Mutual information (MI) is a powerful method for detecting relationships between data sets. There are accurate methods for estimating MI that avoid problems with “binning” when both data sets are discrete or when both data sets are continuous. We present an accurate, non-binning MI estimator for the case of one discrete data set and one continuous data set. This case applies when measuring, for example, the relationship between base sequence and gene expression level, or the effect of a cancer drug on patient survival time. We also show how our method can be adapted to calculate the Jensen–Shannon divergence of two or more data sets.

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).

Usage note: Time zone information was not received by BCO-DMO so it is unknown if the dates and times in this dataset are local or UTC. Please contact the dataset PI if you have questions about this.

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).

This data set consists of chemical and stable isotope data obtained through the analysis of discrete water samples collected from 14 fixed sampling locations in the northern Sacramento-San Joaquin Delta at roughly monthly intervals between April 2011 and November 2012.

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, ..., 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, esti..., , # Data from: How to Use Discrete Choice Experiments to Capture Stakeholder Preferences in Social Work Research

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This dataset supports the worked example in:

Ellis, A. R., Cryer-Coupet, Q. R., Weller, B. E., Howard, K., Raghunandan, R., & Thomas, K. C. (2024). How to use discrete choice experiments to capture stakeholder preferences in social work research. Journal of the Society for Social Work and Research. Advance online publication. https://doi.org/10.1086/731310

The referenced article introduces social work researchers to discrete choice experiments (DCEs) for studying stakeholder preferences. In a DCE, researchers ask participants to complete a series of choice tasks: hypothetical situations in which each participant is presented with alternative scenarios and selects one or more. For example, social work researchers may want to know how parents and other caregivers pr...

This dataset is about book series. It has 1 row and is filtered where the books is Amenability of discrete groups by examples. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The United States Geological Survey (USGS) is studying the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises on the West Florida Shelf and northern Gulf of Mexico regions aboard the research vessel (R/V) Weatherbird II or Bellows, ships of opportunity led by Dr. Kendra Daly, of the University of South Florida (USF) in July and August, 2013. Cruises left from and returned to Saint Petersburg, Florida, but followed different routes. The USGS collected geochemical data pertaining to pH, dissolved inorganic carbon (DIC), total carbon dioxide (TCO2), and total alkalinity (TA) in discrete samples at various depths from predetermined stations. Discrete surface samples were also taken, while in transit, during both cruises.

Example of bias resulting from fitting discrete data with continuous maximum likelihood solutions.

In June and July of 2020, 45 groundwater wells in McHenry County, Illinois, were sampled for water quality (field properties, major ions, nutrients, and trace metals) and 12 wells were sampled for contaminants of emerging concern (pharmaceuticals, pesticides, and wastewater indicator compounds). Quality-assurance and quality-control samples were collected during the June and July 2020 sampling that included equipment blanks, field blanks, and replicates. The results of these samples were used to understand the sources of bias and variability associated with sample collection, processing, storage, and shipping. This data release contains one comma separated values files containing the results of the quality-control sample collection for general water quality (metals, nutrients, and major ions) and contaminants of emerging concern (wastewater indicator compounds and pharmaceuticals). Water-quality data from the associated groundwater monitoring well data are available at the Nationa ...

The Cybersecurity Framework Manufacturing Profile Low Security Level Example Implementations Guide provides example proof-of-concept solutions demonstrating how open-source and commercial off-the-shelf (COTS) products that are currently available today can be implemented in manufacturing environments to satisfy the requirements in the Cybersecurity Framework (CSF) Manufacturing Profile Low Security Level. Example proof-of-concept solutions for a process-based manufacturing environment and a discrete-based manufacturing environment are included in the guide. Depending on factors like size, sophistication, risk tolerance, and threat landscape, manufacturers should make their own determinations about the breadth of the proof-of-concept solutions they may voluntarily implement. The dataset includes all of the raw and processed measurement data for the example implementation of the discrete-based manufacturing system use case.

Discrete bottle data collected at the Bermuda Atlantic Time Time-series Study site in the Sargasso Sea during BATS cruises #1 (October 1988) through #422 (December 2024).

We use individual-level health facility choice data from urban Senegal to estimate consumer preferences for facility characteristics related to maternal health services. We find that consumers consider a large number of quality related facility characteristics, as well as travel costs, when making their health facility choice. In contrast to the typical assumption in the literature, our findings indicate that individuals frequently bypass the facility nearest their home. In light of this, we show that the mismeasured data used commonly in the literature produces biased preference estimates; most notably, the literature likely overestimates consumer distaste for travel.

Data consists of two table with AMS and ChRM data from discrete samples measured from Holes U1507B, U1508C, U1511B of International Ocean Discovery Program (IODP) Exp. 371 (Tasman Frontier subduction initiation and Paleogene Climate). Table S1 contains AMS measurement data, while Table S3 the direction and associated error of the ChRM directions from reliable samples. Table S2 can be found as appendix in the related publication.

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 ...

Averaged performance of rule classifiers for supervised discretisation with transformations of the test sets based on discrete data models obtained for the training samples [%].

The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

This dataset comprises images, geochemical composition, isotopic ratio, and X-ray diffraction (XRD) data from samples of Fe-oxyhydroxide (FeOOH) and seafloor massive sulphide (SMS) collected during the RRS James Cook cruise JC224 (March - April 2022) to the 13°30' N ocean core complex (OCC) region of the Mid-Atlantic Ridge, also known as the Semenov Hydrothermal Field. A total of 23 Robotic Underwater Vehicle (RUV) dives were completed with the HyBIS RUV (Murton et al., 2012) in addition to 6 rock dredges setup with a chain bag dredge and a steel bucket dredge behind it. Sampling was focused on the previously described massive sulphide-hosting areas: Semenov-1, -2, -4 and -5 (Escartin et al., 2017), as well as one dedicated dive to a near-circular feature protruding on the southern slope of the OCC - termed the "bulge". Analysis of the 42 FeOOH and SMS samples occurred onshore at the National Oceanography Centre (NOC) and the University of Southampton and the samples are held for long-term storage at NOC Southampton and Cardiff University. A range of analytical methods were used to collect the data, including: inductively-coupled plasma mass spectrometry (ICP-MS), inductively coupled plasma optical emission spectroscopy (ICP-OES), X-ray diffraction (XRD), and reflective light microphotography. The key aims of these analyses were: (1) to improve understanding regarding the formation of Fe-oxyhydroxide samples at SMS systems, and (2) to determine the potential of Fe-oxyhydroxide as an additional resource at SMS systems. The data were collected as part of a PhD project funded under the Natural Environment Research Council (NERC) project Ultramafic-hosted mineral Resource Assessment (ULTRA) grant, NE/S004068/1. This collection consists only of the data collected under the PhD project and not all data associated with the ULTRA project.

Discrete sample data from manual field collection and laboratory analyses taken since 2020. It contains water quality, sediment, biological, air, and soil samples from monitoring locations across the Lower Brazos Subregion of Texas, Hydrologic Unit Code (HUC) 1207.

Mixture distributions arise in many application areas, for example, as marginal distributions or convolutions of distributions. We present a method of constructing an easily tractable discrete mixture distribution as an approximation to a mixture distribution with a large to infinite number, discrete or continuous, of components. The proposed DIRECT (divergence restricting conditional tesselation) algorithm is set up such that a prespecified precision, defined in terms of Kullback–Leibler divergence between true distribution and approximation, is guaranteed. Application of the algorithm is demonstrated in two examples. Supplementary materials for this article are available online.