Legacy PFC work. Data stored in Phillip Bost’s Lab Notebook #1778; Room D286 (Lindstrom) RTP, NC EPA office.

This dataset is associated with the following publication: Bost, P., M. Strynar, J. Reiner, J. Zweigenbaum, P. Secoura, A. Lindstrom, and J. Dye. U.S. Domestic Cats as Sentinels for Perfluoroalkyl Substances: Associations with Housing, Obesity and Chronic Disease. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 151(0): 145-153, (2016).

An estimated ** million households in the United States owned at least one dog according to a 2024/25 pet owners survey, making them the most widely owned type of pet across the U.S. at this time. Cats and freshwater fish ranked in second and third places, with around ** million and ** million households owning such pets, respectively. Freshwater vs. salt water fish Freshwater fish spend most or all their lives in fresh water. Fresh water’s main difference to salt water is the level of salinity. Freshwater fish have a range of physiological adaptations to enable them to live in such conditions. As the statistic makes clear, Americans keep a large number of freshwater aquatic species at home as pets. American pet owners In 2023, around ** percent of all households in the United States owned a pet. This is a decrease from 2020, but still around a ** percent increase from 1988. It is no surprise that as more and more households own pets, pet industry expenditure has also witnessed steady growth. Expenditure reached over *** billion U.S. dollars in 2022, almost a sixfold increase from 1998. The majority of pet product sales are still made in brick-and-mortar stores, despite the rise and evolution of e-commerce in the United States.

In this competition, you'll write an algorithm to classify whether images contain either a dog or a cat. This is easy for humans, dogs, and cats. Your computer will find it a bit more difficult.

https://www.ethosvet.com/wp-content/uploads/cat-dog-625x375.png" alt="">

The Asirra data set

Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords.

Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface:

Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research. Image recognition attacks

While random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459. State of the art

The current literature suggests machine classifiers can score above 80% accuracy on this task [1]. Therfore, Asirra is no longer considered safe from attack. We have created this contest to benchmark the latest computer vision and deep learning approaches to this problem. Can you crack the CAPTCHA? Can you improve the state of the art? Can you create lasting peace between cats and dogs?

Submission Format

Your submission should have a header. For each image in the test set, predict a label for its id (1 = dog, 0 = cat):

id,label 1,0 2,0 3,0 etc...

This model is based on how house cats utilize wildlands near human habituation. These predators can have detrimental effects on wildlife populations (Alterio et al. 1998). We based our model on the data collected by Odell and Knight (2001) that investigated habitat utilization of these predators with regard to distance from housing and on the probability for a homeowner to possess a house cat. We buffered the populated areas distance layer in ARC/INFO using a probability function [P = 0.216 - 0.96 * Distance (km)] where any cell with distance less than 0.18km received a probability between 0.216 to 0. All distances greater than or equal to 0.18km from populated areas were assigned a probability of 0. The resulting dataset was then resampled to 180m using the bilinear interpolation option.

526038 Global import shipment records of Cat with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The Controlled Anomalies Time Series (CATS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system with 200 injected anomalies.

The CATS Dataset exhibits a set of desirable properties that make it very suitable for benchmarking Anomaly Detection Algorithms in Multivariate Time Series [1]:

• Multivariate (17 variables) including sensors reading and control signals. It simulates the operational behaviour of an arbitrary complex system including:
  • 4 Deliberate Actuations / Control Commands sent by a simulated operator / controller, for instance, commands of an operator to turn ON/OFF some equipment.
  • 3 Environmental Stimuli / External Forces acting on the system and affecting its behaviour, for instance, the wind affecting the orientation of a large ground antenna.
  • 10 Telemetry Readings representing the observable states of the complex system by means of sensors, for instance, a position, a temperature, a pressure, a voltage, current, humidity, velocity, acceleration, etc.
• 5 million timestamps. Sensors readings are at 1Hz sampling frequency.
  • 1 million nominal observations (the first 1 million datapoints). This is suitable to start learning the "normal" behaviour.
  • 4 million observations that include both nominal and anomalous segments. This is suitable to evaluate both semi-supervised approaches (novelty detection) as well as unsupervised approaches (outlier detection).
• 200 anomalous segments. One anomalous segment may contain several successive anomalous observations / timestamps. Only the last 4 million observations contain anomalous segments.
• Different types of anomalies to understand what anomaly types can be detected by different approaches.
• Fine control over ground truth. As this is a simulated system with deliberate anomaly injection, the start and end time of the anomalous behaviour is known very precisely. In contrast to real world datasets, there is no risk that the ground truth contains mislabelled segments which is often the case for real data.
• Obvious anomalies. The simulated anomalies have been designed to be "easy" to be detected for human eyes (i.e., there are very large spikes or oscillations), hence also detectable for most algorithms. It makes this synthetic dataset useful for screening tasks (i.e., to eliminate algorithms that are not capable to detect those obvious anomalies). However, during our initial experiments, the dataset turned out to be challenging enough even for state-of-the-art anomaly detection approaches, making it suitable also for regular benchmark studies.
• Context provided. Some variables can only be considered anomalous in relation to other behaviours. A typical example consists of a light and switch pair. The light being either on or off is nominal, the same goes for the switch, but having the switch on and the light off shall be considered anomalous. In the CATS dataset, users can choose (or not) to use the available context, and external stimuli, to test the usefulness of the context for detecting anomalies in this simulation.
• Pure signal ideal for robustness-to-noise analysis. The simulated signals are provided without noise: while this may seem unrealistic at first, it is an advantage since users of the dataset can decide to add on top of the provided series any type of noise and choose an amplitude. This makes it well suited to test how sensitive and robust detection algorithms are against various levels of noise.
• No missing data. You can drop whatever data you want to assess the impact of missing values on your detector with respect to a clean baseline.

[1] Example Benchmark of Anomaly Detection in Time Series: “Sebastian Schmidl, Phillip Wenig, and Thorsten Papenbrock. Anomaly Detection in Time Series: A Comprehensive Evaluation. PVLDB, 15(9): 1779 - 1797, 2022. doi:10.14778/3538598.3538602”

About Solenix

Solenix is an international company providing software engineering, consulting services and software products for the space market. Solenix is a dynamic company that brings innovative technologies and concepts to the aerospace market, keeping up to date with technical advancements and actively promoting spin-in and spin-out technology activities. We combine modern solutions which complement conventional practices. We aspire to achieve maximum customer satisfaction by fostering collaboration, constructivism, and flexibility.

This statistic shows the results of a survey conducted in the United States in 2017 on pets. Some ** percent of the respondents stated that they prefer dogs.The Survey Data Table for the Statista survey pets in the U.S. 2017 contains the complete tables for the survey including various column headings.

Over the last several decades, feral cats have moved from the fringes to the mainstream in animal welfare and sheltering. Although many best practice guidelines have been published by national non-profits and veterinary bodies, little is known about how groups “in the trenches” actually operate. Our study sought to address that gap through an online survey of feral cat care and advocacy organizations based in the United States. Advertised as “The State of the Mewnion,” its topics included a range of issues spanning non-profit administration, public health, caretaking and trapping, adoptions of friendly kittens and cats, veterinary medical procedures and policies, data collection and program efficacy metrics, research engagement and interest, and relationships with wildlife advocates and animal control agencies. Respondents from 567 organizations participated, making this the largest and most comprehensive study on this topic to date. Respondents came primarily from grassroots organizations. A majority reported no paid employees (74.6%), served 499 or fewer feral cats per year (75.0%), engaged between 1 and 9 active volunteers (54.9%), and did not operate a brick and mortar facility (63.7%). Some of our findings demonstrate a shared community of practice, including the common use of a minimum weight of 2.0 pounds for spay/neuter eligibility, left side ear tip removals to indicate sterilization, recovery holding times after surgery commonly reported as 1 night for male cats and 1 or 1 nights for females, requiring or recommending to adopters of socialized kittens/cats that they be kept indoor-only, and less than a quarter still engaging in routine testing of cats for FIV and FeLV. Our survey also reveals areas for improvement, such as most organizations lacking a declared goal with a measurable value and a time frame, only sometimes scanning cats for microchips, and about a third not using a standardized injection site for vaccines. This study paints the clearest picture yet available of what constitutes the standard practices of organizations serving feral and community cats in the United States.

The Controlled Anomalies Time Series (CATS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system with 200 injected anomalies.

The CATS Dataset exhibits a set of desirable properties that make it very suitable for benchmarking Anomaly Detection Algorithms in Multivariate Time Series [1]:

• Multivariate (17 variables) including sensors reading and control signals. It simulates the operational behaviour of an arbitrary complex system including:
  • 4 Deliberate Actuations / Control Commands sent by a simulated operator / controller, for instance, commands of an operator to turn ON/OFF some equipment.
  • 3 Environmental Stimuli / External Forces acting on the system and affecting its behaviour, for instance, the wind affecting the orientation of a large ground antenna.
  • 10 Telemetry Readings representing the observable states of the complex system by means of sensors, for instance, a position, a temperature, a pressure, a voltage, current, humidity, velocity, acceleration, etc.
• 5 million timestamps. Sensors readings are at 1Hz sampling frequency.
  • 1 million nominal observations (the first 1 million datapoints). This is suitable to start learning the "normal" behaviour.
  • 4 million observations that include both nominal and anomalous segments. This is suitable to evaluate both semi-supervised approaches (novelty detection) as well as unsupervised approaches (outlier detection).
• 200 anomalous segments. One anomalous segment may contain several successive anomalous observations / timestamps. Only the last 4 million observations contain anomalous segments.
• Different types of anomalies to understand what anomaly types can be detected by different approaches. The categories are available in the dataset and in the metadata.
• Fine control over ground truth. As this is a simulated system with deliberate anomaly injection, the start and end time of the anomalous behaviour is known very precisely. In contrast to real world datasets, there is no risk that the ground truth contains mislabelled segments which is often the case for real data.
• Suitable for root cause analysis. In addition to the anomaly category, the time series channel in which the anomaly first developed itself is recorded and made available as part of the metadata. This can be useful to evaluate the performance of algorithm to trace back anomalies to the right root cause channel.
• Affected channels. In addition to the knowledge of the root cause channel in which the anomaly first developed itself, we provide information of channels possibly affected by the anomaly. This can also be useful to evaluate the explainability of anomaly detection systems which may point out to the anomalous channels (root cause and affected).
• Obvious anomalies. The simulated anomalies have been designed to be "easy" to be detected for human eyes (i.e., there are very large spikes or oscillations), hence also detectable for most algorithms. It makes this synthetic dataset useful for screening tasks (i.e., to eliminate algorithms that are not capable to detect those obvious anomalies). However, during our initial experiments, the dataset turned out to be challenging enough even for state-of-the-art anomaly detection approaches, making it suitable also for regular benchmark studies.
• Context provided. Some variables can only be considered anomalous in relation to other behaviours. A typical example consists of a light and switch pair. The light being either on or off is nominal, the same goes for the switch, but having the switch on and the light off shall be considered anomalous. In the CATS dataset, users can choose (or not) to use the available context, and external stimuli, to test the usefulness of the context for detecting anomalies in this simulation.
• Pure signal ideal for robustness-to-noise analysis. The simulated signals are provided without noise: while this may seem unrealistic at first, it is an advantage since users of the dataset can decide to add on top of the provided series any type of noise and choose an amplitude. This makes it well suited to test how sensitive and robust detection algorithms are against various levels of noise.
• No missing data. You can drop whatever data you want to assess the impact of missing values on your detector with respect to a clean baseline.

Change Log

Version 2

• Metadata: we include a metadata.csv with information about:
  • Anomaly categories
  • Root cause channel (signal in which the anomaly is first visible)
  • Affected channel (signal in which the anomaly might propagate) through coupled system dynamics
• Removal of anomaly overlaps: version 1 contained anomalies which overlapped with each other resulting in only 190 distinct anomalous segments. Now, there are no more anomaly overlaps.
• Two data files: CSV and parquet for convenience.

[1] Example Benchmark of Anomaly Detection in Time Series: “Sebastian Schmidl, Phillip Wenig, and Thorsten Papenbrock. Anomaly Detection in Time Series: A Comprehensive Evaluation. PVLDB, 15(9): 1779 - 1797, 2022. doi:10.14778/3538598.3538602”

About Solenix

Solenix is an international company providing software engineering, consulting services and software products for the space market. Solenix is a dynamic company that brings innovative technologies and concepts to the aerospace market, keeping up to date with technical advancements and actively promoting spin-in and spin-out technology activities. We combine modern solutions which complement conventional practices. We aspire to achieve maximum customer satisfaction by fostering collaboration, constructivism, and flexibility.

All samples in the dataset were screened for feline leukemia virus with an FeLV-A specific qPCR. The proviral load of positive samples was quantified via qPCR by normalizing against the puma or domestic cat CCR5 housekeeping gene, depending upon the species of the sample. Additionally, we used conventional PCR to isolate the FeLV env gene from FeLV positive samples, cloned the PCR product, and Sanger sequenced the clones for a phylogenetic analysis of the env gene. This dataset identifies all samples screened and which individuals tested positive. For positive animals, this dataset lists the proviral load and if FeLV isolates from the invidual were sequenced. FeLV sequences for this study are published to GenBank.

The US Cat Food Market is segmented by Pet Food Product (Food, Pet Nutraceuticals/Supplements, Pet Treats, Pet Veterinary Diets) and by Distribution Channel (Convenience Stores, Online Channel, Specialty Stores, Supermarkets/Hypermarkets). The market volume and value are presented in metric ton and USD respectively. The key data points include the market size of pet food by products, distribution channels, and pets.

The number of pet cats adopted from shelters or rescues in the United States reached over *** million in 2023.

A vast global literature documents that free-roaming domestic cats (Felis catus) have substantial negative effects on wildlife, including through predation, fear, disease, and competition-related impacts that have contributed to numerous wildlife extinctions and population declines worldwide. However, no study has synthesized this literature on cat impacts on wildlife to evaluate its overarching biases and major gaps. To direct future research and conservation related to cat impacts on wildlife, we conducted a global literature review that entailed evaluation and synthesis of patterns and gaps in the literature related to the geographic context, methods, and types of impacts studied. Our systematic literature search compiled 2,245 publications. We extracted information from 332 of these meeting inclusion criteria designed to ensure the relevance of studies analyzed. This synthesis of research on cat impacts on wildlife highlights a focus on oceanic islands, Australia, Europe, and North America, and on rural areas, predation, impacts of unowned cats, and impacts at population and species levels. Key research advances needed to better understand and manage cat impacts include more studies in underrepresented, highly biodiverse regions (Africa, Asia, South America), on cat impacts other than predation, and on methods designed to reduce impacts on wildlife. The identified areas of needed research into cat impacts on wildlife will be critical to further clarifying the role of cats in global wildlife declines and to implementing science-driven policy and management that benefit conservation efforts.

The Cloud-Aerosol Transport System (CATS), launched on January 10, 2015, is a lidar remote sensing instrument that will provide range-resolved profile measurements of atmospheric aerosols and clouds from the International Space Station (ISS). CATS is intended to operate on-orbit for at least six months, and up to three years. CATS will provide vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, it will allow scientist to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data product containing geophysical parameters derived from Level 1 data, at 60m vertical and 5km horizontal resolution.

The Cloud-Aerosol Transport System (CATS), launched on January 10, 2015, is a lidar remote sensing instrument that will provide range-resolved profile measurements of atmospheric aerosols and clouds from the International Space Station (ISS). CATS is intended to operate on-orbit for at least six months, and up to three years. CATS will provide vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, it will allow scientist to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data product containing geophysical parameters derived from Level 1 data, at 60m vertical and 5km horizontal resolution.

We toilet trained our cat, but teaching him to flush the toilet was just too difficult, so we decided to train the toilet instead! This is a collection of images used to train a toilet when to flush after a cat has finished his business.

Our cat is used to us flushing the toilet seconds after he's used the bathroom. He usually lingers a bit longer to scratch around the seat to 'cover it up' while it's flushing, so the goal is to try and maintain this same responsiveness. To do this, it's necessary to detect when he's either pooping or peeing, and then flush after he's confidently left either of these states. There are only 3 states:

1. Peeing: He squats low in this state and his back is usually straight with tail raised.
2. Pooping: Generally all 4 paws are close together and his back is arched with tail curled
3. Other: This is any other state that isn't clearly peeing or pooping.

I've been careful to avoid any transition states in my training images, so only very obvious peeing or pooping images. The other images would not be confused with peeing or pooping.

Interestingly I've noticed that when he starts to pee, he briefly transitions through the pooping state, so it will be necessary to measure either state for several seconds before deciding to flush, and then to wait for a few seconds in the other state before starting the flush.

https://i.imgur.com/iXLVwMA.jpg" alt="Flushing Prototype">

For the flushing device itself, I've constructed a wooden box on top of my toilet that uses an Adafruit Feather Bluefruit Sense (for Bluetooth connectivity to the host connected to an OAK-1 camera) as well as FeatherWing DC Motor controller that actuates a 12v micro linear actuator to press the button on top of the toilet. Why Bluetooth? Because the host connected to the camera most certainly should not be networked and I don't want to run a wire from one side of the bathroom to the other. All image detection must be done offline (on the OAK-1).

Originally, we just used a timer to control the flush when we weren't home, but after we went on a trip for a few days, he pooped in the kitchen, so we suspect either he lost confidence that it would flush for him, or he had to use it twice between flush intervals and didn't want to use a dirty toilet (he will sometimes 'dig' in the water before he uses it). Our hope is that he will adjust to the toilet flushing when we would normally flush it, which just isn't possible using a timer (the timer also wastes water).

The flush box itself is just a prototype. Eventually I'll rebuild it and probably share some schematics and code, but right now it's still a work in progress.

Graph and download economic data for Producer Price Index by Industry: Dog and Cat Food Manufacturing: Dog Food (PCU3111113111111) from Dec 1985 to May 2025 about pets, food, manufacturing, PPI, industry, inflation, price index, indexes, price, and USA.

The United States Pet Food Market report segments the industry into Pet Food Product (Food, Pet Nutraceuticals/Supplements, Pet Treats, Pet Veterinary Diets), Pets (Cats, Dogs, Other Pets), and Distribution Channel (Convenience Stores, Online Channel, Specialty Stores, Supermarkets/Hypermarkets, Other Channels). Get five years of historical data alongside five-year market forecasts.

This dataset is comprised of variables coded/extracted from popular press articles about domestic cats (Felis catus), which were evaluated as part of a media-content analysis. Our focus was understanding how a number of issues surrounding free-roaming (feral) cats are presented and discussed in the popular press, including: - The messengers who are quoted or referenced (e.g., cat advocates, veterinarians, naturalists, researchers) - The risks and threats to which feral cats are exposed (e.g., diseases, vehicles, predation)- The impacts feral cats have on the environment, native wildlife (e.g., via predation), and threats they pose to human health (e.g., via disease transmission)- The potential strategies and tools used to manage feral cat populations and their impacts (e.g., trap-neuter-release, bylaws, public education)We used the Lexis Nexus search engine to conduct a systemic search for English-language popular print media, including news articles and bulletins, opinion-editorials, and other public notices (e.g., classifieds) published between 1990 and 2018 (see Search Terms in READ_ME file and Methods: Search in the referenced article). Using a code book we developed (see Questions Coded From Articles in READ_ME), we evaluated each article based on whether they conveyed a variety of different messages. In total, the dataset is comprised of 796 articles, with the bulk (~95%) of articles from the United States and Canada. Most of the people interviewed ("messengers") were from non-governmental organizations, mainly from cat-welfare or cat-rights groups. Researchers, shelter organizations, veterinarians, and groups that differ on how to resolve issues surrounding free-roaming cats were rarely interviewed. Most articles focused on cat welfare issues and the management strategies of euthanasia or trap-neuter-release (TNR), whereas less than one-third of the articles acknowledged that cats have any impact on wildlife or the broader environment.See READ_ME file for a full list of variable definitions.

Co-evolutionary relationships associated with biogeographical context mediate the response of native prey to introduced predators, but this effect has not yet been demonstrated for domestic cats. We investigated the main factors influencing the vulnerability of prey species to domestic cat (Felis catus) predation across Australia, Europe, and North America, where domestic cats are introduced. In addition to prey data from empirical records, we used machine-learning models to compensate for unobserved prey in the diet of cats. We found continent-specific patterns of predation: birds were more frequently depredated by cats in Europe and North America, while mammals were favoured in Australia. Bird prey traits were consistent across continents, but those of mammalian prey diverged, notably in Australia. Differences between prey and non-prey species included mass, distribution, and reproductive traits, except in Australian mammals where there was no evidence for a relationship between mass and the probability of being prey. Many Australian mammal prey also have a high extinction risk, emphasizing their vulnerability compared to European and North American counterparts. Our findings highlight the role of eco-evolutionary context in assessing predation impacts and also demonstrate the potential for machine learning to identify at-risk species, thereby aiding global conservation efforts to reduce the negative impacts of introduced predators.