In 2024, Amazon's net revenue from subscription services segment amounted to 44.37 billion U.S. dollars. Subscription services include Amazon Prime, for which Amazon reported 200 million paying members worldwide at the end of 2020. The AWS category generated 107.56 billion U.S. dollars in annual sales. During the most recently reported fiscal year, the company’s net revenue amounted to 638 billion U.S. dollars. Amazon revenue segments Amazon is one of the biggest online companies worldwide. In 2019, the company’s revenue increased by 21 percent, compared to Google’s revenue growth during the same fiscal period, which was just 18 percent. The majority of Amazon’s net sales are generated through its North American business segment, which accounted for 236.3 billion U.S. dollars in 2020. The United States are the company’s leading market, followed by Germany and the United Kingdom. Business segment: Amazon Web Services Amazon Web Services, commonly referred to as AWS, is one of the strongest-growing business segments of Amazon. AWS is a cloud computing service that provides individuals, companies and governments with a wide range of computing, networking, storage, database, analytics and application services, among many others. As of the third quarter of 2020, AWS accounted for approximately 32 percent of the global cloud infrastructure services vendor market.

Context

Amazon.com, Inc. is an American online retailer with a wide range of products. According to its own information, Amazon, as the market leader in Internet trade, has the world's largest selection of books, CDs and videos. Via the integrated sales platform Marketplace, private individuals or other companies can also offer new and used products as part of online trading. The Amazon Kindle is sold under its own brand as a reader for electronic books, the Amazon Fire HD tablet computer, the Fire TV set-top box, the Fire TV Stick HDMI stick and the Echo speech recognition system.

With sales of $280 billion in 2019, a profit of $11.6 billion, and a market value of $1.32 trillion (June 2020), it was the third most valuable after Apple and Microsoft, and even before Google United States company.

Market cap

Market capitalization of Amazon (AMZN)

Market cap: $2.362 Trillion USD

As of February 2025 Amazon has a market cap of $2.362 Trillion USD. This makes Amazon the world's 4th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Revenue

Revenue for Amazon (AMZN)

Revenue in 2024 (TTM): $637.95 Billion USD

According to Amazon's latest financial reports the company's current revenue (TTM ) is $637.95 Billion USD. an increase over the revenue in the year 2023 that were of $574.78 Billion USD. The revenue is the total amount of income that a company generates by the sale of goods or services. Unlike with the earnings no expenses are subtracted.

Earnings

Earnings for Amazon (AMZN)

Earnings in 2024 (TTM): $71.02 Billion USD

According to Amazon's latest financial reports the company's current earnings are $637.95 Billion USD. , an increase over its 2023 earnings that were of $40.73 Billion USD. The earnings displayed on this page is the company's Pretax Income.

End of Day market cap according to different sources

On Feb 20th, 2025 the market cap of Amazon was reported to be:

$2.362 Trillion USD by Yahoo Finance

$2.362 Trillion USD by CompaniesMarketCap

$2.362 Trillion USD by Nasdaq

Content

Geography: USA

Time period: May 1997- February 2025

Unit of analysis: Amazon Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

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From 2004 to 2024, the net revenue of Amazon e-commerce and service sales has increased tremendously. In the fiscal year ending December 31, the multinational e-commerce company's net revenue was almost 638 billion U.S. dollars, up from 575 billion U.S. dollars in 2023.Amazon.com, a U.S. e-commerce company originally founded in 1994, is the world’s largest online retailer of books, clothing, electronics, music, and many more goods. As of 2024, the company generates the majority of it's net revenues through online retail product sales, followed by third-party retail seller services, cloud computing services, and retail subscription services including Amazon Prime. From seller to digital environment Through Amazon, consumers are able to purchase goods at a rather discounted price from both small and large companies as well as from other users. Both new and used goods are sold on the website. Due to the wide variety of goods available at prices which often undercut local brick-and-mortar retail offerings, Amazon has dominated the retailer market. As of 2024, Amazon’s brand worth amounts to over 185 billion U.S. dollars, topping the likes of companies such as Walmart, Ikea, as well as digital competitors Alibaba and eBay. One of Amazon's first forays into the world of hardware was its e-reader Kindle, one of the most popular e-book readers worldwide. More recently, Amazon has also released several series of own-branded products and a voice-controlled virtual assistant, Alexa. Headquartered in North America Due to its location, Amazon offers more services in North America than worldwide. As a result, the majority of the company’s net revenue in 2023 was actually earned in the United States, Canada, and Mexico. In 2023, approximately 353 billion U.S. dollars was earned in North America compared to only roughly 131 billion U.S. dollars internationally.

Context

Amazon has become a house hold name now and has been around for quite sometime. It comes under the popular FAANG companies and a dream company for many. Today almost anything that you need today is available on Amazon. From groceries to electronics. But it has not only benefited the people purchasing from them. It has benefited those too who invested in the company back then and continue to do till today.

Content

This data set has 7 columns with all the necessary values such as opening price of the stock, the closing price of it, its highest in the day and much more. It has date wise data of the stock starting from 1997 to 2020(August).

Dataset Overview: This dataset compares three major e-commerce companies: Amazon, Alibaba, and eBay, over the decade 2010-2020. It provides insights into sales trends, product availability, user engagement, and other performance metrics. Researchers, analysts, and data enthusiasts can use this dataset for time-series analysis, market comparison, and predictive modeling.

Context: E-commerce has witnessed exponential growth, especially during the last decade. Amazon, Alibaba, and eBay have been key players in shaping the online retail landscape. This dataset serves as a resource to explore how these companies performed in various domains, such as revenue, user base, and product offerings.

Data Description: The dataset contains 12 columns and 6 rows (3 companies across 2 time points: 2010 and 2020).

Column Name Description Year The year the data refers to (2010 or 2020). Company The name of the company (Amazon, Alibaba, or eBay). Total_Sales (USD Billion) Total annual sales/revenue in billion USD. Number_of_Products (Million) The total number of products listed on the platform in millions. Active_Users (Million) Number of active users on the platform (in millions). Market_Share (%) Percentage of the global e-commerce market held by the company. Gross_Margin (%) Gross margin as a percentage of revenue. Operating_Income (%) Operating income as a percentage of revenue. Region_with_Highest_Sales The geographic region where the company had the highest sales (e.g., North America, Asia, Europe). Average_Order_Value (USD) Average monetary value of an order placed on the platform (in USD). Mobile_Transactions (%) Percentage of transactions completed via mobile devices. Number_of_Sellers (Million) Total number of sellers active on the platform (in millions).

Key Insights: Amazon dominates in total sales and market share, with steady growth in the user base and product offerings. Alibaba leads in mobile transactions and seller count, reflecting its focus on mobile-first markets like Asia. eBay maintains strong gross margins but lags in user growth compared to Amazon and Alibaba.

**Potential Uses: **Trend Analysis: Understand how the e-commerce industry evolved during 2010-2020. Market Insights: Compare the performance of Amazon, Alibaba, and eBay. Predictive Modeling: Forecast future trends in e-commerce using regression or machine learning. Visualization: Create graphs and dashboards showcasing the metrics over time.

Description

Amazon.com, Inc., is an American multinational technology company based in Seattle that focuses on e-commerce, cloud computing, digital streaming, and artificial intelligence. It is considered one of the Big Four technology companies, along with Google, Apple, and Facebook.

Context

Time series forecasting for the Amazon Stock Prices. Explore the Data.

About the dataset

Date - in format: yy-mm-dd Open - the price of the stock at market open High - Highest price reached in the day Low - Lowest price reached in the day Close - The stock closing at the end of the Market hours Adj Close - Is the closing price after adjustments for all applicable splits and dividend distributions. Volume - Number of shares traded

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Rare plant and vertebrate species have been documented to contribute disproportionately to the total morphological structure of species assemblages. These species often possess morphologically extreme traits and occupy the boundaries of morphological space. As rare species are at greater risk of extinction than more widely distributed species, human-induced disturbances can strongly affect ecosystem functions related to assemblage morphology. Here, we assess to what extent the distributions of ant morphological traits are supported by morphologically extreme species and how they are distributed among habitats in a global biodiversity hotspot, the Brazilian Amazon. We used a morphological database comprising 15 continuous morphological traits and 977 expert-validated ant species distributed across the Brazilian Amazon. We produced species range estimates using species distribution models or alpha hulls (when few records were available). Next, we conducted a principal components analysis to combine traits into a space with reduced dimensionality (morphospace). Then, we identified morphologically extreme species in this space and quantified their contributions to morphological diversity across different habitat types in the Brazilian Amazon Basin. We identified 114 morphologically extreme ant species across the Amazon ant morphospace. These species also accounted for a large percentage of morphospace filling, exceeding 99% representation in the most disturbed habitats in the Amazon. Our results suggest that a few morphologically extreme species capture most of the variation in ant morphology and therefore, the spectrum of ecosystem functions performed by ants in the Brazilian Amazon Basin. Further, unlike for many other groups, these extreme morphologies were represented by the set of most common species. These results suggest greater functional redundancy and resilience in Brazilian Amazon ants, but more broadly, they contribute to our understanding of ecological processes that sustain ecosystem functions. Methods Occurrence and Morphological Datasets The primary occurrence database comprises historical and current ant records for the Brazilian Amazon (from 1817 to 2020). Data were obtained from the Global Ant Biodiversity Informatics project (GABI: Guénard et al. 2017; but also see database treatment in Andrade-Silva et al. 2022). We updated this dataset by incorporating new literature published in 2021 and 2022. Valid species names were based on the Online Catalog of the Ants of the World (AntCat: Bolton 2022, last checked in November 2022). We only considered nominal ant taxa (valid species and subspecies); informal taxa (morphospecies) were not included. We developed the morphological database from a set of 977 ant species (about 91% of the species recorded for the Brazilian Amazon Basin) and measured 15 continuous morphological traits, including HW = Head width; HL = Head length; CL = Clypeus length; ML = Mandible length; MW = Mandible width; FL = Hind femur length; SL = Scape length; WL = Weber’s length; ID = Inter-ocular distance; EL = Eyes length; PrW = Pronotum width; DEM = Distance of eye to mandible insertion; PeL = Petiole length; PeW = Petiole width; PeH = Petiole height. Vegetation Type Dataset We used a vegetation-type shapefile for the Brazilian Amazon provided by the Instituto Brasileiro de Geografia e Estatística (IBGE 2012) to describe the regional morphological structure of ants from different habitats. The complex vegetation structure in the Amazon Basin is one of the main drivers of animal diversity, hosting varied microhabitats that enable interspecific coexistence (Laurance and Vasconcelos 2009, Fichaux et al. 2019). We defined nine main vegetation types in the Brazilian Amazon: (1) anthropic, (2) white sand forests (Campinaranas), (3) dense ombrophilous forests, (4) open ombrophilous forests, (5) pioneer vegetation (i.e., sand bars, mangroves), (6) savannah, (7) seasonal deciduous forest, (8) seasonal semideciduous forest, and (9) vegetational refuge (i.e., high-altitude fields, peat areas). We reclassified the IBGE vegetation shapefile by grouping vegetation classes within their immediately superior vegetation types. For example, we reclassified the classes "alluvial dense ombrophilous forest" and "lowland dense ombrophilous forest" into "dense ombrophilous forest". We reclassified vegetation types using shapefile dissolve operations in QGIS, version 2.18.2 (QGIS Development Team 2019).

The state of Pará in northern Brazil is located entirely within the Amazon Basin and harbors a great diversity of landscape and vegetation types that support high levels of biodiversity. Here, we provide a comprehensive inventory of ant species and their distribution in Pará. This regional list is based on an extensive review of species records from published and unpublished sources covering a period of 134 years (1886–2020) and includes the five most representative ant collections in Brazil. In total, we documented 12 subfamilies, 90 genera and 753 ant species, including 97 species recorded for the first time in Pará and 12 species newly reported in Brazil. Sampling effort across the state is highly uneven, and most records may be associated with research areas near the state capital, mining areas, hydroelectric dams, and research field stations run by the state or universities. In addition, our results suggest a strong bias in ant collection in Pará in terms of proximity of sampled sites to access routes, such as roads and rivers. We also found that species records were highly unevenly distributed based on areas of endemism within the Amazon, vegetation type, and protected areas within the state. Ant surveys are still lacking from most protected areas of Pará, and further sampling is urgently needed in view of the current trend of expansion of major infrastructure projects and natural resource harvesting within protected areas of Pará. Our database represents an extremely valuable and rich source of information for further studies on ant biodiversity and conservation in the Amazon Basin.

Methods We compiled a dataset of ant species from Pará recorded in the literature from 1886 (the first record in the literature of ants collected in Pará) to 2020. Most published data records were provided by the Global Ant Biodiversity (GABI) project (Guénard et al. 2017), including data extracted from AntWeb (AntWeb 2020). Next, we extracted geographical information from labels of specimens collected in Pará and deposited in one of the five main Brazilian entomological collections, namely Museu Paraense Emílio Goeldi (MPEG); Coleção Entomológica Padre Jesus Santiago Moure at Universidade Federal do Paraná (DZUP); Centro de Pesquisas do Cacau at Comissão Executiva do Plano de Lavoura Cacaueira (CPDC); Museu de Zoologia da Universidade de São Paulo (MZSP); and Instituto Nacional de Pesquisas da Amazônia (INPA). Thirdly, for the MPEG dataset, we retrieved records from the Brazilian Biodiversity Information System (SiBBr), a national online biodiversity database (https://www.sibbr.gov.br/). The SiBBr repository hosts and mirrors the information available from GBIF (Global Biodiversity Information Facility; https://www.gbif.org). Lastly, material from recent surveys conducted by the authors in the state of Pará were also used to update the database.

Dataset on Cloud Network Variability

This is the dataset obtained while benchmarking public and private clouds for the following article:

Alexandru Uta, Alexandru Custura, Dmitry Duplyakin, Ivo Jimenez, Jan Rellermeyer, Carlos Maltzahn, Robert Ricci, Alexandru Iosup. Is Big Data Performance Reproducible in Modern Cloud Networks?. In proceedings of 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2020, February 25-27, Santa Clara, USA.

The dataset contains bandwidth variability data for: - Amazon EC2 - Google Compute Engine - Microsoft Azure (limited data) - Scaleway (limited data) - SURFsara HPCCloud

The dataset contains TCP latency (RTT) data for: - Amazon EC2 - Google Compute Engine

The dataset contains data regarding token bucket sizes (explained in depth in the aforementioned article) for Amazon EC2.

Please note that the full archive is over 3.5 GB of data, made up of hundreds of thousands of small files. This is why we decided to archive the data, which we then split into smaller files, to accommodate for the Github max file size of 100 MB.

===== DETAILS ON THE ARCHIVE FORMAT =====

1. The Bandwidth Variability data:
2. archived in the bandwidth_variability_data.tar.bz2.parta, .partb, .partc

3. after unpacking the archive, the data is split in directories per machine type, and experiment type, for example: --- perfvar-aws-m5xlarge-fullspeed: contains iperf3 output files for continuous communication between 2 m5.xlarge VMs in Amazon EC2. --- perfvar-google-4cpu-bursty-5s30s: contains iperf3 output files for bursty communication (5 seconds communication, 30 seconds break; repeat)

4. The Latency Variability data:

5. archived in the file latency_study.tar.bz2

6. after unpacking the archive, the directories contain TCP dump RTT data and iperf3 outputs

7. The Token Bucket AWS study:

8. archived in the file token_bucket_study.tar.bz2

9. contains files of form INSTANCE_TYPE-REGION-TIMESTAMP.

10. files with extension .raw contain raw iperf3 client output

11. files with extension .bw contain bandwidth samples taken at 1 second intervals from the iperf3 utility

12. files with extension .tb contain a triple of form

13. example of a file name: c5.2xlarge-us-west-1-1567733720.raw

The massive loss of biodiversity in recent years has driven the development of rapid, cost-effective, non-invasive, and efficient sampling alternatives, such as environmental DNA. With this method, a water sample can be used to evaluate a community's diversity, in addition with low abundance, cryptic and threatened species detection. Therefore, in this study, environmental DNA was used to determine the diversity of aquatic, semi-aquatic and terrestrial vertebrates in the Colombian Amazon and Orinoco basins, which included four main subregions: Bojonawi Natural Reserve and adjacent areas (Vichada Department), Sierra de la Macarena National Park and Tillavá (Meta Department), Puerto Nariño and adjacent areas (Amazonas Department) and the Municipality of Solano (Caquetá Department). A total of 709 OTUs were identified for all locations. The Orinoco river showed the highest number of fish genera (68) and the Guayabero river, the largest number of genera for tetrapods (13). New taxonomic records were found on all locations, mianly in Bita, Orinoco and Tillavá rivers, which portrayed the highest record of unknown fish diversity compared with traditional surveys. Likewise, two vulnerable fish species and three vulnerable mammal species were identified, as well as four threatened mammal species, including the giant otter (Pteronura brasiliensis), the giant anteater (Myrmecophaga tridactyla), the two subspecies of the Amazon river dolphin (Inia geoffrensis geoffrensis and Inia geoffrensis humboldtiana) and the tucuxi (Sotalia fluviatilis). It is essential to improve current DNA sequence databases for the neotropics and standardize the methodology according to the animal of interest in order to develop future studies that maximize environmental DNA analyses efficiency. Methods 30 locations were sampled based on the methodologies used by NatureMetrics laboratory (NatureMetrics, 2019) and the studies of Lozano and Caballero (2020) and Caballero et al. (2021a). For each location, up to seven water subsamples (1L each) were taken in a plastic bottle and then were poured into a bucket covered with a plastic bag. The bottle, as well as the plastic bags were previously sterilized with 90 % ethanol thoroughly. Every water sample was taken with sterile gloves to avoid human DNA contamination and all plastic bags were changed after each sampling event to prevent the mixing of water from different sampling locations. The water collection was made each 10-20 m along a linear transect, carried out by boat for the rivers, canoes for the lakes-lagoons and on foot for the streams, taking the coordinates of each sampled point with a GPS (Garmin etrex 12 channel GPS). Once all the subsamples of a location were taken, the process of filtration began using a NatureMetrics eDNA collection kit. A 60 ml syringe filled with the collected water was attached to a filter disk with a 0.8 μm pore size. Then, when the filter disk was clogged and no more water could go through, the syringe was detached and a smaller syringe with a preserving buffer was used to protect the filter and avoid DNA degradation. Each filter was stored in an envelope with their respective field information and were kept cool in styrofoam cooler with ice packs. Filters were transported to Nature Metrics (Egham, Surrey, England), where laboratory procedures took place. DNA was extracted using Qiagen DNeasy Blood & Tissue Kit (see manufacturer instructions), modifying some steps to obtain increased DNA yields. Subsequently, DNA was purified using the DNeasy PowerClean Pro Cleanup kit to remove PCR inhibitors. Then, DNA extracted from each filter was amplified using 12 replicates, with the 12S rRNA mitochondrial gene to target fish as part of the ​eDNA survey - Vertebrates​ pipeline (Milan et al. 2020). Tails were added at the 5′ end of the primers to be complementary with Illumina Nextera index primers. The amplification mixture for each replicate contained 1X DreamTaq PCR Master Mix (Thermo Scientific), 0.4 μM of each of the tailed primers, 1 μL of DNA and PCR grade water (Thermo Scientific) up to a total reaction volume of 10 μL. All PCRs were performed in the presence of both a negative control and a positive control sample (mock community with a known composition, not to occur in Colombia). PCR conditions consisted of an initial denaturation at 95°C for 2 min, followed by 10 cycles of 20 s at 95°C, a 30 s touchdown annealing step (-0.5°C per cycle) starting at 60°C, and 40 s at 72°C, 35 cycles of 20s at 95°C, 30s at 55°C, and 40s at 72°C, and a final elongation step at 72°C for 5 min. Amplification success was determined by gel electrophoresis. Amplicons were pooled and purified with MagBind TotalPure NGS (Omega Biotek) magnetic beads with a ratio 0.8:1 (beads:DNA) to remove primer dimers and then quantified using a Qubit high sensitivity kit according to the manufacturer’s protocol. All purified index PCRs were pooled into a final library with equal concentrations. The final library was sequenced using an Illumina MiSeq V2 kit at 12 pM with a 10% PhiX spike in. Sequence data were processed using a custom bioinformatics pipeline, USEARCH v11, for quality filtering, dereplication and taxonomic assignment (≥ 80% agreement in the overlap). Forward and reverse primers were trimmed from the merged sequences using cutadapt 2.3 (Martin,1994; Mathon et al. 2021) and retained if the trimmed length was between 80 - 120 bp. These sequences were quality filtered to retain only those with an expected error rate per base of 0.01 or below and dereplicated by sample, retaining singletons. Unique reads from all samples were denoised in a single analysis with UNOISE (Dal Pont et al. 2021), requiring retained ZOTUs (zero-radius OTUs) to have a minimum abundance of 8. ZOTUs were clustered at 99% similarity. An OTU-by-sample table was generated by mapping all dereplicated reads for each sample to the OTU representative sequences at an identity threshold of 97%. Taxonomic information was added to each OTU by means of sequence similarity searches against the NCBI ​nt​ database (GenBank) and PROTAX (Somervuo et al. 2016; Lozano and Caballero, 2021). Identifications from either source were accepted and these were consistent at the level at which they were made. Species and genus level assignments were automatically retained if supported by unambiguous matches to reference sequences at ≥99% or ≥95%, respectively. Public records from GBIF were used to assess which hits were most likely to be present in Colombia, in cases where there were equally good matches to multiple species. This allowed numerous uncertain sequences to be resolved to species level. OTUs that were ≥99% similar and had similar co-occurrence patterns were combined with LULU (Frøslev et al. 2017) and the OTU table was then filtered to remove low abundance OTUs from each sample (<0.05% or <10 reads). Finally, human, known food fish and livestock sequences were removed, in addition with OTUs identified above order- level.

📊 Supplement Sales Data (2020–2025) Overview This dataset contains weekly sales data for a variety of health and wellness supplements from January 2020 to April 2025. The data includes products in categories like Protein, Vitamins, Omega, and Amino Acids, among others, and covers multiple e-commerce platforms such as Amazon, Walmart, and iHerb. The dataset also tracks sales in several locations including the USA, UK, and Canada.

Dataset Details Time Range: January 2020 to April 2025

Frequency: Weekly (Every Monday)

Number of Rows: 4,384

Columns:

Date: The week of the sale.

Product Name: The name of the supplement (e.g., Whey Protein, Vitamin C, etc.).

Category: The category of the supplement (e.g., Protein, Vitamin, Omega).

Units Sold: The number of units sold in that week.

Price: The selling price of the product.

Revenue: The total revenue generated (Units Sold * Price).

Discount: The discount applied on the product (as a percentage of original price).

Units Returned: The number of units returned in that week.

Location: The location of the sale (USA, UK, or Canada).

Platform: The e-commerce platform (Amazon, Walmart, iHerb).

Use Cases This dataset is ideal for:

Time-series forecasting and sales trend analysis 📈

Price vs. demand analysis and revenue prediction 📊

Sentiment analysis and impact of promotions (Discounts) on sales 🛍️

Product performance tracking across different platforms and locations 🛒

Business optimization in the health and wellness e-commerce sector 💼

Potential Applications Build predictive models to forecast future sales 📅

Analyze the effectiveness of discounts and promotions 💸

Create recommendation systems for supplement products 🧠

Perform exploratory data analysis (EDA) and uncover trends 🔍

Model return rates and their effect on overall revenue 📉

Why This Dataset? This dataset provides an excellent starting point for those interested in building business intelligence tools, e-commerce forecasting models, or exploring health & wellness trends. It also serves as a perfect dataset for data science learners looking to apply regression, time-series analysis, and predictive modeling techniques.

Overview Supplementary materials for the paper "Comparing Internet experiences and prosociality in Amazon Mechanical Turk and population-based survey samples" by Eszter Hargittai and Aaron Shaw published in Socius in 2020 (https://doi.org/10.1177/2378023119889834). License The materials provided here are issued under the same (Creative Commons Attribution Non-Commercial 4.0) license as the paper. Details and a copy of the license are available at: http://creativecommons.org/licenses/by-nc/4.0/. Manifest The files included are: Hargittai-Shaw-AMT-NORC-2019.rds and Hargittai-Shaw-AMT-NORC-2019.tsv: Two (identical) versions the dataset used for the analysis. The tsv file is provided to facilitate import into software other than R. R analysis code files: 01-import.R - Imports dataset. Creates a mapping of dependent variables and variable names used elsewhere in the figure and analysis. 02-gen_figure.R - Generates Figure 1 in PDF and PNG formats and saves them in the "figures" directory. 03-gendescriptivestats.R - Generates results reported in Table 1. 04-gen_models.R - Fits models reported in Tables 2-4. 05-alternative_specifications.R - Fits models using log-transformed version of the income variable. Makefile: Executes all of the R files in sequence, produces corresponding .log files in the "log" directory that contain the full R session from each file as well as separate error log files (also in the "log" directory) that capture any error messages and warnings generated by R along the way. HargittaiShaw2019Socius-Instrument.pdf: The questions distributed to both the NORC and AMT survey participants used in the analysis reported in this paper. How to reproduce the analysis presented in the paper Depending on your computing environment, reproducing the analysis presented in the paper may be as easy as invoking "make all" or "make" in the directory containing this file on a system that has the appropriate software installed. Once compilation is complete, you can review the log files in a text editor. See below for more on software and dependencies. If calling the makefile fails, the individual R scripts can also be run interactively or in batch mode. Software and dependencies The R and compilation materials provided here were created and tested on a 64-bit laptop pc running Ubuntu 18.04.3 LTS, R version 3.6.1, ggplot2 version 3.2.1, reshape2 version 1.4.3, forcats version 0.4.0, pscl version 1.5.2, and stargazer version 5.2.2 (these last five are R packages called in specific .R files). As with all software, your mileage may vary and the authors provide no warranties. Codebook The dataset consists of 36 variables (columns) and 2,716 participants (rows). The variable names and brief descriptions follow below. Additional details of measurement are provided in the paper and survey instrument. All dichotomous indicators are coded 0/1 where 1 is the affirmative response implied by the variable name: id: Index to identify individual units (participants). svy_raked_wgt: Raked survey weights provided by NORC. amtsample: Data source coded 0 (NORC) or 1 (AMT). age: Participant age in years. female: Participant selected "female" gender. incomecont: Income in USD (continuous) coded from center-points of categories reported in the instruments. incomediv: Income in $1,000s USD (=incomecont/1000). incomesqrt: Square-root of incomecont. lincome: Natural logarithm of incomecont. rural: Participant resides in a rural area. employed: Participant is fully or partially employed. eduhsorless: Highest education level is high school or less. edusc: Highest education level is completed some college. edubaormore: Highest education level is BA or more. white: Race = white. black: Race = black. nativeam: Race = native american. hispanic: Ethnicity = hispanic. asian: Race = asian. raceother: Race = other. skillsmean: Internet use skills index (described in paper). accesssum: Internet use autonomy (described in paper). webweekhrs: Internet use frequency (described in paper). do_sum: Participatory online activities (described in paper). snssumcompare: Social network site activities (described in paper). altru_scale: Generous behaviors (described in paper). trust_scale: Trust scale score (described in paper). pts_give: Points donated in unilateral dictator game (described in paper). std_accesssum: Standardized (z-score) version of accesssum. std_webweekhrs: Standardized (z-score) version of webweekhrs. std_skillsmean: Standardized (z-score) version of skillsmean. std_do_sum: Standardized (z-score) version of do_sum. std_snssumcompare: Standardized (z-score) version of snssumcompare. std_trust_scale: Standardized (z-score) version of trust_scale. std_altru_scale: Standardized (z-score) version of altru_scale. std_pts_give: Standardized (z-score) version of pts_give.

This a Parquet representation of the Open Targets Platform's latest export. The Open Targets Platform integrates evidence from genetics, genomics, transcriptomics, drugs, animal models and scientific literature to score and rank target-disease associations for drug target identification. The Open Targets Platform (https://www.targetvalidation.org) is a freely available resource for the integration of genetics, genomics, and chemical data to aid systematic drug target identification and prioritisation. This dataset is 'Lakehouse Ready'. Meaning, you can query this data in-place straight out of the Registry of Open Data S3 bucket. Deploy this dataset's corresponding CloudFormation template to create the AWS Glue catalog entries into your account in about 30 seconds. That one step will enable you to write SQL with AWS Athena, build dashboards and charts with Amazon Quicksight, perform HPC with AWS EMR, or join into your AWS Redshift clusters. More detail in (the documentation)[https://github.com/aws-samples/data-lake-as-code/blob/roda/README.md.

This dataset comprises the maps and scripts used to generate these maps shown in Figures 2 and 5 of the article "The drivers and impacts of Amazon forest degradation" (https://doi.org/10.1126/science.abp8622). Please see Supplementary Information of the aforementioned publication for further details. Files are sorted by tags: Auxiliary file/data: files needed to run the scripts used to generate the maps in the article's Figures 2. Code: Scripts used to generate the maps in the article's Figures 2 and 5. Data: maps shown in the article's Figures 2 and 5. Documentation: explanatory files on how to use scripts. Figure 2: data explicitly shown in the article's Figure 2. Figure 5: data explicitly shown in the article's Figure 5. 2001-2018 droughts: auxiliary data and scripts relative to extreme drought data shown in Fig. 2. 2001-2018 fire: auxiliary file and scripts relative to burned area (fire) data shown in Fig. 2. 2050 projection: scripts and data related to the projections presented in the article's Figure 5. *** Data on timber extraction shown in Figure 2 did not require the use of any script (see https://doi.org/10.5194/gmd-13-5425-2020). Scripts used to generate the area under edge effects in Figure 2 are available via https://doi.org/10.1038/s41558-021-01026-5.

This data set contains proportional estimates for the vegetative cover types of woody vegetation, herbaceous vegetation, and bare ground over the Amazon Basin for the period 2000-2001. These products were derived from all seven bands of the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Terra satellite. A set of MODIS 32-day composites were used to create the vegetation cover types using the Vegetation Continuous Fields (VCF) (Hansen et al., 2002) approach which shows how much of a land cover such as "forest" or "grassland" exists anywhere on the land surface. The VCF product may depict areas of heterogeneous land cover better than traditional discrete classification schemes which shows where land cover types are concentrated.

The original MODIS products are 500-m spatial resolution and are derived from 2000-2001 data products. The data were resampled to 1-km resolution for the regional study under this project, and provided as 3 separate cover type files in ENVI and GeoTIFF file formats that are provided in six zipped files. These products are registered to the rest of the regional data sets over the Amazon basin.

These data are also available for download from the Global Land Cover Facility Website (http://modis.umiacs.umd.edu/).

The reason behind of this datasets.

When I give a class, I always try to use real-world data from my other jobs (censured, of course), to make my students see how the tools of the class are used out the classroom. We all have been in the Statistics class where we must count our ages, weights, or size, but: how many times the teacher gave us a real-world example? What if we change the game with real information? Tell your students that they can make the menu for their restaurants, prepare their speech to an audience or choose the launch day of their product with the same tool. As a form of gratitude and retribution to all my teachers and mentors, that gave me everything and more that I need to grow, I left here these datasets.

About the info.

1. Here you will have 22 categories for Mexico and 25 for Brazil, in 3 different formats: csv, excel, parquet (choose your favorite). The datasets contain the Top 50 Best Sellers of that hour (they change every hour according to the footer); all obtained by a scraper made with python’s library Beautiful Soup. If you want to know the original names of the categories, here is the landing page:
2. Mexico: https://www.amazon.com.mx/gp/bestsellers/?ref_=nav_cs_bestsellers
3. Brazil: https://www.amazon.com.br/gp/bestsellers/?ref_=nav_cs_bestsellers

Types and context.

The datasets contain: 1. Time: (“%Y-%m-%d %H-%M”) date and time when the request was made. 2. Rank: (Float) Rank position. 3. Product Names: (String) The complete name that the seller puts on the object. 4. Stars: (Float) Average from all the reviews made to the product. 5. Reviews: (Int) Total number of reviews since the product is on sale at Amazon. 6. Authors/Company: (String) In the case of books, kindle, and music, this column is related to the author of the piece, while the rest (in case they use it) contains the company that made the product. 7. Edition/Console: (String) In the case of videogames, this column contains the console where you can play the videogame or use the product (like the headset, keyboards, or controls); books, hardcovers, digital, kindle; and music, CD, vinyl or boxset. 8. Pricestdormin and Maxprices: (Float) In case there is not max price (next column), this just contains the price of the product. On the contrary, contains the lowest price of the range given by Amazon’s algorithm.

Challenges

Also, there are some “issues” left for the beginners (like me): * Some empty rows * In one country there are missing values from one column only, and just for a short period. * (Hint) The same country has an issue with the prices in the same period.

The missing data is not an error, it was deleted for students that are learning how to fill missing info with values. Trust me, it can be filled with a simple Data exploration. The price issue is also left by the same way. The idea came from a Kaggle that has the information of beer sales with a problem like this.

Acknowledgments

I have no words to thank Platzi for his Master program, and my coach Cesar for giving me all the challenges from Kaggle. Three months ago, I did not know a thing about python or Kaggle, and now, look at this!

Tasks left

• Design of dashboards. • Choose a product and predict when it will leave the Top 50. • Scrap the amazon product pages and obtain more info about the products. • Give a hypothesis that explains why that product reaches the top 50, and why did it stay that long. • Which is the variable that affects more in a category: price, stars, reviews, or a mix? • Add an API. • Compare both country markets. • (Spoiler) Fill in the stars and reviews of Brazil from 2020-08-01 to 2020-08-17 • (Spoiler) Correct the prices from Brazil in this time step: 2020-08-01 to 2020-08-17. Hint: thousand = ‘.’, decimal = ‘,’

About the dataset

Historical Amazon stock prices in daily frequency, from 14 May, 1997 to 24 Sep, 2020.

Company Profile

Amazon.com, Inc. engages in the retail sale of consumer products and subscriptions in North America and internationally. The company operates through three segments: North America, International, and Amazon Web Services (AWS). It sells merchandise and content purchased for resale from third-party sellers through physical and online stores. The company also manufactures and sells electronic devices, including Kindle, Fire tablets, Fire TVs, Rings, and Echo and other devices; provides Kindle Direct Publishing, an online service that allows independent authors and publishers to make their books available in the Kindle Store; and develops and produces media content. In addition, it offers programs that enable sellers to sell their products on its Websites, as well as its stores; and programs that allow authors, musicians, filmmakers, skill and app developers, and others to publish and sell content. Further, the company provides compute, storage, database, and other AWS services, as well as fulfillment, advertising, publishing, and digital content subscriptions. Additionally, it offers Amazon Prime, a membership program, which provides free shipping of various items; access to streaming of movies and TV episodes; and other services. The company also operates in the food delivery business in Bengaluru, India. It serves consumers, sellers, developers, enterprises, and content creators. The company also has utility-scale solar projects in China, Australia, and the United States. Amazon.com, Inc. was founded in 1994 and is headquartered in Seattle, Washington.

This data set provides measurements for diameter at breast height (DBH), tree height, distance from tree stems to the furthest canopy element, and a species survey of secondary forests in Para and Rondonia, Brazil, from 2002-2003. The forest areas were defined as Type A and Type B stands. Measurements were made in the overstory, understory, and midstory of each stand.

Type A stands were sampled intensively, with the goal of providing high-fidelity spatial information about the 3-dimensional structure of the stand. These stands were 60 x 60-m (0.36-ha) areas divided into 10 x 10-m grids of uniform clearing and abandonment history and were identifiable from Landsat images. Type B stands were sampled extensively, with the goal of providing unbiased estimates of biomass, along with some information about the vertical structure of the stand and of spatial variability. These stands were polygons of uniform clearing and afforestation history based on multitemporal Landsat imagery, and varied in size and shape. The Landsat files provide classified land cover for each scene and can be used as a time series to evaluate land cover change over time. Each file is a geolocated land cover map based on 30-m Landsat data. NOTE: There were additional files which could not be archived due to file problems. Data Quality Statement: The Data Center has determined that this data set has missing or incomplete data, metadata, or other documentation resulting in diminished usability of this product.

Known Problems: Some unresolved issues remain where data values are inconsistent with the variable descriptions provided with the data set. The site identification and plot identification values are not consistently used in all three data files. The variables are not adequately described.

NEW GOES-19 Data!! On April 4, 2025 at 1500 UTC, the GOES-19 satellite will be declared the Operational GOES-East satellite. All products and services, including NODD, for GOES-East will transition to GOES-19 data at that time. GOES-19 will operate out of the GOES-East location of 75.2°W starting on April 1, 2025 and through the operational transition. Until the transition time and during the final stretch of Post Launch Product Testing (PLPT), GOES-19 products are considered non-operational regardless of their validation maturity level. Shortly following the transition of GOES-19 to GOES-East, all data distribution from GOES-16 will be turned off. GOES-16 will drift to the storage location at 104.7°W. GOES-19 data should begin flowing again on April 4th once this maneuver is complete.

NEW GOES 16 Reprocess Data!! The reprocessed GOES-16 ABI L1b data mitigates systematic data issues (including data gaps and image artifacts) seen in the Operational products, and improves the stability of both the radiometric and geometric calibration over the course of the entire mission life. These data were produced by recomputing the L1b radiance products from input raw L0 data using improved calibration algorithms and look-up tables, derived from data analysis of the NIST-traceable, on-board sources. In addition, the reprocessed data products contain enhancements to the L1b file format, including limb pixels and pixel timestamps, while maintaining compatibility with the operational products. The datasets currently available span the operational life of GOES-16 ABI, from early 2018 through the end of 2024. The Reprocessed L1b dataset shows improvement over the Operational L1b products but may still contain data gaps or discrepancies. Please provide feedback to Dan Lindsey (dan.lindsey@noaa.gov) and Gary Lin (guoqing.lin-1@nasa.gov). More information can be found in the GOES-R ABI Reprocess User Guide.


NOTICE: As of January 10th 2023, GOES-18 assumed the GOES-West position and all data files are deemed both operational and provisional, so no ‘preliminary, non-operational’ caveat is needed. GOES-17 is now offline, shifted approximately 105 degree West, where it will be in on-orbit storage. GOES-17 data will no longer flow into the GOES-17 bucket. Operational GOES-West products can be found in the GOES-18 bucket.

GOES satellites (GOES-16, GOES-17, GOES-18 & GOES-19) provide continuous weather imagery and monitoring of meteorological and space environment data across North America. GOES satellites provide the kind of continuous monitoring necessary for intensive data analysis. They hover continuously over one position on the surface. The satellites orbit high enough to allow for a full-disc view of the Earth. Because they stay above a fixed spot on the surface, they provide a constant vigil for the atmospheric "triggers" for severe weather conditions such as tornadoes, flash floods, hailstorms, and hurricanes. When these conditions develop, the GOES satellites are able to monitor storm development and track their movements. SUVI products available in both NetCDF and FITS.

This dataset is been built based on multiple .csv file (each with reviews on one defined movie from IMDB) from Aditya, P., Abhilash B. & Abhijit M. (2020). IMDb Movie Reviews Dataset. IEEE Dataport. https://dx.doi.org/10.21227/zm1y-b270. Then I added some rules to make this dataset match this one https://www.kaggle.com/datasets/dargolex/french-reviews-on-movies-and-en-translation. Then the dataset has been translated with the API of Google Traduction.