Despite animal testing being a controversial topic for many years, it is still widely used globally to assess the safety of products and test the efficacy of new treatments and products. In 2020, the United States was the world’s largest user of animals in research and testing, with around 20 million animals used research and testing, followed by China where it is estimated that around 16 million animals were used in research and testing in that year. Animal testing is used especially in the medical, cosmetic, and chemical industries.

Animal Testing in the EU

The European Union also reported some 9.4 million animals used research and testing as of 2020. Basic research, and translational and applied research are the two leading purposes of animal testing in the European Union. Mice represent the most commonly used animal in research and testing in the EU, representing almost half of all animals used in research and testing, followed by fish and rats.

Animal Testing in Great Britain

Animal testing in Great Britain was most common in basic scientific research on the nervous system and the immune system, and most procedures on animals for scientific experiments in that year in Great Britain were conducted by universities and medical schools. As in the EU, mice were the most commonly used animals in research and testing, followed by domestic fowl and rats.

In 2019, 797,546 animals were used for research in research facilities in the United States. This is an increase from the previous year, when about 780,070 animals were used for research in the country.

Global Animals Used in Research and Testing market size 2025 was XX Million. Animals Used in Research and Testing Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

This statistic shows the likelihood of consumers in the United States to stop purchasing from their favorite cosmetics/makeup brand if it were reported that the brand test their products on animals, as of April 2017. During the survey, 32 percent of consumers reported that they would very likely stop purchasing from their favorite brand if they tested on animals.

This statistic shows how likely consumers in the United States would be to stop purchasing from their favorite cosmetics/makeup brand if it were reported that they test their products on animals, as of April 2017, by age. During the survey, ** percent of respondents aged 35 to 54 years reported that they would very likely stop purchasing from their favorite brand if they tested on animals.

Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyse animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analysing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analysing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g. sex) on the number and combined strength of an individual’s network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations...

Exports - Other Animal Feeds, N.E.C. (Census Basis) in the United States decreased to 1012.12 USD Million in February from 1030.81 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Exports of Other Animal Feeds, N.e.c..

The initial worksheet comprises raw data for Figure 1, in addition to a sample calculation of the calcium response for a cholinergically stimulated rat sweat gland. The second worksheet contains raw data from freshly isolated and cryopreserved human sweat glands during the cholinergic stimulation shown in Figure 2. The third worksheet contains raw data for multiple sequential cholinergic stimulation of rat and human sweat glands with and without atropine inhibition, as shown in Figure 3. (XLSX)

THIS RESOURCE IS NO LONGER IN SERVICE, it has been replaced by Monarch Initiative. LAMHDI, the initiative to Link Animal Models to Human DIsease, is designed to accelerate the research process by providing biomedical researchers with a simple, comprehensive Web-based resource to find the best animal model for their research. LAMDHI is a free, Web-based, resource to help researchers bridge the gap between bench testing and human trials. It provides a free, unbiased resource that enables scientists to quickly find the best animal models for their research studies. LAMHDI includes mouse data from MGI, the Mouse Genome Informatics website; zebrafish data from ZFIN, the Zebrafish Model Organism Database; rat data from RGD, the Rat Genome Database; yeast data from SGD, the Saccharomyces Genome Database; and fly data from FlyBase. LAMHDI.org is operational today, and data is added regularly. Enhancements are planned to let researchers contribute their knowledge of the animal models available through LAMHDI. The LAMHDI goal is to allow researchers to share information about and access to animal models so they can refine research and testing, and reduce or replace the use of animal models where possible. LAMHDI Database Search: LAMHDI brings together scientifically validated information from various sources to create a composite multi-species database of animal models of human disease. To do this, the LAMHDI database is prepared from a variety of sources. The LAMHDI team takes publicly available data from OMIM, NCBI''s Entrez Gene database, Homologene, and WikiPathways, and builds a mathematical graph (think of it as a map or a web) that links these data together. OMIM is used to link human diseases with specific human genes, and Entrez provides universal identifiers for each of those genes. Human genes are linked to their counterpart genes in other species with Homologene, and those genes are linked to other genes tentatively or authoritatively using the data in WikiPathways. This preparatory work gives LAMHDI a web of human diseases linked to specific human genes, orthologous human genes, homologous genes in other species, and both human and non-human genes involved in specific metabolic pathways associated with those diseases. LAMHDI includes model data that partners provide directly from their data structures. For instance, MGI provides information about mouse models, including a disease for each model, as well as some genetic information (the ID of the model, in fact, identifies one or more genes). ZFIN provides genetic information for each zebrafish model, but no diseases, so zebrafish models are integrated by using the genes as the glue. For instance, a zebrafish model built to feature the zebrafish PKD2 gene would plug into the larger disease-gene map at the node representing the zebrafish PKD2 gene, which is connected to the node representing the human PKD2 gene, which in turn is connected to the node representing the human disease known as polycystic kidney disease. (Some of the partner data LAMHDI receives can even extend the base map. MGI provides a disease for every model, and in some cases this allows the creation of a disease-to-gene relationship in the LAMHDI database that might not already be documented in the OMIM dataset.) With curatorial and model information in hand, LAMHDI runs a lengthy automated process that exhaustively searches for every possible path between each model and each disease in the data, up to a set number of hops, producing for each disease-to-model pair a set of links from the disease to the model. The algorithm avoids circular paths and paths that include more than one disease anywhere in the middle of the path. At the end of this phase, LAMHDI has a comprehensive set of paths representing all the disease-to-model relationships in the data, varying in length from one hop to many hops. Each disease-to-model path is essentially a string of nodes in the data, where each node represents a disease, a gene, a linkage between genes (an orthologue, a homologue, or a pathway connection, referred to as a gene cluster or association), or a model. Each node has a human-friendly label, a set of terms and keywords, and - in most cases - a URL linking the node to the data source where it originated. When a researcher submits a search on the LAMHDI website, LAMHDI searches for the user''s search terms in its precomputed list of all known disease-to-model paths. It looks for the terms not only in the disease and model nodes, but also in every node along each path. The complete set of hits may include multiple paths between any given disease-to-model pair of endpoints. Each of these disease-to-model pair sets is ordered by the number of hops it involves, and the one involving the fewest hops is chosen to represent its respective disease-to-model pair in the search results presented to the user. Results are sorted by scores that represent their matches. The number of hops is one barometer of the strength of the evidence linking the model and the disease; fewer hops indicates the relationship is stronger, more hops indicates it may be weaker. This indicator works best for comparing models from a single partner dataset: MGI explicitly identifies a disease for each mouse model, so there can be disease-to-model hits for mice that involve just one hop. Because ZFIN does not explicitly identify a disease for each model, no zebrafish model will involve fewer than four hops to the nearest disease, from the zebrafish model to a zebrafish gene to a gene cluster to a human gene to a human disease.

Animal Simulators Market Outlook

According to our latest research, the global animal simulators market size in 2024 stands at USD 1.42 billion, reflecting a robust appetite for immersive, animal-centric digital experiences worldwide. The industry is experiencing a healthy compound annual growth rate (CAGR) of 11.8% from 2025 to 2033, with the market projected to reach USD 3.62 billion by the end of the forecast period. This growth is primarily driven by the rising popularity of interactive entertainment, advancements in simulation technology, and an increasing demand for educational and training applications across diverse demographics.

One of the primary growth factors for the animal simulators market is the rapid evolution of simulation technology, which has enabled the creation of highly realistic and engaging animal experiences. Modern animal simulators leverage advanced graphics engines, artificial intelligence, and physics-based modeling to deliver lifelike animal behaviors and ecosystems. These innovations have expanded the appeal of animal simulators beyond traditional gaming audiences, attracting educators, researchers, and conservationists seeking immersive tools for learning and training. Furthermore, the integration of virtual reality (VR) and augmented reality (AR) into animal simulators has significantly enhanced user engagement, allowing players to interact with virtual animals in ways that were previously impossible. This technological leap is not only enriching entertainment experiences but also opening new avenues for scientific exploration and wildlife conservation education.

Another significant driver is the increasing adoption of animal simulators in educational and training environments. Educational institutions, zoos, and wildlife organizations are utilizing these simulators to teach students and the public about animal behavior, ecosystems, and conservation efforts. The interactive nature of these platforms fosters deeper learning and retention compared to traditional methods. Additionally, animal simulators are being used for professional training purposes, such as veterinary education and wildlife management, providing a safe and controlled environment to practice complex scenarios. The growing recognition of the educational and practical value of animal simulators is expected to fuel further market expansion, particularly as more institutions incorporate these tools into their curricula and training programs.

The proliferation of mobile devices and the increasing accessibility of high-speed internet have also played a crucial role in the growth of the animal simulators market. Mobile platforms have democratized access to animal simulators, enabling users from various socioeconomic backgrounds to engage with these experiences. The freemium and microtransaction models prevalent in mobile gaming have further boosted user acquisition and monetization, making animal simulators a lucrative segment for developers and publishers. Additionally, the rise of online communities and social features within animal simulators has fostered a sense of connection among users, driving repeat engagement and organic growth through word-of-mouth and social sharing.

From a regional perspective, North America currently leads the global animal simulators market, followed closely by Europe and Asia Pacific. The North American market benefits from a mature gaming ecosystem, high disposable incomes, and a strong culture of digital entertainment. Europe is witnessing steady growth due to increasing investments in educational technology and wildlife conservation initiatives. Meanwhile, the Asia Pacific region is emerging as a key growth engine, driven by a large population of mobile gamers, rising internet penetration, and growing interest in interactive learning solutions. Latin America and the Middle East & Africa are also showing promising potential, albeit from a smaller base, as digital infrastructure continues to improve and awareness of animal simulators spreads.

Platform Analysis

Proximate analysis of Tithonia rotundifolia was conducted at Fertilizer, Farm Feeds and Remedies Institute laboratory, under the Department of Research and Specialist Services in Harare. The proximate analysis was done on a dry matter basis and the data from the analysis was incorporated in FeedWin for feed formulation. The main aspects considered in proximate analysis being the dry matter content (DM), crude protein content (CP), crude fibre content (CF), ether extract content (EE), nitrogen-free extract content (NFE), calcium content (Ca) and phosphorous content (P), which are important for the diet formulation. The samples were analysed using the procedures of Association of Official Analytical Chemists (A.O.A.C. 2005). For other feed ingredients, the researcher used the standard nutritional composition on FeedWin feed formulation software. FeedWin Interactive v1.01 software was used to formulate the diet. This software ensures the use of nutrient composition in formulating diets. The Tithonia meal was used as an inclusion in the feed, mixed with other ingredients which are maize meal, soybean meal, salt, limestone and mono-calcium-phosphate. These ingredients were used to formulate three diets with 0 %, 10 % and 20 % inclusion levels of the Tithonia meal. The inclusion of Tithonia meal in the diet formulation caused the percentage variation of other ingredients. Thus, becoming a source of variation.Data collected was daily feed intake and weekly weight gain. This data was fed into Microsoft Excel and used to calculate feed conversion efficiency. Before the commencement of the experiment, the weights of the rabbits were taken, using a digital scale for weighing. After this, the rabbits were weighed weekly for weight gain changes. Since the animals were given a predetermined amount of feed, the left-over feed was measured every morning during the feeding trial so as to calculate the feed intake. The feed conversion efficiency was then calculated after the feeding trial. Feed intake was calculated by subtracting the refusals from the feed given. The feed conversion efficiency was then calculated by dividing total feed intake with total weight gain.Statistical Package for Social Sciences (SPSS) also known as Statistical Product and Service Solutions (SPSS) version 20 was used to analyse the data on feed intake, weight gain and feed conversion efficiency. The data was subjected to GLM repeated measures ANOVA for RCBD. Boniferroni’s test was used to separate means. Descriptive statistics were also generated which were presented in form of tables and graphs. Equality of variances was tested using Levene’s test. Wilk’s Lambda’s test was used to assess whether or not there was effect of time or interaction between time and diet.

UNIDO pub. Project completion report on market and product development of sisal and henequen in Kenya (animal feed trials) (reference: testing) - covers (1) background, context and objectives (2) project implementation and results achieved: the bogas recovery system, ensiling the fresh bogas, implementation trials and diet selection (beef steers, cattle, goats, sheep, dairy products). Conclusions and recommendations. Bibliography. Statistics, diagrams. Additional reference: natural fibres.

A systematic search in PubMed and EMBASE yielded 6237 unique publications. After application of inclusion and exclusion criteria, data from 503 publications were included in the meta-analysis and quality assessment.

Sweden Imports from Botswana was US$33.4 Thousand during 2024, according to the United Nations COMTRADE database on international trade. Sweden Imports from Botswana - data, historical chart and statistics - was last updated on July of 2025.

This database contains all the data points used to plot the graphs shown in the 2020 publication by Mitrousis et. al., entitled "Hydrogel-mediated co-transplantation of retinal pigmented epithelium and photoreceptors restores vision in an animal model of advanced retinal degeneration". Please refer to the paper for details.

White-tailed deer Odocoileus virginianus are the most popular big game animal in the United States. Recreational harvest of these animals is a critical tool in population management, as well as an important financial resource for state economies and wildlife agencies. Thus, herd health evaluations can provide information to wildlife managers tasked with developing sustainable harvest practices while monitoring for emergent problems. The purpose of our study was to document causes of illness and natural mortality in New York white-tailed deer submitted for post mortem evaluation. Animals were presented by members of the public and wildlife management personnel due to abnormal behavior or unexplained death. We describe demographic and seasonal associations among gross and histologic evaluation and diagnostic testing. Post mortem examinations were performed on 735 white-tailed deer submitted for necropsy in New York from January 2011 to November 2017. Causes of euthanasia or mortality were classified into nine categories. The most common findings were bacterial infections, trauma not evident at time of collection, and nutritional issues, primarily starvation. Using a multinomial logistic regression model, we looked for associations between the mortality categories and age, sex and season. Compared to the baseline of bacterial deaths, adults were less likely to have died from nutritional and parasitic causes, males were less likely to have died from other causes, and risk of death from nutritional reasons decreased from season to season, with lowest risk in winter. These methods can help wildlife biologists track changes in disease dynamics over time.

Methods Two of the highest priorities, also reflected in the New York State Interagency CWD Risk Minimization Plan, are to detect chronic wasting disease (CWD) in the deer population and document causes of death and disease in white-tailed deer. Standardized criteria for submission in the surveillance program are: 1) live deer behaving abnormally or in poor body condition necessitating humane euthanasia and; 2) deer found dead without an obvious cause of death or found to have some abnormality. DEC may be notified of deer meeting these criteria by members of the public or law enforcement and can submit the animal for necropsy and diagnostic testing. Because the surveillance program specifically excludes deer that died from obvious predation, hunting, and deer-vehicle collisions, animals collected do not represent the New York population as a whole; however, they are valuable for assessing the breadth of diseases affecting wild deer and establishing a standardized baseline for future assessment. A benefit of this program is that these animals can serve as sentinels for emerging diseases. This type of opportunistic surveillance is a widely used method for states to prioritize deer that could be infected by CWD (Joly et al. 2009). Providing a basis for comparison will allow states to refine their surveillance systems to be better informed about white-tailed deer diseases by demo- graphic categories and seasonality.

For the present study, records from deer presented for necropsy through the surveillance program from 2011 to 2017 were compiled to retrospectively evaluate disease occurrence in a subset of the New York deer population. A total of 534 deer out of 735 that died between January 2011 to November 2017 met the criteria for inclusion in the study. Deer that died from obvious, non-natural causes, including deer killed for diagnostic tests (9), forensic studies (102), research (21), hunter killed (49), obvious vehicular trauma and predation (20) were excluded. The study population consisted of 230 females, 169 males, and 135 animals of unknown sex. There were 227 adults, 157 juveniles, 17 neonates, and 133 deer of unknown age. Weight data was available for 215 cases in which full carcasses were submitted.

Experimental Design Assistant (EDA) diagrams prepared for the manuscript. These diagrams can be imported into the EDA tool (see https://eda.nc3rs.org.uk/home) and the designs explored further.

Source data for Figure 2 "Effects of rearing facility on the behavioral and physiological profile of the mice"

This file contains raw source data used to make the graphs presented in Figure 2.

Pet stores prospered in recent years as households have become more financially stable and pet ownership has risen. Pet parents have increasingly lavished their pets with premium food products, designer pet accessories and luxury grooming services. The industry has expanded despite mounting competition from supermarkets, mass merchandisers and online retailers. These competitors offer similar products at greater convenience and competitive prices. Traditional brick-and-mortar stores have successfully positioned themselves as pioneers and exclusive providers of high-quality food and additional service offerings, like grooming or day care. Pet store revenue is expected to climb at a CAGR of 0.3% to $31.6 billion through the end of 2025, including growth of 1.4% in 2025 alone. The revenue growth rate was suppressed because revenue jumped 18.6% to begin the period, as pet ownership skyrocketed in response to the pandemic. Since pets are widely viewed as family members, pet owners have shifted their preferences to higher-quality organic, gluten-free and grain-free pet foods to keep their pets happy and healthy. These premium products and services are high-margin, enabling profit gains for pet stores. Sales of designer dog breeds have also jumped in recent years, contributing to recent growth. While stores have capitalized on growing pet ownership trends, pet store sales growth was constrained by online retailers' surging popularity. Moving forward, pet stores will continue to exhibit revenue growth, albeit slower than before. While positive consumer trends will benefit pet stores, competition from online retailers, mass merchandisers and discount department stores will be more vigorous, limiting the expansion. An aging population will contribute to higher sales of pets and pet-related products as older consumers adopt pets to fulfill their needs for companionship. Younger consumers will continue to buy pets as companions and to round out their budding families. Stores will push premium products and pets to cater to growing appetites for luxury among many consumers. Pet store revenue is expected to swell at a CAGR of 2.4% to $35.6 billion through the end of 2030.