32 datasets found
  1. F

    Financial Database Report

    • marketreportanalytics.com
    doc, pdf, ppt
    Updated Apr 10, 2025
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    Market Report Analytics (2025). Financial Database Report [Dataset]. https://www.marketreportanalytics.com/reports/financial-database-75304
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    pdf, ppt, docAvailable download formats
    Dataset updated
    Apr 10, 2025
    Dataset authored and provided by
    Market Report Analytics
    License

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

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global financial database market is experiencing robust growth, driven by increasing demand for real-time data, sophisticated analytical tools, and the expansion of the financial technology (FinTech) sector. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors. The rising adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting both large financial institutions and smaller firms. Furthermore, the growing complexity of financial markets necessitates access to comprehensive and reliable data for informed decision-making, driving demand for advanced analytical tools integrated within these databases. Regulatory compliance requirements also contribute significantly to market growth, as financial institutions increasingly invest in robust data management systems to meet stringent reporting obligations. The market is segmented by application (personal and commercial use) and database type (real-time and historical), with the commercial segment dominating due to the higher data needs of financial institutions. Key players like Bloomberg, Refinitiv (formerly Thomson Reuters), and FactSet are consolidating their market positions through strategic acquisitions and technological advancements, while smaller specialized providers cater to niche market segments. The geographical distribution shows a concentration in North America and Europe, reflecting the established financial markets in these regions. However, the Asia-Pacific region is expected to exhibit significant growth over the forecast period, fueled by rapid economic expansion and the increasing adoption of financial technologies in emerging markets like India and China. Competition is intense, with established players facing challenges from new entrants offering innovative solutions and disruptive technologies. The primary restraint on market growth is the high cost of these comprehensive databases, particularly for smaller businesses and individual investors. However, the ongoing trend of subscription-based models and cloud-based solutions is partially mitigating this challenge, making the technology more accessible.

  2. I

    Industrial Databases Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Apr 15, 2025
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    Data Insights Market (2025). Industrial Databases Report [Dataset]. https://www.datainsightsmarket.com/reports/industrial-databases-499785
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    ppt, doc, pdfAvailable download formats
    Dataset updated
    Apr 15, 2025
    Dataset authored and provided by
    Data Insights Market
    License

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

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global industrial databases market, valued at $1033 million in 2025, is projected to experience robust growth, exhibiting a compound annual growth rate (CAGR) of 10.8% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of Industry 4.0 and the Internet of Things (IoT) is generating massive volumes of operational data, creating a significant demand for efficient and scalable database solutions capable of handling this influx. Furthermore, the rising need for real-time data analytics and predictive maintenance within manufacturing and other industrial sectors is further stimulating market growth. Companies are increasingly leveraging industrial databases to optimize production processes, improve resource allocation, and enhance overall operational efficiency. The market is segmented by application (market analysis, production analysis, and others) and database type (relational and non-relational). Relational databases currently hold a larger market share, but the adoption of non-relational databases is rapidly growing due to their scalability and flexibility in handling unstructured data. Geographic expansion is also a key factor, with North America and Europe representing significant market shares initially, while the Asia-Pacific region is expected to witness faster growth due to increasing industrialization and technological advancements in countries like China and India. However, challenges such as data security concerns and the high cost of implementation and maintenance could act as potential restraints on market growth. The competitive landscape is characterized by a mix of established players like Oracle and newer entrants offering specialized solutions. The presence of key players such as Dun & Bradstreet, Bloomberg, and Statista highlights the market's importance for providing crucial business intelligence. Companies are focusing on developing advanced analytics capabilities and integrating their offerings with cloud platforms to enhance accessibility and scalability. This strategic focus on cloud-based solutions is driving market expansion and offering greater flexibility for users across various industrial sectors. The forecast period suggests continued strong growth, driven by the ongoing digital transformation within industries and the relentless increase in data generation. The market is expected to witness further consolidation as companies seek strategic partnerships and acquisitions to expand their market reach and product portfolios.

  3. F

    Financial Database Report

    • marketreportanalytics.com
    doc, pdf, ppt
    Updated Apr 10, 2025
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    Market Report Analytics (2025). Financial Database Report [Dataset]. https://www.marketreportanalytics.com/reports/financial-database-75308
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    ppt, pdf, docAvailable download formats
    Dataset updated
    Apr 10, 2025
    Dataset authored and provided by
    Market Report Analytics
    License

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

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global financial database market is experiencing robust growth, driven by the increasing demand for real-time data and advanced analytical capabilities across various sectors. The market's expansion is fueled by several key factors, including the rising adoption of sophisticated investment strategies, regulatory compliance needs, and the burgeoning fintech industry. The market is segmented by application (personal and commercial use) and database type (real-time and historical). Commercial use currently dominates the market, owing to the extensive data requirements of financial institutions, investment banks, and research firms. However, personal use is anticipated to witness significant growth driven by the increasing accessibility and affordability of financial data through online platforms and subscription services. The real-time database segment holds a larger market share due to its critical role in high-frequency trading and real-time risk management. Key players like Bloomberg, Refinitiv (formerly Thomson Reuters), and S&P Capital IQ are establishing themselves as market leaders through continuous product innovation and strategic acquisitions, solidifying their dominant positions through comprehensive data offerings and sophisticated analytical tools. Geographic expansion is another key driver, with regions like North America and Europe currently holding significant market share, while Asia Pacific is poised for substantial growth due to the expanding financial markets and increasing technological adoption in the region. Competitive pressures are evident, with several companies striving to differentiate themselves through specialized data offerings and partnerships. The forecast period (2025-2033) suggests continued market expansion, albeit at a potentially moderating CAGR compared to previous years. This moderation could be attributed to market saturation in some developed regions and the potential for economic fluctuations. However, emerging markets and technological advancements, such as AI-driven analytics and the integration of alternative data sources, will likely continue to fuel market growth. The increasing importance of ESG (environmental, social, and governance) factors in investment decisions is also expected to drive demand for specialized financial databases that incorporate such data. The ongoing evolution of data security and privacy regulations will also play a crucial role in shaping the market's trajectory. Maintaining data integrity and compliance will be critical for market players.

  4. S

    Data on various ESG ratings of sample companies and selected personal data...

    • scidb.cn
    Updated Dec 17, 2024
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    Wen Lin; Zhan Wang; Yuxuan Cai; Linsen Yin (2024). Data on various ESG ratings of sample companies and selected personal data of company executives [Dataset]. http://doi.org/10.57760/sciencedb.18657
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 17, 2024
    Dataset provided by
    Science Data Bank
    Authors
    Wen Lin; Zhan Wang; Yuxuan Cai; Linsen Yin
    Description

    The initial sample of this study covers the A-share companies listed on the Shanghai and Shenzhen stock exchanges during the period 2008-2020. We then screened and processed the initial sample data, including (a) Screening for companies with both RepRisk's ESG rating and Bloomberg's ESG rating. Specifically, the selection is based on samples with the same ISIN code and companies' English names in the Bloomberg and RepRisk lndex (RRI) databases. The ISIN code is a securities coding standard developed by the International Organization for Standardization (ISO) and is a unique code used to identify securities in each country or region around the world. We exclude samples that do not provide ISIN codes or have inconsistent English names. (b) We exclude observations with missing values for the main variables. (c) We exclude the ST, *ST and PT trading status samples during the observation period. Our final sample contains 1352 firm-year observations.The ESG disclosure score data and ESG performance score data required for the ESG-washing construction are respectively obtained from the Bloomberg database and the RepRisk Index (RRI) database of the Wharton Research Centre for Data Studies (WRDS). Positive media coverage data is sourced from the China Research Data Services Platform (CNRDS), while the instrumental variable (IV_population) is obtained from the EPS database and Juhe Data (https://www.gotohui.com/). Unless otherwise stated, all other data in this study are from the China Stock Market and Accounting Research (CSMAR) database. Data on executive company changes were collected manually by the authors back-to-back and independently. Then we compared and reconciled the data collected by each, and where there were discrepancies, we again collected and calibrated the data to maximize their reliability. We first obtained executive biographies from the CSMAR database, and the missing values were retrieved from Sina Finance ( https://finance.sina.com.cn/). Due to the unstructured nature of the resume data, we manually processed more than 30,000 resumes of executives to get the data of executives' company changes, based on which we calculated the per capita number of job hops of all executives in each company. The number of part-time jobs held by executives also reflects their pursuit of career changes and development, so in the robustness test the per capita mean of the number of part-time jobs held by executives is used as a proxy variable for careerist orientation. These data can be obtained directly from the CSMAR database.

  5. .bloomberg TLD Whois Database | Whois Data Center

    • whoisdatacenter.com
    csv
    Updated Jul 20, 2024
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    AllHeart Web Inc (2024). .bloomberg TLD Whois Database | Whois Data Center [Dataset]. https://whoisdatacenter.com/tld/.bloomberg/
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    csvAvailable download formats
    Dataset updated
    Jul 20, 2024
    Dataset provided by
    Authors
    AllHeart Web Inc
    License

    https://whoisdatacenter.com/terms-of-use/https://whoisdatacenter.com/terms-of-use/

    Time period covered
    Jul 10, 2025 - Dec 31, 2025
    Description

    .BLOOMBERG Whois Database, discover comprehensive ownership details, registration dates, and more for .BLOOMBERG TLD with Whois Data Center.

  6. F

    Financial Database Report

    • marketreportanalytics.com
    doc, pdf, ppt
    Updated Apr 10, 2025
    + more versions
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    Market Report Analytics (2025). Financial Database Report [Dataset]. https://www.marketreportanalytics.com/reports/financial-database-75303
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    pdf, doc, pptAvailable download formats
    Dataset updated
    Apr 10, 2025
    Dataset authored and provided by
    Market Report Analytics
    License

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

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global financial database market is experiencing robust growth, driven by increasing demand for real-time data analytics and insights across various financial sectors. The market, currently estimated at $15 billion in 2025, is projected to expand at a compound annual growth rate (CAGR) of 8% from 2025 to 2033, reaching approximately $28 billion by 2033. This expansion is fueled by several key factors. The rise of algorithmic trading and quantitative finance necessitates access to high-quality, comprehensive financial data, driving demand for both real-time and historical databases. Moreover, regulatory compliance requirements are pushing financial institutions to invest in robust data management systems, contributing to market growth. The increasing adoption of cloud-based solutions and advanced analytical tools further accelerates market expansion. The market is segmented by application (personal and commercial use) and database type (real-time and historical). The commercial segment currently dominates, propelled by the needs of large financial institutions, investment banks, and asset management firms. However, the personal use segment is expected to witness significant growth driven by the increasing accessibility of financial data and analytical tools to individual investors. Geographical distribution shows a strong presence in North America and Europe, which are expected to remain dominant markets due to the established financial infrastructure and advanced technological capabilities. However, Asia-Pacific is anticipated to demonstrate the fastest growth, driven by increasing economic activity and the expansion of financial markets in emerging economies. Competition is intense, with established players like Bloomberg and Refinitiv (Thomson Reuters) alongside emerging niche players. The competitive landscape is marked by both established giants and agile newcomers. Established players, like Bloomberg, Thomson Reuters, and WRDS, leverage their extensive data networks and brand reputation. However, these are challenged by newer entrants offering innovative solutions and specialized datasets targeting specific niche markets. The ongoing technological advancements, such as the rise of big data analytics and artificial intelligence, presents both opportunities and challenges. While AI-powered analytics unlock deeper insights from financial data, the need to adapt to evolving technologies and data security concerns require substantial investment. Regulatory changes and data privacy concerns also represent potential restraints, requiring continuous adaptation and compliance measures. The future of the market hinges on the ability of players to innovate, adapt to evolving regulations, and meet the increasing demand for speed, accuracy, and comprehensive financial data insights. The market's trajectory strongly suggests a promising future for both established and emerging companies.

  7. f

    Description of variables.

    • plos.figshare.com
    xls
    Updated Mar 28, 2024
    + more versions
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    Fawad Rauf; Wang Wanqiu; Khwaja Naveed; Yanqiu Zhang (2024). Description of variables. [Dataset]. http://doi.org/10.1371/journal.pone.0299707.t001
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    xlsAvailable download formats
    Dataset updated
    Mar 28, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Fawad Rauf; Wang Wanqiu; Khwaja Naveed; Yanqiu Zhang
    License

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

    Description

    Given the contradictory empirical evidence on the relationship between green R&D expenditure and corporate Green Innovation performance (GIP), The present research study is a distinctive investigation into the moderating impacts of ESG reporting on this relationship. We utilized a data collection of 3,846, firm-year observations of A-share listed firms in China from 2016 to 2022 from CSMAR and Bloomberg databases. The firm’s Corporate GIP is assessed and measured by looking at the total quantity of green patents. Lastly, models with multiple regression analyses and fixed effects were employed. The findings show that ESG reporting has a positive and significant impact on the association between corporate GIP and green R&D expenditure, implying its compensating and supportive function in the form of green signals in green outputs. This research could help executives and lawmakers, especially in developing countries to build innovative environmental strategies for business sustainability.

  8. m

    The impact of capital structure on the market value of technology companies

    • data.mendeley.com
    Updated Feb 26, 2024
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    Эмиль Азизов (2024). The impact of capital structure on the market value of technology companies [Dataset]. http://doi.org/10.17632/8sjcnzpbbn.1
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    Dataset updated
    Feb 26, 2024
    Authors
    Эмиль Азизов
    License

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

    Description

    This dataset presents the main operational and financial indicators of technology companies from S&P500. To implement this study, a sample of 100 companies included in the S&P500 index for 2010-2022 was collected. The sample was obtained from the Bloomberg database. The sample includes companies from two sectors: information technology and biotechnology companies. Within the framework of this study, 2 main hypotheses are put forward: 1) The selected sources of financing have a significant impact on the market value of companies in the technology sector; 2) The ratio of equity and debt capital has a significant impact on the assessment of the company by the market.

  9. Data from: Accounting Measurement of Carbon Credits in Brazil, China, and...

    • zenodo.org
    Updated Apr 24, 2025
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    Valdiva Rossato Souza; Valdiva Rossato Souza; Janilson Antonio da Silva Suzar; Janilson Antonio da Silva Suzar; Maisa de Souza Ribeiro; Maisa de Souza Ribeiro; Eliseu Martins; Eliseu Martins (2025). Accounting Measurement of Carbon Credits in Brazil, China, and India [Dataset]. http://doi.org/10.5281/zenodo.3597501
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    Dataset updated
    Apr 24, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Valdiva Rossato Souza; Valdiva Rossato Souza; Janilson Antonio da Silva Suzar; Janilson Antonio da Silva Suzar; Maisa de Souza Ribeiro; Maisa de Souza Ribeiro; Eliseu Martins; Eliseu Martins
    License

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

    Area covered
    India, China, Brazil
    Description

    its population is characterized as Brazilian, Chinese, and Indian companies that presented financial information to external users through securities markets’ regulatory agencies in Brazil, China, and India and that implemented CDM projects during the 2005–2012 period, ranking in the “registered” status on the UNFCCC website.

    Quantitative data were obtaining to test the statistical hypothesis proposed in the study from information referring to the companies and CDM projects that made up the sample as follows: (i) the financial information referring to the equity (E) of companies that have their shares listed in the capital markets of Brazil, China, and India; and (ii) the emission reduction estimates of CDM projects, available from the UNFCCC website.

    The data collection, referring to the financial information of the companies that have made themselves available via regulatory bodies in the securities markets of the countries under study, was carried out through Thomson Reuters Eikon’s Electronic and Financial Database on July 30, 2013. Thus, when the data collection was carried out, financial information was obtained and converted into euros, referring to the equity (E) of 380 Brazilian companies, 2,584 Chinese companies, and 4,219 Indian companies, for the period under review.

    The collection of data concerning CDM projects with the status “registered” on the UNFCCC site, on the other hand, was carried out using the Bloomberg Economic and Financial Database on July 29, 2013, at which time a total of 289 projects registered by the Brazilian DNA, 3,651 projects registered by the Chinese DNA, and 1,296 projects registered by the Indian DNA were available for analysis for the 2005–2012 period. On November 18, 2004, just one project was registered by the Brazilian DNA, entitled “Brazil NovaGerar Landfill Gas to Energy Project” (UNFCCC, 2014). This project was eliminated from the research because of its set limits defined between 2005 and 2012, the first stage of the Kyoto Protocol.

    However, it was necessary to carry out new searches directly on the UNFCCC site for supplementary information that was crucial to implementing the research, given the fact that it did not include full descriptions concerning the names of the receiving agencies in each country (host party), in the Bloomberg Economic and Financial database, on the date mentioned above, information that was characterized as the only link between the CDM project database (Bloomberg) and the financial information database (Thomson Reuters Eikon). These searches were carried during the October 2013–May 2014 period.

    Subsequently, on September 1, 2014, new searches were carried out on the UNFCCC website to update the information referring to CDM projects registered by the agency during the 2005–2012 period.

    Thus, this research was carried out based on CDM projects located in the “registered” status section of the UNFCCC site over the 2005–2012 period, the records of which were finalized by the body prior to September 1, 2014, containing 299 projects registered by the DNA of Brazil, 3,682 projects registered by the DNA of China, and 1,371 projects registered by the DNA of India, adding up to 5,353 projects, that is, 74.69% of the total implemented projects in all the developing countries that ratified the Kyoto Protocol.

    To allow the measurement to be applied to the fair value of estimates of project emission reduction approved by the companies that make up the research sample, we obtained the interest rate EURIBOR – Euro Interbank Offered Rate (average annual rates) from the Bloomberg Financial and Economic Database on July 29, 2013 to adjust the future flows of economic benefits of CER estimates to the present value.

  10. Main sources of financial information for company analysis in the UK Q1-Q2...

    • statista.com
    Updated Jul 1, 2015
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    Statista (2015). Main sources of financial information for company analysis in the UK Q1-Q2 2015 [Dataset]. https://www.statista.com/statistics/524757/main-sources-of-financial-information-united-kingdom-uk/
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    Dataset updated
    Jul 1, 2015
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 25, 2015 - Apr 27, 2015
    Area covered
    United Kingdom
    Description

    This statistic illustrates main sources of financial information for company analysis according to the chartered financial analysts (members of CFA Society of the UK) in the United Kingdom (UK) as of the the first half of 2015. It can be seen that ** percent of respondents stated that they used databases such as provided by Bloomberg among others as their main source of financial information.

  11. H

    Global Vaccine Distribution Summary with Basemap

    • dataverse.harvard.edu
    rar, tsv, xls
    Updated Apr 17, 2021
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    Harvard Dataverse (2021). Global Vaccine Distribution Summary with Basemap [Dataset]. http://doi.org/10.7910/DVN/2M1WLR
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    tsv(58214), tsv(64745), tsv(40539), rar(1921925), xls(31744), tsv(46582), tsv(11413), tsv(33073)Available download formats
    Dataset updated
    Apr 17, 2021
    Dataset provided by
    Harvard Dataverse
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Dataset funded by
    NSF
    Description

    The dataset is updated to March 21, 2021. The data is collected from Bloomberg https://www.bloomberg.com/graphics/covid-vaccine-tracker-global-distribution/, which gathered vaccine information from government websites, official statements and Bloomberg interviews. Local governments and the CDC sometimes report different totals for the same jurisdiction; in these cases, Bloomberg uses the higher number. It can take several days for counts to be reported to databases. ****There might be several days of data missing because the crawler was down.

  12. f

    GIP and green R&D investment effects on ESG reporting.

    • plos.figshare.com
    xls
    Updated Mar 28, 2024
    + more versions
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    Fawad Rauf; Wang Wanqiu; Khwaja Naveed; Yanqiu Zhang (2024). GIP and green R&D investment effects on ESG reporting. [Dataset]. http://doi.org/10.1371/journal.pone.0299707.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Mar 28, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Fawad Rauf; Wang Wanqiu; Khwaja Naveed; Yanqiu Zhang
    License

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

    Description

    GIP and green R&D investment effects on ESG reporting.

  13. w

    Global Semantic Knowledge Graphing Market Research Report: By Application...

    • wiseguyreports.com
    Updated Dec 3, 2024
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    wWiseguy Research Consultants Pvt Ltd (2024). Global Semantic Knowledge Graphing Market Research Report: By Application (Data Integration, Knowledge Management, Natural Language Processing, Recommendation Systems), By Deployment Type (Cloud-Based, On-Premises), By End User (Healthcare, Banking and Financial Services, Retail, Telecommunications, Government), By Technology (Machine Learning, Artificial Intelligence, Graph Databases, Big Data Analytics) and By Regional (North America, Europe, South America, Asia Pacific, Middle East and Africa) - Forecast to 2032. [Dataset]. https://www.wiseguyreports.com/reports/semantic-knowledge-graphing-market
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    Dataset updated
    Dec 3, 2024
    Dataset authored and provided by
    wWiseguy Research Consultants Pvt Ltd
    License

    https://www.wiseguyreports.com/pages/privacy-policyhttps://www.wiseguyreports.com/pages/privacy-policy

    Area covered
    Global
    Description
    BASE YEAR2024
    HISTORICAL DATA2019 - 2024
    REPORT COVERAGERevenue Forecast, Competitive Landscape, Growth Factors, and Trends
    MARKET SIZE 20232.39(USD Billion)
    MARKET SIZE 20242.68(USD Billion)
    MARKET SIZE 20326.8(USD Billion)
    SEGMENTS COVEREDApplication, Deployment Type, End User, Technology, Regional
    COUNTRIES COVEREDNorth America, Europe, APAC, South America, MEA
    KEY MARKET DYNAMICSGrowing demand for data integration, Increasing adoption of AI technologies, Rising need for contextual insights, Expanding applications across industries, Need for enhanced data interoperability
    MARKET FORECAST UNITSUSD Billion
    KEY COMPANIES PROFILEDMetaMind, Stardog, Facebook, Cytoscape, Microsoft, Google, IBM, Oracle, Graphistry, TigerGraph, Wolfram Research, Amazon, DataStax, Neo4j, Bloomberg
    MARKET FORECAST PERIOD2025 - 2032
    KEY MARKET OPPORTUNITIESIncreased demand for data integration, Growing need for AI-driven insights, Expansion of cloud-based solutions, Rise in automated decision-making processes, Enhanced focus on semantic search capabilities
    COMPOUND ANNUAL GROWTH RATE (CAGR) 12.33% (2025 - 2032)
  14. Data from: Large-scale green grabbing for wind and solar PV development in...

    • data.europa.eu
    • explore.openaire.eu
    • +1more
    unknown
    Updated Nov 20, 2024
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    Zenodo (2024). Large-scale green grabbing for wind and solar PV development in Brazil [Dataset]. https://data.europa.eu/data/datasets/oai-zenodo-org-10360706?locale=bg
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    unknownAvailable download formats
    Dataset updated
    Nov 20, 2024
    Dataset authored and provided by
    Zenodohttp://zenodo.org/
    License

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

    Description

    Large-scale green grabbing for wind and solar PV development in Brazil This repository contains the R code and parts of the data used for the analysis in the paper "Large-scale green grabbing for wind and solar PV development in Brazil" by Michael Klingler, Nadia Amelie, Jamie Rickman, and Johannes Schmidt, available as pre-print. Due to data sharing limitations, we cannot provide all data in the repository. Partly this data is not available publically at all (i.e. Bloomberg data, data by the instituto socio ambiental), partly the data has to be downloaded manually (CAR). We still provide a repository which at least allows to understand the procedures we used during the analysis. Land tenure data set The procedures used to form our final land tenure data set can be found in land-tenure-data/processing.txt It is a mix of analyses in Python and in QGis. Analysis of land tenure data and park ownership/investment information The R-code to analyze the owernship relationships between windpark areas and investors/owners can be found in src/. All required libraries will install automatically. The first two scripts cannot be executed due to data limitations. They create the sankey diagrams linking park areas to onwers and investors: - 1.1-figures-results-1-wind.R - 1.2-figures-results-1-solar.R These three scripts are used to analyze the land tenure types prevailing on parks and comparing them to random areas. They should run with the provided data sets: - 2-random-sampling-areas.R - 3-intersection-parks-land-tenure.R - 4-figures-land-tenure.R This script validates our data against an independent data source. However, it cannot be run as it needs the proprietary Bloomberg database: - 5-validation.R

  15. m

    Data for: Nuclear hazard and asset prices: Implications of nuclear disasters...

    • data.mendeley.com
    Updated Nov 16, 2020
    + more versions
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    Ana Belén Alonso-Conde (2020). Data for: Nuclear hazard and asset prices: Implications of nuclear disasters in the cross-sectional behavior of stock returns [Dataset]. http://doi.org/10.17632/wv94fj59t4.3
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    Dataset updated
    Nov 16, 2020
    Authors
    Ana Belén Alonso-Conde
    License

    Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
    License information was derived automatically

    Description

    Using all stocks listed on the Japanese equity market and macroeconomic data for Japan, the dataset comprises the following series:

    1. Japan_25_Portfolios_MV_PTBV: Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
    2. Japan_25_Portfolios_MV_PE: Monthly returns for 25 size-PE portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
    3. Japan_50_Portfolios_SECTOR: Monthly returns for 50 industry portfolios. (Raw data source: Datastream database)
    4. Japan_3 Factors: Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
    5. Japan_5 Factors: Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology. (Raw data source: Datastream database)
    6. Japan_NUCLEAR_Y: Instrument in years with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
    7. Japan_NUCLEAR_M: Instrument in months with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
    8. Japan_RF_M: Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD)
    9. Company data: Names and general data of the companies that constitute the sample. (Raw data source: Datastream database)
    10. Number of stocks in portfolios: Number of stocks included each year in Japan_25_Portfolios_MV_PTBV, Japan_25_Portfolios_MV_PE and Japan_50_Portfolios_SECTOR. (Raw data source: Datastream database)

    We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) price-to-earnings ratio (PE series), and (vii) industry (SECTOR series). We have used the generic rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. We also exclude stocks with less than twelve observations. Accordingly, our sample comprises a total number of 5,212 stocks.

    REFERENCES:

    Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.

  16. g

    World Bank - EM Thematic Bond Database | gimi9.com

    • gimi9.com
    Updated May 17, 2025
    + more versions
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    (2025). World Bank - EM Thematic Bond Database | gimi9.com [Dataset]. https://gimi9.com/dataset/worldbank_ifc_gb/
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    Dataset updated
    May 17, 2025
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    A proprietary IFC dataset containing data on the total value of IFC-approved green, social, sustainability and sustainability-linked bond issuance value compiled from many sources, including Bloomberg, Environmental Finance, Climate Bond Initiative (CBI), and third-party sources. Visit the 2024 Emerging Markets Green Bond Report here: https://www.ifc.org/en/insights-reports/2025/emerging-market-green-bonds-2024

  17. u

    Analysis of volatility spillovers in the stock, currency and goods market...

    • researchdata.up.ac.za
    xlsx
    Updated May 31, 2023
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    Chevaughn van der Westhuizen; Reneé van Eyden; Goodness C. Aye (2023). Analysis of volatility spillovers in the stock, currency and goods market and the monetary policy efficiency within different uncertainty states in these markets [Dataset]. http://doi.org/10.25403/UPresearchdata.22187701.v1
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    University of Pretoria
    Authors
    Chevaughn van der Westhuizen; Reneé van Eyden; Goodness C. Aye
    License

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

    Description

    South African monthly The FTSE/JSE All Share Index data was procured from Bloomberg and the nominal effective exchange rate (NEER) from South African Reserve Bank (SARB) database, where the data has been seasonally adjusted specifying 2015 as the base year. Volatility measures in these markets are generated through a multivaraite EGARCH model in the WinRATS software. South African monthly consumer price index (CPI) data was procured from the International Monetary Fund’s International Financial Statistics (IFS) database, where the data has been seasonally adjusted, specifying 2010 as the base year. The inflation rate is constructed by taking the year-on-year changes in the monthly CPI figures. Inflation uncertainty was generated through the GARCH model in Eviews software. The following South African macroeconomic variables were procured from the SARB: real industrial production (IP), which is used as a proxy for real GDP, real investment (I), real consumption (C), inflation (CPI), broad money (M3), the 3-month treasury bill rate (TB3) and the policy rate (R), a measure of U.S. EPU developed by Baker et al. (2016) to account for global developments available at http://www.policyuncertainty.com/us_monthly.html.

  18. d

    [Eco-Movement] EV Charging Station DC Hardware Data - CSV updated daily

    • datarade.ai
    .csv
    Updated Feb 26, 2021
    + more versions
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    Eco-Movement (2021). [Eco-Movement] EV Charging Station DC Hardware Data - CSV updated daily [Dataset]. https://datarade.ai/data-products/eco-movement-ev-charge-point-data-complete-coverage-of-euro-eco-movement
    Explore at:
    .csvAvailable download formats
    Dataset updated
    Feb 26, 2021
    Dataset authored and provided by
    Eco-Movement
    Area covered
    Liechtenstein, Turkey, Isle of Man, Netherlands, Monaco, Guadeloupe, Réunion, Lithuania, Slovenia, Chile
    Description

    Eco-Movement is the leading source for EV charging station data. We offer full coverage of all (semi)public EV chargers across Europe, North & Latin America, Oceania, and ever more additional countries. Our real-time database now contains about 1,000,000 unique plugs. Eco-Movement is a specialised B2B data provider focusing 100% on EV charging station data quality and enrichment. Hundreds of quality checks are performed through our proprietary quality dashboard, IT architecture and AI. With the highest quality on the market, we are the trusted choice of mobility industry leaders such as Google, Tesla, Bloomberg, and the European Commission’s EAFO portal.

    Eco-Movement integrates data from 3000+ direct connections with EV Charge Point Operators into a uniform, accurate and complete database. We have an unparalleled set of charge point related attributes, all available on individual charging plug level: from Geolocation to Max Power and from Operator to Hardware and Pricing details. Simple, reliable, and up-to-date: The Eco-Movement database is refreshed every day.

    Whether you are in need of insights, building new products or conducting research, high quality data is more important than ever. Our online Data Retrieval Platform is the easy solution to all your EV Charging Station related data needs. Our DC Hardware Data is an unique dataset developed by Eco-Movement, providing hardware information on individual DC charging station level. This report is for your organisation if you want to gain access to accurate data on the manufacturer and model of charging stations, for example as an essential input for your R&D strategy or competitive analysis.

    The hardware report includes full geolocation, operator/brand, and technical information for each individual station, as well as two specific hardware attributes: DC Hardware Manufacturer and DC Hardware Model. This report is available for all countries in our database (see full list of territories below). The price of the data is dependent on the geographies chosen, the length of the subscription, and the intended use.

    Check out our other Data Offerings available, and gain more valuable market insights on EV charging directly from the experts.

    ALSO AVAILABLE We also offer EV Charging Station Location & Tariffs Data via API (JSON) or online download (CSV). Get detailed insights on Charging Station Locations as well as the prices paid at individual chargers, whether payment is done directly to the CPO or with one of the 200+ eMSP products in our database.

    ABOUT US Eco-Movement's mission is providing the EV ecosystem with the best and most relevant Charging Station information. Based in Utrecht, the Netherlands, Eco-Movement is completely independent from other industry players. We are an active and trusted player in the EV ecosystem and the exclusive source for European Commission charging infrastructure data (EAFO).

  19. m

    [Eco-Movement] EV Charging Station Location & Tariffs Data - CSVs updated...

    • app.mobito.io
    Updated Dec 25, 2022
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    (2022). [Eco-Movement] EV Charging Station Location & Tariffs Data - CSVs updated daily [Dataset]. https://app.mobito.io/data-product/[eco-movement]-ev-charging-station-location-&-tariffs-data---csvs-updated-daily
    Explore at:
    Dataset updated
    Dec 25, 2022
    Area covered
    South Africa, Iceland, Ukraine, Guernsey, Latvia, Russia, Estonia, Greece, Belgium, Israel
    Description

    SUMMARY The most complete, highest quality database of EV charging stations across the globe, with everything you want to know regarding charging locations and tariffs. All attributes are available at individual connector level. The perfect input for network planning, pricing analyses, market projections, go-to market strategies, or other analyses.

    Eco-Movement is the leading source for EV charging station data. We offer full coverage of all (semi)public EV chargers across Europe, North & Latin America, Oceania, and ever more additional countries. Our real-time database now contains about 1,000,000 unique plugs. Eco-Movement is a specialised B2B data provider focusing 100% on EV charging station data quality and enrichment. Hundreds of quality checks are performed through our proprietary quality dashboard, IT architecture and AI. With the highest quality on the market, we are the trusted choice of mobility industry leaders such as Google, Tesla, Bloomberg, and the European Commission’s EAFO portal.

    Eco-Movement integrates data from 300+ direct connections with EV Charge Point Operators into a uniform, accurate and complete database. We have an unparalleled set of charge point related attributes, all available on individual charging plug level: from Geolocation to Max Power and from Operator to Hardware and Pricing details. Simple, reliable, and up-to-date: The Eco-Movement database is refreshed every day.

    When you are in need of insights, high quality data is more important than ever. Our online Data Retrieval Platform is the easy solution to all your EV Charging Station related data needs. It includes various charts that you can filter and group to your preferences, plus the possibility to download all data (or a selection) in CSV format for analysis in your preferred software, e.g. Tableau or Excel.

    Location attributes include coordinates, address, operator, power, connector type, location category, parking type, access type (public / restricted / private), and accepted payment methods. Tariff attributes include price per kWh, per hour charging and/or parking, flat fees, and subscription fees. The reports are available for all countries in our database. The price of the data is dependent on the geographies chosen, the length of the subscription, and the intended use.

    Check out our other Data Offerings available, and gain more valuable market insights on EV charging directly from the experts.

    ALSO AVAILABLE We also offer EV Charging Station Location & Tariffs Data via a real-time API with information about charging station availability, and can offer a separate CSV report focused specifically on DC station hardware manufacturer and model information.

    ABOUT US

    Eco-Movement's mission is providing the EV ecosystem with the best and most relevant Charging Station information. Based in Utrecht, the Netherlands, Eco-Movement is completely independent from other industry players. We are an active and trusted player in the EV ecosystem and the exclusive source for European Commission charging infrastructure data (EAFO).

  20. f

    The influence of GIP, green R&D investment, and the moderating function of...

    • figshare.com
    xls
    Updated Mar 28, 2024
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    Fawad Rauf; Wang Wanqiu; Khwaja Naveed; Yanqiu Zhang (2024). The influence of GIP, green R&D investment, and the moderating function of ESG reporting (robustness test). [Dataset]. http://doi.org/10.1371/journal.pone.0299707.t006
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Mar 28, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Fawad Rauf; Wang Wanqiu; Khwaja Naveed; Yanqiu Zhang
    License

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

    Description

    The influence of GIP, green R&D investment, and the moderating function of ESG reporting (robustness test).

Share
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Market Report Analytics (2025). Financial Database Report [Dataset]. https://www.marketreportanalytics.com/reports/financial-database-75304

Financial Database Report

Explore at:
14 scholarly articles cite this dataset (View in Google Scholar)
pdf, ppt, docAvailable download formats
Dataset updated
Apr 10, 2025
Dataset authored and provided by
Market Report Analytics
License

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

Time period covered
2025 - 2033
Area covered
Global
Variables measured
Market Size
Description

The global financial database market is experiencing robust growth, driven by increasing demand for real-time data, sophisticated analytical tools, and the expansion of the financial technology (FinTech) sector. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors. The rising adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting both large financial institutions and smaller firms. Furthermore, the growing complexity of financial markets necessitates access to comprehensive and reliable data for informed decision-making, driving demand for advanced analytical tools integrated within these databases. Regulatory compliance requirements also contribute significantly to market growth, as financial institutions increasingly invest in robust data management systems to meet stringent reporting obligations. The market is segmented by application (personal and commercial use) and database type (real-time and historical), with the commercial segment dominating due to the higher data needs of financial institutions. Key players like Bloomberg, Refinitiv (formerly Thomson Reuters), and FactSet are consolidating their market positions through strategic acquisitions and technological advancements, while smaller specialized providers cater to niche market segments. The geographical distribution shows a concentration in North America and Europe, reflecting the established financial markets in these regions. However, the Asia-Pacific region is expected to exhibit significant growth over the forecast period, fueled by rapid economic expansion and the increasing adoption of financial technologies in emerging markets like India and China. Competition is intense, with established players facing challenges from new entrants offering innovative solutions and disruptive technologies. The primary restraint on market growth is the high cost of these comprehensive databases, particularly for smaller businesses and individual investors. However, the ongoing trend of subscription-based models and cloud-based solutions is partially mitigating this challenge, making the technology more accessible.

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