As of May 2023, approximately ** percent of all data collected by travel and mobility iOS apps were linked to the users' identity. However, only ** percent of the collected data were users to track users of apps in this category. Shopping and food delivery apps used **** percent of the collected data for tracking purposes, while AI tool apps hosted on the Apple App Store used **** percent of the collected data for tracking their users.

This data set is a summary of status updates for customer certain customer service requests. These data have been assembled for the purposes of feeding a pilot status update tracker.

4,001 People Single Object Multi-view Tracking Data, the data collection site includes indoor and outdoor scenes (such as supermarket, mall and community, etc.) , where each subject appeared in at least 7 cameras. The data diversity includes different ages, different time periods, different cameras, different human body orientations and postures, different collecting scenes. It can be used for computer vision tasks such as object detection and object tracking in multi-view scenes.

Since 2009 PSB has been collecting satellite tag telemetry data from sea turtles and other protected species.

Monitoring animals in their natural habitat is essential for the advancement of animal behavioural studies, especially in pollination studies. We present a novel hybrid detection and tracking algorithm "HyDaT" to monitor unmarked insects outdoors. Our software can detect an insect, identify when a tracked insect becomes occluded from view and when it re-emerges, determine when an insect exits the camera field of view, and our software assembles a series of insect locations into a coherent trajectory. The insect detecting component of the software uses background subtraction and deep learning-based detection together to accurately and efficiently locate the insect.This dataset includes videos of honeybees foraging in two ground-covers Scaevola and Lamb's-ear, comprising of complex background detail, wind-blown foliage, and honeybees moving into and out of occlusion beneath leaves and among three-dimensional plant structures. Honeybee tracks and associated outputs of experiments extracted using HyDaT algorithm are included in the dataset. The dataset also contains annotated images and pre-trained YOLOv2 object detection models of honeybees.

A February 2023 survey in the United Kingdom (UK) found that around 30 percent of respondents felt more wary of what they were reading when they knew the website was tracking their personal information. Another 22 percent said they felt more nervous, while over nine percent felt happier knowing their data was being tracked.

The COVID Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.

This dataset includes data from the Food Tracking Device from several pilot cases within the URBAN-WASTE project. In particular, a few hotels and restaurants were supplied with a food waste tracking device where the kitchen could record their food waste data. The statistics would then be transformed into simple figures to give the kitchen feedback on their development in terms of food waste generation.

This dataset is object tracking data for MOT challenge datasets.The inferenced data is generated by Yolov5 and DeepSort.

The objectives of this project are to use novel satellite tracking methods to provide improved estimation of threats at foraging areas and along migration routes for oceanic stage sea turtles in the Northeast Distant Region of the Atlantic Ocean (NED) and to characterize the in-water habitats used by small, oceanic stage loggerheads (Caretta caretta) so that we better understand the features that likely define their nursery habitats and the potential risks and hazards to the smallest life stages of sea turtle. To accomplish these objectives, our strategy included collaborating with cooperative fishermen in the NED to capture and satellite tag small (30 cm length) loggerhead sea turtles. Using novel satellite telemetry techniques, we were to identify the fine-scale habitat selection, movements, and dispersal of small oceanic loggerheads in the NED.

a register of waste-tracking in Nauru;

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.

The Southern Ocean is a remote, hostile environment where conducting marine biology is challenging, so we know relatively little about this important region, which is critical as a habitat for breeding and foraging of many marine endotherms. Scientists from around the world have been tracking seals, penguins, petrels, whales and albatrosses for more than two decades to learn how they spend their time at sea. The Retrospective Analysis of Antarctic Tracking Data (RAATD), was initiated by the SCAR Expert Group on Marine Mammals (EG-BAMM) in 2010. This team has assembled tracking data shared by 38 biologists from 11 different countries to accumulate the largest animal tracking database in the world, containing information from 15 species, containing over 3,400 individual animals and almost 2.5 million at-sea locations. Analysing a dataset of this size brings its own challenges and the team is developing new and innovative statistical approaches to integrate these complex data. When complete RAATD will provide a greater understanding of fundamental ecosystem processes in the Southern Ocean, help predict the future of top predator distribution and help with spatial management planning.

This anonymized data set consists of one month's (October 2018) web tracking data of 2,148 German users. For each user, the data contains the anonymized URL of the webpage the user visited, the domain of the webpage, category of the domain, which provides 41 distinct categories. In total, these 2,148 users made 9,151,243 URL visits, spanning 49,918 unique domains. For each user in our data set, we have self-reported information (collected via a survey) about their gender and age.

We acknowledge the support of Respondi AG, which provided the web tracking and survey data free of charge for research purposes, with special thanks to François Erner and Luc Kalaora at Respondi for their insights and help with data extraction.

The data set is analyzed in the following paper:

• Kulshrestha, J., Oliveira, M., Karacalik, O., Bonnay, D., Wagner, C. "Web Routineness and Limits of Predictability: Investigating Demographic and Behavioral Differences Using Web Tracking Data." Proceedings of the International AAAI Conference on Web and Social Media. 2021. https://arxiv.org/abs/2012.15112.

The code used to analyze the data is also available at https://github.com/gesiscss/web_tracking.

If you use data or code from this repository, please cite the paper above and the Zenodo link.

Attendance Tracking

## Overview

Attendance Tracking is a dataset for object detection tasks - it contains Person annotations for 551 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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.

Person Tracking

## Overview

Person Tracking is a dataset for object detection tasks - it contains Human, Tag annotations for 1,093 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Berkeley Lab's Tracking the Sun report series is dedicated to summarizing installed prices and other trends among grid-connected, distributed solar photovoltaic (PV) systems in the United States. The present report, the 11th edition in the series, focuses on systems installed through year-end 2017, with preliminary trends for the first half of 2018. As in years past, the primary emphasis is on describing changes in installed prices over time and variation in pricing across projects based on location, project ownership, system design, and other attributes. New to this year, however, is an expanded discussion of other project characteristics in the large underlying data sample. Future editions will include more of such material, beyond the reports traditional focus on installed pricing. The trends described in this report derive primarily from project-level data reported to state agencies and utilities that administer PV incentive programs, solar renewable energy credit (SREC) registration systems, or interconnection processes. In total, data were collected and cleaned for more than 1.3 million individual PV systems, representing 81% of U.S. residential and non-residential PV systems installed through 2017. The analysis of installed pricing trends is based on a subset of roughly 770,000 systems with available installed price data.

A new long video dataset and benchmark for single object tracking. The dataset consists of 50 HD videos from real world scenarios, encompassing a duration of over 400 minutes (676K frames), making it more than 20 folds larger in average duration per sequence and more than 8 folds larger in terms of total covered duration, as compared to existing generic datasets for visual tracking.

An October 2024 survey among adults in the United States found that around 85 percent of respondents assume that companies are always collecting and tracking their personal data, compared to only 10 percent of those who did not think so.