This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

The Metaverse Index (MVI)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

In a recent index benchmarking global luxury brands on a number of different metrics such as awareness, omnichannel capabilities, and ESG efforts, and innovation, Gucci, Louis Vuitton, and Burberry emerged as the brands most ready for the metaverse. Overall, luxury brands did not score high in their metaverse readiness, with the highest scoring brands only reaching an index value of ** out of 100. Nevertheless, in the fashion industry, the luxury sector has taken the metaverse opportunities more seriously than the mainstream names in fashion so far, launching NFTs and collaborating with virtual clothing companies.

This dataset contains gait-based biometric data collected from 5,000 users in a simulated environment for gait authentication in the Metaverse. It includes 16 key gait features extracted using OpenPose and MediaPipe and processed with feature engineering techniques for improved usability.

The dataset is valuable for gait-based authentication, user identification, and biometric security applications. It can be used for machine learning models, deep learning, and anomaly detection in gait recognition research.

Features include:

Stride length, step frequency, stance phase duration, swing phase duration

Hip, knee, and ankle joint angles

Ground reaction forces (GRFs), cadence variability, foot clearance

Gait symmetry index and more

Format: CSVLicense: CC BY 4.0 (Attribution Required)Citation: If using this dataset, please cite:Sandeep Ravikanti (2024). "Metaverse Gait Authentication Dataset (MGAD)." Zenodo. DOI: [10.5281/zenodo.14847773]

1. Dataset OverviewThe Metaverse Gait Authentication Dataset (MGAD) is a large-scale dataset for gait-based biometric authentication in virtual environments. It consists of gait data from 5,000 simulated users, generated using Unity 3D and processed using OpenPose and MediaPipe. This dataset is ideal for researchers working on biometric authentication, gait analysis, and AI-driven identity verification systems.2. Data Structure & FormatFile Format: CSVNumber of Samples: 5,000 usersNumber of Features: 16 gait-based featuresColumns: Each row represents a user with corresponding gait feature valuesSize: Approximately (mention size in MB/GB after upload)3. Feature DescriptionsThe dataset includes 16 extracted gait features:Stride Length (m): Average distance covered in one gait cycle.Step Frequency (steps/min): Number of steps taken per minute.Stance Phase Duration (s): Stance phase in a gait cycle.Swing Phase Duration (s): Duration of the swing phase in a gait cycle.Double Support Phase Duration (s): Time both feet are in contact with the ground.Step Length (m): Distance between consecutive foot placements.Cadence Variability (%): Variability in step rate.Hip Joint Angle (°): Maximum angle variation in the hip joint.Knee Joint Angle (°): Maximum flexion-extension knee angle.Ankle Joint Angle (°): Angle variation at the ankle joint.Avg. Vertical GRF (N): Average vertical ground reaction force.Avg. Anterior-Posterior GRF (N): Ground reaction force in the forward-backward direction.Avg. Medial-Lateral GRF (N): Ground reaction force in the side-to-side direction.Avg. COP Excursion (mm): Center of pressure movement during stance phase.Foot Clearance during Swing Phase (mm): Minimum height of the foot during the swing phase.Gait Symmetry Index (%): Measure of symmetry between left and right gait cycles.4. How to Use the DatasetLoad the dataset in Python using Pandas:Use the features for machine learning models in biometric authentication.Apply preprocessing techniques like normalization and feature scaling.Train and evaluate deep learning or ensemble models for gait recognition.5. Citation & LicenseIf you use this dataset, please cite it as follows:Sandeep Ravikanti, "Metaverse Gait Authentication Dataset (MGAD)," IEEE DataPort, 2025. DOI: https://dx.doi.org/10.21227/rvh5-88426. Contact InformationFor inquiries or collaborations, please contact: bitsrmit2023@gmail.com

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

The Metaverse: Exploring the Future of Digital Interaction and Immersive Experiences

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Meta Platforms (META) on the Metaverse Road to Redemption

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

As the metaverse emerges as a transformative digital realm, its adoption and integration into various aspects of society are subjects of increasing scholarly and practical interest. This research investigated the factors influencing the intention to use metaverse technology (IU) in Bangkok metropolitan areas, with a particular focus on the extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, alongside the role of social media marketing (SMM) and consumer engagement (CE). To verify behavioral intention, gender, age, and experience are proposed as moderating factors affecting the constructs on individuals’ behavioral intention of metaverse technology usage. The study collected data from 403 Thai internet users living in Bangkok and its surrounding areas using an online questionnaire. Subsequently, the PLS-SEM method was employed to validate the research model’s robustness and reliability. Structural model analysis revealed significant relationships among constructs, highlighting SMM’s direct influence on UTAUT2 (β = 0.787) and CE (β = 0.211). Serial mediation analyzes demonstrated a fully mediating role of SMM influencing UI through CE (β = 0.572) and UTAUT2 (β = 0.306). Moderation analyzes revealed the association between SMM and IU, mediated through UTAUT2 and CE, is moderated by age and experience. Additionally, the integration of PLS-SEM and artificial neural network (ANN) models underscored the accuracy and predictive power of the proposed framework. The findings of this study not only contribute to academic literature but also offer practical implications for marketers aiming to navigate the metaverse landscape effectively. They emphasize the pivotal role of UTAUT2 constructs and the subtle interplay between SMM, CE, and IU in shaping successful marketing strategies.

Fashion Token Index Market Outlook

According to our latest research, the Fashion Token Index market size reached USD 2.18 billion globally in 2024, reflecting the rapid integration of blockchain and tokenization within the fashion industry. The market is projected to grow at a robust CAGR of 20.7% from 2025 to 2033, reaching an estimated USD 13.89 billion by the end of the forecast period. This impressive growth is primarily driven by the rising adoption of digital assets, NFTs, and decentralized platforms in the fashion sector, enabling new revenue streams, enhanced transparency, and improved consumer engagement.

One of the most significant growth factors for the Fashion Token Index market is the increasing demand for digital fashion and virtual goods. As consumers, particularly Gen Z and Millennials, spend more time in virtual environments and the metaverse, fashion brands are leveraging blockchain-based tokens to create, sell, and authenticate digital apparel and accessories. This trend not only creates new monetization opportunities for brands and designers but also fosters a thriving secondary market for digital collectibles. The integration of NFTs and other token types into fashion collections is redefining the concept of exclusivity, allowing brands to offer limited edition items, unique experiences, and digital ownership, thereby driving market expansion.

Another key driver is the growing emphasis on authenticity and provenance in the fashion industry. Counterfeiting remains a persistent challenge, costing brands billions annually and eroding consumer trust. Blockchain-powered Fashion Token Index solutions enable brands to embed authentication and provenance data directly into digital tokens, providing immutable proof of origin and ownership. This level of transparency not only protects brand integrity but also empowers consumers to make informed purchasing decisions. As regulatory bodies and industry associations increasingly mandate traceability and sustainability disclosures, the adoption of tokenized solutions is expected to accelerate further, fueling market growth.

The emergence of brand loyalty programs powered by fashion tokens is also contributing to the market's upward trajectory. By leveraging tokenization, fashion brands can create innovative loyalty ecosystems where consumers earn, trade, and redeem tokens for exclusive rewards, early access to collections, or personalized experiences. Such programs foster deeper customer engagement, drive repeat purchases, and enhance brand differentiation in a highly competitive landscape. As more brands experiment with tokenized loyalty initiatives and integrate them with e-commerce and social platforms, the Fashion Token Index market is poised for sustained growth throughout the forecast period.

Regionally, North America currently leads the global Fashion Token Index market, accounting for the largest revenue share in 2024, followed closely by Europe and the Asia Pacific. The dominance of North America is attributed to the strong presence of leading fashion houses, advanced blockchain infrastructure, and a high concentration of tech-savvy consumers. Europe is witnessing rapid adoption, particularly in fashion capitals such as Paris, Milan, and London, where brands are pioneering digital fashion initiatives. The Asia Pacific region, led by China, Japan, and South Korea, is emerging as a significant growth engine, driven by a young, digitally native population and the proliferation of online platforms. The Middle East & Africa and Latin America are also experiencing increased interest, albeit at a more nascent stage, as brands and consumers in these regions begin to explore the benefits of fashion tokenization.

Component Analysis

The Component segment of the Fashion Token Index market is bifurcated into Platform and Services, each playing a pivotal role in shaping the industry landscape. The Platform sub-segment encompasses the underlying blockchain infrastructure, token issuance tools, smart contract development environments, and marketplaces that facilitate the creation, management, and trading of fashion tokens. These platforms are crucial for enabling seamless interoperability, scalability, and security, which are essential for mainstream adoption. Leading platforms are investing heavily in user-friendly interfaces, robust compliance features, and integrations with payment gateways, thereby lowering the entry barriers

Fashion Token Index Market Outlook

According to our latest research, the global Fashion Token Index market size reached USD 1.28 billion in 2024, reflecting a robust expansion driven by the digital transformation in the fashion and retail sectors. The market is projected to grow at a compelling CAGR of 22.7% from 2025 to 2033, reaching an estimated value of USD 9.02 billion by the end of the forecast period. This remarkable growth trajectory is primarily fueled by increased adoption of blockchain technology, rising consumer interest in digital assets, and the proliferation of virtual fashion experiences. As per the latest research, the Fashion Token Index market is witnessing rapid evolution, with both established fashion houses and emerging digital-native brands leveraging tokenization to enhance customer engagement, drive loyalty, and unlock new revenue streams.

One of the key growth factors propelling the Fashion Token Index market is the increasing convergence of fashion and technology. The integration of blockchain-based tokens within the fashion industry enables brands to offer unique digital experiences, authenticate products, and facilitate transparent supply chains. Utility tokens and NFTs are being utilized to provide exclusive access to digital fashion shows, limited-edition collections, and immersive virtual environments. This trend is particularly pronounced among Gen Z and millennial consumers, who are highly receptive to digital ownership and the gamification of brand interactions. The ability to tokenize fashion assets not only enhances consumer engagement but also opens up innovative monetization pathways for designers and brands, further accelerating market growth.

Another significant driver of the Fashion Token Index market is the rise of virtual goods and digital fashion. The burgeoning popularity of the metaverse and online gaming platforms has created a thriving market for digital apparel and accessories, which can be bought, sold, and traded using fashion tokens. Non-fungible tokens (NFTs) are at the forefront of this movement, allowing consumers to own verifiable, scarce digital fashion items. As virtual environments become increasingly sophisticated, brands are investing in NFT collaborations, digital runway events, and avatar customization, thereby expanding the utility and appeal of fashion tokens. The seamless integration of payment and loyalty tokens into these ecosystems further incentivizes consumer participation and fosters brand loyalty.

Furthermore, the Fashion Token Index market is benefiting from the growing emphasis on sustainability and transparency within the fashion industry. Blockchain-powered tokens facilitate traceability, enabling consumers to verify the provenance and ethical credentials of their purchases. Security tokens are being leveraged to fractionalize ownership of high-value fashion assets, democratizing investment opportunities and fostering greater inclusivity. Additionally, the adoption of tokenized loyalty programs is streamlining customer rewards and enhancing the overall shopping experience. As regulatory frameworks around digital assets mature, institutional adoption is expected to rise, paving the way for sustained market expansion.

Regionally, North America and Europe are leading the Fashion Token Index market, driven by advanced digital infrastructure, high consumer awareness, and a vibrant ecosystem of fashion-tech startups. The Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, a burgeoning middle class, and widespread adoption of mobile payment solutions. Latin America and the Middle East & Africa are also witnessing increasing interest, with local brands experimenting with tokenization to differentiate their offerings and tap into global audiences. While regional dynamics vary, the overarching trend is clear: the fusion of blockchain technology and fashion is transforming industry paradigms, creating new value propositions for stakeholders across the value chain.

Token Type Analysis

Scientific collaboration traditionally relies on activities such as workshops, training programs, services, conferences, and personal meetings. In recent years, commercial-academic partnerships and global events such as pandemics have accelerated the development of online interaction platforms. A key challenge is to create immersive and interactive environments that extend beyond conventional search engines and video conferencing. This article introduces a novel 3D model platform compatible with the Metaverse of Academic Nexus for Global Opportunities (MANGOs), highlighting its potential applications in gas chromatography (GC) and comprehensive two-dimensional gas chromatography (GC×GC). The platform offers participants a virtual ecosystem for exploring fundamental concepts, practicing analytical skills, conducting experimental simulations, and sharing databases. Key features include virtual university settings, buildings, laboratories, instruments, a retention index (I) database, and immersive GC and GC×GC simulation environments for a variety of samples. Simulations of chromatograms and contour plots based on literature-reported experimental conditions are demonstrated. This new approach aims to enhance collaboration among gas chromatographers by enabling skill development, data exchange, and collective refinement of results obtained under diverse experimental setups.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Roblox (RBLX) Stock: Building a Metaverse Empire

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data