbsf). The samples, taken in 5 g aliquots of core material, were dried and pulverized in an agate mortar. Powder samples were measured in the conventional XRF Rigaku Supermini200 from the University of São Paulo. The worksheet also presents the average of the XRF scan data for the corresponding depth of each discrete sample. The "1st age model (Sr vs LR04)" worksheet contains the Sr/sum data and the tie points obtained by correlating the Sr/sum data with the LR04 curve (Lisiecki and Raymo; 2005). The "Data with 1st age model" worksheet contains the Fe/sum and ln (Fe/Si) data linearly interpolated to a resolution of 1 kry (using the first age model) and their respective spectral analysis through the Multitaper method using Acycle software. The "2nd age model (Fe vs. NHSI)" worksheet shows the Fe/sum data linearly interpolated to 1 kry (using the first age model) and the Fe/sum data filtered using a bandpass in the precession frequency range. The worksheet also presents the tie points obtained by comparing the filtered Fe/sum data with the insolation curve for the Northern Hemisphere. The "Data with 2nd age model" worksheet contains the Fe/sum and ln (Fe/Si) data linearly interpolated to a resolution of 1 kry (using the second age model) and their respective spectral analysis through the Multitaper method using Acycle software. The "3rd age model (U1471 vs. U1467)" worksheet contains the tie points obtained by correlating the Sr/sum curves of records U1471 and U1467 and their respective ages. The "Data with 3rd age model" worksheet contains the Fe/sum and ln (Fe/Si) data linearly interpolated to a resolution of 1 kry (using the third age model) and their respective spectral analysis through the Multitaper method using Acycle software. The "Normalized data (Zmean)" worksheet contains the Fe/sum and ln (Fe/Si) data normalized to the mean equals zero and standard deviation equals one (that is, zero-mean normalization). The "Normalized data (Zmean)" worksheet contains the Fe/sum and ln (Fe/Si) data normalized to the mean equals zero and standard deviation equals one (that is, zero-mean normalization). The "Detrended data (anomaly)" worksheet contains the detrended Fe/sum and detrended ln (Fe/Si) data. Zmean normalized Fe/sum and ln Fe/Si data were linearly interpolated to a resolution of 500 years. The detrended Fe/sum data was obtained by subtracting LR04 from the Fe/sum record and the detrended ln (Fe/Si) data was obtained by subtracting NHSI from the ln (Fe/Si) record. The worksheet also presents the spectral analysis obtained through the Multitaper method using Acycle software.

The primary article (cited below under "Related works") introduces social work researchers to discrete choice experiments (DCEs) for studying stakeholder preferences. The article includes an online supplement with a worked example demonstrating DCE design and analysis with realistic simulated data. The worked example focuses on caregivers' priorities in choosing treatment for children with attention deficit hyperactivity disorder. This dataset includes the scripts (and, in some cases, Excel files) that we used to identify appropriate experimental designs, simulate population and sample data, estimate sample size requirements for the multinomial logit (MNL, also known as conditional logit) and random parameter logit (RPL) models, estimate parameters using the MNL and RPL models, and analyze attribute importance, willingness to pay, and predicted uptake. It also includes the associated data files (experimental designs, data generation parameters, simulated population data and parameters, simulated choice data, MNL and RPL results, RPL sample size simulation results, and willingness-to-pay results) and images. The data could easily be analyzed using other software, and the code could easily be adapted to analyze other data. Because this dataset contains only simulated data, we are not aware of any legal or ethical considerations. Methods In the worked example, we used simulated data to examine caregiver preferences for 7 treatment attributes (medication administration, therapy location, school accommodation, caregiver behavior training, provider communication, provider specialty, and monthly out-of-pocket costs) identified by dosReis and colleagues in a previous DCE. We employed an orthogonal design with 1 continuous variable (cost) and 12 dummy-coded variables (representing the levels of the remaining attributes, which were categorical). Using the parameter estimates published by dosReis et al., with slight adaptations, we simulated utility values for a population of 100,000 people, then selected a sample of 500 for analysis. Relying on random utility theory, we used the mlogit package in R to estimate the MNL and RPL models, using 5,000 Halton draws for simulated maximum likelihood estimation of the RPL model. In addition to estimating the utility parameters, we measured the relative importance of each attribute, estimated caregivers’ willingness to pay (WTP) for differences in attributes (e.g., how much they would be willing to pay for their child to see one type of provider versus another) with bootstrapped 95% confidence intervals, and predicted the uptake of three treatment packages with different sets of attributes. This submission includes both the simulated source data and the processed results. The online supplement of the primary article describes the methods in greater detail.