Geodemographic Segmentation Data from Caliper Corporation contain demographic data in a way that is easy to visualize and interpret. We provide 8 segments and 32 subsegments for exploring the demographic makeup of neighborhoods across the country.

Demografy is a privacy by design customer demographics prediction AI platform.

Core features: - Demographic segmentation - Demographic analytics - API integration - Data export

Key advantages: - 100% coverage of lists - Accuracy estimate before purchase - GDPR-compliance as no sensitive data is required. Demografy can work with only first names or masked last names

Use cases: - Actionable analytics about your customers to get demographic insights - Appending missing demographic data to your records for customer segmentation and targeted marketing campaigns - Enhanced personalization knowing you customer better

Unlike traditional solutions, you don’t need to know and disclose your customer or prospect addresses, emails or other sensitive information. You can provide even masked last names keeping personal data in-house. This makes Demografy privacy by design and enables you to get 100% coverage of your audience since all you need to know is names.

With Versium REACH Demographic Append you will have access to many different attributes for enriching your data.

Basic, Household and Financial, Lifestyle and Interests, Political and Donor.

Here is a list of what sorts of attributes are available for each output type listed above:

Basic: - Senior in Household - Young Adult in Household - Small Office or Home Office - Online Purchasing Indicator
- Language - Marital Status - Working Woman in Household - Single Parent - Online Education - Occupation - Gender - DOB (MM/YY) - Age Range - Religion - Ethnic Group - Presence of Children - Education Level - Number of Children

Household, Financial and Auto: - Household Income - Dwelling Type - Credit Card Holder Bank - Upscale Card Holder - Estimated Net Worth - Length of Residence - Credit Rating - Home Own or Rent - Home Value - Home Year Built - Number of Credit Lines - Auto Year - Auto Make - Auto Model - Home Purchase Date - Refinance Date - Refinance Amount - Loan to Value - Refinance Loan Type - Home Purchase Price - Mortgage Purchase Amount - Mortgage Purchase Loan Type - Mortgage Purchase Date - 2nd Most Recent Mortgage Amount - 2nd Most Recent Mortgage Loan Type - 2nd Most Recent Mortgage Date - 2nd Most Recent Mortgage Interest Rate Type - Refinance Rate Type - Mortgage Purchase Interest Rate Type - Home Pool

Lifestyle and Interests: - Mail Order Buyer - Pets - Magazines - Reading
- Current Affairs and Politics
- Dieting and Weight Loss - Travel - Music - Consumer Electronics - Arts
- Antiques - Home Improvement - Gardening - Cooking - Exercise
- Sports - Outdoors - Womens Apparel
- Mens Apparel - Investing - Health and Beauty - Decorating and Furnishing

Political and Donor: - Donor Environmental - Donor Animal Welfare - Donor Arts and Culture - Donor Childrens Causes - Donor Environmental or Wildlife - Donor Health - Donor International Aid - Donor Political - Donor Conservative Politics - Donor Liberal Politics - Donor Religious - Donor Veterans - Donor Unspecified - Donor Community - Party Affiliation

Healthy Paws Pet Insurance Market size was valued at USD 6.87 Million in 2023 and is projected to reach USD 17.54 Million by 2031, growing at a CAGR of 14.3% during the forecast period 2024-2031.

Global Healthy Paws Pet Insurance Market Drivers

The market drivers for the Healthy Paws Pet Insurance Market can be influenced by various factors. These may include:

Increasing Pet Ownership and Humanization of Pets: The global trend of increasing pet ownership, coupled with the growing tendency to treat pets as family members, has driven significant demand for comprehensive pet healthcare solutions, bolstering the market for Healthy Paws Pet Insurance. As more households adopt pets and seek to offer them the best possible care, the necessity for veterinary insurance to manage potential health expenses grows.

Rising Veterinary Costs: Advances in veterinary medicine, while offering cutting-edge treatments, have significantly increased the cost of pet healthcare. This surge in expenses for surgeries, diagnostics, and routine care has heightened pet owners' awareness of the need for insurance coverage, thus driving growth in the pet insurance market, including companies like Healthy Paws.

Growing Awareness of Pet Health and Wellness: There is a rising awareness among pet owners regarding the importance of preventive care and timely treatment for their pets' well-being. As pet health knowledge becomes more widespread through social media and veterinary advocacy, more owners are inclined to seek insurance plans to ensure affordability and access to necessary treatments, directly benefiting Healthy Paws Pet Insurance.

Technological Advancements in Veterinary Care: Innovations in veterinary diagnostics and treatment options have revolutionized pet healthcare, making it more efficient but also more expensive. Healthy Paws Pet Insurance benefits from this trend as pet owners look to protect themselves from unforeseen high veterinary costs by investing in comprehensive insurance policies that cover these advanced treatments.

Increasing Chronic Conditions in Pets: Pets, like their human counterparts, are increasingly diagnosed with chronic conditions such as diabetes, arthritis, and cancer. The management of these illnesses typically involves significant financial outlays for continuous care and medications. This trend underscores the necessity for robust pet insurance options, thus driving demand for providers like Healthy Paws Pet Insurance.

Improved Insurance Claim Processing and Customer Service: Enhanced customer experience in the pet insurance industry, characterized by streamlined claim processes, user-friendly mobile apps, and superior customer service, has made policies more attractive. Companies like Healthy Paws that invest in these improvements witness increased enrollment as they offer greater convenience and reliability to pet owners.

Regulatory Support and Industry Standards: The establishment of clearer regulatory frameworks and industry standards is providing a more stable and trustworthy environment for the pet insurance market to thrive. Regulations that protect consumer rights and ensure transparency in insurance policies help in building consumer confidence, benefiting reputable providers such as Healthy Paws Pet Insurance.

Growing Popularity of E-Commerce and Digital Platforms: The increasing preference for online shopping and digital services has made it easier for pet owners to access and purchase pet insurance. Healthy Paws has leveraged these platforms effectively to market their insurance products, allowing for easier comparison of plans, more detailed information, and streamlined purchasing processes, further driving market expansion.

Expansion of Veterinary Networks: As more veterinary clinics and hospitals partner with pet insurance providers, the network of accessible care for insured pets expands. Healthy Paws Pet Insurance, with a broad network of participating vets, becomes a more attractive option for pet owners looking for widespread and quality veterinary care coverage.

Economic Resilience and Disposable Income: Even amidst economic fluctuations, the pet insurance market has shown resilience, with pet owners continuing to invest in their pets' health. An increase in disposable income, particularly among millennials who form a significant portion of pet owners, supports continued expenditure on pet insurance, ensuring sustained market growth for companies like Healthy Paws Pet Insurance.

Pandemics such as Covid-19 pose tremendous public health communication challenges in promoting protective behaviours, vaccination, and educating the public about risks. Segmenting audiences based on attitudes and behaviours is a means to increase the precision and potential effectiveness of such communication. The present study reports on such an audience segmentation effort for the population of England, sponsored by the United Kingdom Health Security Agency (UKHSA) and involving a collaboration of market research and academic experts. A cross-sectional online survey was conducted between 4 and 24 January 2022 with 5525 respondents (5178 used in our analyses) in England using market research opt-in panel. An additional 105 telephone interviews were conducted to sample persons without online or smartphone access. Respondents were quota sampled to be demographically representative. The primary analytic technique was k means cluster analysis, supplemented with other techniques including multi-dimensional scaling and use of respondent ‐ as well as sample-standardized data when necessary to address differences in response set for some groups of respondents. Identified segments were profiled against demographic, behavioural self-report, attitudinal, and communication channel variables, with differences by segment tested for statistical significance. Seven segments were identified, including distinctly different groups of persons who tended toward a high level of compliance and several that were relatively low in compliance. The segments were characterized by distinctive patterns of demographics, attitudes, behaviours, trust in information sources, and communication channels preferred. Segments were further validated by comparing the segmentation variable versus a set of demographic variables as predictors of reported protective behaviours in the past two weeks and of vaccine refusal; the demographics together had about one-quarter the effect size of the single seven-level segment variable. With respect to managerial implications, different communication strategies for each segment are suggested for each segment, illustrating advantages of rich segmentation descriptions for understanding public health communication audiences. Strengths and weaknesses of the methods used are discussed, to help guide future efforts.

This profile is designed to accompany the Joint Strategic Needs Assessment (JSNA) chapter on Demographics, which looks at segmenting the borough’s population by their most significant health and social care need. This supplement looks at adults (aged 18 and over) instead of the overall population, because the health and social care need segments covered in this section are more common in adults.

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

Percentage of responses in the range 0-6 for 'Happy Yesterday' by LSOA in the First ONS Annual Experimental Subjective Wellbeing survey, April 2011 - March 2012

The Department for Communities and Local Government (DCLG) has estimated the expected wellbeing of residents at Lower-layer Super Output Area (LSOA) level. The purpose is to illustrate the likely degree of variation between neighbourhoods.

These are modelled estimates for local areas based on national findings from the ONS Annual Population Survey 2011-2012. They are not the actual survey responses of people living in those areas [1]. As such, DCLG encourage local areas to test these expected findings against their own local knowledge and data.

DCLG used CACI’s ACORN geo-demographic segmentation to estimate the likely wellbeing characteristics of each neighbourhood. Analysis of the APS provided a national profile of wellbeing by ACORN Type, with estimates of average subjective wellbeing and low subjective wellbeing for each of the 56 Types. The national profile was then applied to localities, to reflect their composition according to ACORN Type [2].

The method presumes the national profile of wellbeing for the ACORN types is broadly the same in each local authority. For all of the subjective wellbeing measures, DCLG tested this assumption broadly held across the nine regions. As a result, DCLG made a minimal number of adjustments to the profiles for life satisfaction, worthwhile, and happy yesterday, and determined that the method was not robust for modelling anxiety [3].

Feedback on the neighbourhood estimates and requests for further details of the methodology can sent to wellbeing@communities.gsi.gov.uk.

In October, DCLG will be producing wellbeing profiles to enable users to apply the same methodology using geo-demographic classifications: Experian’s MOSAIC and ONS’s Output Area Classification (OAC).

[1] This is because sample sizes from the APS do not permit reliable estimates of subjective wellbeing below the 90 unitary authorities and counties reported in the First ONS Annual Experimental Subjective Well-being Results.

[2] ACORN is a segmentation based on shared characteristics of people’s life-stage, income, profession and housing, as well as characteristics of places including whether they are urban, suburban or rural. Each respondent on the APS had been classified into one ACORN Type, based on the full postcode in which they live – approximately 16 addresses.) ACORN provided estimates of the population in each ACORN Type in each LSOA and local authority district.

[3] These adjustments were made only where there was reliable evidence (based on samples of more than 100 respondents) from APS that the national wellbeing ACORN profile was substantially different from the regional one, and where the implications for neighbourhood maps would be highly geographically clustered.

Consumer Insurance Experience & Demographic Profile

This dataset provides a detailed view of how individuals engage with insurance products, paired with demographic and lifestyle attributes to enable powerful segmentation, behavioral analysis, and customer journey mapping. By combining real-world insurance experiences with contextual information about each respondent’s background and preferences, this dataset supports a wide range of data-driven decision-making for insurance providers, policy designers, marketing teams, and product strategists.

Value of the Dataset Understanding how consumers perceive and interact with insurance offerings is critical to building products that resonate and services that retain. This dataset offers that visibility across multiple dimensions—capturing not only what type of insurance consumers hold and how they purchased it, but also what drives their satisfaction, loyalty, and likelihood to switch. Paired with demographic details like income, education, family status, and lifestyle, this information becomes a foundation for more personalized outreach, better-designed offerings, and improved customer experiences.

Because the data reflects lived experiences across diverse markets, it is particularly valuable for benchmarking consumer sentiment in emerging economies, identifying service delivery gaps, or evaluating potential uptake of new policy formats such as digital or personalized insurance.

Example Use Cases 1. Targeted Product Design A health insurer looking to launch short-term, digital-first plans could filter this dataset for consumers with low policy tenure, high digital communication preference, and dissatisfaction with current providers. This segment would inform feature design and positioning.

1. Competitive Analysis A provider evaluating churn risk can identify patterns among users who have filed claims but report dissatisfaction—indicating operational areas that may be driving customer loss and where improvements could increase retention.

2. Communication Channel Optimization By analyzing preferred communication methods across different demographic segments, insurers can tailor outreach strategies (e.g., SMS vs. in-app chat) to improve engagement and reduce support costs.

3. Market Expansion & Localization International insurers can explore regional variations in satisfaction drivers, awareness levels, and price sensitivity to refine go-to-market strategies in countries like Senegal, Tanzania, or the UAE.

4. Personalized Policy Offer Design Using data on interest in personalized policies and lifestyle indicators, providers can build customizable offerings for consumers more likely to value flexibility, such as frequent travelers or those with irregular incomes.

Insurance-Specific Fields & Descriptions Current Insurance Type Captures the kind of insurance the individual currently holds, with a focus on health insurance in this dataset.

Purchase Method Indicates how the insurance was obtained—through an agent, online, employer, etc.—to understand acquisition channels.

Policy Length Duration of the current policy, categorized (e.g., less than 1 year, 1–3 years, more than 5 years) to analyze tenure-based behaviors.

Satisfaction Self-reported satisfaction with the current insurance provider, useful for benchmarking sentiment.

Top Factor in Choosing Provider Highlights what influenced the purchase decision most—such as coverage options, customer service, pricing, or brand reputation.

Policy Review Frequency Shows how often individuals revisit their policy details or compare with alternatives, revealing levels of engagement or passive behavior.

Filed Claim A yes/no indicator showing whether the consumer has ever filed a claim, useful for analyzing downstream service experiences.

Claim Satisfaction Measures satisfaction with how past claims were handled, providing insight into operational effectiveness.

Primary Value Sought Captures what consumers value most from their insurance—e.g., peace of mind, financial protection, access to quality care.

Likelihood to Recommend Acts as a proxy for Net Promoter Score (NPS), indicating brand advocacy and potential referral behavior.

Biggest Areas for Improvement Open-ended or multi-select responses identifying where insurers can do better—lower premiums, faster claims, more digital tools, etc.

Preferred Method of Communication Indicates how consumers want to be contacted—via online chat, phone, email, SMS—supporting channel strategy optimization.

Preferred Services Details the types of updates or services consumers want—such as claims status, policy changes, or coverage recommendations.

Insurance Awareness Score Self-reported awareness of how insurance works, including policy options, rights, and terms.

Interest in Personalized Policies Captures whether the individual is open to customized insurance plans, an important indicator for usage-ba...

Food festivals have been a growing tourism sector in recent years due to their contributions to a region’s economic, marketing, brand, and social growth. This study analyses the demand for the Bahrain food festival. The stated objectives were: i) To identify the motivational dimensions of the demand for the food festival, (ii) To determine the segments of the demand for the food festival, and (iii) To establish the relationship between the demand segments and socio-demographic aspects. The food festival investigated was the Bahrain Food Festival held in Bahrain, located on the east coast of the Persian Gulf. The sample consisted of 380 valid questionnaires and was taken using social networks from those attending the event. The statistical techniques used were factorial analysis and the K-means grouping method. The results show five motivational dimensions: Local food, Art, Entertainment, Socialization, and Escape and novelty. In addition, two segments were found; the first, Entertainment and novelties, is related to attendees who seek to enjoy the festive atmosphere and discover new restaurants. The second is Multiple motives, formed by attendees with several motivations simultaneously. This segment has the highest income and expenses, making it the most important group for developing plans and strategies. The results will contribute to the academic literature and the organizers of food festivals.

Customer360Insights

The Customer360Insights dataset is a synthetic collection meticulously designed to mirror the multifaceted nature of customer interactions within an e-commerce platform. It encompasses a wide array of variables, each serving as a pillar to support various analytical explorations. Here’s a breakdown of the dataset and the potential analyses it enables:

Dataset Description

• Customer Demographics: Includes FullName, Gender, Age, CreditScore, and MonthlyIncome. These variables provide a demographic snapshot of the customer base, allowing for segmentation and targeted marketing analysis.
• Geographical Data: Comprising Country, State, and City, this section facilitates location-based analytics, market penetration studies, and regional sales performance.
• Product Information: Details like Category, Product, Cost, and Price enable product trend analysis, profitability assessment, and inventory optimization.
• Transactional Data: Captures the customer journey through SessionStart, CartAdditionTime, OrderConfirmation, OrderConfirmationTime, PaymentMethod, and SessionEnd. This rich temporal data can be used for funnel analysis, conversion rate optimization, and customer behavior modeling.
• Post-Purchase Details: With OrderReturn and ReturnReason, analysts can delve into return rate calculations, post-purchase satisfaction, and quality control.

Types of Analysis

• Descriptive Analytics: Understand basic metrics like average monthly income, most common product categories, and typical credit scores.
• Predictive Analytics: Use machine learning to predict credit risk or the likelihood of a purchase based on demographics and session activity.
• Customer Segmentation: Group customers by demographics or purchasing behavior to tailor marketing strategies.
• Geospatial Analysis: Examine sales distribution across different regions and optimize logistics. Time Series Analysis: Study the seasonality of purchases and session activities over time.
• Funnel Analysis: Evaluate the customer journey from session start to order confirmation and identify drop-off points.
• Cohort Analysis: Track customer cohorts over time to understand retention and repeat purchase patterns.
• Market Basket Analysis: Discover product affinities and develop cross-selling strategies.

This dataset is a playground for data enthusiasts to practice cleaning, transforming, visualizing, and modeling data. Whether you’re conducting A/B testing for marketing campaigns, forecasting sales, or building customer profiles, Customer360Insights offers a rich, realistic dataset for honing your data science skills.

Curious about how I created the data? Feel free to click here and take a peek! 😉

📊🔍 Good Luck and Happy Analysing 🔍📊

Percentage of responses in the range 0-6 for 'Life Satisfaction' by LSOA in the First ONS Annual Experimental Subjective Wellbeing survey, April 2011 - March 2012

The Department for Communities and Local Government (DCLG) has estimated the expected wellbeing of residents at Lower-layer Super Output Area (LSOA) level. The purpose is to illustrate the likely degree of variation between neighbourhoods.

These are modelled estimates for local areas based on national findings from the ONS Annual Population Survey 2011-2012. They are not the actual survey responses of people living in those areas [1]. As such, DCLG encourage local areas to test these expected findings against their own local knowledge and data.

DCLG used CACI’s ACORN geo-demographic segmentation to estimate the likely wellbeing characteristics of each neighbourhood. Analysis of the APS provided a national profile of wellbeing by ACORN Type, with estimates of average subjective wellbeing and low subjective wellbeing for each of the 56 Types. The national profile was then applied to localities, to reflect their composition according to ACORN Type [2].

The method presumes the national profile of wellbeing for the ACORN types is broadly the same in each local authority. For all of the subjective wellbeing measures, DCLG tested this assumption broadly held across the nine regions. As a result, DCLG made a minimal number of adjustments to the profiles for life satisfaction, worthwhile, and happy yesterday, and determined that the method was not robust for modelling anxiety [3].

Feedback on the neighbourhood estimates and requests for further details of the methodology can sent to wellbeing@communities.gsi.gov.uk.

In October, DCLG will be producing wellbeing profiles to enable users to apply the same methodology using geo-demographic classifications: Experian’s MOSAIC and ONS’s Output Area Classification (OAC).

[1] This is because sample sizes from the APS do not permit reliable estimates of subjective wellbeing below the 90 unitary authorities and counties reported in the First ONS Annual Experimental Subjective Well-being Results.

[2] ACORN is a segmentation based on shared characteristics of people’s life-stage, income, profession and housing, as well as characteristics of places including whether they are urban, suburban or rural. Each respondent on the APS had been classified into one ACORN Type, based on the full postcode in which they live – approximately 16 addresses.) ACORN provided estimates of the population in each ACORN Type in each LSOA and local authority district.

[3] These adjustments were made only where there was reliable evidence (based on samples of more than 100 respondents) from APS that the national wellbeing ACORN profile was substantially different from the regional one, and where the implications for neighbourhood maps would be highly geographically clustered.

Sky Packets provides premium first-party data products derived from public and private Wi-Fi networks strategically deployed across high-footfall environments in Mexico, Ecuador, Peru, and Colombia. Leveraging advanced edge infrastructure, our platform captures real-world behavioral, demographic, and emotional signals to fuel powerful consumer insights.

Our datasets are designed for high-end data buyers who require rich, multidimensional intelligence for advanced modeling, targeting, and optimization across sectors including retail, finance, advertising, and urban planning.

Key Highlights

Data Types: Demographic Data, Behavioral Segmentation, Retail Footfall, Points of Interest (POI), and Sentiment Data (captured via AI-enhanced sensors and contextual cues)

Capture Method: First-party data collected through Sky Packets' public and private Wi-Fi infrastructure, embedded across smart city zones, public plazas, and commercial corridors

Geographic Coverage: Mexico, Ecuador, Peru, and Colombia

Delivery Formats: CSV, JSON

Frequency: Weekly or Monthly refresh options are available

Use Cases:

• Retail site selection & competitive benchmarking

• Consumer journey mapping & attribution modeling

• Sentiment trend analysis & predictive demand modeling

• Smart city infrastructure planning

Cross-border investment intelligence

Why Sky Packets?

With a strong reputation for delivering clean, high-granularity datasets from hard-to-source regions, Sky Packets empowers data-driven decisions for enterprise leaders and analysts who demand precision and scale.

Paper

• https://conferences.miccai.org/2022/papers/489-Paper2739.html

Abstract

The previous image synthesis research for surgical vision had limited results for real-world applications with simple simulators, including only a few organs and surgical tools and outdated segmentation models to evaluate the quality of the image. Furthermore, none of the research released complete datasets to the public enabling open research. Therefore, we release a new dataset to encourage further study and provide novel methods with extensive experiments for surgical scene segmentation using semantic image synthesis with a more complex virtual surgery environment. First, we created three cross-validation sets of real image data considering demographic and clinical information from 40 cases of real surgical videos of gastrectomy with the da Vinci Surgical System (dVSS). Second, we created a virtual surgery environment in the Unity engine with five organs from real patient CT data and 22 the da Vinci surgical instruments from actual measurements. Third, We converted this environment photo-realistically with representative semantic image synthesis models, SEAN and SPADE. Lastly, we evaluated it with various state-of-the-art instance and semantic segmentation models. We succeeded in highly improving our segmentation models with the help of synthetic training data. More methods, statistics, and visualizations on https://sisvse.github.io/.

The contribution of our work

• We release the first large-scale instance and semantic segmentation dataset, including both real and synthetic data that can be used for visual object recognition and image-to-image translation research for gastrectomy with the dVSS
• We systematically analyzed surgical scene segmentation using semantic image synthesis with state-of-the-art models with ten combinations of real and synthetic data.
• We found exciting results that synthetic data improved low-performance classes and was very effective for Mask AP improvement while improving the segmentation models overall.

Data generation

We collected 40 cases of real surgical videos of distal gastrectomy for gastric cancer with the da Vinci Surgical System (dVSS), approved by an institutional review board at the medical institution. In order to evaluate generalization performance, we created three cross-validation datasets considering demographic and clinical variations such as gender, age, BMI, operation time, and patient bleeding. Each cross-validation set consists of 30 cases for train/validation and 10 cases for test data. You can find the overall statistics and demographic and clinical information details in the paper.

Object categories

We list five organs (Gallbladder, Liver, Pancreas, Spleen, and Stomach) and 13 surgical instruments that commonly appear from surgeries (Hamonic Ace; HA, Stapler, Cadiere Forceps; CF, Maryland Bipolar Forceps; MBF, Medium-large Clip Applier; MCA, Small Sclip Applier; SCA, Curved Atraumatic Graspers; CAG, Suction, Drain Tube; DT, Endotip, Needle, Specimenbag, Gauze). We classify some rare organs and instruments as “other tissues” and “other instruments” classes. The surgical instruments consist of robotic and laparoscopic instruments and auxiliary tools mainly used for robotic subtotal gastrectomy. In addition, we divide some surgical instruments according to their head, H, wrist; W, and body; B structures, which leads to 24 classes for instruments in total.

Virtual Surgery Environment and Synthetic Data

Abdominal computed tomography (CT) DICOM data of a patient and actual measurements of each surgical instrument are used to build a virtual surgery environment. We aim to generate meaningful synthetic data from a sample patient. We annotated five organs listed for real data and reconstructed 3D models by using VTK. In addition, we precisely measured the actual size of each instrument commonly used for laparoscopic and robotic surgery with dVSS. We built 3D models with commercial software such as 3DMax, Zbrush, and Substance Painter. After that, we integrated 3D organ and instrument models into the unity environment for virtual surgery. A user can control a camera and two surgical instruments like actual robotic surgery through a keyboard and mouse in this environment. To reproduce the same camera viewpoint as dVSS, we set the exact parameters of an endoscope used in the surgery. While the user simulates a surgery, a snapshot function projects a 3D scene into a 2D image. According to the projected 2D image, the environment automatically generates corresponding segmentation masks.

Qualified annotations

Seven annotators trained for surgical tools and organs annotated six organs and 14 surgical instruments divided into 24 instruments according to head, wrist, and body structures with a web-based computer visio...

Introduction: White matter hyperintensities of presumed vascular origin (WMH) are an important magnetic resonance imaging marker of cerebral small vessel disease and are associated with cognitive decline, stroke, and mortality. Their relevance in healthy individuals, however, is less clear. This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.Methods: We evaluated the effect of varying training sample sizes on the accuracy and the robustness of the predicted white matter hyperintensity volume in a population (n = 201) with a low prevalence of confluent WMH and a substantial proportion of participants without WMH. BIANCA was trained with seven different sample sizes between 10 and 40 with increments of 5. For each sample size, 100 random samples of T1w and FLAIR images were drawn and trained with manually delineated masks. For validation, we defined an internal and external validation set and compared the mean absolute error, resulting from the difference between manually delineated and predicted WMH volumes for each set. For spatial overlap, we calculated the Dice similarity index (SI) for the external validation cohort.Results: The study population had a median WMH volume of 0.34 ml (IQR of 1.6 ml) and included n = 28 (18%) participants without any WMH. The mean absolute error of the difference between BIANCA prediction and manually delineated masks was minimized and became more robust with an increasing number of training participants. The lowest mean absolute error of 0.05 ml (SD of 0.24 ml) was identified in the external validation set with a training sample size of 35. Compared to the volumetric overlap, the spatial overlap was poor with an average Dice similarity index of 0.14 (SD 0.16) in the external cohort, driven by subjects with very low lesion volumes.Discussion: We found that the performance of BIANCA, particularly the robustness of predictions, could be optimized for use in populations with a low WMH load by enlargement of the training sample size. Further work is needed to evaluate and potentially improve the prediction accuracy for low lesion volumes. These findings are important for current and future population-based studies with the majority of participants being normal aging people.

Large-scale disturbances, such as megafires, motivate restoration at equally large extents. Measuring the survival and growth of individual plants plays a key role in current efforts to monitor restoration success. However, the scale of modern restoration (e.g., >10,000 ha) challenges measurements of demographic rates with field data. In this study, we demonstrate how unoccupied aerial system (UAS) flights can provide an efficient solution to the tradeoff of precision and spatial extent in detecting demographic rates from the air. We flew two, sequential UAS flights at two sagebrush (Artemisia tridentata) common gardens to measure the survival and growth of individual plants. The accuracy of Bayesian-optimized segmentation of individual shrub canopies was high (73–95%, depending on the year and site), and remotely sensed survival estimates were within 10% of ground-truthed survival censuses. Stand age structure affected remotely sensed estimates of growth; growth was overestimated relative to field-based estimates by 57% in the first garden with older stands, but agreement was high in the second garden with younger stands. Further, younger stands (similar to those just after disturbance) with shorter, smaller plants were sometimes confused with other shrub species and bunchgrasses, demonstrating a need for integrating spectral classification approaches that are increasingly available on affordable UAS platforms. The older stand had several merged canopies, which led to an underestimation of abundance but did not bias remotely sensed survival estimates. Advances in segmentation and UAS structure from motion photogrammetry will enable demographic rate measurements at management-relevant extents.

The Chaigui market, while lacking precise figures in the provided data, exhibits significant growth potential. Considering a typical pharmaceutical market CAGR (let's assume a conservative 5% based on industry averages for emerging markets), and a base year of 2025, we can project substantial expansion over the forecast period (2025-2033). Assuming a 2025 market size of $500 million (a plausible estimate given the involvement of major pharmaceutical groups), the market would be expected to surpass $700 million by 2030 and continue its upward trajectory. This growth is driven by increasing awareness of Chaigui's therapeutic benefits (assuming it's a specific medicine or treatment), a growing aging population (a common demographic driver in the pharmaceutical sector), and advancements in formulation and delivery methods. Government initiatives promoting healthcare access and the adoption of innovative treatment approaches also contribute positively to market expansion. However, potential restraints may include stringent regulatory approvals, the emergence of competitive substitutes, and price sensitivity among consumers. Furthermore, variations in regional economic conditions and the uneven distribution of healthcare infrastructure can influence market penetration. The presence of established players such as Sunflower Pharmaceutical Group, Guizhou Bailing Enterprise Group, and Jilin Aodong Pharmaceutical suggests a competitive landscape where strategic partnerships, R&D investments, and effective marketing campaigns will be crucial for success. Segmentation within the market (for which data is missing) likely focuses on dosage forms, administration routes, and patient demographics, each presenting specific opportunities and challenges for market participants. Analyzing these segments will be key to strategic market entry and positioning.

The purpose of the present research was to create market segmentation of Polish consumers that would capture differences in reactions to Corporate Social Responsibility (CSR), taking into account sociodemographic data and consumers’ value structure. In order to better understand the extracted segments, a mixed method approach was adopted. The first quantitative study was conducted on a nationwide representative sample of Poles aged 18–55 years (N = 1055, CAWI survey). A subsequent qualitative stage covered 24 semi-structured in-depth individual interviews, with representatives of each segment identified in Study 1. Consequently, six segments of Poles were extracted and described, differing in knowledge, attitudes and beliefs about CSR: Sensible Optimists (15%), Sensitive Intellectuals (18%), Family Pragmatics (21%), Passive Poseurs (19%), Excluded and Frustrated (12%) and Corpo-Egoists (15%). The study showed both demographic and psychological differences in between segments. Segments with positive attitudes toward CSR are more female. Segment of least positive attitudes is manly and youngest one. However, results for age, education level and economic status are less conclusive. Personal values proved to be more useful in understanding different attitudes toward CSR than demography. Segments that are more open to CSR prize self-transcendence and maturity values, while less open segments are more oriented toward social status values.

Alto Saxophone Market size was valued at USD 100.1 Billion in 2023 and is projected to reach USD 140.42 Billion by 2030, growing at a CAGR of 5.1% during the forecast period 2024-2030.

Global Alto Saxophone Market Drivers

The market drivers for the Alto Saxophone Market can be influenced by various factors. These may include:

Musical Education Programs: The existence and expansion of music education programs in schools and universities may have an impact on the demand for alto saxophones. These initiatives may increase students' demand for instruments. Music Industry Trends: The demand for alto saxophones can be influenced by trends in the music industry, such as the popularity of particular genres or musicians that use them prominently. Technological Advancements: Design, material, and manufacturing process innovations for saxophones might affect consumer choices. Better materials or features might draw musicians seeking better performance. Economic Factors: The purchasing power of customers can be impacted by the general state of the economy of an area or nation. People and organizations may reduce discretionary expenditure during recessions, which could have an impact on the market. Cultural and Demographic Factors: The markets for musical instruments are influenced by both cultural tastes and demographics. For example, there can be a greater demand for alto saxophones in areas with a strong jazz or classical music background. Promotional Activities: Manufacturers, retailers, and musicians of saxophones can increase interest and sales through marketing and promotional initiatives. Collaborations, endorsements, and sponsorships of musicians could make a big difference. Globalization and Trade Policies: These two factors may have an impact on alto saxophone availability and cost. The dynamics of the market may change as a result of modifications to trade agreements, tariffs, and import/export laws. Online Retail Trends: Saxophone sales may be impacted by the expansion of e-commerce and online retail platforms. Choices made by consumers may be influenced by the ease of internet shopping and the abundance of possibilities. Product Quality and Reputation: Purchase decisions can be greatly influenced by a manufacturer's reputation as well as the quality of their items as viewed by consumers. Sales may be boosted by favorable evaluations and suggestions from established musicians. Environmental Concerns: As people become more conscious of environmental issues, they could choose to use eco-friendly and sustainable materials when making instruments.

In this AqUavplant dataset, we aim to contribute to the aquatic plant mapping benchmarking datasets for agriculture–computer vision researchers. We captured RGB images using a UAV at low altitudes to acquire higher details of small-sized freshwater aquatic plants in Bangladesh. A realistic demographic and geographic variation is added by collecting images of 31 types of invasive and indigenous aquatic species from nine sites in three locations. There are 197 high-resolution images each containing a lofty number of species. The AqUavplant dataset comprising binary and multiclass semantic segmentation annotations, can enhance data-driven models for automatic aquatic plant mapping. Eventually, the reliable baselines mentioned in the manuscript will aid researchers in leveraging the dataset for improved prediction, monitoring, and obtaining demographic data, thereby advancing common and rare aquatic plant mapping.