This dataset contains additional parameters calculated for bottle samples obtained during the 2002 Polar Star Mooring cruise (AWS-02-1). These additional parameters are Potential Temperature (theta), Potential Density (sigma-theta), Oxygen Percent Saturation, Apparent Oxygen Utilization (AOU), Nitric Oxide (NO), and PO.

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.

The 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File (2023-04-03) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations, that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File.

The NMF consists of the full set of privacy-protected statistical queries (counts of individuals or housing units with particular combinations of characteristics) of confidential 2010 Census data relating to the redistricting data portion of the 2010 Demonstration Data Products Suite – Redistricting and Demographic and Housing Characteristics File – Production Settings (2023-04-03). These statistical queries, called “noisy measurements” were produced under the zero-Concentrated Differential Privacy framework (Bun, M. and Steinke, T [2016] https://arxiv.org/abs/1605.02065; see also Dwork C. and Roth, A. [2014] https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] https://arxiv.org/abs/2004.00010), which added positive or negative integer-valued noise to each of the resulting counts. The noisy measurements are an intermediate stage of the TDA prior to the post-processing the TDA then performs to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these 2010 Census demonstration data to enable data users to evaluate the expected impact of disclosure avoidance variability on 2020 Census data. The 2010 Census Production Settings Redistricting Data (P.L.94-171) Demonstration Noisy Measurement File (2023-04-03) has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004).

The data includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2010 Census Edited File (CEF), which includes confidential data initially collected in the 2010 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) (https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/04-Demonstration_Data_Products_Suite/2023-04-03/). As these 2010 Census demonstration data are intended to support study of the design and expected impacts of the 2020 Disclosure Avoidance System, the 2010 CEF records were pre-processed before application of the zCDP framework. This pre-processing converted the 2010 CEF records into the input-file format, response codes, and tabulation categories used for the 2020 Census, which differ in substantive ways from the format, response codes, and tabulation categories originally used for the 2010 Census.

The NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints—information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) —are provided.

PROMICE AWS edition 3 (deprecated). Automatic weather stations (AWS) data from the Greenland ice sheet. Data are collected from the one level tripod design that floats on the ice in the ablation area. See Related Publication for more information. This dataset is not maintained any more. Here is the link to the new and maintained data product: Maintained AWS data product

ecCharts

This licence provides access to ECMWF interactive ecCharts tool to visualise analysis and forecast products Please note that access to the ecCharts is for End User use only (internal).

ECMWF ecCharts web service is available and we are pleased to be able to offer the service for evaluation to customers of ECMWF's web products.

DaDaDa: A Dataset for Data Products in Data Marketplaces

Composition of DaDaDa

DaDaDa contains metadata for 16,147 data products collected from 9 major data marketplaces. The features comprising DaDaDa are detailed below.

• title. The title or short description of the data product.
• url. The web address of the detail page of the data product.
• platform. The name of the data marketplace hosting the data product.
• provider. The name of the data provider as made available by the data marketplace. There are a total of 1,992 data providers, with “Techsalerator” being the leading provider, offering 644 data products.
• description. The detailed description of the data product.
• volume. The number of records.
• size. The data size (in Byte) provided by the data product.
• dimension. The number of data features.
• coverage. The countries covered by the data product.
• update_frequency. The frequency between data product updates as announced by the seller, such as “monthly”, “daily”, and “real-time”. Most data products adopt “no-update” and “daily”.
• data_sample. The filename of the data sample if available. We download and store the data sample of data products in an additional folder.
• category. The original category of data product may vary across different data marketplaces, each with its own way of categorization. We align the data categories from other marketplaces with the AWS Marketplace categories through manual labeling.
• price_mode. The pricing mode of the data product. There are five pricing modes: (1) negotiation mode where data buyers need to negotiate the price with data providers, (2) free mode where the data is provided at no cost, (3) subscription mode where data buyers are charged a recurring fee on a monthly or annual basis, (4) one-off mode where data buyers pay a one-time fee to access the data permanently, and (5) usage-based mode where data buyers are charged based on the amount of data they consume, such as the volume of data downloaded or the number of API calls.
• price. Using USD ($) as the currency unit. If the pricing mode is free or negotiation, the price is set to 0. If the pricing mode is subscription, the price represents the subscription cost for 12 months; If the pricing mode is usage-based, the price reflects the cost for a single usage.

The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.The GEDI instrument was removed from the ISS and placed into storage on March 17, 2023. No data were acquired during the hibernation period from March 17, 2023, to April 24, 2024. GEDI has since been reinstalled on the ISS and resumed operations as of April 26, 2024.The purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI02_A product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.The GEDI02_A data product contains 156 layers for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (e.g., canopy vertical structure), and many other interpreted products from the return waveforms. Additional information for the layers can be found in the GEDI Level 2A Dictionary.Known Issues

• Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).
• Incorrect Reference Ground Track (RGT) number in the filename for select GEDI files: GEDI Science Data Products for six orbits on August 7, 2020, and November 12, 2021, had the incorrect RGT number in the filename. There is no impact to the science data, but users should reference this document for the correct RGT numbers.
• Known Issues: Section 8 of the User Guide provides additional information on known issues.

• Metadata has been updated to include spatial coordinates.
• Granule size has been reduced from one full ISS orbit (5.83 GB) to four segments per orbit (1.48 GB).
• Filename has been updated to include segment number and version number.
• Improved geolocation for an orbital segment.
• Added elevation from the SRTM digital elevation model for comparison.
• Modified the method to predict an optimum algorithm setting group per laser shot.
• Added additional land cover datasets related to phenology, urban infrastructure, and water persistence.
• Added selected_mode_flag dataset to root beam group using selected algorithm.
• Removed shots when the laser is not firing.
• Modified file name to include segment number and dataset version. Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

Global MODIS vegetation indices are designed to provide consistent spatial and temporal comparisons of vegetation conditions. Blue, red, and near-infrared reflectances, centered at 469-nanometers, 645-nanometers, and 858-nanometers, respectively, are used to determine the MODIS daily vegetation indices. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes a new Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI also uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI and EVI products are computed from atmospherically corrected bi-directional surface reflectances that have been masked for water, clouds, heavy aerosols, and cloud shadows. Global MOD13Q1 data are provided every 16 days at 250-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection. Lacking a 250m blue band, the EVI algorithm uses the 500m blue band to correct for residual atmospheric effects, with negligible spatial artifacts. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes and global and regional climate. These data also may be used for characterizing land surface biophysical properties and processes, including primary production and land cover conversion.

This dataset contains transaction data from a fictitious SaaS company selling sales and marketing software to other companies (B2B). In the dataset, each row represents a single transaction/order (9,994 transactions), and the columns include:

Here is the Original Dataset: https://ee-assets-prod-us-east-1.s3.amazonaws.com/modules/337d5d05acc64a6fa37bcba6b921071c/v1/SaaS-Sales.csv

Features

| # | Name of the attribute | Description | | -- | --------------------- | -------------------------------------------------------- | | 1 | Row ID | A unique identifier for each transaction. | | 2 | Order ID | A unique identifier for each order. | | 3 | Order Date | The date when the order was placed. | | 4 | Date Key | A numerical representation of the order date (YYYYMMDD). | | 5 | Contact Name | The name of the person who placed the order. | | 6 | Country | The country where the order was placed. | | 7 | City | The city where the order was placed. | | 8 | Region | The region where the order was placed. | | 9 | Subregion | The subregion where the order was placed. | | 10 | Customer | The name of the company that placed the order. | | 11 | Customer ID | A unique identifier for each customer. | | 13 | Industry | The industry the customer belongs to. | | 14 | Segment | The customer segment (SMB, Strategic, Enterprise, etc.). | | 15 | Product | The product was ordered. | | 16 | License | The license key for the product. | | 17 | Sales | The total sales amount for the transaction. | | 18 | Quantity | The total number of items in the transaction. | | 19 | Discount | The discount applied to the transaction. | | 20 | Profit | The profit from the transaction. |

Inspiration: The CRoss Industry Standard Process for Data Mining (CRISP-DM) CRISP-DM methodology

• [ ] Understanding the business
• [ ] Understanding the data
• [x] Preparing the data
• [ ] Modelling
• [ ] Evaluating
• [ ] Implementing the analysis.

GeoNet provides geological hazard information for Aotearoa New Zealand. This dataset contains data and products recorded by the GeoNet sensor network.

GNSS (Global Navigation Satellite System) data include raw data in proprietary and Receiver Independent Exchange Format (RINEX) and local tie-in survey conducted during equipment changes, more details can be found on the GeoNet geodetic page website.
Coastal gauge data include relative measurement of sea level measured by tsunami monitoring gauges. Raw and quality control data are provided in CREX format (Character Form for the Representtion and eXchange of metereological data), more details can be found on the GeoNet coastal tsunami monitoring gauges page.
Camera images data include webcam images from the GeoNet Volcano monitoring network and Built Environment Instrumentation Programme, more details can be found on the GeoNet camera page.
Waveform data include raw data from weak and strong motion instruments of the GeoNet seismic networks, more details can be found on the GeoNet seismic waveform page.
Seismic data products include strong motion derived data, more details can be found on the GeoNet Strong Motion products page.
Time Series data products include derived time series data from a subgroup of the GeoNet sensor network. Data are in compressed comma separated format (csv.gz), more details can be found on the GeoNet tilde website page.

AWS Marketplace Software Market size was valued at USD 15 Billion in 2024 and is projected to reach USD 40.65 Billion by 2032, growing at a CAGR of 15% during the forecast period 2026 to 2032. The need to streamline software purchasing and management processes is anticipated to support market growth. Centralized billing, flexible pricing models, and simplified license tracking available through AWS Marketplace are being adopted by enterprises to improve operational efficiency and financial transparency.

In the fourth quarter of 2025, net sales of Amazon Web Services (AWS) increased by ** percent from the corresponding period of the previous year. AWS is one of Amazon’s strongest revenue segments, generating ****** billion U.S. dollars in 2025 net sales. On October 20, 2025, the provider experienced a 15-hour outage, which disrupted operations in various industries ranging from social media through banks to smart home devices. Some of the affected companies include Snapchat, Reddit, Lloyds Bank, Roblox, and Zoom. Amazon Web Services Amazon Web Services (AWS) provides on-demand cloud platforms and APIs through a pay-as-you-go model to customers. AWS launched in 2002, providing general services and tools, and produced its first cloud products in 2006. Today, more than 175 different cloud services for a variety of technologies and industries have already been released. AWS ranks as one of the most popular public cloud infrastructure and platform services running applications worldwide, ahead of Microsoft Azure and Google cloud services. Cloud computing Cloud computing is essentially the delivery of online computing services to customers. As enterprises continually migrate their applications and data to the cloud instead of storing them on local machines, it becomes possible to access resources from different locations. Some of the key services of the AWS ecosystem for cloud applications include storage, databases, security tools, and management tools. AWS is among the most popular cloud providers Some of the largest globally operating enterprises use AWS for their cloud services, including Netflix, BBC, and Baidu. Accordingly, AWS is one of the leading cloud providers in the global cloud market. Due to its continuously expanding portfolio of services and deepening of expertise, the company continues to be not only an important cloud service provider but also a business partner.

United States GDPS: IA: PI: Admin & Waste Management Services (AWS) data was reported at 3.723 USD bn in 2017. This records an increase from the previous number of 3.560 USD bn for 2016. United States GDPS: IA: PI: Admin & Waste Management Services (AWS) data is updated yearly, averaging 2.748 USD bn from Dec 1997 (Median) to 2017, with 21 observations. The data reached an all-time high of 3.723 USD bn in 2017 and a record low of 1.438 USD bn in 1997. United States GDPS: IA: PI: Admin & Waste Management Services (AWS) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A103: NIPA 2013: Gross Domestic Product by State: Current Price.

United States GDPS: HI: PI: Admin & Waste Management Services (AWS) data was reported at 2.971 USD bn in 2017. This records an increase from the previous number of 2.925 USD bn for 2016. United States GDPS: HI: PI: Admin & Waste Management Services (AWS) data is updated yearly, averaging 1.874 USD bn from Dec 1997 (Median) to 2017, with 21 observations. The data reached an all-time high of 2.971 USD bn in 2017 and a record low of 989.000 USD mn in 1998. United States GDPS: HI: PI: Admin & Waste Management Services (AWS) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A103: NIPA 2013: Gross Domestic Product by State: Current Price.

PSS data gives you insight into Passenger Service System (PSS) providers for all scheduled airlines as well as each PSS's customer list.

A Passenger Service System or PSS is a network of software applications that help airlines manage all the passenger-related operations from ticketing to boarding. The PSS usually comprises an airline reservations system, an airline inventory system and a departure control system (DCS).

The data set is updated monthly.

The sample dataset shows data for Swiss, Alaska Airlines and Horizon Air.

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=pss/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/pss/

Level-2 interferometric products generated by the Jet Propulsion Lab (JPL) ARIA project. The creation, discovery, and distribution of these products support InSAR science around tectonically active regions, volcanoes, or areas of subsidence/uplift. The generation of the ARIA-S1-GUNW products was in part funded through collaborations with the AWS Open Data Program and NASA ROSES.

Operator Passenger Figures include annual, bi-annual, quarterly and/or monthly passenger figures (passengers, available seat kilometres/miles (ASK/ASM), revenue passenger seat kilometres/miles (RPK/RPM), load factors) for commercial airlines worldwide based on a wide range of operator and government sources.

The data set is updated monthly.

The sample dataset presents passenger figures for Swiss, Alaska Airlines, and Horizon Air beginning in 2011.

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=operator_passenger_figures/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/operator-passenger-figures/

GC-NET automated weather station (AWS) data v01 from the Greenland Ice Sheet. Data are collected from the two-boom mast design located in the accumulation area, with variables as listed in the AWS_variables.csv file provided. See pypromice for how we process the data product. See variables.csv for the most up-to-date description of the the data variables.

Using a combination of OAG flight schedule and ch-aviation fleet data, Capacities - Scheduled provides an overview of future flights scheduled per calendar day with a breakdown of seat capacity for five cabin classes (Economy, Economy Plus/Comfort, Premium Economy, Business, First) by operator and route (Continent, Country, Subdivision, Metro Group, Airport).

The data set is updated weekly.

The sample data shows capacity figures for Alaska Airlines, Swiss, and Horizon Air for one week.

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=capacities_scheduled/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/capacities-scheduled/

The AWS Managed Services market is projected to be valued at $10 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 10.2%, reaching approximately $27 billion by 2034.