Buy Data Sets
We will review requests quickly, but the purchase process can take some time as we negotiate a license between the data vendor and the River Campus Libraries. It can be tricky to estimate when a data product will be available for you to use after we agree to try to buy it, so try to get in touch with us early in your project.
buy data sets
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Data-driven business leaders enhance internal data with third party data products to inform their decisions and drive innovations. In this product demo, you'll learn how Snowflake Marketplace enables you to discover, evaluate and purchase data, data services and applications from some of the world's leading data and solution providers.
Reduce integration costs, delays, and risks by eliminating the need for traditional ETL, API, and FTP processes. Harness the power of the Snowflake Data Cloud and securely access live and ready-to-use data and applications from third-party providers.
Skip lengthy evaluations and realize value more quickly with self-service trials and direct access to data and apps. Simplify procurement with transparent usage-based pricing options directly billed by Snowflake and standardized service terms that help eliminate lengthy legal sign offs for your Marketplace purchases.
There is no other place where customers can find data files, data tables, and data APIs from a vast portfolio of third-party data sets. We continuously innovate to make the world's third-party data easy to find in one data catalog, simple to subscribe to with consistent pricing options, and seamless to use with AWS data and analytics and machine learning services.
Ovation COVID-19 data set contains millions of de-identified diagnostic test results collected in real time (hour by hour) from testing facilities outside a normal hospital setting. This data set includes normalized patient metadata, ICD10 diagnosis codes, qPCR test results and more.
This data set, powered by the Twitter API, contains a curated set of cryptocurrency related Tweets from the past month. It covers a rolling 1-month window and will be updated at least once per week on Mondays to provide recent Tweets from the past week.
IMDb's AP world-renowned 1-10 star rating data, a daily-computed average of fan sentiment derived from IMDb customer votes. TheGraphQL API includes the title, release year or first air date, any known alternative titles (akas) and more.
This innovative resource is designed as a broad compilation of use cases submitted by AWS Marketplace data providers. Each contributor tells a concise but unique story of how they solved a particular business challenge through a reliable third-party data solution.
Type of data: Miscellaneous Data compiled by: Google Access: Free to search, but does include some fee-based search results Sample dataset: Global price of coffee, 1990-present
Type of data: Miscellaneous Data compiled by: Kaggle Access: Free, but registration required Sample dataset: Daily temperature of major cities
Type of data: Machine learning Data compiled by: University of California Irvine Access: Free, no registration required Sample dataset: Behavior of urban traffic in Sao Paulo, Brazil
Type of data: Earth science Data compiled by: NASA Access: Free, no registration required Sample dataset: Environmental conditions during fall moose hunting season in Alaska, 2000-2016
USDA's National Household Food Acquisition and Purchase Survey (FoodAPS) is the first nationally representative survey of U.S. households to collect unique and comprehensive data about household food purchases and acquisitions. Detailed information was collected about foods purchased or otherwise acquired for consumption at home and away from home, including foods acquired through food and nutrition assistance programs. The survey includes nationally representative data from 4,826 households, including Supplemental Nutrition Assistance Program (SNAP) households, low-income households not participating in SNAP, and higher income households. For a more detailed description of the survey, see Background.
Public-use FoodAPS data are available for download in three file formats: SAS, STATA, and CSV. If the public-use files are not sufficient for the researchers' needs, see the Data Access page for instructions about how to gain access to restricted-use data.
The public-use files include: The household-level and individual-level interview files, the food-at-home (FAH) and food-away-from-home (FAFH) event files, the FAH and FAFH item files, data from the Meals and Snacks form, household access to FAFH outlets, and SNAP-authorized FAH retailers. All codebooks and a User's Guide, providing an overview of the survey and data sets as well as general notes about using the data, are also available (see Documentation below).
In an investment climate where passive management through index funds and ETFs continues to capture an increasing share of US investment dollars, are there new sources of data to help active management outperform market benchmarks? And as the availability of new alternative data products provide different perspectives on market activity, will asset managers embrace these new opportunities?
The top story around retail equity investing over the past decade has been the dramatic rise of passive management: according to Bank of America Merrill Lynch, as of May 2018, 45% of US equity assets were in passive investments, up from around 25% ten years ago.
While passive investing looks to remain popular for retail investors, given how it has lowered fees significantly while cost-conscious millennials become an increasing share of the investing public, the direction in active management strategies for institutional investors is to bolster the power of their quantitative and algorithmic models with data that can illuminate important trading trends.
The ability to achieve peak performance depends in no small part on the depth, breadth and quality of data an asset manager can access and utilise in its in-house trading models. With algorithms analysing data at millisecond speed, the inputs to these models are where firms can find their advantage.
An ideal data resource for buy-side asset managers and traders would be a kind of seismograph of the US equity markets revealing factors such as liquidity patterns, trends in short sales of securities, and trading concentrations across the leading broker dealers.
This data informs institutional investors looking to make large investments in a stock, or conversely to exit an existing position, about the number of brokers trading large enough volumes to deliver on their needs.
The power of these models can be further amplified by applying these technologies to process and analyse expansive, multi-perspective trading data like that described above. In an environment where every basis point of return is important, firms will also need to optimise their understanding of liquidity and limit the market impact of trading in and out of positions.
Advancements in data science and AI increasingly have created sophisticated trading models that will become more pervasive; the availability of accurate and consolidated sources of data will ultimately determine if they are effective in driving new insight on the kinetics of equity markets.
Mastery of the new sources of market data and the technologies that mine and package it can become a distinguishing value proposition for buy-side firms and help data-driven active strategies make a comeback. Clients will notice and ask for more.
The Supplemental Nutrition Assistance Program (SNAP) provides low-income families with a monthly benefit, delivered on an Electronic Benefit Transfer (EBT) card, which can be used to purchase food from authorized grocery stores and other food retailers. This data product provides access to two sets of data that provide information about State-level SNAP eligibility rules and administrative policies.
The SNAP Policy Database provides a central data source for information on State policy options in the Supplemental Nutrition Assistance Program (SNAP). The database includes information on State-level SNAP policies relating to eligibility criteria, recertification and reporting requirements, benefit issuance methods, availability of online applications, use of biometric technology (such as fingerprinting), and coordination with other low-income assistance programs. Data are provided for all 50 States and the District of Columbia for each month from January 1996 through December 2016. The information in this database can facilitate research on factors that influence SNAP participation and on SNAP's effects on a variety of outcomes, such as health and dietary intake. For more information, see About the SNAP Policy Database.
Online purchase data tracks purchases that consumers make online and the steps they took before finalizing a purchase. Companies use this information to personalize advertising campaigns and to evaluate the success of their online businesses compared to their competitors.Learn more 041b061a72