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.

This is a point feature class of environmental monitoring stations maintained in the California Department of Water Resources’ (hereafter the Department) Water Data Library Database (WDL) for discrete “grab” water quality sampling stations. The WDL database contains DWR-collected, current and historical, chemical and physical parameters found in drinking water, groundwater, and surface waters throughout the state. This dataset is comprised of a Stations point feature class and a related “Period of Record by Station and Parameter” table. The Stations point feature class contains basic information about each station including station name, station type, latitude, longitude, and the dates of the first and last sample collection events on record. The related Period of Record Table contains the list of parameters (i.e. chemical analyte or physical parameter) collected at each station along with the start date and end date (period of record) for each parameter and the number of data points collected. The Lab and Field results data associated with this discrete grab water quality stations dataset can be accessed from the California Natural Resources Agencies Open Data Platform at https://data.cnra.ca.gov/dataset/water-quality-data or from DWR’s Water Data Library web application at https://wdl.water.ca.gov/waterdatalibrary/index.cfm.

This dataset includes discrete sample and profile data collected from DISCOVERY in the Indian Ocean and Southern Oceans (> 60 degrees South) from 1994-02-19 to 1994-03-30. These data include CHLOROFLUOROCARBON-11 (CFC-11), CHLOROFLUOROCARBON-113 (CFC-113), CHLOROFLUOROCARBON-12 (CFC-12), Carbon tetrachloride (CCL4), DISSOLVED OXYGEN, Delta Oxygen-18, HYDROSTATIC PRESSURE, NITRATE, Potential temperature (theta), SALINITY, WATER TEMPERATURE, phosphate and silicate. The instruments used to collect these data include CTD and bottle. These data were collected by Robert R. Dickson of Fisheries Laboratory - Lowestoft as part of the WOCE_ISS01h_74DI19940219 dataset. CDIAC associated the following cruise ID(s) with this dataset: DIS94 and WOCE_ISS01h_1994 The World Ocean Circulation Experiment (WOCE) was a major component of the World Climate Research Program with the overall goal of better understanding the ocean's role in climate and climatic changes resulting from both natural and anthropogenic causes. The CO2 survey took advantage of the sampling opportunities provided by the WOCE Hydrographic Program (WHP) cruises during this period between 1990 and 1998. The final collection covers approximately 23,000 stations from 94 WOCE cruises.

Dataset

This dataset was created by DANIYAL KHAN

Contents

This dataset is about books. It has 2 rows and is filtered where the book series is Discrete element model and simulation of continuous materials behavior set. It features 9 columns including author, publication date, language, and book publisher.

Discrete Dataset is a collection of the main operators defined on the finite chain L_n={0,1,...,n} up to n=11. These operators have been computationally generated, with the aim of being used to study properties of the operators. The easiest way to use these operators is with the Python package "DiscreteFuzzyOperators", published at https://zenodo.org/doi/10.5281/zenodo.5031268. It currently contains the following operators:

• For n=1,2,3,4, it contains:
  • Discrete aggregation functions.
  • Smooth discrete aggregation functions.
  • Smooth and commutative discrete aggregation functions.
  • Discrete conjunctions.
  • Discrete disjunctions.
  • Discrete t-norms.
  • Discrete t-conorms.
  • Discrete uninorms.
  • Discrete negations.
• For n=5,6, it contains:
  • Smooth and commutative discrete aggregation functions.
  • Discrete conjunctions.
  • Discrete disjunctions.
  • Discrete t-norms.
  • Discrete t-conorms.
  • Discrete uninorms.
  • Discrete negations.
• For n=7,8, it contains:
  • Discrete t-norms.
  • Discrete t-conorms.
  • Discrete uninorms.
  • Discrete negations.
• For n=9,10,11, it contains:
  • Discrete t-norms.
  • Discrete t-conorms.
  • Discrete negations.

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).

Mira1sen/prompt-dataset-discrete dataset hosted on Hugging Face and contributed by the HF Datasets community

Abstract: This data repository contains raw data for the analysis of decomposition error in discrete-time open tandem queues. The data is formatted for the computation and validation of point and interval estimates for decomposition error as well as for the analysis of decomposition error in bottleneck queues. TechnicalRemarks: This data repository contains two folders: 01 Equal Traffic Intensities – Raw data for the analysis of decomposition error in tandem queues with equal traffic intensities, 02 Bottleneck Analyses – Raw data for the analysis of decomposition error in tandem queues with bottlenecks. The first folder contains a training data and a test data file. The second folder contains three files: Data set with downstream bottleneck queues, Data set with upstream bottleneck queues, * Data set with similar traffic intensities.

1. Classic natal dispersal studies focus mainly on distance travelled. Although distances capture some of the main selective pressures related to dispersal, this approach cannot easily incorporate the properties of the actual destination vs. the available alternatives. Recently, movement ecology studies have addressed questions on movement decisions in relation to availability of resources and/or availability of suitable habitats through the use of discrete choice models (DCMs), a widely used type of models within econometrics, which explains individual choices as a function of the properties of a finite number of alternatives. 2. In this contribution, we show how the dispersal discrete choice model (DDCM) can be used for analysing natal dispersal data in patchy environments given that the natal and the breeding area of the disperser are observed. We test this method using a case study on Great Tits (Parus major) in an archipelago of small woodlots. 3. Our results show that DDCMs are able to capture the results of classic distance-based approaches and simultaneously allow testing hypotheses on how departure and settlement are affected by variables that characterize the disperser, the natal patch and the breeding area, as well as their interactions. 4. DDCMs can be applied to any other species and system that uses some form of discrete breeding location or a certain degree of discretization can be applied.

This dataset is about book subjects. It has 2 rows and is filtered where the books is Discrete mathematics : a unified approach. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The Utah FORGE 2024 Discrete Fracture Network (DFN) Model dataset provides a set of files representing discrete fracture network modeling for the FORGE site near Milford, Utah. The dataset includes four distinct DFN model file sets, each corresponding to different time frames and modeling approaches in 2024. These models characterize both natural and induced fractures in the geothermal reservoir, which consists of crystalline granitic and metamorphic rock approximately 8,000 feet below the ground surface. The dataset includes a reference DFN model from February 2024 that incorporates planar fractures and well trajectories, as well as upscaled permeability, porosity, compressibility, and storage values on specified grids. Additionally, there are models based on new microseismic (MEQ) data from May and July 2024, including fracture planes fitted to the latest MEQ catalog datasets, tensile fractures from hydraulic stimulation, and an alternative connected DFN for modeling purposes. Coordinate data is provided in both global and local frames, with detailed instructions on the transformations used to align with principal stress orientations. The dataset also includes notes and calculation files for estimating fracture sizes and differences between various fracture sets. There are subfolders for Global Coordinates and Local Coordinates. To move from the global to the local coordinate frame, fractures and wells were a) rotated 20 degrees counterclockwise looking down about the global point (335376.400482041, 4263189.99998761, 250.093546450195) to better align with the principal stresses; and b) translated by (-335408.68, -4263010.9, 1150). Upscaled permeability values using the _XYZ suffix show directions with respect to the global XYZ coordinate frame, while those using the _IJK suffix are aligned with local coordinate frame.

This paper demonstrates the flexibility of a general approach for the analysis of discrete time competing risks data that can accommodate complex data structures, different time scales for different causes, and nonstandard sampling schemes. The data may involve a single data source where all individuals contribute to analyses of both cause-specific hazard functions, overlapping datasets where some individuals contribute to the analysis of the cause-specific hazard function of only one cause while other individuals contribute to analyses of both cause-specific hazard functions, or separate data sources where each individual contributes to the analysis of the cause-specific hazard function of only a single cause. The approach is modularized into estimation and prediction. For the estimation step, the parameters and the variance-covariance matrix can be estimated using widely available software. The prediction step utilizes a generic program with plug-in estimates from the estimation step. The approach is illustrated with three prognostic models for stage IV male oral cancer using different data structures. The first model uses only men with stage IV oral cancer from population-based registry data. The second model strategically extends the cohort to improve the efficiency of the estimates. The third model improves the accuracy for those with a lower risk of other causes of death, by bringing in an independent data source collected under a complex sampling design with additional other-cause covariates. These analyses represent novel extensions of existing methodology, broadly applicable for the development of prognostic models capturing both the cancer and non-cancer aspects of a patient's health.

Optimised control signals for wind farm flow controlin a two-turbine configuration - dynamic induction and yaw under time-varying wind direction. Additionally, parameter sweep data for motivating choice of model parameters. Data supporting the paper "Adjoint Optimisation for Wind Farm Flow Control with a Free-Vortex Wake Model", submitted to Renewable Energy.

This dataset is about books. It has 1 row and is filtered where the book is The ergodic theory of discrete groups. It features 7 columns including author, publication date, language, and book publisher.

This dataset is about book series. It has 1 row and is filtered where the books is Discrete groups in space and uniformization problems. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset is about book subjects. It has 2 rows and is filtered where the books is Discrete mathematical structures : theory and applications. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Discrete sample data are derived from manual field collection and laboratory analyses and include water quality, sediment, biological, air and soil samples from thousands of monitoring locations across the United States and related territories. Use the data filters to create a set of discrete sample data and select from the available data profiles to download the results. The options available in each filter reflect the available sample data.

High frequency estimated chloride (Cl) and observed specific conductance (SC) data sets, along with response variables derived from those data sets, were used in an analysis to quantify the extent to which deicer applications in winter affect water quality in 93 U.S. Geological Survey water quality monitoring stations across the eastern United States. The analysis was documented in the following publication: Moore, J., R. Fanelli, and A. Sekellick. In review. High-frequency data reveal deicing salts drive elevated conductivity and chloride along with pervasive and frequent exceedances of the EPA aquatic life criteria for chloride in urban streams. Submitted to Environmental Science and Technology. This data release contains five child items: 1) Input datasets of discrete specific conductance (SC) and chloride (Cl) observations used to develop regression models describing the relationship between chloride and SC 2) The predicted chloride concentrations generated by applying the sites-specific and regional regression models to high-frequency SC datasets 3) The regression equations for 56 USGS water quality monitoring stations across the eastern Unite States, as well as three regions 4) Response variables describing temporal patterns in SC and chloride, calculated by using the estimated high-frequency chloride time series datasets and high-frequency SC datasets 5) Watershed characteristics describing the land use, geology, climate, and deicer application rates for the 93 watersheds included in the Moore et. al (in review) study.

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