Many capture-recapture surveys of wildlife populations operate in continuous time but detections are typically aggregated into occasions for analysis, even when exact detection times are available. This discards information and introduces subjectivity, in the form of decisions about occasion definition. We develop a spatio-temporal Poisson process model for spatially explicit capture-recapture (SECR) surveys that operate continuously and record exact detection times. We show that, except in some special cases (including the case in which detection probability does not change within occasion), temporally aggregated data do not provide sufficient statistics for density and related parameters, and that when detection probability is constant over time our continuous-time (CT) model is equivalent to an existing model based on detection frequencies. We use the model to estimate jaguar density from a camera-trap survey and conduct a simulation study to investigate the properties of a CT estimator and discrete-occasion estimators with various levels of temporal aggregation. This includes investigation of the effect on the estimators of spatio-temporal correlation induced by animal movement. The CT estimator is found to be unbiased and more precise than discrete-occasion estimators based on binary capture data (rather than detection frequencies) when there is no spatio-temporal correlation. It is also found to be only slightly biased when there is correlation induced by animal movement, and to be more robust to inadequate detector spacing, while discrete-occasion estimators with binary data can be sensitive to occasion length, particularly in the presence of inadequate detector spacing. Our model includes as a special case a discrete-occasion estimator based on detection frequencies, and at the same time lays a foundation for the development of more sophisticated CT models and estimators. It allows modelling within-occasion changes in detectability, readily accommodates variation in detector effort, removes subjectivity associated with user-defined occasions, and fully utilises CT data. We identify a need for developing CT methods that incorporate spatio-temporal dependence in detections and see potential for CT models being combined with telemetry-based animal movement models to provide a richer inference framework.

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.

Dataset

This dataset was created by Shubh

Contents

In cooperation with the San Antonio Water System, continuous and discrete water-quality data were collected from groundwater wells completed in the Edwards aquifer, Texas, 2014-2015. Discrete measurements of nitrate were made by using a nitrate sensor. Precipitation data from two sites in the National Oceanic and Atmospheric Administration Global Historical Climatology Network are included in the dataset. The continuous monitoring data were collected using water quality sensors and include hourly measurements of nitrate, specific conductance, and water level in two wells. Discrete measurements of nitrate, specific conductance, and vertical flow rate were collected from one well site at different depths throughout the well bore.

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).

Harmful algal blooms (HABs) are overgrowths of algae or cyanobacteria in water and can be harmful to humans and animals directly via toxin exposure or indirectly via changes in water quality and related impacts to ecosystems services, drinking water characteristics, and recreation. While HABs occur frequently throughout the United States, the driving conditions behind them are not well understood, especially in flowing waters. In order to facilitate future model development and characterization of HABs in the Illinois River Basin, this data release publishes a synthesized and cleaned collection of HABs-related water quality and quantity data for river and stream sites in the basin. It includes nutrients, major ions, sediment, physical properties, streamflow, chlorophyll and other types of water data. This data release contains files of harmonized data from the USGS National Water Information System (NWIS), the U.S. Army Corps of Engineers (USACE), the Illinois Environmental Protection Agency (IEPA), and a USGS Open File Report (OFR) containing toxin data in Illinois (Terrio and others, 2013: https://pubs.usgs.gov/of/2013/1019/pdf/ofr2013-1019.pdf). Both discrete data and continuous sensor data for 142 parameters (44 of which returned data) between October 1, 2015 and December 31, 2022 were downloaded from NWIS programmatically. All data were harmonized into a shared format (see files named data_{parameter_group}combined.csv). The USGS NWIS data went through additional cleaning and were also grouped by generic parameters (see pcode_group_xwalk.csv to see what parameter codes are mapped to which generic parameters). Any data not from USGS NWIS were kept outside of the parameter grouping files. Additional streamflow data for select locations was retrieved from the USACE and are available in data_usace_00060_combined.csv. Additional algal toxin data provided by the IEPA and in a USGS OFR report (Terrio and others, 2013), which include some lake sites, are available in data_algaltoxins_combined.csv. We also provide collapsed datasets of daily metrics for each water quality (“generic parameter”) group of USGS NWIS data (files named daily_metrics{parameter_group}.csv). Lastly, we include a site_metadata.csv containing site identification and location information for all sites with water quality and quantity data, and mappings to the National Hydrography Dataset flowlines where available. This work was completed as part of the USGS Proxies Project, an effort supported by the Water Mission Area (WMA) Water Quality Processes program to develop estimation methods for PFAS, harmful algal blooms, and metals, at multiple spatial and temporal scales.

Self-organization of individuals within large collectives occurs throughout biology, with examples including locust swarming and cell formation of embryonic tissues. Mathematical models can help elucidate the individual-level mechanisms behind these dynamics, but analytical tractability often comes at the cost of biological intuition. Discrete models provide straightforward interpretations by tracking each individual yet can be computationally expensive. Alternatively, continuous models supply a large-scale perspective by representing the "effective" dynamics of infinite agents, but their results are often difficult to translate into experimentally relevant insights. We address this challenge by quantitatively linking spatio-temporal dynamics of discrete and continuous models in settings with biologically realistic, time-varying cell numbers. Motivated by zebrafish-skin pattern formation, we create a continuous framework describing the movement and proliferation of a single cell population by upscaling rules from a discrete model. We introduce and fit scaling parameters to account for discrepancies between these two frameworks in terms of cell numbers, considering movement and birth separately. Our resulting continuous models accurately depict ensemble average agent-based solutions when migration or proliferation act alone. Interestingly, the same parameters are not optimal when both processes act simultaneously, highlighting a rich difference in how combining migration and proliferation affects discrete and continuous dynamics. Methods Individual-based data were produced by simulating the ABM listed in the paper, using Matlab (version 2022a). The PDEs were simulated using either Matlab programmes (for the cell birth-only model) or using custom codes in Julia (version 1.7.3). Codes used to simulate the models may be found at the following Github repository: https://github.com/wdmartinson/Self-Organization-One-Species .

Data from: Inferring Continuous and Discrete Population Genetic Structure Across SpaceThis repo contains a snapshot of the data files, analyses, and code for the method conStruct at the time of publication.conStruct.zip

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. Here, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also briefly discuss results on synthetic and real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods.

The most popular general univariate polarization indexes for discrete and continuous variables are extended and combined to describe the extent of polarization between agents in a distribution defined over a collection of many discrete and continuous agent characteristics. A formula for the asymptotic variance of the index is also provided. The implementation of the index is illustrated with an application to Chinese urban household data drawn from six provinces in the years 1987 and 2001 (years spanning the growth and urbanization period subsequent to the economic reforms). The data relates to household adult equivalent log income, adult equivalent living space, which are both continuous variables and the education of the head of household which is a discrete variable. For this data set combining the characteristics changes the view of polarization that would be inferred from considering the indices individually.

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 size of the Continuous and Discrete Non-metallic Fibers (Basalt Fiber) market was valued at USD 64 million in 2024 and is projected to reach USD 80.88 million by 2033, with an expected CAGR of 3.4% during the forecast period.

Study Overview Phylogenetic signals are widely used in the ecological and evolutionary research area. Trait data used to detect phylogenetic signals can be continuous or discrete. Existing indices are either designed for continuous variables or for discrete variables, but not both. Moreover, most existing methods could only perform phylogenetic detection for each trait separately. Here, we developed a method, called M statistic, to detect the phylogenetic signals in continuous traits, discrete traits, and combinations of multiple traits. We compared the performance of our new approach with existing commonly used indices using simulated continuous data. The results showed that our method is not inferior to the existing methods. It also performed well in handling discrete variables and multi-variable combinations. Then we used the trait data of turtles (Testudines) to demonstrate the utility of our new method. We provided an R package called “phylosignalDB” to facilitate all calculations. Description of the Data and Code File "README.txt" - The summary file.Plot Figure 1-4 "Plot.R" - R code for ploting figure 1-4.Case Study for Turtles"ecological trait dataset for turtles.xlsx" - ecological trait dataset for turtles."turtle_signals.R" - R code for detecting phylogenetic signals in ecological traits of turtles.Simulation Study"Simulation.R" - R code for simulation study."sim_signals.rds" - result dataset of simulation study.NotesThe code for the M statistics has been written as the R package phylosignalDB. The package is available on GitHub: https://github.com/Dylan-Yao/phylosignalDB.We used the maximum clade credibility tree with 288 tips provided in Thomson et al. (2021) for the phylogeny of turtles. The phylogeny file "bd.mcc.median_heights.tre" can be obtained from Thomson et al. (2021).Thomson, R.C., Spinks, P.Q. & Shaffer, H.B. (2021) A global phylogeny of turtles reveals a burst of climate-associated diversification on continental margins. Proceedings of the National Academy of Sciences, 118(7): e2012215118.

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 ...

Continuous measurement Surface-water pCO2 was continuously measured from January to September in 2019 and from March to December in 2020. The mooring system was anchored to the bottom of the macroalgal habitat (10 m depth), and the pCO2 sensor package was submerged approximately 0.5 m below the surface. Surface-water pCO2 (or pH), temperature and salinity were concurrently measured at 1-h intervals. Discrete measurement Discrete surface samples for seawater C parameters (pH, total alkalinity, and total dissolved inorganic carbon) and nutrients were measured at 3–4 d intervals.

Finite element mesh, rigid block model coordinates and rigid block CAD models of numerical case study

Abstract A MATLAB-based computer code has been developed for the simultaneous wavelet analysis and filtering of multichannel seismic data. The considered time–frequency transforms include the continuous wavelet transform, the discrete wavelet transform and the discrete wavelet packet transform. The developed approaches provide a fast and precise time–frequency examination of the seismograms at different frequency bands. Moreover, filtering methods for noise, transients or even baseline removal, are im...

Title of program: SeismicWaveTool Catalogue Id: AENG_v1_0

Nature of problem Numerous research works have developed a great number of free or commercial wavelet based software, which provide specific solutions for the analysis of seismic data. On the other hand, standard toolboxes, packages or libraries, such as the MathWorks' Wavelet Toolbox for MATLAB, offers command line functions and interfaces for the wavelet analysis of one-component signals. Thus, software usually is focused on very specific problems or carries out the wavelet analysis from a wide point of view.

Versions of this program held in the CPC repository in Mendeley Data AENG_v1_0; SeismicWaveTool; 10.1016/j.cpc.2012.08.008

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)

High-resolution (15-minute frequency) monitoring data of pH, dissolved oxygen, salinity, temperature, depth, and chlorophyll from July 2015 – April 2016 in two seagrass systems of Puget Sound, WA, USA. Grab samples for instrument validation and carbonate chemistry analysis were periodically taken next to the in-situ instrumentation and are included.

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.

The California Department of Water Resources (DWR) discrete (vs. continuous) water quality datasets contains DWR-collected, current and historical, chemical and physical parameters found in routine environmental, regulatory compliance monitoring, and special studies throughout the state.