In the United States, ** percent of online orders made during Amazon Prime Day 2024 were worth up to ** U.S. dollars, on average. In that edition of the popular online sale event, ** percent of U.S. orders were worth up to *** euros.

In 2024, Amazon Prime Day sales reached a massive $14.2 billion in comparison to $12.9 billion in 2023. This was a huge $1.3 billion increase.

In 2023, Amazon Logistics delivered around 5.86 billion packages in the United States (U.S.). This is an increase of around one billion packages compared with the previous year.

This dataset offers a granular look at the daily Open, High, Low, Close, and Volume (OHLCV) data for some of the Amazon.com Inc. stock (AMZN) Updated every trading day, it aims to serve as a resource for researchers, traders, and finance enthusiasts.

Key Features: - OHLCV: The foundational data points for any financial analysis. - Adjusted Close: This considers dividends and splits, offering a true reflection of value over time. - Change %: Offers a day-to-day percentage change, capturing market momentum. - 20 Day Average Volume: A rolling metric to gauge trading liquidity and interest over roughly a month.

Split Adjusted: Given its history of stock splits, a separate dataset with split-adjusted data is available, ensuring accuracy in historical comparisons.

Image source: https://wallpapers.com/wallpapers/amazon-digital-city-art-npcp6jc782ixp9zs.html

Asset Price Dataset Description

This dataset is a comprehensive collection of historical financial data on a specific asset, covering a wide range of information related to daily prices, trading volume and technical indicators. It is designed to provide a detailed, multi-faceted view of asset performance over time, enabling in-depth analysis and the application of various financial strategies.

Information on the columns of the dataset

1. Date: The specific date of the entry.
2. Opening: The opening price of the asset at the beginning of the day.
3. High: The highest price reached by the asset during the day.
4. Low: The lowest price reached by the asset during the day.
5. Closing: The price of the asset at the end of the day.
6. Adjusted Closing: The closing price adjusted for dividends and stock splits.
7. Volume: The number of shares traded during the day.
8. Amplitude: The difference between the highest and lowest price of the day (High - Low).
9. MA7: Moving average of the closing price of the last 7 days.
10. MA14: Moving average of the closing price of the last 14 days.
11. MA30: Moving average of the closing price over the last 30 days.
12. Daily Return: The percentage change in the closing price in relation to the previous day.
13. ATR (Average True Range): Moving average of the True Range (TR) for a given period, used to measure volatility.
14. RSI (Relative Strength Index): Relative Strength Index, a momentum indicator that measures the speed and change of price movements.
15. Annual growth percentage: Percentage of annual growth.
16. Percentage of daily growth: Percentage of daily growth.
17. Absolute Daily Growth: Daily absolute growth, the absolute difference in the closing price compared to the previous day.
18. Day: The day of the week.
19. Month: The month of the year.
20. TR (True Range): The biggest difference between:
21. The maximum price of the day minus the minimum price of the day.
22. The maximum price of the day minus the closing price of the previous day.
23. The minimum price of the day minus the closing price of the previous day.

Applicability

1. Trend Analysis:
   • Through historical data, it is possible to identify short and long-term price trends, helping analysts and investors make informed decisions about buying and selling assets.
2. Development of Negotiation Strategies:
   • The data can be used to develop and test automated trading strategies, including the use of moving averages, relative strength indexes (RSI), and other technical indicators.
3. Volatility Study:
   • With metrics such as Average True Range (ATR), the dataset allows measuring asset volatility over time, essential for risk management strategies and understanding asset stability.
4. Performance Assessment:
   • The detailed history of opening, closing, high and low prices, as well as trading volume, allows an accurate assessment of the asset's performance in different periods.
5. Modeling and Forecasting:
   • The data can be used to build predictive models using machine learning and statistical analysis techniques, providing predictions about future price movements.
6. Education and Research:
   • For students and researchers, the dataset offers a rich source of real data to study financial markets, test hypotheses and perform simulations.

Importance

1. Informed Decision Making:
   • Access to detailed historical data allows investors and analysts to make evidence-based decisions, reducing uncertainty and risk associated with financial markets.
2. Backtesting:
   • It is possible to apply trading strategies to historical data to verify their effectiveness before implementing them in the real market, a crucial process for developing robust trading systems.
3. Comparative Performance Analysis:
   • With consistent data, you can compare the asset's performance over different periods or with other assets, providing a clear perspective on its relative performance.
4. Pattern Identification:
   • The dataset allows the identification of patterns and anomalies in price movements, which can be explored to develop trading strategies or to better understand the factors that influence the market.
5. Risk Management:
   • Analyzing volatility and price behavior over time helps in building risk management strategies, essential for preserving capital and optimizing returns.

This dataset is a valuable tool for anyone involved in financial markets, from individual investors to market analysts and academic researchers, providing the necessary foundation for detailed analysis and informed financial decisions.

The trade of wild meat in urban markets has become a controversial topic because despite the economic returns it can generate for local communities, it can cause a dramatic increase in harvest rates of game species. The trade of wild meat could be a very accessible and low-cost method to monitor the regional game populations. Nevertheless, the wild meat trade is difficult to monitor because this is an illegal activity and vendors often distrust researchers. In this study, we used two long-term monitoring datasets collected in one of the most important and largest open markets in wildlife in the Amazon, in Iquitos (Peru), to estimate the minimum effort required to obtain reliable information on the amount and trends of wild meat trade. Two 12-month surveys were conducted in the Belén Market between September 2006 and August 2007 (2,443 interviews in 182 sampling days), and between September 2017 and August 2018 (2,081 interviews in 138 sampling days). The data submited in page "interviews year-along" includes the price and the amount of total wild meat, and volume (in kg) of meat of Tayassu pecari -white-lipped peccary-, Cuniculus paca -paca-, Pecari tajacu -collared pecari-, and Mazama sp. -brocket deer- sold in each interview day. In October 2018, at the end of the survey of 2017-2018, we conducted an interview directly to the eleven most frequent wild meat sellers in order to obtain their personal perception on the average price and daily amount of wild meat sold year-along. This information is included in the page "Single questionary".

Amazon Prime is constantly growing in the United States: as of December 2019, there were an estimated 112 million U.S. Amazon Prime subscribers, up from 95 million in June 2018. On average, Amazon Prime members spent 1,400 U.S. dollars on the e-retail platform per year. March 2019 data also states that non-Prime members only spent 600 U.S. dollars annually. Amazon Prime Amazon Prime is a paid subscription service offered by online retail platform Amazon. The subscription includes services such as music and video streaming, free two-day (or faster) shipping, as well as many other benefits. The program was launched in 2005 and is available internationally. In 2019, Amazon generated 19.21 billion U.S. dollars in revenues through its subscription services segment. Subscription services do not only include Amazon Prime revenues, but also audiobook, e-book, digital video, digital music and other non-AWS subscription services. Prime shoppers The most popular product categories purchased by Amazon Prime shoppers in the United States were electronics, apparel, and home and kitchen goods. Amazon Prime shoppers are more engaged that non-members: during a February 2019 survey, 20 percent of Amazon Prime members stated that they shopped on Amazon a few times per week, with seven percent saying that they did so on an (almost) daily basis.

Amazon not only boasts a hugely successful online retail platform but also a thriving digital marketplace which is seamlessly integrated with the main retail shopping experience. That being said, in the first quarter 2025, ** percent of paid units were sold by third-party sellers. 1P and 3P Amazon sellers There are many ways of selling on Amazon. Firstly there are first-party (1P) vendor sales, where vendors send their inventory to Amazon, who in turn control the pricing and include “ships from and sold by Amazon.com” on product listings. The benefits of 1P sales on Amazon are wholesale purchases from Amazon, priority selling and brand trust through Amazon’s credibility as a seller. Amazon also permits third-party (3P) sales on its marketplace. Both individuals and professional sellers can sell on Amazon Marketplace. When it comes to order fulfillment, possible options are Fulfillment by Amazon (FBA) and Fulfillment by Merchant (FBM). Items are displayed as “sold by MERCHANT and Fulfilled by Amazon / Fulfilled by MERCHANT”. 3P sales are a popular strategy for sellers to make up for certain 1P sales disadvantages, namely improved margins through better pricing control, more favorable payment terms and less reliance on the relationship with Amazon. Amazon seller revenues This magic formula has ultimately cashed in for Amazon, which has seen its net revenues multiply in recent years. In 2023, the e-commerce giant generated approximately *** billion dollars in third-party seller services, an increase of about ** billion dollars from the previous year. While these figures are the product of orders throughout the year, a significant chunk is attributable to special offer and discount days. According to a survey, Black Friday is the shopping event driving the largest sales increase for Amazon sellers, followed by two of the company's own events, Prime Day and Amazon Summer Sale. In the context of the coronavirus pandemic, Amazon Prime Day played a particularly decisive role for small and medium-sized businesses around the world, many of which had to turn to online sales overnight in order to survive.

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.

Amazon Stock: A Hold for the Next Month, But With Potential for Upside

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

The online revenue of amazon.ae amounted to US$769.8m in 2024. Discover eCommerce insights, including sales development, shopping cart size, and many more.

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.

Amazon.com Inc. (AMZN): The Future of Retail?

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.

Is JD (JD) E-Commerce the Next Amazon?

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.

Amazon Escapes $268 Million Tax Bill in Luxembourg

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 2024, Amazon was the online marketplace with the highest gross merchandise value in the United States, amounting to approximately *** billion U.S. dollars. It was followed by Walmart, with a GMV of roughly *** billion U.S. dollars. Amazon: the U.S.’s clear favorite While Amazon might not be the leading online marketplace worldwide, it is the front-runner in the United States. As of April 2023, Amazon dominated the list of leading online marketplaces in the U.S. based on number of monthly visits, with over *** billion website visitors. Regarding the market share of leading retail e-commerce companies, Amazon was also in the lead with over ** percent difference from the next competitor, Walmart. Even when it comes to holiday shopping, consumers favor Amazon. Nearly ** percent of consumers stated that they prefer the online marketplace for their holiday shopping needs over other websites. A prime day for shopping Consumer purchasing behavior often relies on shopping events that occur throughout the year. Amazon incurs the largest increase of sales during Black Friday, which is followed by Prime Day, an event exclusively for Amazons’ Prime members. Consumers often plan their shopping around this event, due to the deals offered to Prime members. In 2023, most shoppers planned on purchasing electronics, at almost ** percent of consumers. The most purchased items on Prime Day, however, fell into the category of home goods, with around ** percent of shoppers, and only around ** percent of shoppers purchased electronics. In general, the average value of Prime Day orders was worth up to ** U.S. dollars, with ** percent of orders.

With 438 billion U.S. dollars in net sales, the United States were Amazon’s biggest market in 2024. Germany was ranked second with 41 billion U.S. dollars, ahead of the UK with 37.9 billion U.S. dollars. Biggest internet company Founded in 1994, Amazon has grown into one of the biggest e-commerce marketplaces and cloud computing platforms worldwide. In 2020, Amazon was ranked first in terms of company revenue among global publicly traded internet companies. With an annual revenue of approximately 386 billion U.S. dollars, the e-retailer ranked far ahead of closest competitors Google (181.7 billion U.S. dollars) and Alibaba (109.5 billion U.S. dollars). Amazon shopping Amazon is the leading e-retailer in the United States. In September 2021, 65 percent of Amazon customers in the United States held a membership with the company’s subscription service Amazon Prime, benefitting from free 2-day shipping, music and video streaming and exclusive offers and deals. Amazon Prime members are very engaged shoppers: a June 2021 survey found that over four in ten of them were likely to shop not just during Prime days, but also during other retail events, with 56 percent of them showing interest for Cyber Monday.

In 2022, big e-commerce sites providing Buy Now, Pay Later (BNPL) options could count on higher average online orders (AOV). A worldwide study showed that online stores with annual revenue between one and ten million U.S. dollars would generate online orders worth nearly 280 U.S. dollars when providing BNPL options, compared to under 250 U.S. dollars without offering BNPL payment. In addition to that, online stores providing BNPL options reported higher online traffic.

Buy Now, Pay Later on Amazon Prime Day

In the United States, BNPL plays an important role in sales events. The value of purchases made on Amazon on Prime Day increased between 2022 and 2023. In the latest year, shoppers spent 461 million U.S. dollars on the first Prime Day and 466 million U.S. dollars on day two using BNPL options. This represented nearly a 100 million U.S. dollar increase compared to the previous year.

Buy Now, Pay Later in online shopping

BNPL contributes to the success of several e-commerce formats in the United States. In a 2023 survey, 17 percent of shoppers considered BNPL options a leading feature driving sign-ups to retail subscription boxes. In 2022, an even higher number of U.S. shoppers appreciated BNPL services offered in live commerce events, with Gen X consumers being the most enthusiastic about it (62 percent).

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.

WHAT IS A COURTESY ADJUSTMENT ON AMAZON?

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.

Amazon's Future: Bright, But the Stock May Be Overvalued

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 repository includes HRLDAS Noah-MP model output generated as part of Bieri et al. (2025) - Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): Impact on Amazon dry-season transpiration.

These data are distributed in two different formats: Raw model output files and subsetted files that include data for a specific variable. All files are .nc format (NetCDF) and aggregated into .tar files to facilitate download. Given the size of these datasets, Globus transfer is the best way to download them.

Raw model output for four model experiments is available: FD (control), GW, SOIL, and ROOT. See the associated publication for information on the different experiments. These data span an approximately 20 year period from 01 Jun 2000 to 31 Dec 2019. The data have a spatial resolution of 4 km and a temporal frequency of 3 hours. These data are for a domain in the southern Amazon basin (see Figure 1 in the associated publication). Data for each experiment is available as a .tar file which includes 3-hourly NetCDF files. All default Noah-MP output variables are included in each file. As a result, the .tar files are quite large and may take many hours or even days to transfer depending on your network speed and local configurations. These files are named 'noahmp_output_2000_2019_EXP.tar', where EXP is the name of the experiment (FD, GW, SOIL, or ROOT).

Subsetted model output at a daily temporal resolution for all four model experiments is also available. These .tar files include the following variables: water table depth (ZWT), latent heat flux (LH), sensible heat flux (HFX), soil moisture (SOIL_M), canopy evaporation (ECAN), ground evaporation (EDIR), transpiration (ETRAN), rainfall rate at the surface (QRAIN), and two variables that are specific to the ROOT experiment: ROOTACTIVITY (root activity function) and GWRD (active root water uptake depth). There is one file for each variable within the tarred files. These files are named 'noahmp_output_subset_2000_2019_EXP.tar', where EXP is the name of the experiment (FD, GW, SOIL, or ROOT).

Finally, there is a sample dataset with raw 3-hourly output from the ROOT experiment for one day. The purpose of this sample dataset is to allow users to confirm if these data meet their needs before initiating a full transfer via Globus. This file is named 'noahmp_output_sample_ROOT.tar'.

The README.txt file provides information on the Noah-MP output variables in these datasets, among other specifications.

Information on HRLDAS Noah-MP and names/definitions of model output variables that are useful in working with these data are available here: http://dx.doi.org/10.5065/ew8g-yr95. Note that some output variables may be listed in this document under a different variable name, so searching for the long name (e.g. 'baseflow' instead of 'QRF') is recommended. Information on additional output variables that were added to the model as part of this study is available here: https://github.com/bieri2/bieri-et-al-2025-EGU-GMD/tree/DynaRoot. Model code, configuration files, and forcing data used to carry out the model simulations are linked in the related resources section.

During the first quarter 2025, Amazon generated total net sales of nearly *** billion U.S. dollars, surpassing the *** billion U.S. dollars in the same quarter of 2024. From books to billions Launched in 1995 in the United States as an online bookshop, Amazon has since grown into an international e-commerce giant. In April 2023 worldwide visits to amazon.com amounted to over *** billion considering both desktop and mobile traffic. Prime time in the U.S. Although a global company, Amazon truly thrives in the United States where the company is the leading e-commerce platform by sales value. In the North American country, the number of subscribers using Amazon Prime services has been growing steadily over the last several years and is forecast to reach new heights in 2024.