Big Data and Society Abstract & Indexing - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

Forecast: Production Volumes of High (2-Digit Definition) R&D Intensive Activities in Italy 2024 - 2028 Discover more data with ReportLinker!

Big Data and Society Acceptance Rate - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus

Big Data and Society CiteScore 2024-2025 - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus

Click to view Metadata CLOCAGWIaa183 grid represents sum of ugsaa with ussaa to obtain total groundwater infiltration (gwiaa). This 2007 draft data set was generated using the PRMS surface water model by Earthfx for CLOCA's Tier 1 water budget contract/source water protection program (2007-2008). Values are in mm/a transient (long-term annual averages) for a 19-year period of record of 1981-1999. Annual and monthly average grids are also available (summed daily means). Spatial resolution is a 25m grid covering CLOCA watersheds. The south-west corner of the data set is omitted due to the original grid definition. Zonal Statistics (mean) for CLOCA Watershed was calculated and CLOCAgwiaa183 was extracted. Raster calculator was then used to extract 1.15 or 15% above the mean for the jurisdiction.

The database includes effluent volumes, contaminant mass emissions (ME), average constituent concentrations, and toxicity. Constituent concentration data were standardized to monthly time steps. For constituents analyzed more than once per month, the arithmetic mean of all results in a given month was calculated. Where the frequency of constituent analysis was less than monthly or data for a given month were not available, the arithmetic mean of available data within the given year was calculated and used to populate months for which no data existed. The monthly flow and concentration data were then used to calculate annual discharge volumes and constituent mass emissions for each facility. Annual average flow-weighted concentrations (FWC) were calculated by dividing the annual ME for a given constituent by the total annual effluent volume (V).

Summary

Facial expression is among the most natural methods for human beings to convey their emotional information in daily life. Although the neural mechanisms of facial expression have been extensively studied employing lab-controlled images and a small number of lab-controlled video stimuli, how the human brain processes natural facial expressions still needs to be investigated. To our knowledge, this type of data specifically on large-scale natural facial expression videos is currently missing. We describe here the natural Facial Expressions Dataset (NFED), a fMRI dataset including responses to 1,320 short (3-second) natural facial expression video clips. These video clips are annotated with three types of labels: emotion, gender, and ethnicity, along with accompanying metadata. We validate that the dataset has good quality within and across participants and, notably, can capture temporal and spatial stimuli features. NFED provides researchers with fMRI data for understanding of the visual processing of large number of natural facial expression videos.

Data Records

Data Records The data, which is structured following the BIDS format53, were accessible at https://openneuro.org/datasets/ds00504754. The “sub-

Stimulus. Distinct folders store the stimuli for distinct fMRI experiments: "stimuli/face-video", "stimuli/floc", and "stimuli/prf" (Fig. 2b). The category labels and metadata corresponding to video stimuli are stored in the "videos-stimuli_category_metadata.tsv”. The “videos-stimuli_description.json” file describes category and metadata information of video stimuli(Fig. 2b).

Raw MRI data. Each participant's folder is comprised of 11 session folders: “sub-

Volume data from pre-processing. The pre-processed volume-based fMRI data were in the folder named “pre-processed_volume_data/sub-

Surface data from pre-processing. The pre-processed surface-based data were stored in a file named “volumetosurface/sub-

FreeSurfer recon-all. The results of reconstructing the cortical surface are saved as “recon-all-FreeSurfer/sub-

Surface-based GLM analysis data. We have conducted GLMsingle on the data of the main experiment. There is a file named “sub--

Validation. The code of technical validation was saved in the “derivatives/validation/code” folder. The results of technical validation were saved in the “derivatives/validation/results” folder (Fig. 2h). The “README.md” describes the detailed information of code and results.

Real-Time Video Storage Market Outlook

The global real-time video storage market size is projected to reach approximately USD 45 billion by 2032, up from USD 15 billion in 2023, with a compound annual growth rate (CAGR) of 11.6% during the forecast period. This robust growth is fueled by the increasing demand for video content across various platforms and applications, coupled with advancements in storage technologies. The exponential increase in video data being generated for applications such as surveillance, broadcasting, and streaming is a major contributing factor to the market's expansion. Additionally, the proliferation of high-definition and 4K video content has necessitated the development of more efficient and scalable storage solutions.

One of the primary growth factors for the real-time video storage market is the surge in demand for video surveillance solutions. With increasing concerns about security and safety across the globe, governments and private sectors are investing heavily in surveillance systems. These systems require robust storage solutions that can handle large volumes of high-definition video data in real-time. The integration of artificial intelligence and analytics with video surveillance systems has further boosted the need for advanced storage solutions that can facilitate quick retrieval and processing of video data. Additionally, the adoption of smart city initiatives in many regions is driving the demand for comprehensive surveillance and storage systems.

Another crucial growth factor is the rise of online video streaming platforms. With the shift in consumer preferences towards on-demand video content, platforms like Netflix, Amazon Prime, and YouTube are witnessing unprecedented growth. These platforms require large-scale, efficient storage solutions to manage and deliver content seamlessly to millions of users worldwide. The increasing penetration of high-speed internet and mobile devices has further fueled the growth of online streaming services, thereby augmenting the demand for real-time video storage solutions. Furthermore, advancements in compression technologies are enabling more efficient storage and transmission of video data, driving the market forward.

The growing trend of remote work and the subsequent increase in video conferencing activities is another significant driver for the real-time video storage market. As businesses and educational institutions continue to adopt remote working and learning models, there is a heightened need for robust video conferencing solutions. These solutions rely on effective storage systems to ensure seamless communication and collaboration among users. The integration of features such as recording and transcription in video conferencing platforms has further increased the demand for storage solutions that can handle and store large volumes of video data efficiently.

In terms of regional outlook, North America dominates the real-time video storage market, accounting for a significant share of the global market. The region's technological advancements, coupled with high adoption rates of advanced storage solutions in sectors such as media and entertainment, government, and healthcare, drive its market position. Europe follows closely, with a substantial share, driven by increasing demand for video surveillance in public and private sectors. The Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, owing to rapid urbanization, increasing internet penetration, and rising investments in digital infrastructure. Countries such as China, India, and Japan are expected to be at the forefront of this growth.

Component Analysis

In the real-time video storage market, the component segment is divided into hardware, software, and services, each playing a crucial role in the deployment and operation of video storage solutions. Hardware components form the backbone of any video storage solution, encompassing servers, storage arrays, and other physical infrastructure required to store and manage video data. The demand for high-capacity and high-performance storage hardware has risen significantly with the increasing volume of video content being generated for various applications. Innovations in high-density storage solutions, such as solid-state drives (SSDs), are transforming the hardware landscape by offering faster data access speeds and improved reliability compared to traditional hard disk drives (HDDs).

Software solutions are equally important in the real-time video storage market, providing the necessary tools for managing, optimizing, and se

Blu Ray Archive System Market Outlook

The global Blu Ray Archive System market size was estimated at USD 1.5 billion in 2023 and is expected to reach USD 2.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.9%. Key growth factors driving this market include the increasing need for cost-effective and reliable data storage solutions, the rise in data generation across various industries, and the proliferation of high-definition media content.

One of the primary drivers of the Blu Ray Archive System market is the exponential increase in data generation. With the digital transformation sweeping across industries, the volume of data generated globally has surged, necessitating reliable and scalable storage solutions. Blu Ray technology provides a cost-effective and durable solution for long-term data storage, making it an attractive option for businesses that need to archive large volumes of data securely.

Another significant growth factor is the increasing demand for high-definition content. The media and entertainment industry, in particular, requires high-capacity storage solutions to archive high-definition videos, movies, and other media content. Blu Ray discs, with their high storage capacity and longevity, offer an ideal solution for this industry. Furthermore, the growing adoption of 4K and 8K resolution in video content has only heightened the need for efficient and high-capacity archiving solutions.

The healthcare industry is also contributing to the growth of the Blu Ray Archive System market. With the increasing adoption of electronic health records (EHRs) and medical imaging technologies, the volume of data generated by healthcare providers is growing rapidly. Blu Ray Archive Systems provide a secure and durable solution for storing medical records, imaging data, and other critical healthcare information, ensuring compliance with data retention regulations and safeguarding patient privacy.

Regionally, North America is expected to dominate the Blu Ray Archive System market due to the high adoption rate of advanced technologies and the presence of major market players. The Asia Pacific region is projected to exhibit the highest growth rate, driven by the rapid digitalization of industries and the increasing demand for data storage solutions in emerging economies such as China and India. Europe, Latin America, and the Middle East & Africa are also expected to witness significant growth, fueled by the rising awareness of data storage needs and technological advancements in these regions.

Component Analysis

The Blu Ray Archive System market is segmented into three key components: hardware, software, and services. Each of these segments plays a crucial role in the overall functionality and effectiveness of Blu Ray Archive Systems. The hardware segment primarily includes Blu Ray drives and discs, which are essential for data storage and retrieval. The increasing demand for high-capacity storage devices is driving the growth of this segment. Blu Ray drives have evolved to support larger storage capacities and faster data transfer rates, making them a preferred choice for archiving purposes.

The software segment encompasses the various applications and solutions used to manage, organize, and retrieve data stored on Blu Ray discs. This includes archiving software, data management tools, and retrieval applications. The growth of this segment is driven by the need for efficient and user-friendly software solutions that can handle large volumes of data and provide quick access to archived information. Advances in software technology have enhanced the capabilities of Blu Ray Archive Systems, making them more versatile and efficient.

The services segment includes installation, maintenance, and support services provided by companies specializing in Blu Ray Archive Systems. These services are crucial for ensuring the smooth operation and longevity of the systems. The increasing adoption of Blu Ray Archive Systems across various industries has led to a growing demand for professional services to support these systems. Service providers offer a range of solutions, including system integration, troubleshooting, and regular maintenance, to ensure optimal performance and minimize downtime.

Overall, the component analysis reveals that the Blu Ray Archive System market is a comprehensive ecosystem that relies on the seamless integration of hardware, software, and services. Each component is interdependent, and advancements in one area often drive improvements in the others

Russia Imports: Transportation Means: Volume: Vehicles data was reported at 4,021.000 Unit th in 2011. This records an increase from the previous number of 2,087.100 Unit th for 2010. Russia Imports: Transportation Means: Volume: Vehicles data is updated yearly, averaging 1,751.400 Unit th from Dec 2005 (Median) to 2011, with 7 observations. The data reached an all-time high of 4,021.000 Unit th in 2011 and a record low of 984.200 Unit th in 2009. Russia Imports: Transportation Means: Volume: Vehicles data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Foreign Trade – Table RU.JAA038: Imports: Transportation Means.

Forecast: Production Volumes of High (3-Digit Definition) R&D Intensive Activities in the US 2024 - 2028 Discover more data with ReportLinker!

228 Global exporters importers export import shipment records of High definition television with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Video Surveillance Data Storage Market Outlook

The global video surveillance data storage market size in 2023 is estimated to be around $XX billion and is projected to reach approximately $XX billion by 2032, growing at a compound annual growth rate (CAGR) of X%. The market's expansion is driven by the increasing demand for enhanced video surveillance systems across various sectors, including government, retail, and healthcare. The rapid technological advancements in data storage solutions, coupled with the escalating concerns regarding public safety and security, are propelling the market’s growth.

One of the key growth factors for the video surveillance data storage market is the rising need for high-resolution imaging and advanced video analytics. With the proliferation of high-definition (HD) and ultra-high-definition (UHD) cameras, the amount of data generated by video surveillance systems has surged. This necessitates the deployment of robust and scalable data storage solutions capable of handling large volumes of data efficiently. In addition, the implementation of advanced analytics such as facial recognition and license plate recognition requires substantial storage capacity to process and store the data effectively.

Another significant factor contributing to market growth is the increasing adoption of cloud-based storage solutions. Cloud storage offers numerous benefits, including scalability, cost-efficiency, and ease of access. Organizations are increasingly shifting towards cloud storage to manage the growing volumes of video data, as it provides a flexible and scalable infrastructure that can be adjusted according to the data requirements. Furthermore, cloud storage solutions facilitate seamless data access and sharing across different locations, enhancing the overall efficiency and effectiveness of video surveillance systems.

The integration of artificial intelligence (AI) and machine learning (ML) technologies in video surveillance systems is also a major driver for market growth. AI and ML algorithms enable real-time video analysis, predictive analytics, and automated event detection, which significantly improve the efficacy of surveillance operations. These technologies generate a vast amount of data that needs to be stored and analyzed, thereby driving the demand for advanced data storage solutions. Moreover, the growing trend of smart cities and IoT applications is further augmenting the need for efficient video surveillance data storage solutions.

From a regional perspective, North America holds a significant share in the video surveillance data storage market, driven by the high adoption rate of advanced surveillance technologies and stringent security regulations. The Asia Pacific region is expected to witness substantial growth over the forecast period, attributed to the increasing urbanization, infrastructure development, and rising security concerns in countries such as China and India. Europe is also a prominent market for video surveillance data storage, supported by the growing implementation of smart city projects and advancements in data storage technologies.

Storage Type Analysis

In the video surveillance data storage market, the storage type segment is categorized into Network Attached Storage (NAS), Direct Attached Storage (DAS), Storage Area Network (SAN), and Cloud Storage. Network Attached Storage (NAS) is a significant segment due to its high scalability and ease of access. NAS systems are designed to handle large volumes of data and provide seamless data sharing across different devices and networks, making them ideal for video surveillance applications. The increasing adoption of NAS solutions in small and medium enterprises (SMEs) and large enterprises is driving the growth of this segment.

Direct Attached Storage (DAS) is another critical segment in the market. DAS systems offer direct connectivity to the video surveillance system, providing high-speed data transfer and low latency. These systems are particularly beneficial for applications requiring real-time data access and high-performance storage solutions. The growing demand for high-resolution video surveillance, coupled with the need for efficient and reliable data storage, is fueling the adoption of DAS solutions.

Storage Area Network (SAN) is a prominent segment, especially in large enterprises and data centers. SAN solutions provide a high-speed network that connects storage devices with servers, enabling efficient data management and storage. The ability of SAN systems to handle massive data volumes and provide hi

Portable Storage Solutions Market Outlook

The global market size for portable storage solutions was valued at approximately $14.8 billion in 2023 and is projected to reach $23.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.3% during the forecast period. This growth is primarily driven by the increasing demand for flexible and convenient storage options across different sectors, the surge in data generation, and advancements in storage technologies.

One of the primary growth factors for the portable storage solutions market is the exponential increase in data generation. With the proliferation of digital content, such as high-definition videos, high-resolution images, and expansive software applications, individuals and organizations are experiencing unprecedented data storage needs. Portable storage solutions offer the required flexibility and convenience to manage and transport large volumes of data efficiently, which significantly contributes to the market's growth. Moreover, the expanding use of sophisticated devices, such as smartphones, tablets, and laptops, necessitates reliable and high-capacity portable storage options, thus driving the market forward.

Technological advancements in storage solutions are another critical factor boosting the portable storage solutions market. Innovations such as solid-state drives (SSDs) and advancements in USB technology (such as USB 3.0 and USB-C) have revolutionized portable storage by offering higher data transfer speeds, increased storage capacities, and enhanced durability compared to traditional hard drives. These improvements make portable storage solutions more attractive to consumers and businesses alike, thereby fueling market growth. Additionally, continuous advancements in data security features integrated within these storage solutions, such as encryption and biometric access, further enhance their appeal, particularly in sectors where data protection is paramount.

The rise of remote work and the increasing trend of digital nomadism have also significantly impacted the portable storage solutions market. As more individuals and enterprises adopt flexible work arrangements, the need for portable, reliable, and secure data storage solutions has become more pronounced. Portable storage devices enable users to access and transfer data seamlessly across different locations, ensuring continuity and productivity. This shift towards remote work and mobile computing is anticipated to sustain the demand for portable storage solutions, thereby contributing to market expansion over the forecast period.

The increasing demand for Mobile Hard Disk solutions is a testament to the evolving needs of both consumers and enterprises for robust and flexible storage options. As digital content continues to proliferate, mobile hard disks offer a reliable means to store and transport large volumes of data without relying on constant internet connectivity. These devices provide a practical solution for users who require substantial storage capacity on-the-go, especially in areas with limited cloud access. The convenience and portability of mobile hard disks make them an indispensable tool for professionals who frequently travel or work remotely, ensuring that their data is always accessible and secure.

Regionally, North America holds a substantial share of the portable storage solutions market, driven by the high adoption rate of advanced technologies, a robust IT infrastructure, and the presence of leading market players. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the rapid digitization, increasing number of internet users, and expanding consumer electronics market in countries such as China and India. Europe also presents significant growth opportunities owing to the growing adoption of cloud computing and the increasing reliance on portable storage for business continuity and disaster recovery solutions.

Product Type Analysis

The portable storage solutions market is segmented by product type into external hard drives, USB flash drives, portable SSDs, memory cards, and others. External hard drives remain a dominant segment due to their high storage capacities and cost-effectiveness. These drives are widely used across personal and enterprise applications, providing an efficient solution for backing up large volumes of data. Despite facing competition from more advanced storage technologies, the affordability and high sto

The World Top Incomes Database provides statistical information on the shares of top income groups for 30 countries. The construction of this database was possible thanks to the research of over thirty contributing authors. There has been a marked revival of interest in the study of the distribution of top incomes using tax data. Beginning with the research by Thomas Piketty of the long-run distribution of top incomes in France, a succession of studies has constructed top income share time series over the long-run for more than twenty countries to date. These projects have generated a large volume of data, which are intended as a research resource for further analysis. In using data from income tax records, these studies use similar sources and methods as the pioneering work by Kuznets for the United States.The findings of recent research are of added interest, since the new data provide estimates covering nearly all of the twentieth century -a length of time series unusual in economics. In contrast to existing international databases, generally restricted to the post-1970 or post-1980 period, the top income data cover a much longer period, which is important because structural changes in income and wealth distributions often span several decades. The data series is fairly homogenous across countries, annual, long-run, and broken down by income source for several cases. Users should be aware also about their limitations. Firstly, the series measure only top income shares and hence are silent on how inequality evolves elsewhere in the distribution. Secondly, the series are largely concerned with gross incomes before tax. Thirdly, the definition of income and the unit of observation (the individual vs. the family) vary across countries making comparability of levels across countries more difficult. Even within a country, there are breaks in comparability that arise because of changes in tax legislation affecting the definition of income, although most studies try to correct for such changes to create homogenous series. Finally and perhaps most important, the series might be biased because of tax avoidance and tax evasion. The first theme of the research programme is the assembly and analysis of historical evidence from fiscal records on the long-run development of economic inequality. “Long run” is a relative term, and here it means evidence dating back before the Second World War, and extending where possible back into the nineteenth century. The time span is determined by the sources used, which are based on taxes on incomes, earnings, wealth and estates. Perspective on current concerns is provided by the past, but also by comparison with other countries. The second theme of the research programme is that of cross-country comparisons. The research is not limited to OECD countries and will draw on evidence globally. In order to understand the drivers of inequality, it is necessary to consider the sources of economic advantage. The third theme is the analysis of the sources of income, considering separately the roles of earned incomes and property income, and examining the historical and comparative evolution of earned and property income, and their joint distribution. The fourth theme is the long-run trend in the distribution of wealth and its transmission through inheritance. Here again there are rich fiscal data on the passing of estates at death. The top income share series are constructed, in most of the cases presented in this database, using tax statistics (China is an exception; for the time being the estimates come from households surveys). The use of tax data is often regarded by economists with considerable disbelief. These doubts are well justified for at least two reasons. The first is that tax data are collected as part of an administrative process, which is not tailored to the scientists' needs, so that the definition of income, income unit, etc., are not necessarily those that we would have chosen. This causes particular difficulties for comparisons across countries, but also for time-series analysis where there have been substantial changes in the tax system, such as the moves to and from the joint taxation of couples. Secondly, it is obvious that those paying tax have a financial incentive to present their affairs in a way that reduces tax liabilities. There is tax avoidance and tax evasion. The rich, in particular, have a strong incentive to understate their taxable incomes. Those with wealth take steps to ensure that the return comes in the form of asset appreciation, typically taxed at lower rates or not at all. Those with high salaries seek to ensure that part of their remuneration comes in forms, such as fringe benefits or stock-options which receive favorable tax treatment. Both groups may make use of tax havens that allow income to be moved beyond the reach of the national tax net. These shortcomings limit what can be said from tax data, but this does not mean that the data are worthless. Like all economic data, they measure with error the 'true' variable in which we are interested. References Atkinson, Anthony B. and Thomas Piketty (2007). Top Incomes over the Twentieth Century: A Contrast between Continental European and English-Speaking Countries (Volume 1). Oxford: Oxford University Press, 585 pp. Atkinson, Anthony B. and Thomas Piketty (2010). Top Incomes over the Twentieth Century: A Global Perspective (Volume 2). Oxford: Oxford University Press, 776 pp. Atkinson, Anthony B., Thomas Piketty and Emmanuel Saez (2011). Top Incomes in the Long Run of History, Journal of Economic Literature, 49(1), pp. 3-71. Kuznets, Simon (1953). Shares of Upper Income Groups in Income and Savings. New York: National Bureau of Economic Research, 707 pp. Piketty, Thomas (2001). Les Hauts Revenus en France au 20ème siècle. Paris: Grasset, 807 pp. Piketty, Thomas (2003). Income Inequality in France, 1901-1998, Journal of Political Economy, 111(5), pp. 1004-42.

Russia Import Volume Index: Machines, Equipment & Transportation Means data was reported at 122.335 Prev Year=100 in 2021. This records an increase from the previous number of 100.521 Prev Year=100 for 2020. Russia Import Volume Index: Machines, Equipment & Transportation Means data is updated yearly, averaging 119.700 Prev Year=100 from Dec 1999 (Median) to 2021, with 23 observations. The data reached an all-time high of 146.500 Prev Year=100 in 2004 and a record low of 49.100 Prev Year=100 in 2009. Russia Import Volume Index: Machines, Equipment & Transportation Means data remains active status in CEIC and is reported by Federal Customs Service. The data is categorized under Russia Premium Database’s Foreign Trade – Table RU.JAG003: Export and Import: Volume Index: by Product: Annual.

Commercial Blu Ray Discs Market Outlook

The global commercial Blu Ray discs market size was valued at USD 1.2 billion in 2023 and is projected to reach USD 2.1 billion by 2032, growing at a CAGR of 6.2% during the forecast period. This growth can be attributed to various factors, including the increasing demand for high-definition content and advancements in Blu Ray technology that enhance storage capacity and durability. The rising consumption of digital media, coupled with the increasing popularity of 4K and 8K resolution content, fuels the demand for Blu Ray discs, thereby propelling the market's expansion.

One of the key growth factors for the commercial Blu Ray discs market is the significant advancements in Blu Ray technology. With capacities reaching up to 128GB per disc, Blu Ray technology offers superior storage solutions compared to traditional DVDs and CDs. This makes it an ideal choice for storing large volumes of high-definition video and audio content, which is increasingly demanded in both consumer and professional settings. Additionally, the advancement in 3D Blu Ray and Ultra HD Blu Ray formats has further solidified Blu Ray's position as a superior physical media option, driving market growth.

The rise in the entertainment industry, particularly the film and gaming sectors, has significantly contributed to the growth of the Blu Ray discs market. Consumers' increasing preference for high-definition and immersive viewing experiences has led to a surge in demand for Blu Ray discs. The gaming industry, in particular, benefits from Blu Ray's high storage capacity, which is essential for storing complex and high-quality gaming graphics and data. Moreover, the trend of collecting physical media among enthusiasts and collectors continues to support the market's growth.

Moreover, the increasing penetration of Blu Ray technology in various educational and corporate sectors has opened new avenues for market growth. Blu Ray discs are being extensively used for archiving large data volumes, due to their durability and longevity compared to other storage mediums. Educational institutions utilize Blu Ray technology for distributing educational materials, including high-definition educational videos and interactive content. Similarly, enterprises leverage Blu Ray technology for data storage and backup solutions, ensuring data integrity and security over extended periods.

The integration of Blue-ray Drive technology into modern devices has further enhanced the appeal of Blu Ray discs. Blue-ray Drives are essential for reading and writing data on Blu Ray discs, making them indispensable for both personal and professional use. These drives have evolved significantly, offering faster read and write speeds, which are crucial for handling large files associated with high-definition content. The compatibility of Blue-ray Drives with various formats, including BD-R, BD-RE, and BD-ROM, ensures versatility and convenience for users. As more consumers and businesses adopt Blu Ray technology, the demand for reliable Blue-ray Drives continues to grow, supporting the overall expansion of the Blu Ray discs market.

Regionally, the commercial Blu Ray discs market exhibits diverse growth patterns, with North America and Asia Pacific leading the charge. North America holds a significant market share, driven by the high consumption of digital content and the presence of major entertainment and gaming industries. The Asia Pacific region is expected to witness the highest growth rate, propelled by the rising disposable incomes, increasing adoption of high-definition content, and the expanding entertainment industry in countries like China, Japan, and India. The growing popularity of Blu Ray technology in these regions is anticipated to offer lucrative opportunities for market players.

Type Analysis

The commercial Blu Ray discs market is segmented into various types, including BD-R (Blu-ray Disc Recordable), BD-RE (Blu-ray Disc Rewritable), and BD-ROM (Blu-ray Disc Read-Only Memory). BD-R discs, which allow users to record data only once, are widely used for archiving and data storage purposes. Their one-time writable feature ensures data integrity and prevents accidental deletion, making them a reliable option for long-term storage. The demand for BD-R discs is particularly high in the enterprise sector, where data security and integrity are of paramount importance.

BD-RE discs, on the other hand, o

Data becomes an important issue in conducting research activities. Each research institute and R & D requires data was documented from previous research, whether derived from the institution itself or other institutions. Currently, each work unit already has several databases, but there is yet one means to store a safe and reliable. Therefore, a large scientific data repository system is required. In addition to being a means of sharing data, the repository is also intended to provide access and preserve data. The repository is expected to support intergovernmental research collaboration. The various data held by Indonesian Institute of Sciences (LIPI)'s work units, especially the Life Science and Earth Science can be categorized as big data because it has a very large volume, variety, and velocity (high speed) needed to process the data. The data are still scattered in part still managed by individually and partly. Individual data management causes lack of access, data is only accessible to a limited audience. Lack of access leads to duplication of research, wasted government funds, and lack of benefits for further research. ---------------------------------------------------------------------- Data menjadi masalah yang penting dalam melakukan kegiatan penelitian. Setiap lembaga penelitian dan badan litbang memerlukan data-data yang dokumentasi dari penelitian sebelumnya, baik yang berasal dari institusi sendiri atau institusi lain. Saat ini masing-masing satuan kerja sudah memiliki beberapa pangkalan data, akan tetapi belum ada satu sarana untuk menyimpan yang aman dan handal. Oleh karena itu, perlu dibuat sistem repositori big data ilmiah. Selain sebagai sarana berbagi data, repositori juga dimaksudkan untuk menyediakan akses dan melestarikan data. Dengan repositori diharapkan akan mendukung kolaborasi penelitian antar lembaga. Berbagai macam data yang dimiliki oleh satuan kerja di lingkungan LIPI, khususnya Kedeputian Ilmu Hayati dan Kedeputian Kebumian dapat dikategorikan big data imiah karena memiliki volume yang sangat besar, variety (jenis) yang sangat beragam, dan velocity (kecepatan) tinggi yang dibutuhkan untuk memproses data tersebut. Data-data tersebut masih tersebar sebagian masih dikelola secara individu dan sebagian sudah dikelola oleh satuan kerja. Pengelolaan data secara individu menyebabkan kurangnya akses, data hanya dapat diakses oleh kalangan terbatas. Kurangnya akses menyebabkan terjadinya duplikasi penelitian, dana pemerintah terbuang, dan kurangnya manfaat untuk penelitian lebih lanjut.

Disclaimer!!! Data uploaded here are collected from the internet. The sole purposes of uploading these data are to provide this Kaggle community with a good source of data for analysis and research. I don't own these datasets and am also not responsible for them legally by any means. I am not charging anything (either monetary or any favor) for this dataset.

Overview

This dataset contains historical daily prices for Nifty 100 stocks and indices currently trading on the Indian Stock Market. - Data samples are of 15-minute intervals and the availability of data is from Jan 2015 to Feb 2022. - Along with OHLCV (Open, High, Low, Close, and Volume) data, we have created 55 technical indicators. - More details about these technical indicators are provided in the Data description file.

Content

The whole dataset is around 5 GB, and 100 stocks (Nifty 100 stocks) and 2 indices (Nifty 50 and Nifty Bank indices) are present in this dataset. Details about each file are - - OHLCV (Open, High, Low, Close, and Volume) data

Inspiration

• Data is uploaded for Research and Educational purposes.

Possible problem statements

• Univariate and Multi-variate time series forecasting of stock prices and index prices
• Multi-variate data can be used to predict the trend of the stock price (Buy or Sell or Hold)
• Different intraday or positional trading strategies can be built out of this multivariate data. [technical indicators are already added here]
• EDA on time series data