100+ datasets found
  1. T

    United States Fed Funds Interest Rate

    • tradingeconomics.com
    • ko.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 2, 2025
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    TRADING ECONOMICS (2025). United States Fed Funds Interest Rate [Dataset]. https://tradingeconomics.com/united-states/interest-rate
    Explore at:
    xml, excel, json, csvAvailable download formats
    Dataset updated
    Jul 2, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Aug 4, 1971 - Jun 18, 2025
    Area covered
    United States
    Description

    The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  2. T

    Pakistan Interest Rate

    • tradingeconomics.com
    • jp.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated May 5, 2025
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    TRADING ECONOMICS (2025). Pakistan Interest Rate [Dataset]. https://tradingeconomics.com/pakistan/interest-rate
    Explore at:
    csv, xml, excel, jsonAvailable download formats
    Dataset updated
    May 5, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Feb 3, 1992 - Jun 16, 2025
    Area covered
    Pakistan
    Description

    The benchmark interest rate in Pakistan was last recorded at 11 percent. This dataset provides - Pakistan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  3. c

    The global GPU Database market size is USD 455 million in 2024 and will...

    • cognitivemarketresearch.com
    pdf,excel,csv,ppt
    Updated Apr 15, 2025
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    Cognitive Market Research (2025). The global GPU Database market size is USD 455 million in 2024 and will expand at a compound annual growth rate (CAGR) of 20.7% from 2024 to 2031. [Dataset]. https://www.cognitivemarketresearch.com/gpu-database-market-report
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset updated
    Apr 15, 2025
    Dataset authored and provided by
    Cognitive Market Research
    License

    https://www.cognitivemarketresearch.com/privacy-policyhttps://www.cognitivemarketresearch.com/privacy-policy

    Time period covered
    2021 - 2033
    Area covered
    Global
    Description

    According to Cognitive Market Research, the global GPU Database market size will be USD 455 million in 2024 and will expand at a compound annual growth rate (CAGR) of 20.7% from 2024 to 2031. Market Dynamics of GPU Database Market Key Drivers for GPU Database Market Growing Demand for High-Performance Computing in Various Data-Intensive Industries- One of the main reasons the GPU Database market is growing demand for high-performance computing (HPC) across various data-intensive industries. These industries, including finance, healthcare, and telecommunications, require rapid data processing and real-time analytics, which GPU databases excel at providing. Unlike traditional CPU databases, GPU databases leverage the parallel processing power of GPUs to handle complex queries and large datasets more efficiently. This capability is crucial for applications such as machine learning, artificial intelligence, and big data analytics. The expansion of data and the increasing need for speed and scalability in processing are pushing enterprises to adopt GPU databases. Consequently, the market is poised for robust growth as organizations continue to seek solutions that offer enhanced performance, reduced latency, and greater computational power to meet their evolving data management needs. The increasing demand for gaining insights from large volumes of data generated across verticals to drive the GPU Database market's expansion in the years ahead. Key Restraints for GPU Database Market Lack of efficient training professionals poses a serious threat to the GPU Database industry. The market also faces significant difficulties related to insufficient security options. Introduction of the GPU Database Market The GPU database market is experiencing rapid growth due to the increasing demand for high-performance data processing and analytics. GPUs, or Graphics Processing Units, excel in parallel processing, making them ideal for handling large-scale, complex data sets with unprecedented speed and efficiency. This market is driven by the proliferation of big data, advancements in AI and machine learning, and the need for real-time analytics across industries such as finance, healthcare, and retail. Companies are increasingly adopting GPU-accelerated databases to enhance data visualization, predictive analytics, and computational workloads. Key players in this market include established tech giants and specialized startups, all contributing to a competitive landscape marked by innovation and strategic partnerships. As organizations continue to seek faster and more efficient ways to harness their data, the GPU database market is poised for substantial growth, reshaping the future of data management and analytics.< /p>

  4. T

    Brazil Interest Rate

    • tradingeconomics.com
    • tr.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 18, 2025
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    TRADING ECONOMICS (2025). Brazil Interest Rate [Dataset]. https://tradingeconomics.com/brazil/interest-rate
    Explore at:
    xml, json, csv, excelAvailable download formats
    Dataset updated
    Jun 18, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Mar 5, 1999 - Jun 18, 2025
    Area covered
    Brazil
    Description

    The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  5. Amount of data created, consumed, and stored 2010-2023, with forecasts to...

    • statista.com
    Updated Jun 30, 2025
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    Statista (2025). Amount of data created, consumed, and stored 2010-2023, with forecasts to 2028 [Dataset]. https://www.statista.com/statistics/871513/worldwide-data-created/
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    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    May 2024
    Area covered
    Worldwide
    Description

    The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

  6. AGNC: Will Mortgage REIT Thrive in a Rising Rate Environment? (Forecast)

    • kappasignal.com
    Updated Dec 29, 2023
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    KappaSignal (2023). AGNC: Will Mortgage REIT Thrive in a Rising Rate Environment? (Forecast) [Dataset]. https://www.kappasignal.com/2023/12/agnc-will-mortgage-reit-thrive-in.html
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    Dataset updated
    Dec 29, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

    AGNC: Will Mortgage REIT Thrive in a Rising Rate Environment?

    Financial data:

    • Historical daily stock prices (open, high, low, close, volume)

    • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

    • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

    Machine learning features:

    • Feature engineering based on financial data and technical indicators

    • Sentiment analysis data from social media and news articles

    • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

    Potential Applications:

    • Stock price prediction

    • Portfolio optimization

    • Algorithmic trading

    • Market sentiment analysis

    • Risk management

    Use Cases:

    • Researchers investigating the effectiveness of machine learning in stock market prediction

    • Analysts developing quantitative trading Buy/Sell strategies

    • Individuals interested in building their own stock market prediction models

    • Students learning about machine learning and financial applications

    Additional Notes:

    • The dataset may include different levels of granularity (e.g., daily, hourly)

    • Data cleaning and preprocessing are essential before model training

    • Regular updates are recommended to maintain the accuracy and relevance of the data

  7. J

    Discounting the distant future: How much does model selection affect the...

    • journaldata.zbw.eu
    • jda-test.zbw.eu
    txt
    Updated Dec 8, 2022
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    Ben Groom; Phoebe Koundouri; Ekaterini Panopoulou; Theologos Pantelidis; Ben Groom; Phoebe Koundouri; Ekaterini Panopoulou; Theologos Pantelidis (2022). Discounting the distant future: How much does model selection affect the certainty equivalent rate? (replication data) [Dataset]. http://doi.org/10.15456/jae.2022319.0714833919
    Explore at:
    txt(1844)Available download formats
    Dataset updated
    Dec 8, 2022
    Dataset provided by
    ZBW - Leibniz Informationszentrum Wirtschaft
    Authors
    Ben Groom; Phoebe Koundouri; Ekaterini Panopoulou; Theologos Pantelidis; Ben Groom; Phoebe Koundouri; Ekaterini Panopoulou; Theologos Pantelidis
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Recent work in evaluating investments with long-term consequences has turned towards establishing a schedule of Declining Discount Rates (DDRs). Using US data we show that the employment of models that account for changes in the interest rate generating mechanism has important implications for operationalising a theory of DDRs that depends upon uncertainty. The policy implications of DDRs are then analysed in the context of climate change for the USA, where the use of a state space model can increase valuations by 150% compared to conventional constant discounting.

  8. T

    Norway Interest Rate

    • tradingeconomics.com
    • fa.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 19, 2025
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    TRADING ECONOMICS (2025). Norway Interest Rate [Dataset]. https://tradingeconomics.com/norway/interest-rate
    Explore at:
    xml, excel, csv, jsonAvailable download formats
    Dataset updated
    Jun 19, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1991 - Jun 19, 2025
    Area covered
    Norway
    Description

    The benchmark interest rate in Norway was last recorded at 4.25 percent. This dataset provides the latest reported value for - Norway Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  9. d

    Emergency Services Billing Rates - Code Rates

    • catalog.data.gov
    • data.texas.gov
    • +1more
    Updated Feb 25, 2024
    + more versions
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    data.austintexas.gov (2024). Emergency Services Billing Rates - Code Rates [Dataset]. https://catalog.data.gov/dataset/emergency-services-billing-rates-code-rates
    Explore at:
    Dataset updated
    Feb 25, 2024
    Dataset provided by
    data.austintexas.gov
    Description

    The Texas Department of Insurance (TDI) collects and reports information about billing rates for emergency service providers by procedure code as set by the political subdivisions. The procedure codes include Healthcare Common Procedure Coding System (HCPCS) Codes and any other codes reported by the political subdivisions. This dataset lists the codes and the rates for residents of that political subdivision and for non-residents if that rate differs. There is a row for each procedure code and the rates set by a political subdivision. Political subdivisions with more than one code with a rate set will be listed in multiple rows. The data includes the year and quarter the information applies to as well as the date the political subdivision submitted their report. The Texas Legislature amended Texas Insurance Code Chapter 38 via Senate Bill 2476 during the 88th session to add reporting “relating to consumer protections against certain medical and health care billing by emergency medical services providers. A political subdivision may submit to the department a rate set, controlled, or regulated by the political subdivision for emergency services.” ► For contact information, refer to dataset:  Emergency Services Billing Rates - Contact List. ► For National Provider Identifier Standard (NPI) information reported in each political subdivision, refer to dataset: Emergency Services Billing Rates - NPI. ► For ZIP codes within political subdivisions, refer to dataset:  Emergency Services Billing Rates - ZIPs. Users are responsible for reviewing and updating data before the submission deadlines. Information entered or found in this dataset is subject to change. Visit TDI’s web site disclaimer for more information. For more information related to this data, visit TDI’s FAQ page.

  10. In-Memory Data Grids Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Dec 3, 2024
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    Dataintelo (2024). In-Memory Data Grids Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-in-memory-data-grids-market
    Explore at:
    pdf, pptx, csvAvailable download formats
    Dataset updated
    Dec 3, 2024
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    In-Memory Data Grids Market Outlook



    The global In-Memory Data Grids market size is projected to grow from $2.5 billion in 2023 to an estimated $4.8 billion by 2032, reflecting a compound annual growth rate (CAGR) of 7.5%. This impressive growth trajectory is driven by the increasing demand for real-time data processing capabilities across various industries, necessitating faster data storage and retrieval solutions. The enhanced speed and performance of in-memory data grids are crucial as businesses strive for efficiency in data management, contributing to a robust market expansion over the forecast period.



    One of the primary growth factors for the In-Memory Data Grids market is the escalating volume of data generated globally, which necessitates more efficient data management solutions. Organizations across sectors such as retail, finance, and healthcare are increasingly focused on harnessing data for strategic insights, which in turn fuels demand for advanced data processing tools. In-memory data grids provide a high-performance solution for handling large datasets, allowing for faster data access and manipulation, and are therefore becoming integral to modern data strategies. Moreover, as businesses continue to explore big data analytics, the need for systems that can support real-time analytics is propelling the market further.



    The rise of digital transformation initiatives across various industries is another significant factor driving the in-memory data grids market. Companies are increasingly adopting digital technologies to enhance operational efficiencies, improve customer experiences, and maintain competitive advantage. In-memory data grids serve as a critical infrastructure component in these digital transformation efforts by enabling rapid data processing and supporting real-time decision-making. The ability to process large volumes of data swiftly assists organizations in developing agile responses to market changes, thus fostering market growth.



    Technological advancements and the increasing adoption of cloud computing are also contributing to market growth. Cloud-based in-memory data grids offer scalability, flexibility, and cost-efficiency, which are appealing to organizations seeking to optimize IT infrastructure. As more companies migrate to cloud environments, the demand for cloud-enabled data grids is expected to rise, driving further market expansion. Additionally, innovations in technology, such as the integration of artificial intelligence (AI) and machine learning (ML) with in-memory data grids, are enhancing grid capabilities, thus attracting greater interest from businesses looking to leverage these advanced technologies for enhanced data processing and analytics.



    Regionally, North America is anticipated to maintain a dominant position in the in-memory data grids market due to the presence of major technology firms and high adoption rates of advanced technologies. The robust IT and telecommunications infrastructure in this region supports the widespread implementation of in-memory data grids. Meanwhile, Asia Pacific is projected to witness the highest growth rate, driven by rapid technological advancements, increasing investments in IT infrastructure, and growing awareness of data-driven decision-making. Europe is also expected to see significant growth, fueled by digital transformation initiatives and stringent data protection regulations that necessitate efficient data management solutions.



    Component Analysis



    In the realm of components, the in-memory data grids market is segmented into software and services. The software component is pivotal, as it encompasses the actual framework that facilitates data storage and retrieval within the grid. These software solutions are designed to enhance data processing capabilities, enabling organizations to manage and analyze vast datasets efficiently. With advancements in technology, software solutions have evolved to offer sophisticated features such as data replication, partitioning, and distributed caching, which are essential for ensuring data reliability and performance. The software segment is expected to hold a significant market share, driven by continuous innovation and the ongoing demand for high-performance data management solutions.



    The services component of the in-memory data grids market plays a crucial role in supporting the implementation and optimization of grid solutions. This includes consulting, deployment, and support services that ensure seamless integration of in-memory data grids with existing IT infrastructures. As organizations increasingly adopt these solutions to enhance t

  11. Z

    Forex News Annotated Dataset for Sentiment Analysis

    • data.niaid.nih.gov
    • paperswithcode.com
    • +1more
    Updated Nov 11, 2023
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    Kalliopi Kouroumali (2023). Forex News Annotated Dataset for Sentiment Analysis [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_7976207
    Explore at:
    Dataset updated
    Nov 11, 2023
    Dataset provided by
    Kalliopi Kouroumali
    Georgios Fatouros
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

    To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

    We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

    Examples of Annotated Headlines
    
    
        Forex Pair
        Headline
        Sentiment
        Explanation
    
    
    
    
        GBPUSD 
        Diminishing bets for a move to 12400 
        Neutral
        Lack of strong sentiment in either direction
    
    
        GBPUSD 
        No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft 
        Positive
        Positive sentiment towards GBPUSD (Cable) in the near term
    
    
        GBPUSD 
        When are the UK jobs and how could they affect GBPUSD 
        Neutral
        Poses a question and does not express a clear sentiment
    
    
        JPYUSD
        Appropriate to continue monetary easing to achieve 2% inflation target with wage growth 
        Positive
        Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply
    
    
        USDJPY
        Dollar rebounds despite US data. Yen gains amid lower yields 
        Neutral
        Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other
    
    
        USDJPY
        USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains 
        Negative
        USDJPY is expected to reach a lower value, with the USD losing value against the JPY
    
    
        AUDUSD
    

    RBA Governor Lowe’s Testimony High inflation is damaging and corrosive

        Positive
        Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD.
    

    Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.

  12. d

    Foreign Exchange Rates

    • data.world
    csv, zip
    Updated Dec 2, 2024
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    The Associated Press (2024). Foreign Exchange Rates [Dataset]. https://data.world/associatedpress/foreign-exchange-rates
    Explore at:
    zip, csvAvailable download formats
    Dataset updated
    Dec 2, 2024
    Authors
    The Associated Press
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    This dataset was generated by parsing PDFs released by the US Treasury for foreign exchange. An edited version (quarterly-edited.csv) includes fixes for typos in the Treasury data.

    Usage caveats from the documentation:

    "Exceptions to using the reporting rates as shown in the report are: * collections and refunds to be valued at specified rates set by international agreements, * conversions of one foreign currency into another, * foreign currencies sold for dollars, and * other types of transactions affecting dollar appropriations. (See Volume I Treasury Financial Manual 2-3200 for further details.)

    Since the exchange rates in this report are not current rates of exchange, they should not be used to value transactions affecting dollar appropriations."

    Additional caveats:

    This unified dataset should be used only for reference or ballpark estimation, and not for anything like automated valuation. The reason is because there's still a lot of messiness involving countries and changing units- when in doubt or if required, please do additional research to confirm the historical rates are indeed as stated.

    Future plans:

    • Clean this up so that it can indeed be used in an automated system for converting currencies
    • Fill in the missing entries
    • Standardize country names and currency names

    Documentation

    Source

  13. c

    The global Big Data market size is USD 40.5 billion in 2024 and will expand...

    • cognitivemarketresearch.com
    pdf,excel,csv,ppt
    Updated Apr 9, 2025
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    Cognitive Market Research (2025). The global Big Data market size is USD 40.5 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 12.9% from 2024 to 2031. [Dataset]. https://www.cognitivemarketresearch.com/big-data-market-report
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset updated
    Apr 9, 2025
    Dataset authored and provided by
    Cognitive Market Research
    License

    https://www.cognitivemarketresearch.com/privacy-policyhttps://www.cognitivemarketresearch.com/privacy-policy

    Time period covered
    2021 - 2033
    Area covered
    Global
    Description

    According to Cognitive Market Research, the global Big Data marketsize is USD 40.5 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 12.9% from 2024 to 2031. Market Dynamics of Big Data Market Key Drivers for Big Data Market Increasing demand for decision-making based on data - One of the main reasons the Big Data market is growing is due to the increasing demand for decision-making based on data. Organizations understand the strategic benefit of using data insights to make accurate and informed decisions in the current competitive scenario. This change marks a break from conventional decision-making paradigms as companies depend more and more on big data analytics to maximize performance, reduce risk, and open up prospects. Real-time processing, analysis, and extraction of actionable insights from large datasets enables businesses to react quickly to consumer preferences and market trends. The increasing need to maximize performance, reduce risk, and open up prospects is anticipated to drive the Big Data market's expansion in the years ahead. Key Restraints for Big Data Market The lack of integrator and interoperability poses a serious threat to the Big Data industry. The market also faces significant difficulties because of the realization of its full potential. Introduction of the Big Data Market Big data software is a category of software used for gathering, storing, and processing large amounts of heterogeneous, dynamic data produced by humans, machines, and other technologies. It is concentrated on offering effective analytics for extraordinarily massive datasets, which help the organization obtain a profound understanding by transforming the data into superior knowledge relevant to the business scenario. Additionally, the programmer assists in identifying obscure correlations, market trends, customer preferences, hidden patterns, and other valuable information from a wide range of data sets. Due to the widespread use of digital solutions in sectors such as finance, healthcare, BFSI, retail, agriculture, telecommunications, and media, data is increasing dramatically on a worldwide scale. Smart devices, soil sensors, and GPS-enabled tractors generate massive amounts of data. Large data sets, such as supply tracks, natural trends, optimal crop conditions, sophisticated risk assessment, and more, are analyzed in agriculture through the application of big data analytics.

  14. T

    Sweden Interest Rate

    • tradingeconomics.com
    • pt.tradingeconomics.com
    • +12more
    csv, excel, json, xml
    Updated May 8, 2025
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    TRADING ECONOMICS (2025). Sweden Interest Rate [Dataset]. https://tradingeconomics.com/sweden/interest-rate
    Explore at:
    csv, excel, xml, jsonAvailable download formats
    Dataset updated
    May 8, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    May 26, 1994 - Jun 18, 2025
    Area covered
    Sweden
    Description

    The benchmark interest rate in Sweden was last recorded at 2 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  15. c

    The global AI Training Dataset Market size will be USD 2962.4 million in...

    • cognitivemarketresearch.com
    pdf,excel,csv,ppt
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    Cognitive Market Research, The global AI Training Dataset Market size will be USD 2962.4 million in 2025. [Dataset]. https://www.cognitivemarketresearch.com/ai-training-dataset-market-report
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset authored and provided by
    Cognitive Market Research
    License

    https://www.cognitivemarketresearch.com/privacy-policyhttps://www.cognitivemarketresearch.com/privacy-policy

    Time period covered
    2021 - 2033
    Area covered
    Global
    Description

    According to Cognitive Market Research, the global AI Training Dataset Market size will be USD 2962.4 million in 2025. It will expand at a compound annual growth rate (CAGR) of 28.60% from 2025 to 2033.

    North America held the major market share for more than 37% of the global revenue with a market size of USD 1096.09 million in 2025 and will grow at a compound annual growth rate (CAGR) of 26.4% from 2025 to 2033.
    Europe accounted for a market share of over 29% of the global revenue, with a market size of USD 859.10 million.
    APAC held a market share of around 24% of the global revenue with a market size of USD 710.98 million in 2025 and will grow at a compound annual growth rate (CAGR) of 30.6% from 2025 to 2033.
    South America has a market share of more than 3.8% of the global revenue, with a market size of USD 112.57 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.6% from 2025 to 2033.
    Middle East had a market share of around 4% of the global revenue and was estimated at a market size of USD 118.50 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.9% from 2025 to 2033.
    Africa had a market share of around 2.20% of the global revenue and was estimated at a market size of USD 65.17 million in 2025 and will grow at a compound annual growth rate (CAGR) of 28.3% from 2025 to 2033.
    Data Annotation category is the fastest growing segment of the AI Training Dataset Market
    

    Market Dynamics of AI Training Dataset Market

    Key Drivers for AI Training Dataset Market

    Government-Led Open Data Initiatives Fueling AI Training Dataset Market Growth

    In recent years, Government-initiated open data efforts have strongly driven the development of the AI Training Dataset Market through offering affordable, high-quality datasets that are vital in training sound AI models. For instance, the U.S. government's drive for openness and innovation can be seen through portals such as Data.gov, which provides an enormous collection of datasets from many industries, ranging from healthcare, finance, and transportation. Such datasets are basic building blocks in constructing AI applications and training models using real-world data. In the same way, the platform data.gov.uk, run by the U.K. government, offers ample datasets to aid AI research and development, creating an environment that is supportive of technological growth. By releasing such information into the public domain, governments not only enhance transparency but also encourage innovation in the AI industry, resulting in greater demand for training datasets and helping to drive the market's growth.

    India's IndiaAI Datasets Platform Accelerates AI Training Dataset Market Growth

    India's upcoming launch of the IndiaAI Datasets Platform in January 2025 is likely to greatly increase the AI Training Dataset Market. The project, which is part of the government's ?10,000 crore IndiaAI Mission, will establish an open-source repository similar to platforms such as HuggingFace to enable developers to create, train, and deploy AI models. The platform will collect datasets from central and state governments and private sector organizations to provide a wide and rich data pool. Through improved access to high-quality, non-personal data, the platform is filling an important requirement for high-quality datasets for training AI models, thus driving innovation and development in the AI industry. This public initiative reflects India's determination to become a global AI hub, offering the infrastructure required to facilitate startups, researchers, and businesses in creating cutting-edge AI solutions. The initiative not only simplifies data access but also creates a model for public-private partnerships in AI development.

    Restraint Factor for the AI Training Dataset Market

    Data Privacy Regulations Impeding AI Training Dataset Market Growth

    Strict data privacy laws are coming up as a major constraint in the AI Training Dataset Market since governments across the globe are establishing legislation to safeguard personal data. In the European Union, explicit consent for using personal data is required under the General Data Protection Regulation (GDPR), reducing the availability of datasets for training AI. Likewise, the data protection regulator in Brazil ordered Meta and others to stop the use of Brazilian personal data in training AI models due to dangers to individuals' funda...

  16. o

    Data from: Dataset For: A Guide to Residential Energy Storage and Rooftop...

    • openenergyhub.ornl.gov
    Updated Jun 12, 2024
    + more versions
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    (2024). Dataset For: A Guide to Residential Energy Storage and Rooftop Solar: State Net Metering Policies and Utility Rate Tariff Structures [Dataset]. https://openenergyhub.ornl.gov/explore/dataset/dataset-for-a-guide-to-residential-energy-storage-and-rooftop-solar-state-net-me/
    Explore at:
    Dataset updated
    Jun 12, 2024
    Description

    Note: Find data at source. ・ Federal and state decarbonization goals have led to numerous financial incentives and policies designed to increase access and adoption of renewable energy systems. In combination with the declining cost of both solar photovoltaic and battery energy storage systems and rising electric utility rates, residential renewable adoption has become more favorable than ever. However, not all states provide the same opportunity for cost recovery, and the complicated and changing policy and utility landscape can make it difficult for households to make an informed decision on whether to install a renewable system. This paper is intended to provide a guide to households considering renewable adoption by introducing relevant factors that influence renewable system performance and payback, summarized in a state lookup table for quick reference. Five states are chosen as case studies to perform economic optimizations based on net metering policy, utility rate structure, and average electric utility price; these states are selected to be representative of the possible combinations of factors to aid in the decision-making process for customers in all states. The results of this analysis highlight the dual importance of both state support for renewables and price signals, as the benefits of residential renewable systems are best realized in states with net metering policies facing the challenge of above-average electric utility rates.This dataset is intended to allow readers to reproduce and customize the analysis performed in this work to their benefit. Suggested modifications include: location, household load profile, rate tariff structure, and renewable energy system design.

  17. Space Missions

    • kaggle.com
    Updated Apr 30, 2024
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    Monis Amir (2024). Space Missions [Dataset]. https://www.kaggle.com/datasets/monisamir/space-missions/discussion
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 30, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Monis Amir
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    I found this Interesting Dataset on Maven Analytics about Space Missions and decided to work on it. The Dataset comes with the Data of Space Missions from 1957 to 2022. It consist of Date, Location, Rocket Name, Rocket Status, Mission Name, Mission Status, and the Company Launch the Mission. 🚀

    Firstly, I ensure Data quality by meticulously Cleaning and Preparing it for Analysis. Then, I create Pivot Tables to Summarize and Analyze the Data from different angles. Next, I dive into Visualization, leveraging Tools to Transform complex Datasets into Clear, Actionable Insights. After Creating the Visuals, I Delve Deeper to Uncover Valuable Trends and Patterns, Empowering informed Decision-Making Insights. Every step, from Cleaning the Data to Visualization to Extracting Insights, is essential in Unlocking the True Power of Data-Driven Strategies. 📊 📈

    ACTIONABLE DATA-DRIVEN INSIGHTS FROM THIS DASHBOARD:

    1. THE NUMBER OF SPACE MISSIONS BY YEAR IS INCREASING. This suggests that there is a Growing Interest in Space Exploration. Businesses and Organizations Involved in Space Exploration could take Advantage of this Trend by Developing New Products and Services.
    2. THE OVERALL SUCCESS RATE OF SPACE MISSIONS IS INCREASING. This could be due to a Number of Factors, such as Improvements in Technology and Engineering. Companies Involved in Space Exploration can Leverage this Information to Market their Services to Potential Customers.
    3. (RVSN USSR) IS THE COMPANY WITH THE MOST TOTAL MISSIONS. As of 2022, they have Launched 1777 Missions. This suggests that they are a Leader in the Space Exploration Industry. Other Companies Looking to Enter the Space Exploration Industry may want to Study (RVSN USSR)'s Business Model.
    4. ARIANESPACE HAS THE HIGHEST SUCCESS RATE OF ANY COMPANY LISTED ON THE DATASET AT 96.25%. This suggests that they are a Reliable Provider of Space Launch Services. Companies Looking to Launch Satellites or other Spacecraft into Orbit may want to consider Using Arianespace's Services.
    5. THE MAJORITY OF SPACE MISSIONS (4162) HAVE BEEN SUCCESSFUL. This is a Positive Sign for the Future of Space Exploration. It suggests that Space Missions are Becoming more Routine and Less Risky. This could lead to an Increase in the Number of Private Companies and Organizations Involved in Space Exploration.

    Overall, the Data in this Dashboard suggests that Space Exploration is a Growing Industry with a Bright Future. Companies and Organizations that are Involved in Space Exploration can take Advantage of this Trend by Developing New Products and Services. 🚀 📊

    TOOL USED: Microsoft Excel

    DataAnalytics #DataScience #DataAnalyst #DataVisualization #BusinessIntelligence #DataAnalysis #DataStorytelling #DataDrivenDecisions #DataDriven

  18. T

    Euro Area Interest Rate

    • tradingeconomics.com
    • zh.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Feb 1, 2001
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    TRADING ECONOMICS (2001). Euro Area Interest Rate [Dataset]. https://tradingeconomics.com/euro-area/interest-rate
    Explore at:
    xml, json, csv, excelAvailable download formats
    Dataset updated
    Feb 1, 2001
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 18, 1998 - Jun 5, 2025
    Area covered
    Euro Area
    Description

    The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  19. FIREX-AQ NOAA-CHEM Twin Otter Photolysis Rate (j value) Data - Dataset -...

    • data.nasa.gov
    • data.staging.idas-ds1.appdat.jsc.nasa.gov
    Updated Apr 1, 2025
    + more versions
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    nasa.gov (2025). FIREX-AQ NOAA-CHEM Twin Otter Photolysis Rate (j value) Data - Dataset - NASA Open Data Portal [Dataset]. https://data.nasa.gov/dataset/firex-aq-noaa-chem-twin-otter-photolysis-rate-j-value-data-eefbe
    Explore at:
    Dataset updated
    Apr 1, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    FIREXAQ_jValue_AircraftInSitu_N48_Data are in situ photolysis rate (j value) data collected onboard the NOAA-CHEM Twin Otter aircraft during FIREX-AQ. Data collection for this product is complete.Completed during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA’s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. The Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite’s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.

  20. T

    Canada Interest Rate

    • tradingeconomics.com
    • ko.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 4, 2025
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    TRADING ECONOMICS (2025). Canada Interest Rate [Dataset]. https://tradingeconomics.com/canada/interest-rate
    Explore at:
    csv, xml, json, excelAvailable download formats
    Dataset updated
    Jun 4, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Feb 7, 1990 - Jun 4, 2025
    Area covered
    Canada
    Description

    The benchmark interest rate in Canada was last recorded at 2.75 percent. This dataset provides - Canada Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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TRADING ECONOMICS (2025). United States Fed Funds Interest Rate [Dataset]. https://tradingeconomics.com/united-states/interest-rate

United States Fed Funds Interest Rate

United States Fed Funds Interest Rate - Historical Dataset (1971-08-04/2025-06-18)

Explore at:
127 scholarly articles cite this dataset (View in Google Scholar)
xml, excel, json, csvAvailable download formats
Dataset updated
Jul 2, 2025
Dataset authored and provided by
TRADING ECONOMICS
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Time period covered
Aug 4, 1971 - Jun 18, 2025
Area covered
United States
Description

The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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