This dataset is about book subjects. It has 2 rows and is filtered where the books is Statistical monitoring of clinical trials : a unified approach. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Life tests for highly reliable products often take a long time even using accelerated life testing with censoring. When the production process is monitored by control charts with the product lifetime as the key quality characteristic, the time spent on life testing could incur significant delays for practitioners to make decisions after sampling. However, shortening the test duration, that results in excessive right-censored observations, inevitably degrades the test power for anomaly detection. This study pays close attention to the determination of censoring time in life tests when monitoring lifetime data with the likelihood-based control charts. To interpret the optimal censoring time, the performance metric—out-of-control average time to signal (OC ATS), is deconstructed into two parts: the original OC ATS and the delay caused by life testing. Finite-sample analytical and large-sample asymptotic expressions of ATS metrics are derived for Type-I censored exponential lifetimes. Similar analytical expressions are also derived for the Weibull case. For general distributions, a Monte Carlo simulation procedure is developed for obtaining approximate results. Our numerical investigation uncovers the 2-fold impact of censoring time on the actual performance of control charts under various scenarios and provides useful references for practitioners to set more sensible censoring times in life testing.

River water quality monitoring statistics.........

Number of weeks needed to achieve a CumSe = 50% for the different statistical monitoring methods based on the DGLM model.

Introduction

Remote Patient Monitoring Statistics: Remote Patient Monitoring (RPM) is revolutionizing healthcare by allowing continuous, real-time observation of patients' health outside traditional medical environments. By utilizing wearable devices, mobile applications, and connected sensors, RPM enables tracking vital signs and chronic conditions, minimizing the need for frequent hospital visits.

The implementation of RPM has accelerated in recent years, driven by the growing prevalence of chronic diseases, an aging population, and technological advancements in healthcare.

As healthcare systems move toward more efficient and patient-focused models, RPM is vital in improving patient outcomes, optimizing disease management, and lowering healthcare costs. These statistics provide an in-depth look at the key trends shaping the RPM market, offering insights into its rapid growth and future potential.

The results of the water quality monitoring of Bazhangxi, Pozixi, and the drainage trunk line in the second quarter of 2017 in Chiayi City and Lantan Reservoir.

The water quality monitoring results of the main drainage line in Chiayi City, the water quality monitoring results of Bazhang River and Puzi River in the third quarter of 2017, and the water quality monitoring results of Lantan Reservoir in Chiayi City in the third quarter of 2017.

The Patent Technology Monitoring Team (PTMT) studies patent statistical information and prepares reports in a variety of formats.

Enetwild consortium developed a data standard on wildlife monitoring data to aggregate raw data such as occurrences, drive hunt hunting bags, distance sampling data, or aggregated or estimated data such as density estimates, summarized hunting bags, probability of presence. This standard is based on the Darwin Core standard, using the Event core, the Occurrence extension, the extended measurement or fact extension. We proposed to extend the measurement or fact extension to allow them to be nested among themselves for recording confidence intervals and precision information. We offer the possibility to nest occurrences within each other as well. We propose controlled vocabularies in relation to statistical information, in both data and metadata. In addition to a single metadata sheet, this Excel file proposes two ways to enter a dataset. The first option is to record events, occurrences, and measurements in different sheets, which is similar to the option proposed by GBIF in its Integrated Publishing Toolkit. The second option is to record events, occurrences and measurements in a single sheet (data_AllInOne) with the help of conditional formatting, working on the most recent version of Excel.

This is in line with the EFSA data model harmonisation under the SIGMA project.

CumSe achieved 52 weeks after the events were started for the different statistical monitoring methods based on the DGLM model.

Context

This data set contains some basic statistics about user count and user growth as well as crash count for a real mobile app. The dataset contains a basic timeseries of 1 hour resolution for a period of one week.

Content

The data set contains columns for total concurrent user count, new users acquired in that period of time, number of sessions and crash count.

Acknowledgements

This data set would not be available without the Real User Monitoring capabilities of Dynatrace and its flexibility to export and expose this data for scientific experiments.

Inspiration

The data set was intended to play around with seasonality, trend and prediction of timeseries.

Introduction

Emission Monitoring System Statistics: An Emission Monitoring System (EMS) is a critical tool used to measure and report the concentration of pollutants, such as COâ‚‚, NOx, SOâ‚‚, and particulate matter, from industrial processes.

It consists of sensors, analyzers, sampling probes, and data acquisition systems that continuously or periodically monitor emissions, ensuring compliance with environmental regulations.

EMS data supports both regulatory reporting and operational optimization, helping industries reduce emissions, improve energy efficiency, and maintain air quality standards.

By providing real-time insights and enabling corrective actions, EMS plays a key role in environmental protection, regulatory compliance, and sustainable industrial practices.

The PRONTO heterogeneous benchmark dataset is based on an industrial-scale multiphase flow facility. It includes data from heterogeneous sources, including process measurements, alarm records, high frequency ultrasonic flow and pressure measurements, an operation log and video recordings. The study collected data from various operational conditions with and without induced faults to generate a multi-rate, multi-modal dataset. The dataset is suitable for developing and validating algorithms for fault detection and diagnosis (FDD) and data fusion.

When using the dataset please cite the following publication:

A. Stief, R. Tan, Y. Cao, J. R. Ottewill, N. F. Thornhill, J. Baranowski, A heterogeneous benchmark dataset for data analytics: Multiphase flow facility case study, Journal of Process Control, 79 (2019) 41–55, DOI: https://doi.org/10.1016/j.jprocont.2019.04.009

The dataset has been used in the following works:

A. Stief, R. Tan, Y. Cao, J. R. Ottewill. Analytics of heterogeneous process data: Multiphase flow facility case study. IFAC-PapersOnLine, 51(18):363–368, 2018. DOI: https://doi.org/10.1016/j.ifacol.2018.09.327

A. Stief, J. R. Ottewill, R. Tan, Y. Cao. Process and alarm data integration under a two-stage Bayesian framework for fault diagnostics. IFAC-PapersOnLine, 51(24):1220–1226, 2018. DOI: https://doi.org/10.1016/j.ifacol.2018.09.696

A. Stief, J. R. Ottewill, J. Baranowski. Investigation of the diagnostic properties of sensors and features in a multiphase flow facility case study. in: 12^th IFAC Symposium on Dynamics and Control of Process Systems (in press), 2019

M. Lucke, X. Mei, A. Stief, M. Chioua, N. F. Thornhill. Variable selection for fault detection and identification based on mutual information of multi-valued alarm series, in: 12^th IFAC Symposium on Dynamics and Control of Process Systems (in press), 2019

R. Tan, T. Cong, N. F. Thornhill, J. R. Ottewill, J. Baranowski. Statistical monitoring of processes with multiple operating modes, in: 12^th IFAC Symposium on Dynamics and Control of Process Systems (in press), 2019.

Data for benchmarking SPC against other process monitoring methods. The data consist of a one-dimensional timeseries of floats (x.csv). Addititionally information whether the data are within the specifications are provided as another time series (y.csv). The data are generated by solving an optimization problem for each time to generate a mixture distribution of different probability distributions. Then for each timestep one record is sampled. Inputs for the optimization problem are the given probability distributions, the lower and upper limit of the tolerance interval as well as the desired median of the data. Additionally weights of the different probability distributions can be given as boundary condions for the different time steps. Metadata generated from the solving are stored in k_matrix.csv (wheights at each time step) and distribs (probability distribution objects). The data consists of phases with data from a stable mixture distribution and phases with data from a mixture distribution that do not fulfill the stability criteria.

In 2024, over ** percent of respondents among U.S. workers reported experiencing at least one form of electronic monitoring on the job. Technology monitoring, including tracking of work-related smartphones, tablets, and computers, was the most common at around ** percent. Camera monitoring followed at ** percent, while productivity and location tracking were reported by ** percent and ** percent of workers, respectively.

Information on the incidence of gender-based violence on women in the Spanish population as well as the distribution of known cases according to the level of risk estimated by police specialists responsible for the safety of victims