The Former Prisoner of War (POW) Statistical Tracking System database is a registry designed to comply with Public Law 97-37, the Former Prisoner of War Benefits Act of 1981. This database contains information about the Medical Evaluation Program for ex-POWs at VA facilities. The program provides a complete medical and psychiatric evaluation of ex-POWs. Only ex-POWs who volunteer to participate in the program are included in this registry. Health examinations are given to ex-POWs at VA facilities. The findings are then recorded on a special coding sheet, VA Form 10-0048a. Quarterly, these code sheets are sent to the Austin Information Technology Center, where they are manually keyed into the database. The main users of this registry are: * The Advisory Committee on Former Prisoners of War * Congress * National Academy of Sciences * Researchers * The National Center for Veteran Analysis and Statistics.

An April 2023 survey of Americans found that around 57 percent of the respondents were concerned about the government tracking their online behavior daily. A further 46 percent were concerned about companies tracking their online data. Approximately 34 percent of respondents were afraid of being tracked online.

An October 2024 survey among adults in the United States found that around ** percent of respondents assume that companies are always collecting and tracking their personal data, compared to only ** percent of those who did not think so.

Cognitive workload is an important component in performance psychology, ergonomics, and human factors. Unfortunately, benchmarks and publicly available datasets are scarce, making it difficult to establish new approaches and comparative studies. In this work, COLET-COgnitive workLoad state estimation based on Eye-Tracking dataset is presented. Forty-seven (47) individuals' eye movements were monitored as they solved puzzles involving visual search tasks of varying complexity and duration. The authors give an in-depth study of the participants' performance during the experiments while eye and gaze features were derived from low-level eye recorded metrics, and their relationships with the experiment tasks were investigated. Finally, the results from the classification of cognitive workload levels solely based on eye and gaze data, by employing and testing a set of machine learning algorithms are provided. The dataset is made available to the public.

Please cite the following work:

Ktistakis, E., Skaramagkas, V., Manousos, D., Tachos, N. S., Tripoliti, E., Fotiadis, D. I., & Tsiknakis, M. (2022). Colet: A dataset for cognitive workload estimation based on eye-tracking. Computer Methods and Programs in Biomedicine, 106989. https://doi.org/10.1016/j.cmpb.2022.106989

Data files for analysis reported in M&C manuscript

County/city, township, date (subgroup indicators such as confirmed cases, gender, age, bacteriology positivity), usage instructions: If interfacing with the machine daily, it is recommended to select the single-day dataset. If selecting the annual cumulative dataset, there are approximately 100,000 to 300,000 records, the data volume is relatively large, and it is recommended to confirm the demand before downloading. Tuberculosis is a chronic infectious disease, and the treatment for individual cases may last 6-8 months or longer. Therefore, the "under management" cases in this dataset refer to cases still under tracking and treatment, regardless of the year of illness. Updated every morning, the previous day's township indicators are summarized. The daily dataset contains up to 369 records, while the annual cumulative dataset contains approximately 100,000 to 300,000 records.

This statistic shows the leading application tracking systems (ATS) used by enterprise and mid-market companies who have their headquarters in the United States in 2020. At that time, Workday was the most widely used ATS among these companies, being the primary ATS for nearly 22 percent of the reviewed 1,063 companies.

This dataset supports our study "Statistical Analysis of Fluorescence Intensity Transients with Bayesian Methods," which introduces Fluorescence Intensity Trace Statistical Analysis (FITSA), a Bayesian approach for direct analysis of fluorescence intensity traces. From these traces, FITSA estimates diffusion coefficient and molecular brightness. The repository contains all fluorescence intensity traces used in our comparative analysis of FITSA and fluorescence correlation spectroscopy (FCS). A README file describes the data structure. We provide both synthetic and experimental datasets that demonstrate various applications of FITSA. When combined with our separately published code, these datasets enable reproduction of our analysis and support further methodological development in the field. Based on our analysis of these traces, we demonstrate that FITSA achieves precision comparable to FCS while requiring substantially fewer photons and shorter measurement times., , , # Experimental and synthetic datasets supporting FITSA: Statistical analysis of fluorescence intensity transients with Bayesian methods

This repository contains the complete set of traces used in the study:

"Statistical Analysis of Fluorescence Intensity Transients with Bayesian Methods"

Authors: Hamed Karimi, Martin Laasmaa, Margus Pihlak, Marko Vendelin

Repository Structure

The datasets are organized in subfolders corresponding to the figures in the study. Since some datasets were used across multiple figures, all relevant figure numbers are included in the subfolder names.

Synthetic Datasets

Multiple synthetic datasets were generated with varying molecular brightness levels, as shown in Figure 5 and associated Supporting Materials figures. These datasets are stored in dedicated subfolders, with the molecular brightness indicated in the subfolder name. For example:

• mu_mol-50k represents data with a molecular brightness of 50,000 1/s

Additional Experimental Dat...,

Examples of descriptive statistics that can be gleaned from Tracker data that could not be determined from standard beam cross data (Track CASK-β N = 30, Track Control N = 29).

The dataset contains all data used during the evaluation of statistical characteristics preservation. Archives are protected by password "trace-share" to avoid false detection by antivirus software.

For more information, see the project repository at https://github.com/Trace-Share.

Selected Attack Traces

We selected 72 different traces of network attacks obtained from various internet databases. File names refer to common names of contained vulnerabilities, malware, or attack tools.

Background Traffic Data

Publicly available dataset CSE-CIC-IDS-2018 was used as a background traffic data. The evaluation uses data from the day Thursday-01-03-2018 containing a sufficient proportion of regular traffic without any statistically significant attacks. Only traffic aimed at victim machines (range 172.31.69.0/24) is used to reduce less significant traffic.

Evaluation Results and Dataset Structure

Traces variants (traces-normalized.zip, traces-adjusted.zip)

./traces-normalized/ — normalized PCAP files and details in YAML format;

./traces-adjusted/ — configuration files for traces combination in YAML format.

Computed statistics (statistics.zip)

./statistics-background/ — background traffic statistics computed by ID2T;

./statistics-combination/ — combined traces statistics computed by ID2T for all adjust options (selected only combinations where ID2T provided all statistics files);

./statistics-difference/ — computed mean and median differences of background and combined traffic traces.

Evaluation results

statistics-difference.ipynb — file containing visualization of statistics differences.

The latest Apple iOS version includes a new privacy feature, which means that mobile apps are forced to ask users for permission to allow them to collect tracking data. Among those that have already installed the iOS 14.5 update, the opt-in rate (how many people are choosing to allow app tracking) is around ** percent, as of April 2022. With so many users concerned about their online activities being tracked, a low opt-in rate had been predicted.

This dataset contain information about Statistics of trips, passengers, drivers and vehicles by year and month and activity in tracking vehicles for qualified drivers

One data set was used to run the t-tests; the other contains all the data necessary to run the LMMs reported in the paper

The Online Attendance Tracking System market is experiencing significant growth as organizations increasingly transition to digital solutions for managing attendance records and employee time management. These systems provide a streamlined approach to tracking attendance, reducing manual errors, and ensuring accurat

This repository contains a table with parameters (including sensing time - ST parameter) and an R script for statistical analysis. This is supplementary material for the preprint available on arXiv and titled "Sensing Time Effectiveness for Fitness to Drive Evaluation in Neurological Patients" authored by Nadica Miljković and Jaka Sodnik .

ST parameter was calculated in overall 56 patients during selected scenario with pedestrian collision in a driving simulator produced by Nervtech. Together with other parameters, ST parameters are stored in tableParameters.csv, while R programming code for statistical analysis of all parameters is placed in statisticalAnalysis.R.

Dataset contents

1. tableParameters.csv, table with parameters, csv (comma-separated values) format
2. statisticalAnalysis.R, code in R programming language for statistical analysis

Table with parameters has the following structure

1. column - no which is ordinary number in consecutive order from 1 to 56
2. column - id presents an internal patient's id
3. column - st presents ST parameter in ms
4. column - fitness presents a categorical variable and can be either fit-, unfit-, or conditionally-fit-to-drive (cond fit)
5. column - speed at the collision onset in km/h
6. column - ttc presents time-to-collision in s
7. column - manual_correction is categorical variable: 0 means that no manual correction was required for ST calculation, while 1 means that manual correction was required
8. column - igd presents initial gaze distance in pixels

Missing data are presented with NA (Not Available).

NOTE: Python code for ST calculation and sample eye tracker video are available on GitHub repository https://github.com/NadicaSm/Sensing-Time-Calculation-from-the-Eye-Tracker-Videos under GNU GPL license and released on Zenodo with doi (https://doi.org/10.5281/zenodo.6560419).

If you find these parameters and R code useful for your own research and teaching class, please cite the following references:

1. Miljković, N., & Sodnik, J. (2022). Parameters for Statistical Evaluation of Sensing Time Effectiveness for Assessment of Fitness to Drive [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6560246

2. Miljković, N., & Sodnik, J. (2022, May). Sensing Time Effectiveness for Fitness to Drive Evaluation in Neurological Patients. Preprint in arXiv (pp. 1-23). https://doi.org/10.48550/arXiv.2205.08942

3. Motnikar, L., Stojmenova, K., Štaba, U. Č., Klun, T., Robida, K. R., & Sodnik, J. (2020). Exploring driving characteristics of fit-and unfit-to-drive neurological patients: A driving simulator study. Traffic Injury Prevention, 21(6), 359-364. https://doi.org/10.1080/15389588.2020.1764547

Acknowledgements

J.S. kindly acknowledges University Rehabilitation Institute Soča employees and the Nervtech team. Authors gratefully appreciate the support from Nenad B. Popović, PhD from University of Belgrade – School of Electrical Engineering for his valuable assistance in design of illustrations and for provided feedback for the initial manuscript structure. Also, both Authors thank Nebojša Jovanović, MSc from University of Belgrade - School of Electrical Engineering for his kind contribution to earlier stages of the project, especially for his work on developing Python code to capture sensing time parameter.

The Package Tracking Systems market has witnessed a significant transformation over the past decade, driven by the rapid evolution of e-commerce and logistic operations. As businesses and consumers increasingly demand real-time visibility into the status of their shipments, package tracking systems have emerged as e

1. The use of bio-logging devices to track animal movement continues to grow as technological advances and device miniaturisation allow researchers to study animal behaviour in unprecedented detail. Balanced against the remarkable data that bio-loggers can provide is a need to understand the impact of devices on animal behaviour and welfare.
2. Recent meta-analyses have demonstrated impacts of device attachment on animal behaviour, but there is concern about the frequency and clarity with which device effects are reported. One aspect lacking in many studies is assessment of the statistical power of tests of device effects, yet such information would assist the interpretation of results. We address this issue by providing an overview of the statistical power, as well as the Type M (magnitude) and Type S (sign) error rate, of tests of device effects within the avian tracking literature across a range of assumed effect sizes.
3. The median power of statistical tests ranged from 9% to 65% across a range of assumed effect sizes corresponding to benchmark values for small, moderate and large effects (d = 0.2, 0.5, 0.8 respectively). Moreover, when using effect sizes derived from previous a meta-analysis (d = 0.1) median power was only 6%. When assuming smaller effect sizes, statistical tests were characterised by high Type M and Type S error rates, suggesting that statistically significant results of device effects will tend to exaggerate the size of such effects and may estimate the sign of an effect in the wrong direction.
4. Well-designed tracking studies will reduce device effects to low levels and consequently issues associated with low power will be commonplace. Nevertheless, assessment of device effects remains important, particularly when embarking on novel tracking studies. We recommend that statistical tests of device effects are reported clearly and are routinely accompanied by assessment of statistical power, including Type M and Type S errors, based upon realistic external estimates of effect size. Reporting the statistical power can help avoid the pitfalls of overstating results from individual studies, shift the emphasis to accurate reporting of effect sizes and guide decisions about the ethical impacts of device attachment.