This table shows overall ATCEMS response interval performance for entire fiscal years. Data in the table is broken out by incident response priority and service area (City of Austin or Travis County).

Examples demonstrating how confidence intervals change depending on the level of confidence (90% versus 95% versus 99%) and on the size of the sample (CI for n=20 versus n=10 versus n=2). Developed for BIO211 (Statistics and Data Analysis: A Conceptual Approach) at Stony Brook University in Fall 2015.

This dataset comprises comprehensive information from ranked matches played in the game League of Legends, spanning the time frame between January 12, 2023, and May 18, 2023. The matches cover a wide range of skill levels, specifically from the Iron tier to the Diamond tier.

The dataset is structured based on time intervals, presenting game data at various percentages of elapsed game time, including 20%, 40%, 60%, 80%, and 100%. For each interval, detailed match statistics, player performance metrics, objective control, gold distribution, and other vital in-game information are provided.

This collection of data not only offers insights into how matches evolve and strategies change over different phases of the game but also enables the exploration of player behavior and decision-making as matches progress. Researchers and analysts in the field of esports and game analytics will find this dataset valuable for studying trends, developing predictive models, and gaining a deeper understanding of the dynamics within ranked League of Legends matches across different skill tiers.

Dataset

This dataset was created by Md Mahmud Ferdous

Contents

This table contains data describing ATCEMS performance in delivering patients with time-sensitive conditions (aka “Alert Patients”) to receiving facilities in a timely manner. The call-to-door interval begins when the first 911 call for an incident is answered in the Communications Center, and ends when the patient is recorded in CAD as arriving at a receiving facility.

Introductory statistical inference texts and courses treat the point estimation, hypothesis testing, and interval estimation problems separately, with primary emphasis on large-sample approximations. Here, I present an alternative approach to teaching this course, built around p-values, emphasizing provably valid inference for all sample sizes. Details about computation and marginalization are also provided, with several illustrative examples, along with a course outline. Supplementary materials for this article are available online.

This is the data set behind the Wind Generation Interactive Query Tool created by the CEC. The visualization tool interactively displays wind generation over different time intervals in three-dimensional space. The viewer can look across the state to understand generation patterns of regions with concentrations of wind power plants. The tool aids in understanding high and low periods of generation. Operation of the electric grid requires that generation and demand are balanced in each period.

These data show the results of four tests, one pretest and three posttest. It consist of three variables. Each task is performed three times (three trials). The movement times, the time it took to perform three different functional tasks. The duration of the maximal handopening during one of these tasks. And the deviation of the grip force control, in a task where a handle needed to be grasped with the correct amount of force.

The Jones Cove 40 ft Interval Contour Lines is the primary Jones Cove 40 ft Interval Contour Line data product produced and distributed by the National Park Service, Great Smoky Mountains National Park.

This dataset presents a high-resolution tracking log of the International Space Station (ISS), captured every 10 seconds over a continuous 24-hour period on June 7, 2025. It contains 8,641 data points, each representing the ISS’s exact location and motion as it orbits the Earth approximately every 90 minutes. Each record includes a timestamp, latitude, longitude, altitude (in kilometers), orbital speed (in km/h), the hemisphere in which the station was located, and the geographical region or body of water it was passing over. The data has been enriched with geolocation insights to help identify where the ISS was positioned above the Earth. This dataset is ideal for those interested in space exploration, orbital mechanics, geospatial analysis, educational demonstrations, or real-time data visualization. Whether you're a student, data scientist, or space enthusiast, this rich time-series dataset offers a valuable glimpse into the motion of one of humanity’s most iconic space assets.

Key Highlights: - 8641 entries captured at 10-second intervals (1 full day) - Tracks latitude, longitude, altitude, and speed of the ISS - Includes hemisphere and region metadata for context - Suitable for geospatial visualization, orbital simulation, and data science - Based on publicly available ISS tracking sources - Released under CC0 (Public Domain) for unrestricted use

Inter-Visit-Interval Descriptive Statistics for Each Group.

Descriptive statistics for the interval-scaled predictors and dependent variables.

Data sets (R-wave to R-wave interval) used in the study.

This dataset is about books. It has 2 rows and is filtered where the book is Survival analysis with interval-censored data : a practical approach with R, SAS and WinBUGS. It features 7 columns including author, publication date, language, and book publisher.

ABSTRACT

The issue of diagnosing psychotic diseases, including schizophrenia and bipolar disorder, in particular, the objectification of symptom severity assessment, is still a problem requiring the attention of researchers. Two measures that can be helpful in patient diagnosis are heart rate variability calculated based on electrocardiographic signal and accelerometer mobility data. The following dataset contains data from 30 psychiatric ward patients having schizophrenia or bipolar disorder and 30 healthy persons. The duration of the measurements for individuals was usually between 1.5 and 2 hours. R-R intervals necessary for heart rate variability calculation were collected simultaneously with accelerometer data using a wearable Polar H10 device. The Positive and Negative Syndrome Scale (PANSS) test was performed for each patient participating in the experiment, and its results were attached to the dataset. Furthermore, the code for loading and preprocessing data, as well as for statistical analysis, was included on the corresponding GitHub repository.

BACKGROUND

Heart rate variability (HRV), calculated based on electrocardiographic (ECG) recordings of R-R intervals stemming from the heart's electrical activity, may be used as a biomarker of mental illnesses, including schizophrenia and bipolar disorder (BD) [Benjamin et al]. The variations of R-R interval values correspond to the heart's autonomic regulation changes [Berntson et al, Stogios et al]. Moreover, the HRV measure reflects the activity of the sympathetic and parasympathetic parts of the autonomous nervous system (ANS) [Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, Matusik et al]. Patients with psychotic mental disorders show a tendency for a change in the centrally regulated ANS balance in the direction of less dynamic changes in the ANS activity in response to different environmental conditions [Stogios et al]. Larger sympathetic activity relative to the parasympathetic one leads to lower HRV, while, on the other hand, higher parasympathetic activity translates to higher HRV. This loss of dynamic response may be an indicator of mental health. Additional benefits may come from measuring the daily activity of patients using accelerometry. This may be used to register periods of physical activity and inactivity or withdrawal for further correlation with HRV values recorded at the same time.

EXPERIMENTS

In our experiment, the participants were 30 psychiatric ward patients with schizophrenia or BD and 30 healthy people. All measurements were performed using a Polar H10 wearable device. The sensor collects ECG recordings and accelerometer data and, additionally, prepares a detection of R wave peaks. Participants of the experiment had to wear the sensor for a given time. Basically, it was between 1.5 and 2 hours, but the shortest recording was 70 minutes. During this time, evaluated persons could perform any activity a few minutes after starting the measurement. Participants were encouraged to undertake physical activity and, more specifically, to take a walk. Due to patients being in the medical ward, they received instruction to take a walk in the corridors at the beginning of the experiment. They were to repeat the walk 30 minutes and 1 hour after the first walk. The subsequent walks were to be slightly longer (about 3, 5 and 7 minutes, respectively). We did not remind or supervise the command during the experiment, both in the treatment and the control group. Seven persons from the control group did not receive this order and their measurements correspond to freely selected activities with rest periods but at least three of them performed physical activities during this time. Nevertheless, at the start of the experiment, all participants were requested to rest in a sitting position for 5 minutes. Moreover, for each patient, the disease severity was assessed using the PANSS test and its scores are attached to the dataset.

The data from sensors were collected using Polar Sensor Logger application [Happonen]. Such extracted measurements were then preprocessed and analyzed using the code prepared by the authors of the experiment. It is publicly available on the GitHub repository [Książek et al].

Firstly, we performed a manual artifact detection to remove abnormal heartbeats due to non-sinus beats and technical issues of the device (e.g. temporary disconnections and inappropriate electrode readings). We also performed anomaly detection using Daubechies wavelet transform. Nevertheless, the dataset includes raw data, while a full code necessary to reproduce our anomaly detection approach is available in the repository. Optionally, it is also possible to perform cubic spline data interpolation. After that step, rolling windows of a particular size and time intervals between them are created. Then, a statistical analysis is prepared, e.g. mean HRV calculation using the RMSSD (Root Mean Square of Successive Differences) approach, measuring a relationship between mean HRV and PANSS scores, mobility coefficient calculation based on accelerometer data and verification of dependencies between HRV and mobility scores.

DATA DESCRIPTION

The structure of the dataset is as follows. One folder, called HRV_anonymized_data contains values of R-R intervals together with timestamps for each experiment participant. The data was properly anonymized, i.e. the day of the measurement was removed to prevent person identification. Files concerned with patients have the name treatment_X.csv, where X is the number of the person, while files related to the healthy controls are named control_Y.csv, where Y is the identification number of the person. Furthermore, for visualization purposes, an image of the raw RR intervals for each participant is presented. Its name is raw_RR_{control,treatment}_N.png, where N is the number of the person from the control/treatment group. The collected data are raw, i.e. before the anomaly removal. The code enabling reproducing the anomaly detection stage and removing suspicious heartbeats is publicly available in the repository [Książek et al]. The structure of consecutive files collecting R-R intervals is following:

    Phone timestamp
    RR-interval [ms]


    12:43:26.538000
    651


    12:43:27.189000
    632


    12:43:27.821000
    618


    12:43:28.439000
    621


    12:43:29.060000
    661


    ...
    ...

The first column contains the timestamp for which the distance between two consecutive R peaks was registered. The corresponding R-R interval is presented in the second column of the file and is expressed in milliseconds.
The second folder, called accelerometer_anonymized_data contains values of accelerometer data collected at the same time as R-R intervals. The naming convention is similar to that of the R-R interval data: treatment_X.csv and control_X.csv represent the data coming from the persons from the treatment and control group, respectively, while X is the identification number of the selected participant. The numbers are exactly the same as for R-R intervals. The structure of the files with accelerometer recordings is as follows:

    Phone timestamp
    X [mg]
    Y [mg]
    Z [mg]


    13:00:17.196000
    -961
    -23
    182


    13:00:17.205000
    -965
    -21
    181


    13:00:17.215000
    -966
    -22
    187


    13:00:17.225000
    -967
    -26
    193


    13:00:17.235000
    -965
    -27
    191


    ...
    ...
    ...
    ...

The first column contains a timestamp, while the next three columns correspond to the currently registered acceleration in three axes: X, Y and Z, in milli-g unit.

We also attached a file with the PANSS test scores (PANSS.csv) for all patients participating in the measurement. The structure of this file is as follows:

    no_of_person
    PANSS_P
    PANSS_N
    PANSS_G
    PANSS_total


    1
    8
    13
    22
    43


    2
    11
    7
    18
    36


    3
    14
    30
    44
    88


    4
    18
    13
    27
    58


    ...
    ...
    ...
    ...
    ..

The first column contains the identification number of the patient, while the three following columns refer to the PANSS scores related to positive, negative and general symptoms, respectively.

USAGE NOTES

All the files necessary to run the HRV and/or accelerometer data analysis are available on the GitHub repository [Książek et al]. HRV data loading, preprocessing (i.e. anomaly detection and removal), as well as the calculation of mean HRV values in terms of the RMSSD, is performed in the main.py file. Also, Pearson's correlation coefficients between HRV values and PANSS scores and the statistical tests (Levene's and Mann-Whitney U tests) comparing the treatment and control groups are computed. By default, a sensitivity analysis is made, i.e. running the full pipeline for different settings of the window size for which the HRV is calculated and various time intervals between consecutive windows. Preparing the heatmaps of correlation coefficients and corresponding p-values can be done by running the utils_advanced_plots.py file after performing the sensitivity analysis. Furthermore, a detailed analysis for the one selected set of hyperparameters may be prepared (by setting sensitivity_analysis = False), i.e. for 15-minute window sizes, 1-minute time intervals between consecutive windows and without data interpolation method. Also, patients taking quetiapine may be excluded from further calculations by setting exclude_quetiapine = True because this medicine can have a strong impact on HRV [Hattori et al].

The accelerometer data processing may be performed using the utils_accelerometer.py file. In this case, accelerometer recordings are downsampled to ensure the same timestamps as for R-R intervals and, for each participant, the mobility coefficient is calculated. Then, a correlation

The described database was created using data obtained from the California Independent System Operator (CAISO) and the National Renewable Energy Laboratory (NREL). All data was collected at five-minute intervals, and subsequently cleaned and modified to create a database comprising three time series: solar energy production, wind energy production, and electricity demand. The database contains 12 columns, including date, station (1: Winter, 2: Spring, 3: Summer, 4: Autumn), day of the week (0: Monday, ... , 6: Sunday), DHI (W/m2), DNI (W/m2), GHI (W/m2), wind speed (m/s), humidity (%), temperature (degrees), solar energy production (MW), wind energy production (MW), and electricity demand (MW).

With the rapid development of data acquisition and storage space, massive datasets exhibited with large sample size emerge increasingly and make more advanced statistical tools urgently need. To accommodate such big volume in the analysis, a variety of methods have been proposed in the circumstances of complete or right censored survival data. However, existing development of big data methodology has not attended to interval-censored outcomes, which are ubiquitous in cross-sectional or periodical follow-up studies. In this work, we propose an easily implemented divide-and-combine approach for analyzing massive interval-censored survival data under the additive hazards model. We establish the asymptotic properties of the proposed estimator, including the consistency and asymptotic normality. In addition, the divide-and-combine estimator is shown to be asymptotically equivalent to the full-data-based estimator obtained from analyzing all data together. Simulation studies suggest that, relative to the full-data-based approach, the proposed divide-and-combine approach has desirable advantage in terms of computation time, making it more applicable to large-scale data analysis. An application to a set of interval-censored data also demonstrates the practical utility of the proposed method.

A STUDY OF DETERMINANTS OF INTERPREGNANCY INTERVAL IN MULTIPAROUS WOMEN IN A TERTIARY HOSPITAL

Includes accelerometer data using an ActiGraph to assess usual sedentary, moderate, vigorous, and very vigorous activity at baseline, 6 weeks, and 10 weeks. Includes relative reinforcing value (RRV) data showing how participants rated how much they would want to perform both physical and sedentary activities on a scale of 1-10 at baseline, week 6, and week 10. Includes data on the breakpoint, or Pmax of the RRV, which was the last schedule of reinforcement (i.e. 4, 8, 16, …) completed for the behavior (exercise or sedentary). For both Pmax and RRV score, greater scores indicated a greater reinforcing value, with scores exceeding 1.0 indicating increased exercise reinforcement. Includes questionnaire data regarding preference and tolerance for exercise intensity using the Preference for and Tolerance of Intensity of Exercise Questionnaire (PRETIEQ) and positive and negative outcome expectancy of exercise using the outcome expectancy scale (OES). Includes data on height, weight, and BMI. Includes demographic data such as gender and race/ethnicity. Resources in this dataset:Resource Title: Actigraph activity data. File Name: AGData.csvResource Description: Includes data from Actigraph accelerometer for each participant at baseline, 6 weeks, and 10 weeks.Resource Title: RRV Data. File Name: RRVData.csvResource Description: Includes data from RRV at baseline, 6 weeks, and 10 weeks, OES survey data, PRETIE-Q survey data, and demographic data (gender, weight, height, race, ethnicity, and age).

The Cardiac Arrhythmia Suppression Trial (CAST) was a landmark NHLBI-sponsored study designed to test the hypothesis that the suppression of asymptomatic or mildly symptomatic ventricular premature complexes (PVCs) in survivors of myocardial infarction (MI) would decrease the number of deaths from ventricular arrhythmias and improve survival. Enrollment required an acute MI within the preceding 2 years and 6 or more PVCs per hour during a pre-treatment (qualifying) long-term ECG (Holter) recording. Those subjects enrolled within 90 days of the index MI were required to have left ventricular ejection fractions less than or equal to 55%, while those enrolled after this 90 day window were required to have an ejection fraction less than or equal to 40%. CAST enrolled 3,549 patients in all.