This paper builds on the identification results and estimation tools for continuous DiD designs in Callaway, Goodman-Bacon, and Sant'Anna (2023) to discuss aggregation strategies for event studies with continuous treatments. Estimates from continuous designs are functions of the treatment dosage/intensity variable. Nonparametric plots of these functions show heterogeneity across doses, but not heterogeneity over time. Event-study-type plots of aggregated parameters achieve the opposite. We describe how partially aggregating across treatment doses and event time can lead to readable yet nuanced figures that reflect how causal effects evolve over time, potentially in different parts of the treatment dose distribution.

This data release contains three different datasets that were used in the Scientific Investigations Report: Spatial and Temporal Distribution of Bacterial Indicators and Microbial Source Tracking within Tumacácori National Historical Park and the Upper Santa Cruz River, Arizona, 2015-16. These datasets contain regression model data, estimated discharge data, and calculated flux and yields data. Regression Model Data: This dataset contains data used in a regression model development in the SIR. The period of data ranged from May 25, 1994 to May 19, 2017. Data from 2015 to 2017 were collected by the U.S. Geological Survey. Data prior to 2015 were provided by various agencies. Listed below are the different data contained within this dataset: - Season represented as an indicator variable (Fall, Spring, Summer, and Winter) - Hydrologic Condition represented as an indicator variable (rising limb, recession limb, peak, or unable to classify) - Flood (binary variable indicating if the sample was collected during a flood event or not) - Decimal Date (DT) represented as a continuous variable - Sine of DT represented as a continuous variable for periodic function to describe seasonal variation - Cosine of DT represented as a continuous variable for periodic function to describe seasonal variation Estimated Discharge: This dataset contains estimated discharge at four different sites between 03/02/2015 and 12/14/2016. The discharge was estimated using nearby streamgage relations and methods are described in detail in the SIR . The sites where discharge was estimated are listed below. - NW8; 312551110573901; Nogales Wash at Ruby Road - SC3; 312654110573201; Santa Cruz River abv Nogales Wash - SC10; 313343110024701; Santa Cruz River at Santa Gertrudis Lane - SC14; 09481740; Santa Cruz River at Tubac, AZ Calculated Flux and Yields: This dataset contains calculated flux and yields for E. coli and suspended sediment concentrations. Mean daily flux was calculated when mean daily discharge was available at a corresponding streamgage. Instantaneous flux was calculated when instantaneous discharge (at 15-minute intervals) were available at a corresponding streamgage, or from a measured or estimated discharge value. The yields were calculated using the calculated flux values and the area of the different watersheds. Methods and equations are described in detail in the SIR. Listed below are the data contained within this dataset: - Mean daily E. coli flux, in most probable number per day - Mean daily suspended sediment, in flux, in tons per day - Instantaneous E. coli flux, in most probable number per second - Instantaneous suspended sediment flux, in tons per second - E. coli, in most probable number per square mile - Suspended sediment, in tons per square mile

Description. Real-world data used to test our implementation of the average continuous Straightness, and associated results.

Source code. The source code is available on GitHub: https://github.com/CompNet/SpatialMeasures

Citation. If you use these data, please cite the following article:

V. Labatut, “Continuous Average Straightness in Spatial Graphs,” Journal of Complex Networks, 6(2):269–296, 2018. ⟨hal-01571212⟩ DOI: 10.1093/comnet/cnx033

@Article{Labatut2018, author = {Labatut, Vincent}, title = {Continuous Average Straightness in Spatial Graphs}, journal = {Journal of Complex Networks}, year = {2018}, volume = {6}, number = {2}, pages = {269-296}, doi = {10.1093/comnet/cnx033},}

The objective of this study was to derive continuous fields of vegetation cover from multi-temporal Advanced Very High Resolution Radiometer (AVHRR) data using all available bands and derived Normalized Difference Vegetation Index (NDVI). The continuous fields describe sub-pixel proportions of cover for tree, herbaceous, bare ground and water cover types. For tree cover, additional fields describing leaf longevity (evergreen and deciduous) and leaf morphology (broadleaf and needleleaf) were also generated. The modeling of carbon dynamics and climate require knowing tree characteristics such as these. These products were resampled and aggregated to 0.25, 0.5 and 1.0 degree grids for the International Satellite Land Surface Climatology Project (ISLSCP) data initiative II. The data set describes the geographic distributions of three fundamental vegetation characteristics: tree, herbaceous and bare ground cover, plus a water layer. For tree cover, leaf longevity and morphology layers were produced.

This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.

ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.

The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August, 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloudelectrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil Magnetometer, Accelerometers, Gerdien Conductivity Probe, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels.

When dealing with missing data in clinical trials, it is often convenient to work under simplifying assumptions, such as missing at random (MAR), and follow up with sensitivity analyses to address unverifiable missing data assumptions. One such sensitivity analysis, routinely requested by regulatory agencies, is the so-called tipping point analysis, in which the treatment effect is re-evaluated after adding a successively more extreme shift parameter to the predicted values among subjects with missing data. If the shift parameter needed to overturn the conclusion is so extreme that it is considered clinically implausible, then this indicates robustness to missing data assumptions. Tipping point analyses are frequently used in the context of continuous outcome data under multiple imputation. While simple to implement, computation can be cumbersome in the two-way setting where both comparator and active arms are shifted, essentially requiring the evaluation of a two-dimensional grid of models. We describe a computationally efficient approach to performing two-way tipping point analysis in the setting of continuous outcome data with multiple imputation. We show how geometric properties can lead to further simplification when exploring the impact of missing data. Lastly, we propose a novel extension to a multi-way setting which yields simple and general sufficient conditions for robustness to missing data assumptions.

Capturing qualitative features of animal behavior requires recording occurrences of behavior over time. Continuous sampling is best for capturing brief behaviors, but can be very time consuming. Instantaneous sampling can reduce the amount of labor required, but can miss short-duration behaviors. We therefore synthesized these techniques by continuously sampling during randomly scattered time intervals; a technique we call piecewise continuous sampling. To optimize and test the efficacy of this technique, we collected a continuous behavioral dataset of harvester ant workers, and then we developed a protocol to estimate the amount of sampling time necessary to reconstruct the proportion of time animals spend in different behavioral states. This protocol finds the sample size needed for the variance of the sample to converge on the variation of the population. We then divided this estimated time into equal-duration intervals that were randomly distributed across the entire continuous data..., In order to create a continuously-sampled dataset to compare sampling methods against, we manually coded the behavior of nine Pogonomyrmex californicus ants continuously over a three-hour timespan. Six were from a small colony (~30 workers, 2 queens) and three were from a larger one (~110 workers, 2 queens), though both colonies were still considered small as colonies in this species typically reach a size of 2,000–4,500 workers in the field (Johnson 2000). The nest was partitioned into a foraging arena and a brood chamber with a total surface area of 242 cm2. The workers that were followed were selected based on the task they were doing at the beginning of the video, so as to capture a range of repertoires; brood care (interacting with brood), food processing (interacting with seeds or artificial diet), or resting (immobile). Two from the small colony, and one from the larger, were selected for each task group. Switches between activities were manually coded using the program Cowlog (V..., , # Piecewise continuous sampling: a method for minimizing bias and sampling effort for estimated metrics of animal behavior

https://doi.org/10.5061/dryad.p8cz8w9z5

This archive contains all of the code and data used for the manuscript titled, "Piecewise continuous sampling: a method for minimizing bias and sampling effort for estimated metrics of animal behavior". The goal of this manuscript is to establish piecewise continuous sampling as a flexible method for capturing animal behavior and to give suggestions as to how ethologists can optimally sample their data. This work is based on the observation of ant behavior, but should have applications for other animals as well.

Description of the data and file structure

The file rawDataTask.csv contains second-by-second measures of the tasks 9 Pogonomyrmex californicus ants were performing over 11,041 seconds. This data is uses for nearly all analyses used in this manuscript, everything from...

Continuous measurements of temperature, pH, conductivity and dissolved oxygen from river water at ten sites located within the rivers Swale, Derwent, Aire, Calder, Trent, Ouse and Nidd. Part of the Land Ocean Interaction Study (LOIS) project. Hydrolab H20 water quality monitors were installed at ten sites and used to log water temperature, pH, conductivity and dissolved oxygen between 1994 and 1997. Data were collected continuously at 30 minute intervals (for periods of variable lengths depending on site) between 1994 and 1997. Data were collected using Hydrolab DataSonde 3 continuous monitoring units. Hydrolabs at River Nidd (Hunsingore) and the River Swale (Crakehill) were suspended from trees. The other hydrolabs were located in large steel pipes running from the bank into the rivers which allowed the flow of water over the probes but offered a high degree of safety from damage by vandals and large water borne objects. The units on the Trent and the Ouse at Skelton were fitted with stirrers, as the probes were prone to fouling by the high levels of suspended solids often encountered in these rivers during spate conditions. The deployment of the units and the collection of data were carried out by members of the field sampling team at York University, as part of the Land Ocean Interaction Study (LOIS).

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

In Fiji, monthly HFPS data collection commenced in February 2024 on topics including employment, income, food security, health, food prices, assets and well-being. Fieldwork took place in rounds roughly one month in length in a panel method, where each household was only recontacted at least thirty days after the previous interview. Each month has approximately 700 households in the sample and is representative of urban and rural areas and divisions. This dataset contains combined monthly survey data between February and October 2024. There is one date file for household level data with a unique household ID, and a separate file for individual level data within each household data, that can be matched to the household file using the household ID, and which also has a unique individual ID within the household data which can be used to track individuals over time within households

Geographic coverage

Urban and rural areas of Fiji.

Analysis unit

Household, invidiual.

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each division month to month. It had a probability-based weighted design, with a proportionate stratification to achieve geographical representation. A panel was established from the outset, where in each subsequent round after February 2024, the survey firm would first attempt to contact all households from the previous month and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households. This dataset includes 4,120 completed interviews with 1,360 unique households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire, which can be found in the External Resources of this documentation, is available in English, with iTaukei translation available. There were few changes to the questionnaire across the survey months, with some sections only asked in some rounds, such as the digital governance module in rounds 3 and 4. The survey instrument consists of the following modules, with notes in parentheses on dates of collection for questions which were not collected consistently across the whole survey period: - Basic information, - Household roster, - Access to Services and Shocks (additional questions on water disruption were asked since April 2024) - Subjective well-being - Food insecurity experience scale (FIES) - Views on the economy and government (some questions were added since May 2024) - Household income - Labor - Agriculture - Medical service utilization - Climate migration (April 2024) - Digital government services (May and June 2024)

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey's monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, food security, household income, agriculture activities, social protection, subjective well-being, access to services, shocks, and perceptions. The household identifier (panel_hid) is available in both the household dataset and the individual dataset. The individual identifier (panel_indid) can be found in the individual dataset.

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 examined, and their statistical behaviour is well understood. Also, continuous measurements avoid the substantial ambiguity often associated with the assignment of discrete characters to states. I present a set of simulations to determine whether use of continuous characters is a feasible alternative or supplement to discrete characters for inferring phylogeny. I compare relative reconstruction accuracy by inferring phylogenies from simulated continuous and discrete characters. These tests demonstrate significant promise for continuous traits by demonstrating their higher overall accuracy as compared to reconstruction from discrete characters under Mk when simulated under unbounded Brownian motion, and equal performance when simulated under an Ornstein-Uhlenbeck model. Continuous characters also perform reasonably well in the presence of covariance between sites. I argue that inferring phylogenies directly from continuous traits may be benefit efforts to maximise phylogenetic information in morphological datasets by preserving larger variation in state space compared to many discretisation schemes. I also suggest that the use of continuous trait models in phylogenetic reconstruction may alleviate potential concerns of discrete character model adequacy, while identifying areas that require further study in this area. This study provides an initial controlled demonstration of the efficacy of continuous characters in phylogenetic inference.

Analysis of ‘Patient Treatment Classification’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/manishkc06/patient-treatment-classification on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

In hospitals, medical treatments and surgeries can be categorized into inpatient and outpatient procedures. For patients, it is important to understand the difference between these two types of care, because they impact the length of a patient’s stay in a medical facility and the cost of a procedure.

Inpatient Care (Incare Patient) and Outpatient Care (Outcare Patient)

The difference between an inpatient and outpatient care is how long a patient must remain in the facility where they have the procedure done.

Inpatient care requires overnight hospitalization. Patients must stay at the medical facility where their procedure was done (which is usually a hospital) for at least one night. During this time, they remain under the supervision of a nurse or doctor.

Patients receiving outpatient care do not need to spend a night in a hospital. They are free to leave the hospital once the procedure is over. In some exceptional cases, they need to wait while anesthesia wears off or to make sure there are not any complications. As long as there are not any serious complications, patients do not have to spend the night being supervised. [source of information: pbmhealth]

Content

Problem Statement In today’s world of automation, the skills and knowledge of a person could be utilized at the best places possible by automating tasks wherever possible. As a part of the hospital automation system, one can build a system that would predict and estimate whether the patient should be categorized as an incare patient or an outcare patient with the help of several data points about the patients, their conditions and lab tests.

Objective Build a machine learning model to predict if the patient should be classified as in care or out care based on the patient's laboratory test result.

Data

About the data The dataset is Electronic Health Record Predicting collected from a private Hospital in Indonesia. It contains the patient's laboratory test results used to determine next patient treatment whether in care or out care.

Attribute Information

Given is the attribute name, attribute type, the measurement unit and a brief description.

Name / Data Type / Value Sample/ Description

HAEMATOCRIT /Continuous /35.1 / Patient laboratory test result of haematocrit

HAEMOGLOBINS/Continuous/11.8 / Patient laboratory test result of haemoglobins

ERYTHROCYTE/Continuous/4.65 / Patient laboratory test result of erythrocyte

LEUCOCYTE /Continuous /6.3 / Patient laboratory test result of leucocyte

THROMBOCYTE/Continuous/310/ Patient laboratory test result of thrombocyte

MCH/Continuous /25.4/ Patient laboratory test result of MCH

MCHC/Continuous/33.6/ Patient laboratory test result of MCHC

MCV/Continuous /75.5/ Patient laboratory test result of MCV

AGE/Continuous/12/ Patient age

SEX/Nominal – Binary/F/ Patient gender

SOURCE/Nominal/ {1,0}/The class target 1.= in care patient, 0 = out care patient

Acknowledgements

This dataset was downloaded from Mendeley Data. Sadikin, Mujiono (2020), “EHR Dataset for Patient Treatment Classification”, Mendeley Data, V1, doi: 10.17632/7kv3rctx7m.1

--- Original source retains full ownership of the source dataset ---

Traffic volume data from automatic permanent counting stations on federal trunk roads are made available to BASt by the Federal Motorway GmbH and the federal states in a uniform data format. These raw data available to BASt are hourly data and have not been checked for plausibility by BASt, nor have data preparations taken place. Thus, the files may contain, among other things, data gaps, incomplete measuring cross-sections, incorrect directions and implausible lane arrangements. In addition, time shifts, incorrect vehicle type distinctions, format errors and incorrect numerical values may be present. The hourly data are available as raw data in the BASt stock band format for traffic volume data as ANSI dataset. Based on this raw data, aggregated hourly raw data were created for directional values. The general information on the respective automatic continuous counters is provided as monthly CSV files together with the direction-aggregated raw data in the zip files. Both the hourly data and the metadata only reflect the current status at the time of provision. In general, no guarantee can be given by BASt for completeness and quality at this stage of data collection. There is a complete disclaimer. BASt assumes no liability for damages resulting from the use of the information provided. A monthly update is planned. The results based on the plausible and finally prepared hourly data as well as the associated hourly data from 2003 onwards are also made available by BASt: Automatic counting points on motorways and federal roads Dataset description: Dataset description for directional traffic volume data (PDF) Each file can contain several million records. A correspondingly powerful editing software is recommended.

No description found

Summary and description of objective number 1 variables of the study.

This data release contains three different datasets that were used in the Scientific Investigations Report: Spatial and Temporal Distribution of Bacterial Indicators and Microbial Source Tracking within Tumacacori National Historical Park and the Upper Santa Cruz River, Arizona, 2015-16. These datasets contain regression model data, estimated discharge data, and calculated flux and yields data. Regression Model Data: This dataset contains data used in a regression model development in the SIR. The period of data ranged from May 25, 1994 to May 19, 2017. Data from 2015 to 2017 were collected by the U.S. Geological Survey. Data prior to 2015 were provided by various agencies. Listed below are the different data contained within this dataset: Season represented as an indicator variable (Fall, Spring, Summer, and Winter) Hydrologic Condition represented as an indicator variable (rising limb, recession limb, peak, or unable to classify) Flood (binary variable indicating if the sample was collected during a flood event or not) Decimal Date (DT) represented as a continuous variable Sine of DT represented as a continuous variable for periodic function to describe seasonal variation Cosine of DT represented as a continuous variable for periodic function to describe seasonal variation Estimated Discharge: This dataset contains estimated discharge at four different sites between March 2, 2015 and December 14, 2016. The discharge was estimated using nearby streamgage relations and methods are described in detail in the SIR. The sites where discharge was estimated are listed below. NW8; 312551110573901; Nogales Wash at Ruby Road SC3; 312654110573201; Santa Cruz River abv Nogales Wash SC10; 313343110024701; Santa Cruz River at Santa Gertrudis Lane SC14; 09481740; Santa Cruz River at Tubac, AZ Calculated flux and Yields: This dataset contains calculated flux and yields for E. coli and suspended sediment concentrations. Mean daily flux was calculated when mean daily discharge was available at a corresponding streamgage. Instantaneous flux was calculated when instantaneous discharge (at 15-minute intervals) were available at a corresponding streamgage, or from a measured or estimated discharge value. The yields were calculated using the calculated flux values and the area of the different watersheds. Methods and equations are described in detail in the SIR. Listed below are the data contained within this dataset: Mean daily E. coli flux, in most probable number per day Mean daily suspended sediment flux, in tons per day Instantaneous E. coli flux, in most probable number per second Instantaneous suspended sediment flux, in tons per second E. coli, in most probable number per square mile Suspended sediment, in tons per square mile

This is a point feature class of environmental monitoring stations maintained in the California Department of Water Resources’ (hereafter the Department) Hydstra continuous database management system used for collection of high frequency continuous timeseries data for groundwater, surface water, water quality and tidal station types. The QA/QC data timeseries data associated with these stations is published through the Departments Water Data Library web application. This dataset is comprised of a “Stations Table” and a related “Period of Record Table”. Stations table is the primary feature class and contains basic information about each station including Station Name, Latitude, Longitude and Description. The Period of Record Table is a related feature class that contains a list of parameters (i.e. stage, flow, depth to groundwater, water temperature, turbidity, pH, etc.) collected at each station along with the start date and end date (period of record) for each parameter and the number of data points collected.

Continuous measurements of conductivity, dissolved oxygen, pH, temperature and water level from the Frome Piddle; Pang Lambourn and Tern catchments, recorded between 2002 and 2007. YSI sondes were installed at 16 sites in these catchments to record continuous measurements of conductivity, dissolved oxygen, pH and temperature. Druck pressure transducers were installed at the same sites to measure continuous water level values. The instruments were installed as part of the NERC funded Lowland Catchment Research (LOCAR) Programme to provide comparable baseline river water chemistry data across the LOCAR catchments. Data were collected continuously at 15-minutes intervals for periods of variable length, depending on site.

This is a point feature class of environmental monitoring stations maintained in the California Department of Water Resources’ (hereafter the Department) Hydstra continuous database management system used for collection of high frequency continuous timeseries data for groundwater, surface water, water quality and tidal station types. The QA/QC data timeseries data associated with these stations is published through the Departments Water Data Library web application. This dataset is comprised of a “Stations Table” and a related “Period of Record Table”. Stations table is the primary feature class and contains basic information about each station including Station Name, Latitude, Longitude and Description. The Period of Record Table is a related feature class that contains a list of parameters (i.e. stage, flow, depth to groundwater, water temperature, turbidity, pH, etc.) collected at each station along with the start date and end date (period of record) for each parameter and the number of data points collected.

Understanding how and why rates of character evolution vary across the Tree of Life is central to many evolutionary questions; e.g., does the trophic apparatus (a set of continuous characters) evolve at a higher rate in fish lineages that dwell in reef versus non-reef habitats (a discrete character)? Existing approaches for inferring the relationship between a discrete character and rates of continuous-character evolution rely on comparing a null model (in which rates of continuous-character evolution are constant across lineages) to an alternative model (in which rates of continuous-character evolution depend on the state of the discrete character under consideration). However, these approaches are susceptible to a "straw-man" effect: the influence of the discrete character is inflated because the null model is extremely unrealistic. Here, we describe MuSSCRat, a Bayesian approach for inferring the impact of a discrete trait on rates of continuous-character evolution in the presence of alternative sources of rate variation ("background-rate variation"). We demonstrate by simulation that our method is able to reliably infer the degree of state-dependent rate variation, and show that ignoring background-rate variation leads to biased inferences regarding the degree of state-dependent rate variation in grunts (the fish group Haemulidae).