This data set describes the holding temperatures and developmental stages of larvae of red and blue king crab (Paralithodes camtschaticus and P. platypus) reared to the first crab stage at the Kodiak Laboratory.

Background: Colour polymorphic species provide invaluable insight into processes that generate and maintain intra-specific variation. Despite an increasing understanding of the genetic basis of discrete morphs, sources of colour variation within morphs remain poorly understood. Here we use the polymorphic tawny dragon lizard Ctenophorus decresii to test simple Mendelian models for the inheritance of discrete morphs, and to investigate the genetic basis of continuous variation among individuals across morphs. Males of this species express either orange, yellow, orange surrounded by yellow, or grey throats. Although four discrete morphs are recognised, the extent of orange and yellow varies greatly. We artificially elevated testosterone in F0 females and F1 juveniles to induce them to express the male throat colour polymorphism, and quantified colour variation across the pedigree. Results: Inheritance of discrete morphs in C. decresii best fit a model whereby two autosomal loci with complete dominance respectively determine the presence of orange and yellow. However, a single locus model with three co-dominant alleles for orange, yellow and grey could not be definitively rejected. Additionally, quantitative expression of the proportion of orange and yellow on the throat was strongly heritable (orange: h2 = 0.84 ± 0.14; yellow: h2 = 0.67 ± 0.19), with some evidence for covariance between the two. Conclusions: Our study supports the theoretical prediction that polymorphism should be governed by few genes of major effect, but implies broader genetic influence on variation in constituent morph traits.

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 data collected through the APGAR test aimed to measure family functions and changes in a postmodern context. Over three years, this study has been conducted to 77 individuals, 71 of whom were adults while the remaining 6 were underage people. These participants belong to 37 families from different social strata in the city of Quito-Ecuador, which were selected through convenience and non-probabilistic sampling. The APGAR design used a modification of the work [1], extrapolated to the Ecuadorian context. The data has been collected, cleaned, and unified in a single file in a CSV structured format and without missing values. The participants’ personal information has been concealed to guarantee their identity remains anonymous. Additionally, those who participated in the project have given their consent for the use of their information for academic purposes, which include: scientific journals, presentations, and digital academic repositories. The structured data, within the file, has a distribution in the form of rows and columns. Each row (instance) represents an APGAR test executed on an individual, while the columns represent the different variables (attributes) of the dataset. Each APGAR test has metadata collected during the process. The metadata corresponding to the informative data of the individuals are located in attributes 1 to 7 of the dataset and their description is as follows. • Person_ID: Identifier of each individual who participated in the APGAR test. Discrete quantitative variable. • Year: Data collection year. Discrete quantitative variable. • Family_ID: Unique identifier of each family. Discrete quantitative variable. • Age: Participant age in years. Discrete quantitative variable. • Familiar_Rol: Self-identification of the role played by the individual in the family. Nominal qualitative variable with open categories. Seven different classes were identified: father, mother, son, daughter, nephew, grandmother, and stepfather. • Gender: Self-identification of the individual's gender. Nominal qualitative variable with open categories. Two classes were identified: male and female. • Location: Geographical location of the family home. Nominal qualitative variable with closed categories determined by the official zones that make up the metropolitan district of Quito. Nineteen classes were identified in total. In the Ecuadorian context, a person is of legal age if he has reached an age equal to or greater than 18 years. Therefore, in order to discern these two segments of subpopulations within the family, the design of two different question types for the APGAR tests was required. The Questions (Qi) for adults were: • Q1: I am satisfied with the help I receive from my family when I have a problem or need. • Q2: I am satisfied with the participation that my family gives me and allows me. • Q3: I am satisfied with how my family accepts and supports my desire to undertake new activities. • Q4: I am satisfied with how my family expresses affection and responds to my emotions, such as anger, sadness, love, etc. • Q5: I am satisfied with how we share in my family: a) time to be together, b) spaces in the house, c) money. The Questions (Qi) for underage people were: • Q1: When I am worried about anything, I can ask my family for help. • Q2: I like how my family talks and shares their problems with me. • Q3: I like how my family allows me to do the new things I want to do. • Q4: I like what my family does when I am happy, sad, angry, etc. • Q5: I like how my family and I spend time together. On the other hand, for each question, 5 possible answers were designed with different weights based on a linear symmetric likert scale, and with the same ratings for adults and underage people. The Likert scale weighted Answers (Ai), offered for the participants were: • A1: Never (0 Points) • A2: Almost Never (1 Point) • A3: Sometimes (2 Points) • A4: Almost Always (3 Points) • A5: Always (4 Points) Variables 8 to 32 correspond to the execution of the APGAR test, per se, and were coded in the form of a tuple, Question-Answer (Qi-Aj). The ‘i’ value identifies the 5 types of questions, while the ‘j’ index determines the 5 types of answers. All the tuples Qi-Aj were encoded through a boolean variable (0/1). Where ‘0’ indicates the absence of a value in the tuple and ‘1’ the presence of a value in the tuple. All the APGAR tests were taken in Spanish since it is the official language of Ecuador, and then transcribed into English. Although the data has been collected by using the APGAR test to measure family functions and their changes in the postmodern context, it is important to note that the collected data could be used for other different purposes.

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...

Sexual selection theory maintains that traits under selection honestly advertise qualities of signaling individuals and that only individuals in the best condition can survive while displaying maximally elaborated secondary sexual ornaments. Recent trends in mate choice studies have favored the consideration of the effects of multiple traits on mating success, including suites of traits transmitted in different sensory modalities. Sexual selection is expected to be especially strong in lek-mating species. The lesser prairie-chicken (Tympanuchus pallidicinctus) is a grouse species which breeds on leks. Males of this species perform audio and locomotor displays and exhibit two pairs of brightly colored ornaments. We performed an observational study of mate choice in spring 2016-2018 and noted 40 copulations on seven leks. We quantified behavioral, territorial, and morphological characteristics of males along with ornament color properties. We found that variables from all categories considered predicted female choice. Age had the strongest effect, with adult males experiencing higher female choice relative to yearlings. Allocation of aggressive behavior and comb color properties were also strongly correlated with female choice. Our results show that modulating behavior depending on whether females are present contributes to male mating success and highlight the importance of the size and color properties of secondary ornaments. Further research is required to determine how the variables most strongly associated with mate choice relate to one another and which aspects of individual quality, if any, are signaled by color ornaments. Future studies could also incorporate variables related to females to determine if selection on females occurs.

Methods The data was collected in the field and is based on readings and measurements taken from wild Lesser Prairie-Chickens. The variables beginning with "pc" resulted from a principal components analysis of several behavioral variables. The raw values of the other variables were standardized (mean = 0, SD=1).

The columns beginning with "as" and "cmb" represent color readings from air sacs and combs respectively. The raw spectra obtained with spectrometry were processed using color space models in the R package pavo.

Dist and mcp are spatial variables which were determined through the use of GIS software.

Herbaceous plants: Marta ChilinaAbundance Native Plants:Abundance of native plants, the number of native plants within a 1m2 quadrat, was collected through a biological survey method. Native species comprised of flowering plants (like goldenrod), shamrocks/clovers, and several other unidentifiable species. Species were counted over the entire 1m2 quadrat, rather than a quarter. This variable is quantitative and discrete. Data was collected near a pond and a nearby impermeable area (baseball diamond). Sampling at pond area began at 3:00 pm and sampling at impermeable area began at 4:00 pm.Abundance Exotic Plants: Abundance of exotic plants, the number of exotic plants within a 1m2 quadrat, was collected through a biological survey method. Grass was categorized as an exotic species, and surveyed over one quarter of the 1m2 quadrat, which was then multiplied by four to get a total estimate of colonies in the entire quadrat. This variable is quantitative and discrete. Data was collected near a pond and a nearby impermeable area (baseball diamond). Sampling at pond area began at 3:00 pm and sampling at impermeable area began at 4:00 pm.Total Number of Flowers:

Total number of flowers, the number of flowers on plants within a 1m2 quadrat, was collected through a biological survey method. Flowers counts were obtained from one plant, and then multiplied by the abundance of the same plant in the 1m2 quadrat to record the total number of flowers present on that plant species in that quadrat. When counting goldenrod flowers, only stems were counted and multiplied by the total number of goldenrods in the quadrat, not individual flower buds. Only flower blossoms were considered in this experiment. This variable is quantitative and discrete. Data was collected near a pond and a nearby impermeable area (baseball diamond). Sampling at pond area began at 3:00 pm and sampling at impermeable area began at 4:00 pm.Woody plants: Noah MalaquiA 50 metre transect was placed near the shore of a pond and in an impermeable area. Data for the abundance of woody plants, canopy coverage, vegetation/ground coverage, and abundance of flowers was measured every 2 metres--starting at one end of the transect--in each location through visual inspection. For the abundance of woody plants, the number of trees in a 0.5m radius from a spot, only trees that had a height of at least 1.5 metres or more side were considered woody plants. This variable is a quantitative discrete variable. Canopy coverage, the percent of the sky covered by canopy from trees above a point of observation, was measured by remaining in place and looking up at the sky. This variable is a quantitative, continuous variable. Similarly, vegetation/ground coverage was measured by standing still and looking down at the ground; data was measured as a percentage of how much bare ground was not visible in that distinct spot. This variable is a quantitative continuous variable. For the abundance of flowers, the number of flowers within 1m on each side of the transect, only plants that had visible flowers sprouting were considered and recorded. This variable is a quantitative discrete variable. A 50 metre transect yielded a total of 25 replicates since data was measured and recorded every 2 metres.Vertebrates & Invertebrates: Mihails DitmansVertebrate Abundance:The vertebrate abundance is the number of vertebrates seen within a 50m radius of the observation point over a course of 15 minutes. Vertebrates were counted on trees, on the ground, in the air, and in the pond. This is a quantitative discrete variable. Data collection began in the pond area at 3:00 for the vertebrate observation and 3:15 for the invertebrate observations and in the impermeable area began at 3:45 for vertebrate and 4:00 for invertebrate.Vertebrate Diversity:The vertebrate diversity is the number of different types of vertebrates found within a 50m radius of the observation point over a course of 15 minutes. Birds of observably different morphology were counted separate in terms of diversity, for example seagulls, small black birds and large black birds. Vertebrates were counted on trees, on the ground, and in the air. This is a quantitative discrete variable. Data collection began in the pond area at 3:00 for the vertebrate observation and 3:15 for the invertebrate observations and in the impermeable area began at 3:45 for vertebrate and 4:00 for invertebrate.Human Abundance:The human abundance is the number of humans seen within a 50m radius of the observation point over a course of 15 minutes. Cars were counted as 1 person unless more could be seen through the windows. One person passing through multiple times was only counted once. This is a quantitative discrete variable. Data collection began in the pond area at 3:00 for the vertebrate observation and 3:15 for the invertebrate observations and in the impermeable area began at 3:45 for vertebrate and 4:00 for invertebrate.Observed Invertebrate Abundance:The observed abundance of invertebrates is the number of invertebrates seen in a 5m radius of the observation point over a course of 15 minutes. Both land invertebrates and flying invertebrates were included. If a fast moving invertebrate was seen multiple times it was counted as multiple individuals. This is a quantitative discrete variable. Data collection began in the pond area at 3:00 for the vertebrate observation and 3:15 for the invertebrate observations and in the impermeable area began at 3:45 for vertebrate and 4:00 for invertebrate.Invertebrates: Miriam BastawrousA total of 6 pan traps were distributed 3 metres apart in alternating colours of yellow, white, and blue. They were filled with soapy water from 3:00pm and left out until 3:30pm near the shore of a pond (total duration of 30 minutes) and from 3:40pm to 4:00pm in the impermeable area (total duration of 20 minutes). At the end of each period the abundance of invertebrates, which is the number of invertebrates seen inside the liquid of the bowl, were recorded. This variable is a quantitative discrete variable. Next, a sweep net was used along the 50 metre length of the transect 10 separate times. Each time the sweep net reached the end of the transect, the abundance of invertebrates, which is the number of invertebrates within any part of the net, found in the net was recorded. This variable is a quantitative discrete variable. This was done in the pond area at 3:10 and in the impermeable area at 3:45.Methods:A 50 metre transect was used to measure several variables throughout the data collecting process. Quadrats were placed every 2 metres alternating left and right starting at one end of the transect for a total of 25 replicates. Total abundance of native and exotic plants were recorded as well as total number of flowers within each quadrat. Additionally, the transect was used to measure the abundance of woody plants, canopy coverage, vegetation/ground coverage, and abundance of flowers. Here, observations were made after stopping every 2 metres and observing the immediate surroundings for all variables. Canopy coverage was estimated by looking straight up while vegetation/ground coverage and abundance of flowers was estimated by looking down. This process was also repeated for a total of 25 replicates. The abundance of vertebrates and number of vertebrate species within a 50 metre radius at one end of the transect was measured and recorded through observation for at least 15 minutes. In another 15 minute interval, the abundance of invertebrates was recorded using the same methods. Finally, sweep nets were used to collect the abundance of invertebrates by walking along the transect for a total of 10 replicates. Subsequently, 6 pan traps placed 3 metres apart were used to also collect the abundance of invertebrates at one end of the transect. The traps alternated in colours of yellow, white, and blue. Each pan trap was left to collect invertebrates for at least 30 minutes. All of the methods listed above were conducted in both a pond area and impermeable area at the Keele Campus of York University on a foggy and humid afternoon from 3pm-5pm.Hypothesis:Plant and animal species would be more abundant in pond areas compared to impermeable areas. This would be due to plants having soft soil for nutrients to grow and consequently the animals having plants to eat.Predictions:1) There will be a greater abundance of native, flowering, and exotic plants in the pond area compared to the impermeable area.2) Canopy coverage and vegetation coverage will be greater in the pond area than in the impermeable area.3) Abundance of invertebrates, abundance of vertebrates, and diversity of vertebrates will be greater in the pond area than in the impermeable area.

In this compressed .zip file, the open data is provide to reproduce the data on the paper: A quantitative comparison of in-line coating thickness distributions obtained from a pharmaceutical tablet mixing process using discrete element method and terahertz pulsed imaging. The data can be divided into 3 categories: DEM input files are used to generate the dynamics of tablets in drum mixing; Ray-tracing codes and input files use the position and orientation of tablets to model the terahertz in-line sensing process; .m files are Matlab scripts for data analysis

The latent class model is a widely used mixture model for multivariate discrete data. Besides the existence of qualitatively heterogeneous latent classes, real data often exhibit additional quantitative heterogeneity nested within each latent class. The modern latent class analysis also faces extra challenges, including the high-dimensionality, sparsity, and heteroskedastic noise inherent in discrete data. Motivated by these phenomena, we introduce the Degree-heterogeneous Latent Class Model and propose an easy-to-implement HeteroClustering algorithm for it. HeteroClustering uses heteroskedastic PCA with l2 normalization to remove degree effects and perform clustering in the top singular subspace of the data matrix. We establish the result of exact clustering under minimal signal-to-noise conditions. We further investigate the estimation and inference of the high-dimensional continuous item parameters in the model, which are crucial to interpreting and finding useful markers for latent classes. We provide comprehensive procedures for global testing and multiple testing of these parameters with valid error controls. The superior performance of our methods is demonstrated through extensive simulations and applications to three diverse real-world datasets from political voting records, genetic variations, and single-cell sequencing.

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 dynami...

Although we live in an era of unprecedented quantities and access to data, deriving actionable information from raw data is a hard problem. Earth observation systems (EOS) have experienced rapid growth and uptake in recent decades, and the rate at which we obtain remotely sensed images is increasing. While significant effort and attention has been devoted to designing systems that deliver analytics ready imagery faster, less attention has been devoted to developing analytical frameworks that enable EOS to be seamlessly integrated with other data for quantitative analysis. Discrete global grid systems (DGGS) have been proposed as one potential solution that addresses the challenge of geospatial data integration and interoperability. Here, we propose the systematic extension of EASE-Grid in order to provide DGGS-like characteristics for EOS data sets. We describe the extensions as well as present implementation as an application programming interface (API), which forms part of the University of Minnesota’s GEMS (Genetic x Environment x Management x Socioeconomic) Informatics Center’s API portfolio.

Based on the context of your article, your target labels are Efficiency Ratio and Quality Maintenance Ratio, which are continuous variables derived from multiple factors such as fuel consumption, logistics costs, spoilage, and supply chain bottlenecks.

These labels are not binary, as they represent ratios that indicate performance and quality metrics in agricultural logistics and supply chain management. The values of these ratios are continuous and are used for regression tasks, rather than classification tasks with binary outputs.

So, your article is focused on predicting these continuous values rather than performing binary classification.

You said: write description of the dataset along iwth features it is downloaded form EURO crops etc like that

ChatGPT said: ChatGPT The dataset used in this research was obtained from a large-scale agricultural logistics study conducted across European farms, specifically focusing on key regions known for extensive crop production, such as France, Germany, and the Netherlands. The dataset, named EURO Crops Agricultural Logistics Dataset, contains detailed records of various logistical operations, environmental conditions, and product quality metrics across multiple agricultural supply chains. It spans from June 2018 to June 2024, with hourly data collected from IoT-enabled sensors and GPS devices installed on vehicles, storage units, and monitoring stations.

The dataset includes a total of 53,305 records, with data points capturing critical aspects of agricultural logistics operations, such as transportation efficiency, storage conditions, and product quality. The information is collected in real-time through IoT sensors deployed across the logistics network, tracking the movement and conditions of agricultural products. The data also encompasses environmental monitoring systems, providing insights into weather patterns, soil conditions, and crop health.

The key features of the dataset include:

Vehicle_Type: Categorical data indicating the type of vehicle used for transportation (e.g., Truck, Van). Crop_Type: Categorical data specifying the type of crops being transported (e.g., Wheat, Corn, Rice). Harvest_Date: Date indicating when the crops were harvested. Crop_Yield: Quantitative data showing the total yield of the crop (in kilograms). Storage_Temperature: Continuous data representing the temperature inside the storage unit (in degrees Celsius). Storage_Humidity: Continuous data representing the humidity levels inside the storage unit (in percentage). Fuel_Consumption: Continuous data indicating the amount of fuel used during transportation (in liters per 100 km). Route_Distance: Continuous data showing the total distance covered by the vehicle (in kilometers). Delivery_Time: Continuous data representing the total time taken for the delivery (in hours). Traffic_Level: Continuous data showing the level of traffic congestion on the route (on a scale of 0 to 100). Temperature: Environmental temperature during transportation (in degrees Celsius). Humidity: Environmental humidity during transportation (in percentage). Vehicle_Load_Capacity: The total load capacity of the vehicle (in kilograms). Vibration_Level: Data from sensors measuring the vibration experienced during transportation, which affects crop quality (in arbitrary units). Queue_Time: Time spent in queues or waiting during transit (in hours). Weather_Impact: Index measuring the impact of weather conditions on logistics operations (e.g., heavy rain, wind, etc.). Station_Capacity: Storage capacity of the distribution or logistics station (in kilograms). Operational_Cost: The total cost of logistics operations, including fuel, labor, and storage costs (in USD). Energy_Consumption: Total energy consumption of storage and transportation units (in kWh). IoT_Sensor_Reading_Temperature: Continuous data from IoT sensors monitoring the temperature of the crops during transit (in degrees Celsius). IoT_Sensor_Reading_Humidity: Continuous data from IoT sensors monitoring the humidity of the crops during transit (in percentage). IoT_Sensor_Reading_Light: Continuous data from IoT sensors monitoring light exposure during transportation (in lumens). Warehouse_Storage_Time: Time spent by the crops in warehouse storage before further transportation (in days). Inventory_Levels: Current inventory levels at various storage facilities (in units). Fuel_Costs: Cost of fuel consumed during transportation (in USD per liter). Spoilage_Risk: Probability of spoilage during transportation, based on environmental and operational conditions (as a percentage). The target labels in the dataset include:

Efficiency Ratio: A composite ratio calculated based on fuel consumption, logistics costs, and delivery times, aimed at measuring the overall efficiency of the logistics operation. Quality Maintenance Ratio: A ratio derived from spoi...

Each row corresponds to an individual. Some individuals have two entries, as they were photographed and measured twice to test for repeatability of the measurements. WR1 describes the forewing wing region which was measured, WR2 described the hindwing region which was measured. The mean, mode, and standard deviation of each colour channel (R, G & B), in each wing region are reported in separate columns.

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.

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).

None of the files underwent filtering.

Healthy participants exhibited some of the features of disease.

The data provide the basis to determine how a session may predict future performance.

Discrete morphological data have been widely used to study species evolution, but the use of quantitative (or continuous) morphological characters is less common. Here, we implement a Bayesian method to estimate species divergence times using quantitative characters. Quantitative character evolution is modelled using Brownian diffusion with character correlation and character variation within populations. Through simulations, we demonstrate that ignoring the population variation (or population “noise”) and the correlation among characters leads to biased estimates of divergence times and rate, especially if the correlation and population noise are high. We apply our new method to the analysis of quantitative characters (cranium landmarks) and molecular data from carnivoran mammals. Our results show that time estimates are affected by whether the correlations and population noise are accounted for or ignored in the analysis. The estimates are also affected by the type of data analysed, with analyses of morphological characters only, molecular data only, or a combination of both; showing noticeable differences among the time estimates. Rate variation of morphological characters among the carnivoran species appears to be very high, with Bayesian model selection indicating that the independent-rates model fits the morphological data better than the autocorrelated-rates model. We suggest that using morphological continuous characters, together with molecular data, can bring a new perspective to the study of species evolution. Our new model is implemented in the MCMCtree computer program for Bayesian inference of divergence times.

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

Meta-Data:

Herbaceous plants: Marta Chilina

Abundance Native Plants:

Abundance was collected through a survey method. Grass was categorized as an exotic species, and surveyed over one quarter of the quadrant, which was then multiplied by four to get a total estimate of colonies in the entire quadrant. This variable is quantitative and discrete. Data was collected in the grassland and a nearby disturbed area, with approximately 1 hour spent in each area. The weather was cloudy and slightly humid.

Abundance Exotic Plants:

Abundance was collected through a survey method. Native species comprised of flowering plants, shamrocks/clovers, buckthorn, and several other unidentifiable species. Species were counted over the entire quadrant, rather than a quarter. This variable is quantitative and discrete. Data was collected in the grassland and a nearby disturbed area, with approximately 1 hour spent in each area. The weather was cloudy and slightly humid.

Total Number of Flowers:

Abundance was collected through a survey method. Flowers counts were obtained from one plant, and then multiplied by the abundance of the same plant in the quadrant to record the total number of flowers present in that quadrant, of that particular plant species. Only flower blossoms were considered in this experiment. This variable is quantitative and discrete. Data was collected in the grassland and a nearby disturbed area, with approximately 1 hour spent in each area. The weather was cloudy and slightly humid.

Woody plants: Noah Malaqui

A 50 metre transect was placed in both the grassland and disturbed area. Starting from one end of the transect, data for the abundance of woody plants, canopy coverage, vegetation/ground coverage, and abundance of flowers was collected every 2 metres in each location through observation. Only trees that were at least 1.5 metres or greater in height and 0.5 metres away from the transect on either side were considered woody plants. Canopy coverage was measured by standing still and directly looking up; data was recorded as a percentage of how much sky was not visible in that particular spot. Similarly, vegetation/ground coverage was measured by standing still and directly looking down; data was recorded as a percentage of how much bare ground was not visible in that particular spot. Finally, plants that had visible flowers sprouting within 1 metre on each side of the transect were recorded. Since data was recorded every 2 metres of the 50 metre transect, there was a total of 25 replicates. The weather during the time of data collection was cloudy and slightly humid.

Vertebrates & Invertebrates: Mihails Ditmans

Vertebrate Abundance:

The vertebrate abundance is the number of vertebrates seen within 50m of the observation point over a course of 15 minutes. Vertebrates were counted on trees, on the ground, and in the air. This is a quantitative discrete variable. The weather was cloudy and slightly humid.

Vertebrate Diversity:

The vertebrate diversity is the number of different types of vertebrates found within 50m of the observation point over a course of 15 minutes. Birds of observably different morphology were counted separate in terms of diversity, for example seagulls, small black birds and large black birds. Vertebrates were counted on trees, on the ground, and in the air. This is a quantitative discrete variable. The weather was cloudy and slightly humid.

Human Abundance:

The human abundance is the number of humans seen within 50m of the observation point over a course of 15 minutes. Cars were counted as 1 person unless more could be seen through the windows. One person passing through multiple times was only counted once. This is a quantitative discrete variable. The weather was cloudy and slightly humid.

Observed Invertebrate Abundance:

The observed abundance of invertebrates is the number of invertebrates seen in a 5m radius of the observation point over a course of 15 minutes. Both land invertebrates and flying invertebrates were included. If a fast moving invertebrate was seen multiple times it was counted as multiple individuals. This is a quantitative discrete variable. The weather was cloudy and slightly humid.

Invertebrates: Mariam Bastawrous

A total of 6 pan traps were distributed 3 metres apart in alternating colours of yellow, white, and blue. They were filled with soapy water from 3:05pm and left out until 3:45pm in the disturbed area (total duration of 40 minutes) and from 3:55pm to 4:30pm in the grassland (total duration of 35 minutes). At the end of each period a count of the abundance of invertebrates were recorded. Next, a 50 metre transect was measured and data was collected with a sweep net. A sweep net was used along the 50 metre length of the transect 10 separate times. Each time the sweep net reached the end of the transect, the abundance of invertebrates found in the net was recorded. This was repeated in both the grassland as well as the disturbed area. The weather this data was collected in was cloudy and humid.

Methods:

A 50 metre transect was used to measure several variables throughout the data collecting process. Quadrats were placed every 2 metres alternating left and right starting at one end of the transect for a total of 25 replicates. Total abundance of native and exotic plants were recorded as well as total number of flowers within each quadrat. Additionally, the transect was used to measure the abundance of woody plants, canopy coverage, vegetation/ground coverage, and abundance of flowers. Here, observations were made after stopping every 2 metres and observing the immediate surroundings for all variables. Canopy coverage was estimated by looking straight up while vegetation/ground coverage and abundance of flowers was estimated by looking down. This process was also repeated for a total of 25 replicates. The abundance of vertebrates and number of vertebrate species within a 50 metre radius at one end of the transect was measured and recorded through observation for at least 15 minutes. In another 15 minute interval, the abundance of invertebrates was recorded using the same methods. Finally, sweep nets were used to collect the abundance of invertebrates by walking along the transect for a total of 10 replicates. Subsequently, 6 pan traps placed 3 metres apart were used to also collect the abundance of invertebrates at one end of the transect. Each pan trap was left to collect invertebrates for at least 30 minutes. All of the methods listed above were conducted in both a grassland and disturbed area at the Keele Campus of York University on a cloudy and slightly humid afternoon from 3pm-5pm.

Hypothesis:

Plant and animal species would be more abundant in grassland areas than in disturbed areas. This would be due to increased competition for space and other resources among the animals and plants that occupy the habitat, including humans.

Predictions:

1) There will be a greater abundance of humans and fewer animals in the disturbed area when compared to the grassland area.

2) Native and flowering plant species will be more abundant in the grassland than in the disturbed area.

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.

Detection of movements in the extremities of people with 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). This data represents the administration of a low-cost quantitative continuous measurement of movements in the extremities of people with Parkinson's disease by examiners certified in the Movement Disorders Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) (Goetz, et al., 2008) to healthy men with typical development 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. A technologist recorded the output of the instrumentation of each assessment (Harrigan, et al., 2020) and operated a video camera to record the movements of the participants during the testing session. A second (retest) assessment of the protocol was performed after a month for each participant.

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). The participants are identified by their original identification number (Harrigan, et al., 2020). The first (test) session was designated with "F." The second (retest) session was designated with "S."