These datasets contain peer-to-peer trades from various recommendation platforms.

Metadata includes

• peer-to-peer trades

• have and want lists

• image data (tradesy)

This dataset contains images (scenes) containing fashion products, which are labeled with bounding boxes and links to the corresponding products.

Metadata includes

• product IDs

• bounding boxes

Basic Statistics:

• Scenes: 47,739

• Products: 38,111

• Scene-Product Pairs: 93,274

We introduce PDMX: a Public Domain MusicXML dataset for symbolic music processing, including over 250k musical scores in MusicXML format. PDMX is the largest publicly available, copyright-free MusicXML dataset in existence. PDMX includes genre, tag, description, and popularity metadata for every file.

Merchant API will provide you with all essential data and metrics for conducting comprehensive competitor analysis, price monitoring, and market niche research.

With Google Shopping API you can get:

• Google Shopping Products listed for the specified keyword. The results include product title, description in Google Shopping SERP, product rank, price, reviews, and rating as well as the related domain. • Full detailed Google Shopping Product Specification. You will receive all product attributes and their content from the product specification page. • A list of Google Shopping Sellers of the specified product. The provided data for each seller includes related product base and total price, shipment and purchase details, and special offers. • Google Shopping Sellers Ad URL with all additional parameters set by the seller.

With Amazon API you can get:

• Results from Amazon product listings according to the specified keyword (product name), location, and language parameters. • A list of ASINs (unique product identifiers assigned by Amazon) of all modifications listed for the specified product and information about the product prices based on ASIN • Amazon Choice products

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

The Merchant Pig Iron market serves as a crucial segment within the global iron and steel industry, primarily utilized as an intermediate product for the production of cast iron and steel. This raw material is characterized by its high carbon content, which enhances the fluidity and castability of iron when manufact

MDC06CaseStudy - In September 2023, the University of Miami Industrial Assessment Center (MIIAC) performed an assessment at CGI Merchant Group. A report presented no cost and low-cost recommendations on how to save energy, water, and money.Open PDF - https://gisweb.miamidade.gov/agolpdf/MDC06CaseStudy.pdf

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.

MHI MERCHANT HOUSE INTERNATIONAL LIMITED

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.

Merchants Trust (MRCH) Poised for Growth: A Deep Dive

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

KAV 4858 cover memo. Visit https://dataone.org/datasets/sha256%3A8e764b03b8d0ffb28e02c9c91554e1435510bc41505353347abbf4499b21f633 for complete metadata about this dataset.

Ethical ref #. 2022_FBMSREC 026The attraction and retention of female seafarers in Cape Town, South Africa, present multifaceted challenges and opportunities within the maritime industry. This study explored the factors influencing the recruitment and retention of female seafarers to provide insights into enhancing gender diversity and inclusivity in maritime professions. It employed a qualitative research approach with semi-structured interviews with 12 female seafarers employed by a prominent maritime company in Cape Town. The study investigated the motivations driving female seafarers to pursue maritime careers, the challenges they encounter in their roles, and potential strategies for improving attraction and retention efforts. Key themes emerged, including the influence of family tradition, perceptions of the maritime profession, workplace challenges, and the importance of mentorship and support networks. Findings underscored the significance of addressing gender-specific barriers and implementing targeted initiatives to attract and retain female talent in the maritime sector. By shedding light on the experiences and perspectives of female seafarers in Cape Town, this study contributes to broader discussions on gender equality and workforce diversity in maritime industries worldwide.The study employed convenience sampling to select 12 female seafarers, utilising semi-structured interviews to gather insights into motivating factors and challenges. Methodological rigour was ensured through the alignment of questions with research objectives and the use of the same interview guide for all participants. Qualitative data analysis followed a systematic process to derive meaningful insights, with saturation being crucial. Ethical considerations were prioritised, with approval obtained from relevant ethics committees and measures taken to maintain participant confidentiality. The study aimed to contribute to the transformation of the maritime industry by addressing gender diversity issues and providing valuable insights for decision-making. While the findings are expected to inform future research and strategies for improving gender diversity and retention, limitations such as the sample size and focus on a single organization should be acknowledged. The study employed convenience sampling as the participant selection method, acknowledging its suitability given the challenges in accessing female seafarers due to their unique work environments and schedules. Initial contact was established through email, presenting a comprehensive invitation explaining the study's justification, purpose, and primary goals. Participants were requested to confirm their willingness to participate via return email. Follow-up communications, encompassing both email reminders and telephone calls, were employed to confirm participation and interview appointments. Before the interviews, participants received an informed consent form as an annexure, allowing for a thorough review and evaluation of its contents, including details about the study's purpose, participant rights, and the voluntary nature of participation.In the data collection phase, face-to-face interviews in a closed office within AMSOL’s premises, lasting between 30 and 45 minutes served as the primary method. Structured interviews guided the process, ensuring consistency while allowing flexibility to explore emerging themes. This approach facilitated in-depth exploration of participants' experiences, motivations, and perspectives. Active engagement during interviews involved note-taking, capturing noteworthy comments and personal reactions, particularly regarding pregnancy, raising a family whilst away and the financial implications of maternity. The use of different colours aided categorisation for subsequent coding. Different colours were used to identify the different factors i.e. Attraction (Green), Retention (Blue) and Transformation (Red). The same colour for each factor was then used to identify themes and link quotes to each theme e.g. Remuneration, Travel, Maternity etc. A different colour (Yellow) was then used to identify any quotes that were linked across more than one factor e.g. Remuneration under the Attraction factor was linked to financial stability whilst on maternity leave under the Retention factor.Data saturation, denoting the point at which new data cease to provide novel insights, was a crucial consideration in qualitative research. Recurring themes such as remuneration, travel and maternity leave, gender imbalance and sexual harassment across early interviews indicated an approaching saturation point. By the twelfth interview, a comprehensive understanding of participants' experiences, motivations, challenges, and perspectives was achieved. While theoretically reaching saturation earlier, the decision to continue interviews aimed to enhance rigour. Data saturation was confirmed at the twelve-interview point, marked by theme repetition, an absence of new information, a comprehensive understanding, and a rigorous data collection approach. This ensured a thorough depiction of female seafarers' experiences and insights regarding attraction and retention.The data analysis process in the study followed a systematic approach to extract meaningful insights from the collected data. It began with importing and organising the data, which involved transcribing interviews and structuring them for analysis. Next, the process moved to coding and identifying clusters of meaning within the data. This step involved systematically tagging segments of the text with descriptive labels, allowing for pattern and theme identification. These codes were then grouped into themes, representing broader concepts or ideas that emerged from the data. The interpretation and understanding phase followed, where the study analysed the themes in depth and considered their implications and relationships with the research questions. Finally, establishing trustworthiness was essential to ensure the validity and reliability of the findings. This involved employing strategies such as member checking, peer debriefing, and maintaining an audit trail to ensure transparency and rigour throughout the analysis process. Overall, these steps worked together cohesively to transform raw data into meaningful insights that addressed the study's objectives.In conclusion, the multifaceted exploration of factors influencing maritime careers reveals a nuanced interplay between historical allure and evolving considerations. Remuneration emerges as a timeless magnet, with its historical significance echoed in the participants' narratives. To sustain this appeal, it is recommended to regularly review remuneration packages, incorporating industry benchmarks and innovative structures, while collaborating with associations for fair and competitive standards. The allure of Travel represents a profound aspect of maritime careers, emphasising unique experiences and transformative journeys. To enhance this allure, recruitment efforts should spotlight the adventurous dimensions, incorporating real-life narratives and fostering cultural exchange programmes to enrich seafarers' professional and personal lives. Family Tradition, once dominant, now yields to nuanced individual choices, prompting a reconsideration of its impact. The Influence of Parents and Friends emerges as a pivotal theme, advocating for inclusive outreach initiatives and positive narratives to empower informed decision-making within social circles. Guidance Teachers and Instructors play a pivotal role in influencing maritime career choices through exposure and education. Recommendations include continuous professional development, collaboration with institutions for curriculum integration, mentorship programmes, and tailored informational materials to amplify educators' impact.

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

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.

First Merchants' Shares Projected to See Growth Ahead (FRME)

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.

First Merchants (FRME): Local Treasures, National Reach?

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.

Minimalist Merchants' (MNSO) Market Move: Expansion or Stagnation?

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 of 2022, the average age of all ships in the world merchant fleet was just over 20 years. General cargo ships were the oldest type of vessels, with an average age of around 27 years. Some 68 percent of the world's cargo ships were older than 15 years, in contrast with about 21 percent of bulk carriers.

Ship scrapping activity worldwide Ship scrapping is the activity of dismantling old vessels to recycle the metals. Before the existence of ship scrapping, companies simply abandoned or sank the older vessels to get rid of them. From 2013 to 2021, the number of commercial ships dismantled globally almost halved. Bangladesh and India dismantled more than 50 percent of the total number of ocean-going commercial vessels in 2021. Most of the ship scrapping activity is carried out in countries where the labor market and environmental regulations are weak.

Impact of COVID on ship scrapping The economic crisis caused by the COVID-19 pandemic deeply impacted the global economy, leading to the worst decline in industrial production over the recent century. This had severe implications for all industries across the globe at varying degrees. Since the end of 2019, global ship demolition activity has increased sharply as a consequence of higher fuel prices and adverse market conditions. Due to the COVID-19 pandemic, the monthly demolition activity peaked at 2.3 million deadweight tons in February 2020. This trend is expected to continue throughout 2020, as owners face the increased underutilization of vessels brought about by the COVID-19 pandemic. Besides, as a result of strict lockdown measures across the globe, the impact of the COVID-19 crisis persists. Over 12 million deadweight tons of commercial fleet were delivered in January 2020, which declined to roughly 5.9 million deadweight tons by March 2020. Although some countries have started to make progress in normalizing economic and social activity again, the recovery to previous levels could take longer than expected.