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.

Background Pairs of related individuals are widely used in linkage analysis. Most of the tests for linkage analysis are based on statistics associated with identity by descent (IBD) data. The current biotechnology provides data on very densely packed loci, and therefore, it may provide almost continuous IBD data for pairs of closely related individuals. Therefore, the distribution theory for statistics on continuous IBD data is of interest. In particular, distributional results which allow the evaluation of p-values for relevant tests are of importance. Results A technology is provided for numerical evaluation, with any given accuracy, of the cumulative probabilities of some statistics on continuous genome data for pairs of closely related individuals. In the case of a pair of full-sibs, the following statistics are considered: (i) the proportion of genome with 2 (at least 1) haplotypes shared identical-by-descent (IBD) on a chromosomal segment, (ii) the number of distinct pieces (subsegments) of a chromosomal segment, on each of which exactly 2 (at least 1) haplotypes are shared IBD. The natural counterparts of these statistics for the other relationships are also considered. Relevant Maple codes are provided for a rapid evaluation of the cumulative probabilities of such statistics. The genomic continuum model, with Haldane's model for the crossover process, is assumed. Conclusions A technology, together with relevant software codes for its automated implementation, are provided for exact evaluation of the distributions of relevant statistics associated with continuous genome data on closely related individuals.

Suppose we observe a random vector X from some distribution in a known family with unknown parameters. We ask the following question: when is it possible to split X into two pieces f(X) and g(X) such that neither part is sufficient to reconstruct X by itself, but both together can recover X fully, and their joint distribution is tractable? One common solution to this problem when multiple samples of X are observed is data splitting, but Rasines and Young offers an alternative approach that uses additive Gaussian noise—this enables post-selection inference in finite samples for Gaussian distributed data and asymptotically when errors are non-Gaussian. In this article, we offer a more general methodology for achieving such a split in finite samples by borrowing ideas from Bayesian inference to yield a (frequentist) solution that can be viewed as a continuous analog of data splitting. We call our method data fission, as an alternative to data splitting, data carving and p-value masking. We exemplify the method on several prototypical applications, such as post-selection inference for trend filtering and other regression problems, and effect size estimation after interactive multiple testing. Supplementary materials for this article are available online.

Many variables in biological research - from body size to life history timing to environmental characteristics - are measured continuously (e.g., body mass in kilograms) but analyzed as categories (e.g., large versus small), which can lower statistical power and change interpretation. We conducted a mini-review of 72 recent publications in six popular ecology, evolution, and behavior journals to quantify the prevalence of categorization. We then summarized commonly categorized metrics and simulated a dataset to demonstrate the drawbacks of categorization using common variables and realistic examples. We show that categorizing continuous variables is common (31% of publications reviewed). We also underscore that predictor variables can and should be collected and analyzed continuously. Finally, we provide recommendations on how to keep variables continuous throughout the entire scientific process. Together, these pieces comprise an actionable guide to increasing statistical power and fac..., , , # Overcoming the pitfalls of categorizing continuous variables in ecology and evolutionary biology

https://doi.org/10.5061/dryad.5x69p8d9r

We simulated data to quantify the detrimental impact of categorizing continuous variables using various statistical breakpoints and sample sizes (details below). To give the example biological relevance, we created a dataset that illustrates the complexity of life history theory and climate change impacts, and contains a predictor variable that is frequently categorized (Table 2) - reproductive timing in one year and its effect on body size in the following year. A reasonable research question would be: How does timing of reproduction in year t influence body mass at the start of the breeding season in year t+1? For illustrative purposes, let’s say we collected data from individually banded penguins in Antarctica. Based on the mechanistic relationships between seasonally available sea ice and food availabi...

This code estimates the generalized continuous-discrete model with a t-distributed error kernel. It also replicates the Monte Carlo study of this paper. If you use any part of this code in any form, please cite this paper.

Mobile manipulators, which are robotic systems integrating an automatic or autonomous mobile base with a manipulator, can potentially enhance automation in many industrial and unstructured environments. Namely, large-scale manufacturing processes, typical in the aerospace, energy, transportation, and conformal additive manufacturing fields, encompass a notable subset of potential future mobile manipulator use-cases. Utilizing autonomous mobility for manipulator re-positioning could allow for continuous, simultaneous arm and mobile base cooperation, which is referred to as i.e., continuous performance. Continuous mobile manipulator capabilities may hold particular benefit for large, curved, and complex workpieces. However, such flexibility can also introduce additional sources of performance uncertainty, preventing mobile manipulators from satisfying stringent pose repeatability and accuracy requirements. To identify and quantify this uncertainty, the Configurable Mobile Manipulator Apparatus (CMMA) was developed by the National Institute of Standards and Technology. Previous test implementations with the apparatus included non-continuous mobile manipulator performance, such as static and indexed performance, but continuous performance measurement had only been previously demonstrated in simulation and on proof-of-concept hardware. This dataset was obtained through the transfer of simulations and algorithms for continuous registration to an industrial mobile manipulator platform and through a subsequent 2^3 factorial designed experiment to compare the performance and robustness of two continuous localization methods: 1) A deterministic spiral search and 2) A stochastic Unscented Kalman Filter (UKF) search across two selected mobile base speeds and sides of the CMMA. Supplementary data obtained prior to the experiment, such as source code, calibration data, mobile base map and configuration data, coordinate system measurements, and robot/client to ground-truth system time synchronization is also included, along with the analysis source code and results files generated in conducting the performance evaluation. The experiment included the following improvements from the prior experiment conducted in February 2022: 1) Further manual tuning of the UKF hyper-parameters, 2) added retro-reflective tape edge detection to assist initial coordinate registration and to eliminate anomalies where the first fiducial was not detected, 3) eliminated infrared reflections on the CMMA and from the lab windows to improve ground-truth data capture quality, and 4) the coordinate system measurement between the cart transporter map and the ground truth system was re-done.*Certain commercial equipment, instruments, or materials are identified in this dataset to foster understanding. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.

The Continuous Data Protection (CDP) and Recovery Software market has rapidly evolved, becoming an essential component for organizations aiming to safeguard their critical data assets in an increasingly digital world. CDP software enables businesses to continuously capture and save data changes, allowing for real-ti

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

Annual descriptive statistics of hourly contaminant concentrations measured continuously by the Quebec Air Quality Monitoring Network.

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

Continuous Population Statistics: Average household size of persons residing in family dwellings by date. Quarterly. Provinces.

Summarized data in each Experiment

Continuous Population Statistics: Resident population by date, sex, nationality (Spanish/foreign). Quarterly. Provinces.

Brazil Unemployment Rate: Roraima: Total data was reported at 16.500 % in Mar 2020. This records an increase from the previous number of 14.800 % for Dec 2019. Brazil Unemployment Rate: Roraima: Total data is updated quarterly, averaging 8.900 % from Mar 2012 (Median) to Mar 2020, with 33 observations. The data reached an all-time high of 16.500 % in Mar 2020 and a record low of 5.200 % in Jun 2014. Brazil Unemployment Rate: Roraima: Total data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.GBA048: Continuous National Household Sample Survey: Unemployment Rate: by Sex.

While causal mediation analysis has seen considerable recent development for a single measured mediator (M) and final outcome (Y), less attention has been given to repeatedly measured M and Y. Previous methods have typically involved discrete-time models that limit inference to the particular measurement times used, and do not recognize the continuous nature of the mediation process over time. To overcome such limitations, we present a new continuous time approach to causal mediation analysis that uses a differential equations model in a potential outcomes framework to describe the causal relationships among model variables over time. A connection between the differential equations models and standard repeated measures models is made to provide convenient model formulation and fitting. A continuous time extension of the sequential ignorability assumption allows for identifiable natural direct and indirect effects as functions of time, with estimation based on a two-step approach to model fitting in conjunction with a continuous time mediation formula. Novel features include a measure of an overall mediation effect based on the ‘area between the curves’, and an approach for predicting the effects of new interventions. Simulation studies show good properties of estimators and the new methodology is applied to data from a cohort study to investigate sugary drink consumption as a mediator of the effect of socioeconomic status on dental caries in children.

Provides an aggregate of data for the Office of the Actuary and the Office of Research, Evaluation and Statistics.