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.

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.

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...

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 all field and laboratory-based data associated with discrete surface water samples collected as part of the CMS and boat mapping components of the study. The data provided in the table herein constitute a collection of field-based and laboratory-based measurements that coincide with the timestamps of samples collected at 33 sites across the Delta. Laboratory-based measurements presented herein were conducted by the U.S. Geological Survey (USGS) Organic Matter Research Laboratory (OMRL) in Sacramento, CA, the USGS Earths System Processes Division (ESPD) microbial biogeochemistry laboratory in Menlo Park, CA, the USGS Reston Stable Isotope Laboratory (RSIL) in Reston, VA and the USGS National Water Quality Laboratory (NWQL) in Denver, CO. The machine-readable (comma separated value, *.csv) file presented herein includes laboratory-based measurements for discrete samples collected from 33 established field sites (sampled repeatedly). In addition, field-based sensor data from continuous measurement platforms (CMS locations or as part of the mapping boat flow-through system) are also included in this discrete sample dataset by ensuring that the field sensor measurements were both spatially and temporally coincident with the physically discrete water sample collected for laboratory analysis.

Discrete sample data from manual field collection and laboratory analyses taken since 2010. It contains water quality, sediment, biological, air, and soil samples from monitoring locations across the Upper Colorado Subregion of Texas, Hydrologic Unit Code (HUC) 1208.

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 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.

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

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://wate ...

Discrete choice (DC) methods provide a convenient approach for preference elicitation and they lead to unbiased estimates of preference model parameters if the parameterization of the value function allows for a good description of the preferences. On the other hand, indifference elicitation (IE) has been suggested as a direct trade-off estimator for preference elicitation in decision analysis decades ago, but has not found widespread application in statistical analysis frameworks as for discrete choice methods. We develop a hierarchical, probabilistic model for IE that allows us to do Bayesian inference similar to DC methods. A case study with synthetically generated data allows us to investigate potential bias and to estimate parameter uncertainty over a wide range of numbers of replies and elicitation uncertainties for both DC and IE. Through an empirical case study with laboratory-scale choice and indifference experiments, we investigate the feasibility of the approach and the excess time needed for indifference replies. Our results demonstrate (i) the absence of bias of the suggested methodology, (ii) a reduction in the uncertainty of estimated parameters by about a factor of three or a reduction of the required number of replies to achieve a similar accuracy as with DC by about a factor of ten, (iii) the feasibility of the approach, and (iv) a median increase in time needed for indifference reply of about a factor of three. If the set of respondents is small, the higher elicitation effort may be worth to achieve a reasonable accuracy in estimated value function parameters.

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 Central Texas Coastal Subregion of Texas, Hydrologic Unit Code (HUC) 1210.

The Ocean Observatories Initiative (OOI) is a long-term NSF-funded program that deploys autonomous sensors on both moored and mobile platforms at multiple locations, including the Global Irminger Sea Array (60.46°N, 38.44°W) (Trowbridge et al., 2019). The OOI program conducts yearly turn-around cruises to the Irminger Sea Array to recover and redeploy moorings and gliders deployed year-round at this site. During these cruises the OOI program routinely conducts Conductivity Temperature Depth (CTD) casts and collects water samples from Niskin bottles on the CTD rosette for discrete sample analysis. These turn-around cruise data are critical for validation and calibration of the data from sensors deployed year-round and also provide a valuable dataset in and of themselves (Palevsky et al., 2023).

For this project, our team participated in two of the yearly turn-around cruises to the OOI Irminger Sea Array (AR30-03, 4-24 June, 2018 and AR35-05, 2-25 August, 2019) and collected supplementary additional samples from CTD casts to further support efforts to improve the capacity to produce high-quality data products from OOI’s biogeochemical sensors to enable analysis of scientific questions about the ocean’s biological carbon pump and other carbon cycling processes (Palevsky and Nicholson, 2018; Palevsky et al., 2023).

These supplemental data provided here were collected in coordination with data collected by the OOI program. The complete collection of shipboard data and cruise documentation from these cruises is available from an OOI managed document storage system called Alfresco (see related publications), following the path: OOI > Global Irminger Sea Array > Cruise Data > {Cruise ID}. For more information on OOI data access options and recommendations for use of cruise data to calibrate OOI biogeochemical sensors, see the OOI Biogeochemical Sensor Data Best Practices and User Guide (Palevsky et al., 2023).

Full cruise data from the OOI Irminger Sea cruises for which discrete sample data are presented here can be accessed via the Rolling Deck to Repository (R2R, see deployments) and OOI’s Alfresco data management server (see related publications):

• Irminger Sea 5 cruise, June 2018, AR30-03. Alfresco path: Global Irminger Sea Array > Cruise Data > Irminger_Sea-05_AR30-03_2018-06-05

• Irminger Sea 6 cruise, August 2019, AR35-05. Alfresco path: Global Irminger Sea Array > Cruise Data > Irminger_Sea-06_AR35-05_2019-08-02

The McRaven 2022 datasets (see related datasets) provide salinity-calibrated Conductivity Temperature Depth (CTD) data from the OOI Irminger Sea cruises for which discrete sample data are presented here.

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.

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.

Confocal quadrics constitute a special example of orthogonal coordinate systems. In this cumulative thesis we propose two approaches to the discretization of confocal coordinates, and study the closely related checkerboard incircular nets. First, we propose a discretization based on factorizable solutions to an integrable discretization of the Euler-Poisson-Darboux equation. The constructed solutions are discrete Koenigs nets and feature a novel discrete orthogonality constraint defined on pairs of dual discrete nets, as well as a corresponding discrete isothermicity condition. The coordinate functions of these discrete confocal coordinates are explicitly given in terms of gamma functions. Secondly, we show that classical confocal coordinates and their reparametrizations along coordinate lines are characterized by orthogonality and the factorization property. We use these two properties to propose another discretization of confocal coordinates, while again employing the aforementioned discrete orthogonality constraint. In comparison to the first approach, this definition results in a broader class of nets capturing arbitrary reparametrizations also in the discrete case. We show that these discrete confocal coordinate systems may equivalently be constructed geometrically via polarity with respect to a sequence of classical confocal quadrics. Different sequences correspond to different discrete parametrizations. We give several explicit examples, including parametrizations in terms of Jacobi elliptic functions. A particular example of discrete confocal coordinates in the two-dimensional case is closely related to incircular nets, that is, congruences of straight lines in the plane with the combinatorics of the square grid such that each elementary quadrilateral admits an incircle. Thus, thirdly, we classify and integrate the class of checkerboard incircular nets, which constitute the Laguerre geometric generalization of incircular nets. Further aspects of the novel discrete orthogonality constraint are studied in the introduction of this thesis. These include discrete Lamé coefficients, discrete focal nets, discrete parallel nets, and discrete isothermicity, as well as the relation to pairs of circular and conical nets.

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

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).

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.

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 ...

NODC Accession 0115607 includes chemical, discrete sample, physical and profile data collected from SHOYO in the North Pacific Ocean from 1993-10-14 to 1993-11-27 and retrieved during cruise PACIFICA_492S19931014 and WOCE P02E. These data include DISSOLVED OXYGEN, HYDROSTATIC PRESSURE, NITRATE + NITRITE CONTENT (CONCENTRATION), PHOSPHATE, Potential temperature (theta), SALINITY, SILICATE and WATER TEMPERATURE. The instruments used to collect these data include CTD and bottle. These data were collected by Tamotsu Bando of Japan Oceanographic Data Center as part of the PACIFICA_492S19931014 data set.

PACIFICA (PACIFic ocean Interior CArbon) was an international collaborative project for the data synthesis of ocean interior carbon and its related parameters in the Pacific Ocean. The North Pacific Marine Science Organization (PICES), Section of Carbon and Climate (S-CC) supported the project.