Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

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 recent surge in enthusiasm for simultaneously inferring relationships from extinct and extant species has reinvigorated interest in statistical approaches for modelling morphological evolution. Current statistical methods use the Mk model to describe substitutions between discrete character states. Although representing a significant step forward, the Mk model presents challenges in biological interpretation, and its adequacy in modelling morphological evolution has not been well explored. Another major hurdle in morphological phylogenetics concerns the process of character coding of discrete characters. The often subjective nature of discrete character coding can generate discordant results that are rooted in individual researchers' subjective interpretations. Employing continuous measurements to infer phylogenies may alleviate some of these issues. Although not widely used in the inference of topology, models describing the evolution of continuous characters have been well examine...

Many aspects of morphological phylogenetics are controversial in the theoretical systematics literature and yet are often poorly explained and justified in empirical studies. In this paper, I argue that most morphological characters describe variation that is fundamentally quantitative, regardless of whether it is coded qualitatively or quantitatively by systematists. Given this view, three fundamental problems in morphological character analysis (character state definition, delimitation, and ordering) may have a common solution: coding morphological characters as continuous quantitative traits. A new parsimony method (step-matrix gap-weighting, a modification of Thiele's approach) is proposed that allows quantitative traits to be analyzed as continuous variables. The problem of scaling or weighting quantitative characters relative to qualitative characters (and to each other) is reviewed, and three possible solutions are described. The new coding method is applied to data from hoplocercid lizards, and the results show the sensitivity of phylogenetic conclusions to different scaling methods. Although some authors reject the use of continuous, overlapping, quantitative characters in phylogenetic analysis, quantitative data from hoplocercid lizards that are coded using the new approach do contain significant phylogenetic structure, and exhibit levels of homoplasy that are similar to those seen in data that are coded qualitatively.

We hypothesized that (1) correlation of (A) the output of instrumentation to generate quantitative continuous measurements of movements and (B) the quantitative measurements of trained examiners using structured ratings of movements would generate the tools to differentiate the movements of (A) Parkinson's disease (PD), (B) parkinsonian syndromes, and health, and (2) continuous quantitative measurements of movements would improve the ratings generated by visual observations of trained raters, and provide pathognomonic signatures to identify PD and parkinsonian syndromes.

A protocol for a low-cost quantitative continuous measurement of movements in the extremities of people with PD (McKay, et al., 2019) was administered to people with PD and multiple system atrophy-parkinsonian type (MSA-P) and age- and sex-matched healthy control participants. Data from instrumentation was saved as WinDaq files (Dataq Instruments, Inc., Akron, Ohio) and converted into Excel files (McKay, et al., 2019) using the WinDaq Waveform Data Browser (Dataq Instruments, Inc., Akron, Ohio).

Participants were asked to sit in a straight-back chair with arms approximately six inches from the wall to minimize the risk of hitting the wall. The examiner sat in a similar chair facing the participant. The examiner asked the technologist and the videographer to begin recording immediately before instructing the participant to perform each item.

Items were scored live by the examiner at the same time that the quantitative continuous measurements of movements were recorded by the instrumentation.

Healthy control participants were matched for age and sex with participants with PD. The key identifies the diagnosis (PD = Parkinson's disease, MSA-P = Multiple system atrophy - parkinsonian type, HC = healthy control, 1 = male, 0 = female). Participants with PD completed a single test session (0002, 0005, 0007-0009, 0012, 0017-0018, and 0021), a test and a retest session (0001, 0003, 0006, 0010-0011, 0013, 0015, 0019, 0022-0023), or a test and two retest sessions (0014). HC participants completed test and retest sessions (0020, 0024-0030). A participant with MSA-P (0004) completed a test session. Individual files for the WinDaq, Excel, and coding forms for each testing are entered in the dataset. The Excel files for the five repetitive items were converted to fast Fourier transforms (FFTs) and continuous wavelet transforms (CWTs) (MatLab).

The laterality of signals and transforms for test ratings of the upper extremity for participant 30 were reversed.

No files were filtered.

Findings were presented at the MDS Congress Virtual.

Coevolution is relentlessly creating and maintaining biodiversity, and therefore has been a central topic in evolutionary biology. Previous theoretical studies have mostly considered coevolution between genetically symmetric traits (i.e., coevolution between two continuous quantitative traits or two discrete Mendelian traits). However, recent empirical evidence indicates that coevolution can occur between genetically asymmetric traits (e.g., between quantitative and Mendelian traits). We examine consequences of antagonistic coevolution mediated by a quantitative predator trait and a Mendelian prey trait, such that predation is more intense with decreased phenotypic distance between their traits (phenotype matching). This antagonistic coevolution produces a complex pattern of bifurcations with bistability (initial state dependence) in a two-dimensional model for trait coevolution. Further, with eco-evolutionary dynamics (so that the trait evolution affects predator-prey population dynamics), we find that coevolution can cause rich dynamics including anti-phase cycles, in-phase cycles, chaotic dynamics, and deterministic predator extinction. Predator extinction is more likely to occur when the prey trait exhibits complete dominance rather than semidominance and when the predator trait evolves very rapidly. Our study illustrates how recognizing the genetic architectures of interacting ecological traits can be essential for understanding the population and evolutionary dynamics of coevolving species.

NB: This is version 1 of the data; Version 2 is at https://ora.ox.ac.uk/objects/uuid:49e681d2-1566-473f-9a5e-419402417b54. Microsoft Excel file containing the data tables corresponding to figures in this study. Not included are the example whole-brain images.

As our generation and collection of quantitative digital data increase, so do our ambitions for extracting new insights and knowledge from those data. In recent years, those ambitions have manifested themselves in so-called “Grand Challenge” projects coordinated by academic institutions. These projects are often broadly interdisciplinary and attempt to address to major issues facing the world in the present and the future through the collection and integration of diverse types of scientific data. In general, however, disciplines that focus on the past are underrepresented in this environment – in part because these grand challenges tend to look forward rather than back, and in part because historical disciplines tend to produce qualitative, incomplete data that are difficult to mesh with the more continuous quantitative data sets provided by scientific observation. Yet historical information is essential for our understanding of long-term processes, and should thus be incorporated into our efforts to solve present and future problems. Archaeology, an inherently interdisciplinary field of knowledge that bridges the gap between the quantitative and the qualitative, can act as a connector between the study of the past and data-driven attempts to address the challenges of the future. To do so, however, we must find new ways to integrate the results of archaeological research into the digital platforms used for the modeling and analysis of much bigger data.

Planet Texas 2050 is a grand challenge project recently launched by The University of Texas at Austin. Its central goal is to understand the dynamic interactions between water supply, urbanization, energy use, and ecosystems services in Texas, a state that will be especially affected by climate change and population mobility by the middle of the 21st century. Like many such projects, one of the products of Planet Texas 2050 will be an integrated data platform that will make it possible to model various scenarios and help decision-makers project the results of present policies or trends into the future. Unlike other such projects, however, PT2050 incorporates data collected from past societies, primarily through archaeological inquiry. We are currently designing a data integration and modeling platform that will allow us to bring together quantitative sensor data related to the present environment with “fuzzier” data collected in the course of research in the social sciences and humanities. Digital archaeological data, from LiDAR surveys to genomic information to excavation documentation, will be a central component of this platform. In this paper, I discuss the conceptual integration between scientific “big data” and “medium-sized” archaeological data in PT2050; the process that we are following to catalogue data types, identify domain-specific ontologies, and understand the points of intersection between heterogeneous datasets of varying resolution and precision as we construct the data platform; and how we propose to incorporate digital data from archaeological research into integrated modeling and simulation modules.

This data is based on the Seshat data release in https://zenodo.org/record/6642230 and aims to dissect the time series of each NGA into culturally and institutionally continuous time series. For both continuity criteria, the central continuous time series is marked in the data (central meaning that this is the time interval during which the NGA has crossed a specified threshold between low-complexity and high-complexity societies). Details can be found in v3 of https://arxiv.org/abs/2212.00563

The dataset and replication package of the study "A continuous open source data collection platform for architectural technical debt assessment".

Abstract

Architectural decisions are the most important source of technical debt. In recent years, researchers spent an increasing amount of effort investigating this specific category of technical debt, with quantitative methods, and in particular static analysis, being the most common approach to investigate such a topic.

However, quantitative studies are susceptible, to varying degrees, to external validity threats, which hinder the generalisation of their findings.

In response to this concern, researchers strive to expand the scope of their study by incorporating a larger number of projects into their analyses. This practice is typically executed on a case-by-case basis, necessitating substantial data collection efforts that have to be repeated for each new study.

To address this issue, this paper presents our initial attempt at tackling this problem and enabling researchers to study architectural smells at large scale, a well-known indicator of architectural technical debt. Specifically, we introduce a novel approach to data collection pipeline that leverages Apache Airflow to continuously generate up-to-date, large-scale datasets using Arcan, a tool for architectural smells detection (or any other tool).

Finally, we present the publicly-available dataset resulting from the first three months of execution of the pipeline, that includes over 30,000 analysed commits and releases from over 10,000 open source GitHub projects written in 5 different programming languages and amounting to over a billion of lines of code analysed.

Dataset of tree (.tre) files and R code for running generalized Robinson-Foulds distance (Smith, 2020a;b) analysis.

The .tre files can be read into R (R Core Team., 2023) using the ape::read.tree function (Paradis et al., 2003), full details in R code file.

Paradis, E., Claude, J., & Strimmer, K. (2004). APE: analyses of phylogenetics and evolution in R language. Bioinformatics, 20(2), 289-290.

R Core Team. (2023). R: A Language and Environment for Statistical Computing. (Version 4.2.2). R Foundation for Statistical Computing, Vienna, Austria: https://www.R-project.org/.

Smith, M. R. (2020a). Information theoretic generalized Robinson–Foulds metrics for comparing phylogenetic trees. Bioinformatics, 36(20), 5007-5013. https://doi.org/10.1093/bioinformatics/btaa614

Smith, M. R. (2020b). TreeDist: distances between phylogenetic trees. R package version 2.7.0. doi:10.5281/zenodo.3528124.

As our generation and collection of quantitative digital data increase, so do our ambitions for extracting new insights and knowledge from those data. In recent years, those ambitions have manifested themselves in so-called “Grand Challenge” projects coordinated by academic institutions. These projects are often broadly interdisciplinary and attempt to address to major issues facing the world in the present and the future through the collection and integration of diverse types of scientific data. In general, however, disciplines that focus on the past are underrepresented in this environment – in part because these grand challenges tend to look forward rather than back, and in part because historical disciplines tend to produce qualitative, incomplete data that are difficult to mesh with the more continuous quantitative data sets provided by scientific observation. Yet historical information is essential for our understanding of long-term processes, and should thus be incorporated into our efforts to solve present and future problems. Archaeology, an inherently interdisciplinary field of knowledge that bridges the gap between the quantitative and the qualitative, can act as a connector between the study of the past and data-driven attempts to address the challenges of the future. To do so, however, we must find new ways to integrate the results of archaeological research into the digital platforms used for the modeling and analysis of much bigger data.

Planet Texas 2050 is a grand challenge project recently launched by The University of Texas at Austin. Its central goal is to understand the dynamic interactions between water supply, urbanization, energy use, and ecosystems services in Texas, a state that will be especially affected by climate change and population mobility by the middle of the 21st century. Like many such projects, one of the products of Planet Texas 2050 will be an integrated data platform that will make it possible to model various scenarios and help decision-makers project the results of resent policies or trends into the future. Unlike other such projects, however, PT2050 incorporates data collected from past societies, primarily through archaeological inquiry. We are currently designing a data integration and modeling platform that will allow us to bring together quantitative sensor data related to the present environment with “fuzzier” data collected in the course of research in the social sciences and humanities. Digital archaeological data, from LiDAR surveys to genomic information to excavation documentation, will be a central component of this platform. In this paper, I discuss the conceptual integration between scientific “big data” and “medium-sized” archaeological data in PT2050; the process that we are following to catalog data types, identify domain-specific ontologies, and understand the points of intersection between heterogeneous data sets of varying resolution and precision as we construct the data platform; and how we propose to incorporate digital data from archaeological research into integrated modeling and simulation modules.

A quantitative continuous variable that reflects the risk of tree dieoff during a significant drought period (SPI48 drought = -2).

"A protocol for a low-cost quantitative continuous measurement of movements in the extremities of people with PD (McKay, et al., 2019) was administered to people with PD . . . and age- and sex-matched healthy control participants" (Harrigan, et al., Quantitative continuous measurement of movements in the extremities, 2020). "Healthy control participants were matched for age and sex with participants with PD. Participants with PD completed a single test session . . . , a test and a retest session . . . , or a test and two retest sessions . . . . HC participants completed test and retest sessions " (Harrigan, et al., Quantitative continuous measurement of movements in the extremities, 2020). Thirty-two trained raters who were certified in the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) (Goetz, et al., 2008) were presented the output of the ten participants with PD who completed a single test session (Pilot Test and Retest). Raters were presented two sets of 40 quizzes containing five representations for scoring of (A) output signals and FFTs and (B) CWTs (Pilot Test and Retest). Each quiz contained the panels of the x, y, and z representations of the finger and wrist or the toe and ankle of the five repetitive tasks. Each panel to be scored included six images corresponding to the signals of the three dimensions of the two accelerometers on a single extremity. The laterality of the representations was not stated. Raters were presented five sets of six images of the original signal and the fast Fourier transform (FFT) or the continuous wavelet transforms (CWTs). Raters were presented either five panels of output signals and FFTs or CWTs. Panels did not include output signals and FFTs and CWTs simultaneously. Raters were instructed to score (A) output signals and FFTs and (B) CWTs analogously to the clinical coding forms as indicated the the instructions in the data. The raters also completed the output of the ten participants with PD and eight HCs who completed a two test session (CWT Test and Retest). Raters were presented two sets of 72 quizzes containing five representations for scoring of (CWTs (Pilot Test and Retest). Each quiz contained the panels of averaged signals of the x, y, and z representations of the finger and wrist or the toe and ankle of the five repetitive tasks. Each panel to be scored included two images corresponding to the signals of the three dimensions of the two accelerometers on a single extremity. The laterality of the representations was not stated. Raters were asked to complete ratings independently at convenient times during the week.

Despite strong interest in how noise affects marine mammals, little is known about the most abundant and commonly exposed taxa. Social delphinids occur in groups of hundreds of individuals that travel quickly, change behavior ephemerally, and are not amenable to conventional tagging methods, posing challenges in quantifying noise impacts. We integrated drone-based photogrammetry, strategically-placed acoustic recorders, and broad-scale visual observations to provide complimentary measurements of different aspects of behavior for short- and long-beaked common dolphins. We measured behavioral responses during controlled exposure experiments (CEEs) of military mid-frequency (3-4 kHz) active sonar (MFAS) using simulated and actual Navy sonar sources. We used latent-state Bayesian models to evaluate response probability and persistence in exposure and post-exposure phases. Changes in sub-group movement and aggregation parameters were commonly detected during different phases of MFAS CEEs but not control CEEs. Responses were more evident in short-beaked common dolphins (n=14 CEEs), and a direct relationship between response probability and received level was observed. Long-beaked common dolphins (n=20) showed less consistent responses, although contextual differences may have limited which movement responses could be detected. These are the first experimental behavioral response data for these abundant dolphins to directly inform impact assessments for military sonars. Methods We used complementary visual and acoustic sampling methods at variable spatial scales to measure different aspects of common dolphin behavior in known and controlled MFAS exposure and non-exposure contexts. Three fundamentally different data collection systems were used to sample group behavior. A broad-scale visual sampling of subgroup movement was conducted using theodolite tracking from shore-based stations. Assessments of whole-group and sub-group sizes, movement, and behavior were conducted at 2-minute intervals from shore-based and vessel platforms using high-powered binoculars and standardized sampling regimes. Aerial UAS-based photogrammetry quantified the movement of a single focal subgroup. The UAS consisted of a large (1.07 m diameter) custom-built octocopter drone launched and retrieved by hand from vessel platforms. The drone carried a vertically gimballed camera (at least 16MP) and sensors that allowed precise spatial positioning, allowing spatially explicit photogrammetry to infer movement speed and directionality. Remote-deployed (drifting) passive acoustic monitoring (PAM) sensors were strategically deployed around focal groups to examine both basic aspects of subspecies-specific common dolphin acoustic (whistling) behavior and potential group responses in whistling to MFAS on variable temporal scales (Casey et al., in press). This integration allowed us to evaluate potential changes in movement, social cohesion, and acoustic behavior and their covariance associated with the absence or occurrence of exposure to MFAS. The collective raw data set consists of several GB of continuous broadband acoustic data and hundreds of thousands of photogrammetry images. Three sets of quantitative response variables were analyzed from the different data streams: directional persistence and variation in speed of the focal subgroup from UAS photogrammetry; group vocal activity (whistle counts) from passive acoustic records; and number of sub-groups within a larger group being tracked by the shore station overlook. We fit separate Bayesian hidden Markov models (HMMs) to each set of response data, with the HMM assumed to have two states: a baseline state and an enhanced state that was estimated in sequential 5-s blocks throughout each CEE. The number of subgroups was recorded during periodic observations every 2 minutes and assumed constant across time blocks between observations. The number of subgroups was treated as missing data 30 seconds before each change was noted to introduce prior uncertainty about the precise timing of the change. For movement, two parameters relating to directional persistence and variation in speed were estimated by fitting a continuous time-correlated random walk model to spatially explicit photogrammetry data in the form of location tracks for focal individuals that were sequentially tracked throughout each CEE as a proxy for subgroup movement. Movement parameters were assumed to be normally distributed. Whistle counts were treated as normally distributed but truncated as positive because negative count data is not possible. Subgroup counts were assumed to be Poisson distributed as they were distinct, small values. In all cases, the response variable mean was modeled as a function of the HMM with a log link: log(Responset) = l0 + l1Z t where at each 5-s time block t, the hidden state took values of Zt = 0 to identify one state with a baseline response level l0, or Zt = 1 to identify an “enhanced” state, with l1 representing the enhancement of the quantitative value of the response variable. A flat uniform (-30,30) prior distribution was used for l0 in each response model, and a uniform (0,30) prior distribution was adopted for each l1 to constrain enhancements to be positive. For whistle and subgroup counts, the enhanced state indicated increased vocal activity and more subgroups. A common indicator variable was estimated for the latent state for both the movement parameters, such that switching to the enhanced state described less directional persistence and more variation in velocity. Speed was derived as a function of these two parameters and was used here as a proxy for their joint responses, representing directional displacement over time.
To assess differences in the behavior states between experimental phases, the block-specific latent states were modeled as a function of phase-specific probabilities, Z t ~ Bernoulli (pphaset), to learn about the probability pphase of being in an enhanced state during each phase. For each pre-exposure, exposure, and post-exposure phase, this probability was assigned a flat uniform (0,1) prior probability. The model was programmed in R (R version 3.6.1; The R Foundation for Statistical Computing) with the nimble package (de Valpine et al. 2020) to estimate posterior distributions of model parameters using Markov Chain Monte Carlo (MCMC) sampling. Inference was based on 100,000 MCMC samples following a burn-in of 100,000, with chain convergence determined by visual inspection of three MCMC chains and corroborated by convergence diagnostics (Brooks and Gelman, 1998). To compare behavior across phases, we compared the posterior distribution of the pphase parameters for each response variable, specifically by monitoring the MCMC output to assess the “probability of response” as the proportion of iterations for which pexposure was greater or less than ppre-exposure and the “probability of persistence” as the proportion of iterations for which ppost-exposre was greater or less than ppre-exposure. These probabilities of response and persistence thus estimated the extent of separation (non-overlap) between the distributions of pairs of pphase parameters: if the two distributions of interest were identical, then p=0.5, and if the two were non-overlapping, then p=1. Similarly, we estimated the average values of the response variables in each phase by predicting phase-specific functions of the parameters: Mean.responsephase = exp(l0 + l1pphase) and simply derived average speed as the mean of the speed estimates for 5-second blocks in each phase.

Parkinson's disease is one of the most common neurodegenerative disorders, caused by the progressive deterioration of dopaminergic cells in the substantia nigra pars compacta. It is the second leading cause of death in the United States, following Alzheimer's disease. The diagnosis of Parkinson's disease primarily relies on physical and neurological examinations, supported by laboratory data and structured interviews. Motor assessments are typically performed through the visual observation of trained raters, who evaluate patients from a distance as they perform motor tasks (Goetz CG, et al. Mov Disord 2008).

However, quantifying the severity of motor symptoms by the naked human eye presents significant challenges and limitations. The utilization of advanced technologies is required to address these challenges and provide more precise and objective assessments.

To reduce the uncertainty in the motor assessment of people with Parkinson’s disease by visual observation from several feet away, we developed a low-cost, quantitative, continuous measurement of movements in the extremities of individuals with Parkinson’s disease (McKay GN, et al. MethodsX 2019).

A low-cost, quantitative, continuous measurement of movements in the extremities of people with Parkinson's disease was conducted by trained raters on 20 individuals with Parkinson’s disease and 8 age- and sex-matched healthy participants with typical development. Representations of the output signals and their transforms (Harrigan TP, et al., Data Brief 2022) were evaluated by 35 trained raters. Signals and fast Fourier and continuous wavelet transforms were presented to trained raters, without clinical assessments, to be scored for halts, interruptions, amplitude decrements, and slowing. This scoring utilized a scheme (Hernandez ME, et al., MethodsX 2022) similar to the schemes used for rating clinical assessments based on visual observation (Goetz CG, et al. Mov Disord 2008; McKay GN, et al. MethodsX 2019).

An online procedure allowed 35 trained raters to complete structured ratings, including halts, amplitude decrements, and slowing of the signals and transforms (Hernandez ME, et al., MethodsX 2022). The scores for movements with no, minimal, or mild impairments were more challenging to classify compared to those indicating moderate or worse impairments. These results were analyzed using a parent regression exponential model (Y=-0.00291e1.13124+0.44694) with an alpha level of 0.05 (Brasic JR, et al., Mov Disord in press).

This poster was presented at Neurology Exchange Virtual Conference, September 20-22, 2022, www.neurology-exchange.com

Quantitative studies in many fields involve the analysis of multivariate data of diverse types, including measurements that we may consider binary, ordinal and continuous. One approach to the analysis of such mixed data is to use a copula model, in which the associations among the variables are parameterized separately from their univariate marginal distributions. The purpose of this article is to provide a simple, general method of semiparametric inference for copula models via a type of rank likelihood function for the association parameters. The proposed method of inference can be viewed as a generalization of marginal likelihood estimation, in which inference for a parameter of interest is based on a summary statistic whose sampling distribution is not a function of any nuisance parameters. In the context of copula estimation, the extended rank likelihood is a function of the association parameters only and its applicability does not depend on any assumptions about the marginal distributions of the data, thus making it appropriate for the analysis of mixed continuous and discrete data with arbitrary marginal distributions. Estimation and inference for parameters of the Gaussian copula are available via a straightforward Markov chain Monte Carlo algorithm based on Gibbs sampling. Specification of prior distributions or a parametric form for the univariate marginal distributions of the data is not necessary.

BackgroundFailure to recognize acute deterioration in hospitalized patients may contribute to cardiopulmonary arrest, unscheduled intensive care unit admission and increased mortality.PurposeIn this systematic review we aimed to determine whether continuous non-invasive respiratory monitoring improves early diagnosis of patient deterioration and reduces critical incidents on hospital wards.Data SourcesStudies were retrieved from Medline, Embase, CINAHL, and the Cochrane library, searched from 1970 till October 25, 2014.Study SelectionElectronic databases were searched using keywords and corresponding synonyms ‘ward’, ‘continuous’, ‘monitoring’ and ‘respiration’. Pediatric, fetal and animal studies were excluded.Data ExtractionSince no validated tool is currently available for diagnostic or intervention studies with continuous monitoring, methodological quality was assessed with a modified tool based on modified STARD, CONSORT, and TREND statements.Data SynthesisSix intervention and five diagnostic studies were included, evaluating the use of eight different devices for continuous respiratory monitoring. Quantitative data synthesis was not possible because intervention, study design and outcomes differed considerably between studies. Outcomes estimates for the intervention studies ranged from RR 0.14 (0.03, 0.64) for cardiopulmonary resuscitation to RR 1.00 (0.41, 2.35) for unplanned ICU admission after introduction of continuous respiratory monitoring,LimitationsThe methodological quality of most studies was moderate, e.g. ‘before-after’ designs, incomplete reporting of primary outcomes, and incomplete clinical implementation of the monitoring system.ConclusionsBased on the findings of this systematic review, implementation of routine continuous non-invasive respiratory monitoring on general hospital wards cannot yet be advocated as results are inconclusive, and methodological quality of the studies needs improvement. Future research in this area should focus on technology explicitly suitable for low care settings and tailored alarm and treatment algorithms.

Detection of movements in the extremities of people in Parkinson disease was developed to enhance the gold-standard structured assessment of people with Parkinson’s disease assessed by the visual observation by the examiner of the person with Parkinson’s disease (Goetz, et al., 2008).The examiner administered a low-cost quantitative continuous measurement of movements of the extremities of people with Parkinson’s disease (McKay, et al., 2019) to men with Parkinson’s disease in person. The examiner instructed the participant how to perform each task. The examiner demonstrated the movements. The examiner did not continue to perform the task while the participant was performing the tasks. The examiner instructed the participant to perform each movement as quickly and fully as possible. The examiner encouraged the participant to execute each motion with the maximal speed and range of motion. The examiner sought to capture at least 60 optimal repetitions for each motion. The data from this procedure performed on cohorts of individuals with Parkinson’s disease and multiple system atrophy and healthy age- and sex-matched individuals with typical development have been published (Harrigan, et al., 2020; Hernandez, et al., 2022).The data from the participants are included in the publications (Harrigan, et al., 2020; Hernandez, et al., 2022).

Two experts certified in the MDS-UPDRS (Goetz, et al., 2008) then edited the original videotapes to extract only the administration of each task. One expert had participated as examiner and participant in the videotaped segments. The videotape segments correspond to the tasks of the protocol (3.17RTU: 3.17 Rest tremor amplitude upper limbs, 3.17RTUC: 3.17 Rest tremor amplitude upper limbs counting, 3.15PT: 3.15 Postural tremor of the hands, 3.4FT: 3.4 Finger tapping, 3.5HM: 3.5 Hand movements, 3.6PS: 3.6 Pronation-supination movements of the hands, 3.9ACU: 3.9 Arising from chair upper limbs, 3.9ACL: 3.9 Arising from chair upper limbs, 3.17RTL: 3.17 Rest tremor amplitude lower limbs, 3.17RTLC: 3.17 Rest tremor amplitude lower limbs counting, 3.7TT: 3.7 Toe tapping, 3.8LA: 3.8 Leg agility) (McKay, et al., 2019).

In this study, we used morphometric data to test species boundaries in the genus Burmeistera (Campanulaceae). Morphometrics measurements were made on herbarium specimens from a monophyletic clade of three species with recurved corolla lobes:Â B. crispiloba, B. sodiroana, B. succulenta. We used using both hierarchical and normal mixture model-based clustering methods to test the current species hypotheses. Our results support the recognition of the three known species plus a new species described in the paper., Morphometric measurements were made from 95 herbarium specimens using a ruler and digital calipers. The herbarium specimens were sourced from three herbaria: MO, NY, and QCA. Measurements include 23 continuous quantitative variables and two qualitative variables, which are described in more detail in the README file. The measurements are unprocessed., , # Morphometrics in the Recurved Corolla Clade of Burmeistera (Campanulaceae)

This REAME file was generated on 2023-11-29 by Brock Mashburn.

GENERAL INFORMATION

1. Title: Morphometrics in the Recurved Corolla Clade of Burmeistera (Campanulaceae)

2. Author Information

   1. Brock Mashburn; Department of Biology, University of Missouri–St. Louis, St. Louis, Missouri 63121, U.S.A. brockmashburn@gmail.com
   2. Andrea Trigueros; Department of Biology, University of Missouri–St. Louis, St. Louis, Missouri 63121, U.S.A.
   3. Carmen Ulloa Ulloa; Missouri Botanical Garden, 4344 Shaw, St. Louis, Missouri 63110, U.S.A.
   4. Nathan Muchhala; Department of Biology, University of Missouri–St. Louis, St. Louis, Missouri 63121, U.S.A.

3. Date of data collection: 2017-2018

4. Funding sources: National Science Foundation under Grant No. 1754802 awarded to Nathan Muchhala

DATA & FILE OVERVIEW

1. File List:

   1. Morphometric_measurements_RCC.cs...