The Registry of Open Data on AWS contains publicly available datasets that are available for access from AWS resources. Note that datasets in this registry are available via AWS resources, but they are not provided by AWS; these datasets are owned and maintained by a variety of government organizations, researchers, businesses, and individuals. This dataset contains derived forms of the data in https://github.com/awslabs/open-data-registry that have been transformed for ease of use with machine interfaces. Currently, only the ndjson form of the registry is populated here.

The Data Exchange Platform Services Marketsize was valued at USD 1.3 billion in 2023 and is projected to reach USD 7.5 billion by 2032, exhibiting a CAGR of 19.15 % during the forecast period. Recent developments include: November 2019: Amazon Web Services (AWS) partnered with Deloitte to help customers securely find, subscribe to, and use third-party data in the cloud using AWS Data Exchange, a new service that can help address unique healthcare issues. March 2020: Google Cloud Platform (GCP) launched Anthos, a new platform that enables businesses to run their applications across multiple clouds and on-premises environments. Anthos includes a data exchange feature that allows businesses to share data between their applications, regardless of where they are deployed.. Key drivers for this market are: Increasing Adoption of Cloud-based Managed Services to Drive Market Growth. Potential restraints include: Misuse of Virtual Currency and Security Attacks Confines the Adoption of Cryptocurrencies. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

A multidisciplinary repository of public data sets such as the Human Genome and US Census data that can be seamlessly integrated into AWS cloud-based applications. AWS is hosting the public data sets at no charge for the community. Anyone can access these data sets from their Amazon Elastic Compute Cloud (Amazon EC2) instances and start computing on the data within minutes. Users can also leverage the entire AWS ecosystem and easily collaborate with other AWS users. If you have a public domain or non-proprietary data set that you think is useful and interesting to the AWS community, please submit a request and the AWS team will review your submission and get back to you. Typically the data sets in the repository are between 1 GB to 1 TB in size (based on the Amazon EBS volume limit), but they can work with you to host larger data sets as well. You must have the right to make the data freely available.

The AWS Public Blockchain Data initiative provides free access to blockchain datasets through collaboration with data providers. The data is optimized for analytics by being transformed into compressed Parquet files, partitioned by date for efficient querying.

Datasets

Blockchain dataset - Maintained by - Path:
- Bitcoin - AWS - s3://aws-public-blockchain/v1.0/btc/
- Ethereum - AWS - s3://aws-public-blockchain/v1.0/eth/
- Arbitrum - SonarX - s3://aws-public-blockchain/v1.1/sonarx/arbitrum/
- Aptos - SonarX - s3://aws-public-blockchain/v1.1/sonarx/aptos/
- Base - SonarX - s3://aws-public-blockchain/v1.1/sonarx/base/
- Provenance - SonarX - s3://aws-public-blockchain/v1.1/sonarx/provenance/
- XRP Ledger - SonarX - s3://aws-public-blockchain/v1.1/sonarx/xrp/
- Stellar(XDR files) - Stellar - s3://aws-public-blockchain/v1.1/stellar/
- The Open Network (TON) - TON - s3://aws-public-blockchain/v1.1/ton/
- Cronos - Cronos - s3://aws-public-blockchain/v1.1/cronos/

Become a Data Provider

We welcome additional blockchain data providers to join this initiative. If you're interested in contributing datasets to the AWS Public Blockchain Data program, please contact our team at aws-public-blockchain@amazon.com.

This dataset can be used to test Minimal Viable Product 2 from Research Data Exchange. This means that the data can only be analyzed in a closed and secure environment and that the output will be checked before it is shared with the data requester. The link to access the data can be found in the references. The data contains customer sentiments regarding Baby products purchased on Amazon.com, on the basis of their written reviews. The data is used for an assignment from the master track Behavioural Data Science, which be found on Kaggle, see reference.

The Sentinel-2 mission is a land monitoring constellation of two satellites that provide high resolution optical imagery and provide continuity for the current SPOT and Landsat missions. The mission provides a global coverage of the Earth's land surface every 5 days, making the data of great use in on-going studies. L1C data are available from June 2015 globally. L2A data are available from November 2016 over Europe region and globally since January 2017.

Malawi Internet Usage: Device Vendor Market Share: Mobile: Amazon data was reported at 0.000 % in 01 May 2024. This stayed constant from the previous number of 0.000 % for 30 Apr 2024. Malawi Internet Usage: Device Vendor Market Share: Mobile: Amazon data is updated daily, averaging 0.080 % from Apr 2024 (Median) to 01 May 2024, with 9 observations. The data reached an all-time high of 0.080 % in 27 Apr 2024 and a record low of 0.000 % in 01 May 2024. Malawi Internet Usage: Device Vendor Market Share: Mobile: Amazon data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Malawi – Table MW.SC.IU: Internet Usage: Device Vendor Market Share.

Automated Weather Station and AWS-like networks are the primary source of surface-level meteorological data in remote polar regions. These networks have developed organically and independently, and deliver data to researchers in idiosyncratic ASCII formats that hinder automated processing and intercomparison among networks. Moreover, station tilt causes significant biases in polar AWS measurements of radiation and wind direction. Researchers, network operators, and data centers would benefit from AWS-like data in a common format, amenable to automated analysis, and adjusted for known biases. This project addresses these needs by developing a scientific software workflow called "Justified AWS" (JAWS) to ingest Level 2 (L2) data in the multiple formats now distributed, harmonize it into a common format, and deliver value-added Level 3 (L3) output suitable for distribution by the network operator, analysis by the researcher, and curation by the data center. Polar climate researchers currently face daunting problems including how to easily: 1. Automate analysis (subsetting, statistics, unit conversion) of AWS-like L2 ASCII data. 2. Combine or intercompare data and data quality from among unharmonized L2 datasets. 3. Adjust L2 data for biases such as AWS tilt angle and direction. JAWS addresses these common issues by harmonizing AWS L2 data into a common format, and applying accepted methods to quantify quality and estimate biases. Specifically, JAWS enables users and network operators to 1. Convert L2 data (usually ASCII tables) into a netCDF-based L3 format compliant with metadata conventions (Climate-Forecast and ACDD) that promote automated discovery and analysis. 2. Include value-added L3 features like the Retrospective, Iterative, Geometry-Based (RIGB) tilt angle and direction corrections, solar angles, and standardized quality flags. 3. Provide a scriptable API to extend the initial L2-to-L3 conversion to newer AWS-like networks and instruments. Polar AWS network experts and NSIDC DAAC personnel, each with decades of experience, will help guide and deliberate the L3 conventions implemented in Stages 2-3. The project will start on July 1, 2017 at entry Technology Readiness Level 3 and will exit on June 30, 2019 at TRL 6. JAWS is now a heterogeneous collection of scripts and methods developed and validated at UCI over the past 15 years. At exit, JAWS will comprise three modular stages written in or wrapped by Python, installable by Conda: Stage 1 ingests and translates L2 data into netCDF. Stage 2 annotates the netCDF with CF and ACDD metadata. Stage 3 derives value-added scientific and quality information. The labor-intensive tasks include turning our heterogeneous workflow into a robust, standards-compliant, extensible workflow with an API based on best practices of modern scientific information systems and services. Implementation of Stages 1-2 may be straightforward though tedious due to the menagerie of L2 formats, instruments, and assumptions. The RIGB component of Stage 3 requires ongoing assimilation of ancillary NASA data (CERES, AIRS) and use of automated data transfer protocols (DAP, THREDDS). The immediate target recipient elements are polar AWS network managers, users, and data distributors. L2 borehole data suffers from similar interoperability issues, as does non-polar AWS data. Hence our L3 format will be extensible to global AWS and permafrost networks. JAWS will increase in situ data accessibility and utility, and enable new derived products (both are AIST goals). The PI is a long-standing researcher, open source software developer, and educator who understands obstacles to harmonizing disparate datasets with NASA interoperability recommendations. Our team participates in relevant geoscience communities, including ESDS working groups, ESIP, AGU, and EarthCube.

Norway Internet Usage: Device Vendor Market Share: Mobile: Amazon data was reported at 0.000 % in 22 Aug 2024. This stayed constant from the previous number of 0.000 % for 21 Aug 2024. Norway Internet Usage: Device Vendor Market Share: Mobile: Amazon data is updated daily, averaging 0.050 % from Aug 2024 (Median) to 22 Aug 2024, with 9 observations. The data reached an all-time high of 0.050 % in 18 Aug 2024 and a record low of 0.000 % in 22 Aug 2024. Norway Internet Usage: Device Vendor Market Share: Mobile: Amazon data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Norway – Table NO.SC.IU: Internet Usage: Device Vendor Market Share.

Global, aggregated physical air quality data from public data sources provided by government, research-grade and other sources. These awesome groups do the hard work of measuring these data and publicly sharing them, and our community makes them more universally-accessible to both humans and machines.

Discover the booming Data Marketplace Platform market! This in-depth analysis reveals market size, CAGR, key drivers, trends, and leading companies like AWS, Snowflake, and Acxiom. Learn about the future of data monetization and the opportunities within this rapidly expanding sector. Explore regional market shares and forecast projections for 2025-2033.

A collection of downscaled climate change projections, derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) [Taylor et al. 2012] and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs) [Meinshausen et al. 2011]. The NASA Earth Exchange group maintains the NEX-DCP30 (CMIP5), NEX-GDDP (CMIP5), and LOCA (CMIP5).

This data set provides measurements of carbon dioxide flux rates (FCO2), gas transfer velocity (k), and partial pressures (pCO2) at 75 sites on rivers and streams of the Amazon River system in South America for the period beginning July 1, 2004, and ending January 23, 2007. Several fieldwork campaigns occurred between June 2004 and January 2007 in the Amazon River basin, with discharge conditions ranging from low to high flow. The sampled areas span the spectrum of chemical characteristics observed across the entire basin, including, for example, both low and high pH values and suspended sediment loads. There is one comma-delimited data file in this data set.

The NASA Earth Exchange (NEX) Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate scenarios that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) [Taylor et al. 2012] and across the two of the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs) [Meinshausen et al. 2011] developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The dataset is an ensemble of projections from 21 different models and two RCPs (RCP 4.5 and RCP 8.5), and provides daily estimates of maximum and minimum temperatures and precipitation using a daily Bias-Correction - Spatial Disaggregation (BCSD) method (Thrasher, et al., 2012). The data spans the entire globe with a 0.25 degree (~25-kilometer) spatial resolution for the periods from 1950 through 2005 (Historical) and from 2006 to 2100 (Climate Projections).

The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four "Tier 1" greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.

In the fourth quarter of 2024, the most popular vendor in the cloud infrastructure services market, Amazon Web Services (AWS), controlled ** percent of the entire market. Microsoft Azure takes second place with ** percent market share, followed by Google Cloud with ** percent market share. Together, these three cloud vendors account for ** percent of total spend in the fourth quarter of 2024. Organizations use cloud services from these vendors for machine learning, data analytics, cloud native development, application migration, and other services. AWS Services Amazon Web Services is used by many organizations because it offers a wide variety of services and products to its customers that improve business agility while being secure and reliable. One of AWS’s most used services is Amazon EC2, which lets customers create virtual machines for their strategic projects while spending less time on maintaining servers. Another important service is Amazon Simple Storage Service (S3), which offers a secure file storage service. In addition, Amazon also offers security, website infrastructure management, and identity and access management solutions. Cloud infrastructure services Vendors offering cloud services to a global customer base do so through different types of cloud computing, which include infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Further, there are different cloud computing deployment models available for customers, namely private cloud and public cloud, as well as community cloud and hybrid cloud. A cloud deployment model is defined based on the location where the deployment resides, and who has access to and control over the infrastructure.

This data package contains raw leaf gas exchange data used to support the publication "Leaf isoprene and monoterpene emission distribution across hyperdominant tree genera in the Amazon basin," Phytochemistry, in-press. Data was collected from 162 individuals, distributed among 25 botanical families and 113 species, from four sites in the Amazon. The data was collected using a modified portable photosynthesis system for environmental control coupled to a thermal desorption tube for volatile isoprenoid collections. Collected TD tube samples were analysed in Manaus, Brazil, using thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). The dataset includes both photosynthesis and isoprene/monoterpene emission rates from leaves under standard environmental conditions as well as controlled light response curves. For detailed methods and raw data, refer to the publication and related material cited in Dataset References.

This data contains in-phase and quadrature waveforms of AWS-1 LTE uplink emissions collected at NASA Langley Research Center's Langley Research Antenna System. The collection campaign is part of the NASCTN effort on AWS-3 LTE impacts on Aeronautical Mobile Telemetry https://www.nist.gov/programs-projects/aws-3-lte-impacts-amt. The work is detailed in the NTIA Technical Report TR-21-553 In-Situ Captures of AWS-1 LTE for Aeronautical Mobile Telemetry System Evaluation available at https://www.its.bldrdoc.gov/publications/details.aspx?pub=3262 . Additional pertinent details on recording parameter settings are outlined in the format provided in NTIA Technical Memo TM-21-553 available at https://www.its.bldrdoc.gov/publications/details.aspx?pub=3261 .

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS. See all usage examples for datasets listed in this registry. See datasets from Facebook Data for Good, NASA Space Act Agreement, NIH STRIDES, NOAA Big Data Program, Space Telescope Science Institute, and Amazon Sustainability Data Initiative.

Amazon (AMZN) Stock Price Time-Series Dataset: May 2012 - November 2012

Dataset Overview

This dataset provides a detailed, intraday view of Amazon's stock (AMZN) price movements from May 21, 2012, to November 14, 2012. Meticulously compiled, it offers a granular perspective on market dynamics, enabling robust quantitative analysis and modeling.

Content

The dataset encompasses the following key financial metrics for each trading day:

• Date: The specific date of the trading session.
• Open: The initial price at the commencement of trading.
• High: The maximum price attained during the trading day.
• Low: The minimum price recorded during the trading day.
• Close: The final trading price at the market's close.
• Adj Close: The closing price adjusted for corporate actions like dividends and stock splits, providing a true return on investment.
• Volume: The number of shares exchanged throughout the trading day, indicating market activity and liquidity.

Intended Use Cases

This dataset is tailored for sophisticated financial analysis, model development, and academic research. Potential applications include:

• Algorithmic Trading Strategy Development: Design and back-test trading algorithms using historical price movements and volume data.
• Volatility Modeling: Analyze and forecast stock price volatility using time-series analysis techniques (e.g., GARCH models).
• Financial Forecasting: Implement machine learning models to predict future stock prices based on historical patterns.
• Event Study Analysis: Examine the impact of specific events or news announcements on Amazon's stock price.
• Risk Management: Evaluate potential risks associated with investing in Amazon stock during this period.
• Academic Research: Conduct studies on market efficiency, price discovery, and the impact of market microstructure on stock behavior.

Data Considerations

• Time Zone: Data is timestamped with Eastern Time (ET).
• Data Cleaning: The dataset has been verified for accuracy, but users are encouraged to perform their own data quality checks.

Contect info:

You can contect me for more data sets if you want any type of data to scrape

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