On the continental scale, climate is an important determinant of the distributions of plant taxa and ecoregions. To quantify and depict the relations between specific climate variables and these distributions, we placed modern climate and plant taxa distribution data on an approximately 25-kilometer (km) equal-area grid with 27,984 points that cover Canada and the continental United States (Thompson and others, 2015). The gridded climatic data include annual and monthly temperature and precipitation, as well as bioclimatic variables (growing degree days, mean temperatures of the coldest and warmest months, and a moisture index) based on 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and absolute minimum and maximum temperatures for 1951-1980 interpolated from climate-station data (WeatherDisc Associates, 1989). As described below, these data were used to produce portions of the "Atlas of relations between climatic parameters and distributions of important trees and shrubs in North America" (hereafter referred to as "the Atlas"; Thompson and others, 1999a, 1999b, 2000, 2006, 2007, 2012a, 2015). Evolution of the Atlas Over the 16 Years Between Volumes A & B and G: The Atlas evolved through time as technology improved and our knowledge expanded. The climate data employed in the first five Atlas volumes were replaced by more standard and better documented data in the last two volumes (Volumes F and G; Thompson and others, 2012a, 2015). Similarly, the plant distribution data used in Volumes A through D (Thompson and others, 1999a, 1999b, 2000, 2006) were improved for the latter volumes. However, the digitized ecoregion boundaries used in Volume E (Thompson and others, 2007) remain unchanged. Also, as we and others used the data in Atlas Volumes A through E, we came to realize that the plant distribution and climate data for areas south of the US-Mexico border were not of sufficient quality or resolution for our needs and these data are not included in this data release. The data in this data release are provided in comma-separated values (.csv) files. We also provide netCDF (.nc) files containing the climate and bioclimatic data, grouped taxa and species presence-absence data, and ecoregion assignment data for each grid point (but not the country, state, province, and county assignment data for each grid point, which are available in the .csv files). The netCDF files contain updated Albers conical equal-area projection details and more precise grid-point locations. When the original approximately 25-km equal-area grid was created (ca. 1990), it was designed to be registered with existing data sets, and only 3 decimal places were recorded for the grid-point latitude and longitude values (these original 3-decimal place latitude and longitude values are in the .csv files). In addition, the Albers conical equal-area projection used for the grid was modified to match projection irregularities of the U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977) from which plant taxa distribution data were digitized. For the netCDF files, we have updated the Albers conical equal-area projection parameters and recalculated the grid-point latitudes and longitudes to 6 decimal places. The additional precision in the location data produces maximum differences between the 6-decimal place and the original 3-decimal place values of up to 0.00266 degrees longitude (approximately 143.8 m along the projection x-axis of the grid) and up to 0.00123 degrees latitude (approximately 84.2 m along the projection y-axis of the grid). The maximum straight-line distance between a three-decimal-point and six-decimal-point grid-point location is 144.2 m. Note that we have not regridded the elevation, climate, grouped taxa and species presence-absence data, or ecoregion data to the locations defined by the new 6-decimal place latitude and longitude data. For example, the climate data described in the Atlas publications were interpolated to the grid-point locations defined by the original 3-decimal place latitude and longitude values. Interpolating the data to the 6-decimal place latitude and longitude values would in many cases not result in changes to the reported values and for other grid points the changes would be small and insignificant. Similarly, if the digitized Little (1971, 1976, 1977) taxa distribution maps were regridded using the 6-decimal place latitude and longitude values, the changes to the gridded distributions would be minor, with a small number of grid points along the edge of a taxa's digitized distribution potentially changing value from taxa "present" to taxa "absent" (or vice versa). These changes should be considered within the spatial margin of error for the taxa distributions, which are based on hand-drawn maps with the distributions evidently generalized, or represented by a small, filled circle, and these distributions were subsequently hand digitized. Users wanting to use data that exactly match the data in the Atlas volumes should use the 3-decimal place latitude and longitude data provided in the .csv files in this data release to represent the center point of each grid cell. Users for whom an offset of up to 144.2 m from the original grid-point location is acceptable (e.g., users investigating continental-scale questions) or who want to easily visualize the data may want to use the data associated with the 6-decimal place latitude and longitude values in the netCDF files. The variable names in the netCDF files generally match those in the data release .csv files, except where the .csv file variable name contains a forward slash, colon, period, or comma (i.e., "/", ":", ".", or ","). In the netCDF file variable short names, the forward slashes are replaced with an underscore symbol (i.e., "_") and the colons, periods, and commas are deleted. In the netCDF file variable long names, the punctuation in the name matches that in the .csv file variable names. The "country", "state, province, or territory", and "county" data in the .csv files are not included in the netCDF files. Data included in this release: - Geographic scope. The gridded data cover an area that we labelled as "CANUSA", which includes Canada and the USA (excluding Hawaii, Puerto Rico, and other oceanic islands). Note that the maps displayed in the Atlas volumes are cropped at their northern edge and do not display the full northern extent of the data included in this data release. - Elevation. The elevation data were regridded from the ETOPO5 data set (National Geophysical Data Center, 1993). There were 35 coastal grid points in our CANUSA study area grid for which the regridded elevations were below sea level and these grid points were assigned missing elevation values (i.e., elevation = 9999). The grid points with missing elevation values occur in five coastal areas: (1) near San Diego (California, USA; 1 grid point), (2) Vancouver Island (British Columbia, Canada) and the Olympic Peninsula (Washington, USA; 2 grid points), (3) the Haida Gwaii (formerly Queen Charlotte Islands, British Columbia, Canada) and southeast Alaska (USA, 9 grid points), (4) the Canadian Arctic Archipelago (22 grid points), and (5) Newfoundland (Canada; 1 grid point). - Climate. The gridded climatic data provided here are based on the 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and include annual and monthly temperature and precipitation. The CRU CL 2.0 data were interpolated onto the approximately 25-km grid using geographically-weighted regression, incorporating local lapse-rate estimation and correction. Additional bioclimatic variables (growing degree days on a 5 degrees Celsius base, mean temperatures of the coldest and warmest months, and a moisture index calculated as actual evapotranspiration divided by potential evapotranspiration) were calculated using the interpolated CRU CL 2.0 data. Also included are absolute minimum and maximum temperatures for 1951-1980 interpolated in a similar fashion from climate-station data (WeatherDisc Associates, 1989). These climate and bioclimate data were used in Atlas volumes F and G (see Thompson and others, 2015, for a description of the methods used to create the gridded climate data). Note that for grid points with missing elevation values (i.e., elevation values equal to 9999), climate data were created using an elevation value of -120 meters. Users may want to exclude these climate data from their analyses (see the Usage Notes section in the data release readme file). - Plant distributions. The gridded plant distribution data align with Atlas volume G (Thompson and others, 2015). Plant distribution data on the grid include 690 species, as well as 67 groups of related species and genera, and are based on U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977), regional atlases (e.g., Benson and Darrow, 1981), and new maps based on information available from herbaria and other online and published sources (for a list of sources, see Tables 3 and 4 in Thompson and others, 2015). See the "Notes" column in Table 1 (https://pubs.usgs.gov/pp/p1650-g/table1.html) and Table 2 (https://pubs.usgs.gov/pp/p1650-g/table2.html) in Thompson and others (2015) for important details regarding the species and grouped taxa distributions. - Ecoregions. The ecoregion gridded data are the same as in Atlas volumes D and E (Thompson and others, 2006, 2007), and include three different systems, Bailey's ecoregions (Bailey, 1997, 1998), WWF's ecoregions (Ricketts and others, 1999), and Kuchler's potential natural vegetation regions (Kuchler, 1985), that are each based on distinctive approaches to categorizing ecoregions. For the Bailey and WWF ecoregions for North America and the Kuchler potential natural vegetation regions for the contiguous United States (i.e.,

Data DescriptionWater Quality Parameters: Ammonia, BOD, DO, Orthophosphate, pH, Temperature, Nitrogen, Nitrate.Countries/Regions: United States, Canada, Ireland, England, China.Years Covered: 1940-2023.Data Records: 2.82 million.Definition of ColumnsCountry: Name of the water-body region.Area: Name of the area in the region.Waterbody Type: Type of the water-body source.Date: Date of the sample collection (dd-mm-yyyy).Ammonia (mg/l): Ammonia concentration.Biochemical Oxygen Demand (BOD) (mg/l): Oxygen demand measurement.Dissolved Oxygen (DO) (mg/l): Concentration of dissolved oxygen.Orthophosphate (mg/l): Orthophosphate concentration.pH (pH units): pH level of water.Temperature (°C): Temperature in Celsius.Nitrogen (mg/l): Total nitrogen concentration.Nitrate (mg/l): Nitrate concentration.CCME_Values: Calculated water quality index values using the CCME WQI model.CCME_WQI: Water Quality Index classification based on CCME_Values.Data Directory Description:Category 1: DatasetCombined Data: This folder contains two CSV files: Combined_dataset.csv and Summary.xlsx. The Combined_dataset.csv file includes all eight water quality parameter readings across five countries, with additional data for initial preprocessing steps like missing value handling, outlier detection, and other operations. It also contains the CCME Water Quality Index calculation for empirical analysis and ML-based research. The Summary.xlsx provides a brief description of the datasets, including data distributions (e.g., maximum, minimum, mean, standard deviation).Combined_dataset.csvSummary.xlsxCountry-wise Data: This folder contains separate country-based datasets in CSV files. Each file includes the eight water quality parameters for regional analysis. The Summary_country.xlsx file presents country-wise dataset descriptions with data distributions (e.g., maximum, minimum, mean, standard deviation).England_dataset.csvCanada_dataset.csvUSA_dataset.csvIreland_dataset.csvChina_dataset.csvSummary_country.xlsxCategory 2: CodeData processing and harmonization code (e.g., Language Conversion, Date Conversion, Parameter Naming and Unit Conversion, Missing Value Handling, WQI Measurement and Classification).Data_Processing_Harmonnization.ipynbThe code used for Technical Validation (e.g., assessing the Data Distribution, Outlier Detection, Water Quality Trend Analysis, and Vrifying the Application of the Dataset for the ML Models).Technical_Validation.ipynbCategory 3: Data Collection SourcesThis category includes links to the selected dataset sources, which were used to create the dataset and are provided for further reconstruction or data formation. It contains links to various data collection sources.DataCollectionSources.xlsxOriginal Paper Title: A Comprehensive Dataset of Surface Water Quality Spanning 1940-2023 for Empirical and ML Adopted ResearchAbstractAssessment and monitoring of surface water quality are essential for food security, public health, and ecosystem protection. Although water quality monitoring is a known phenomenon, little effort has been made to offer a comprehensive and harmonized dataset for surface water at the global scale. This study presents a comprehensive surface water quality dataset that preserves spatio-temporal variability, integrity, consistency, and depth of the data to facilitate empirical and data-driven evaluation, prediction, and forecasting. The dataset is assembled from a range of sources, including regional and global water quality databases, water management organizations, and individual research projects from five prominent countries in the world, e.g., the USA, Canada, Ireland, England, and China. The resulting dataset consists of 2.82 million measurements of eight water quality parameters that span 1940 - 2023. This dataset can support meta-analysis of water quality models and can facilitate Machine Learning (ML) based data and model-driven investigation of the spatial and temporal drivers and patterns of surface water quality at a cross-regional to global scale.Note: Cite this repository and the original paper when using this dataset.

The dataset contains a stratified survey of ecological and soil states at sites where fine scale patterns of covariation between vegetation and edaphic characteristics were recorded. Key data collection included leaf area index, moss and organic matter thickness, surface and deeper soil moisture. Data were collected at sites in the Yukon (2013) and Northwest Territories (2014), Canada. Full details about this dataset can be found at https://doi.org/10.5285/36f4e380-d01d-44a7-8321-7a677e6996b2

This data set includes microseismic and structural geological data collected at Aquistore (Canada). They cover a period from 26th April - 21st June 2015, during which CO2 was being injected in the Aquistore injection well at 3.5 km depth. The data were collected in the framework of a research project funded by UKCCSRC (EPSRC) and based at Aquistore in order to identify whether any microseismic events, that could be related to the CO2 injection, took place during this period and use of these events to image potential flowpathways at depth. The data were collected at a sampling rate of 1000Hz using a short-period microseismic array with a 25m aperture, consisting of one three-component and three one-component sensors (Lennartz, MKIII and MKII lite). The array was placed at 50cm depth, approximately 150m away from the injection well. Acquisition was continuous during the above period. The microseismic data are available in PASCAL or ASCII format. Full details on equipment used in data collection and data formats are available in the README file. Due to commercial constraints this dataset is currently under embargo until the end of 2017. Due to the large size of the dataset additional information and access requirements can be requested via the contact email supplied.

This dataset is a contribution to the development of a kelp distribution vector dataset. Bull kelp (Nereocystis leutkeana) and giant kelp (Macrocystis pyrifera) are important canopy-forming kelp species found in marine nearshore habitats on the West coast of Canada. Often referred to as a foundation species, beds of kelp form structural underwater forests that offer habitat for fishes and invertebrates. Despite its far-ranging importance, kelp has experienced a decline in the west coast of North America. The losses have been in response to direct harvest, increase in herbivores through the removal of predators by fisheries or diseases, increase in water turbidity from shoreline development as well as sea temperature change, ocean acidification, and increased storm activates. Understanding these impacts and the level of resilience of different kelp populations requires spatiotemporal baselines of kelp distribution.

The area covered by this dataset includes the BC coast and extends to portions of the Washington and Alaska coasts. This dataset was created using 137 British Admiralty (BA) charts, including insets, with scales ranging from 1:6,080 to 1:500,000, created between 1858 and 1956. All surveys were based on triangulation, in which a sextant or theodolite was used to determine latitude and angles, while a chronometer was used to help determine longitude.

First, each BA chart was scanned by the Canadian Hydrographic Service (CHS) using the CHS Colortrac large format scanner, and saved as a Tagged Image Format at 200 DPI, which was deemed sufficient resolution to properly visualize all the features of interest. Subsequently, the scanned charts were imported into ESRI ArcMap and georeferenced directly to WGS84 using CHS georeferencing standards and principles (charts.gc.ca). In order to minimize error, a hierarchy of control points was used, ranging from high survey order control points to comparing conspicuous stable rock features apparent in satellite imagery.

The georeferencing result was further validated against satellite imagery, CHS charts and fieldsheets, the CHS-Pacific High Water Line (charts.gc.ca), and adjacent and overlapping BA charts. Finally, the kelp features were digitized, and corresponding chart information (scale, chart number, title, survey start year, survey end year, and comments) was added as attributes to each feature.

Given the observed differences in kelp feature representation at different scales, when digitizing kelp features, polygons were used to represent the discrete observations, and as such, they represent presence of kelp and not kelp area. Polygons were created by tracing around the kelp feature, aiming to keep the outline close to the stipe and blades.

The accuracy of the location of the digitized kelp features was defined using a reliability criterion, which considers the location of the digitized kelp feature (polygon) in relation to the local depth in which the feature occurs. For this, we defined a depth threshold of 40 m to represent a low likelihood of kelp habitat in areas deeper than the threshold. An accuracy assessment of the digitized kelp features concluded that 99% of the kelp features occurred in expected areas within a depth of less than 40 m, and only about 1% of the features occurred completely outside of this depth. For more information, visit: https://open.canada.ca/data/en/dataset/bac83470-bc8f-4065-8ef3-bf76463c4ef2

Revolutionize Customer Engagement with Our Comprehensive Ecommerce Data

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Our Ecommerce Data is not just a standalone product; it is a critical piece of our broader data ecosystem. It seamlessly integrates with our comprehensive suite of business and consumer datasets, offering you a holistic approach to data-driven decision-making: - Tailored Packages: Choose customized data packages that meet your specific business needs, combining Ecommerce Data with other relevant datasets for a complete view of your market. - Holistic Insights: Whether you are looking for industry-specific details or a broader market overview, our integrated data solutions provide you with the insights necessary to stay ahead of the competition and make informed business decisions.

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Computer Servers Market Size 2024-2028

The computer servers market size is forecast to increase by USD 50.2 billion, at a CAGR of 10.05% between 2023 and 2028.

The market is experiencing significant growth, driven by escalating investments in the construction of hyperscale data centers. These data centers demand an increasing number of servers to accommodate the surging digital transformation and cloud computing adoption. Moreover, technological advancements in the market, such as the integration of artificial intelligence and machine learning capabilities, are fueling the demand for high-performance servers. However, this market landscape is not without challenges. Security concerns, particularly the threat of cyberattacks and data breaches, pose a significant obstacle. As businesses increasingly rely on servers to store sensitive information, ensuring robust security measures becomes crucial. Companies must prioritize implementing advanced security features and protocols to mitigate these risks and maintain customer trust. In summary, the market presents lucrative opportunities for growth, fueled by the digital transformation and technological advancements. However, companies must navigate the challenges, particularly the security concerns, to capitalize on these opportunities and maintain a competitive edge.

What will be the Size of the Computer Servers Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
Request Free SampleThe market continues to evolve, driven by the ever-increasing demand for advanced technology solutions across various sectors. AI workloads and network connectivity are key factors fueling this growth, with an emphasis on CPU cores and operating systems that can efficiently handle complex data processing tasks. Big data analytics and edge computing require significant power consumption, leading to innovations in thermal management and server procurement. Database servers and server management software play a crucial role in ensuring system performance and data security. Virtual machines and server virtualization enable businesses to optimize their data center infrastructure and reduce costs. Disaster recovery and server decommissioning are essential components of server lifecycle management, ensuring business continuity and reducing IT expenses. The ongoing evolution of server technology includes the adoption of ARM architecture and high availability features, as well as software-defined networking and high performance computing. Web servers and cloud computing have transformed the way businesses operate, while gaming servers cater to the growing demand for immersive entertainment experiences. Network attached storage and storage area networks provide the necessary capacity for managing and storing large volumes of data. Server uptime is a critical metric for businesses, and machine learning applications are being integrated into server systems to improve performance and efficiency. In the dynamic and ever-changing landscape of the market, these trends and patterns continue to unfold, shaping the future of technology solutions.

How is this Computer Servers Industry segmented?

The computer servers industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. End-userLarge enterprisesSmall and medium enterprisesGeographyNorth AmericaUSCanadaEuropeGermanyUKAPACChinaRest of World (ROW).

By End-user Insights

The large enterprises segment is estimated to witness significant growth during the forecast period.The market is witnessing significant growth, driven by the increasing demand for advanced IT infrastructure from large enterprises. These businesses, particularly in sectors such as IT, telecom, healthcare, banking, financial services, and insurance (BFSI), and defense, are investing heavily to manage escalating enterprise data volumes. Upgrading IT infrastructure offers numerous benefits, including enhanced storage capacity, improved security, and faster processing speeds for high-volume data. Network connectivity and server virtualization are crucial factors fueling this trend. The adoption of big data analytics, machine learning applications, and edge computing is driving the need for more powerful and efficient servers. Operating systems, such as Linux and Windows, continue to dominate the market, while database servers and application servers are essential components of modern IT infrastructure. Power consumption and thermal management are critical concerns for data center infrastructure, leading to the increasing popularity of x86 architecture, blade servers, and rackmount servers. High availability and disaster recovery s

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Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million, at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. This fusion enables organizations to derive deeper insights from their data, fueling business innovation and decision-making. Another trend shaping the market is the emergence of containerization and microservices in data science platforms. This approach offers enhanced flexibility, scalability, and efficiency, making it an attractive choice for businesses seeking to streamline their data science operations. However, the market also faces challenges. Data privacy and security remain critical concerns, with the increasing volume and complexity of data posing significant risks. Ensuring robust data security and privacy measures is essential for companies to maintain customer trust and comply with regulatory requirements. Additionally, managing the complexity of data science platforms and ensuring seamless integration with existing systems can be a daunting task, requiring significant investment in resources and expertise. Companies must navigate these challenges effectively to capitalize on the market's opportunities and stay competitive in the rapidly evolving data landscape.

What will be the Size of the Data Science Platform Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the increasing demand for advanced analytics and artificial intelligence solutions across various sectors. Real-time analytics and classification models are at the forefront of this evolution, with APIs integrations enabling seamless implementation. Deep learning and model deployment are crucial components, powering applications such as fraud detection and customer segmentation. Data science platforms provide essential tools for data cleaning and data transformation, ensuring data integrity for big data analytics. Feature engineering and data visualization facilitate model training and evaluation, while data security and data governance ensure data privacy and compliance. Machine learning algorithms, including regression models and clustering models, are integral to predictive modeling and anomaly detection. Statistical analysis and time series analysis provide valuable insights, while ETL processes streamline data integration. Cloud computing enables scalability and cost savings, while risk management and algorithm selection optimize model performance. Natural language processing and sentiment analysis offer new opportunities for data storytelling and computer vision. Supply chain optimization and recommendation engines are among the latest applications of data science platforms, demonstrating their versatility and continuous value proposition. Data mining and data warehousing provide the foundation for these advanced analytics capabilities.

How is this Data Science Platform Industry segmented?

The data science platform industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentOn-premisesCloudComponentPlatformServicesEnd-userBFSIRetail and e-commerceManufacturingMedia and entertainmentOthersSectorLarge enterprisesSMEsApplicationData PreparationData VisualizationMachine LearningPredictive AnalyticsData GovernanceOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.In the dynamic the market, businesses increasingly adopt solutions to gain real-time insights from their data, enabling them to make informed decisions. Classification models and deep learning algorithms are integral parts of these platforms, providing capabilities for fraud detection, customer segmentation, and predictive modeling. API integrations facilitate seamless data exchange between systems, while data security measures ensure the protection of valuable business information. Big data analytics and feature engineering are essential for deriving meaningful insights from vast datasets. Data transformation, data mining, and statistical analysis are crucial processes in data preparation and discovery. Machine learning models, including regression and clustering, are employed for model training and evaluation. Time series analysis and natural language processing are valuable tools for understanding trends and customer sen

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Alternative Data Market Size 2025-2029

The alternative data market size is forecast to increase by USD 60.32 billion, at a CAGR of 52.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increased availability and diversity of data sources. This expanding data landscape is fueling the rise of alternative data-driven investment strategies across various industries. However, the market faces challenges related to data quality and standardization. As companies increasingly rely on alternative data to inform business decisions, ensuring data accuracy and consistency becomes paramount. Addressing these challenges requires robust data management systems and collaboration between data providers and consumers to establish industry-wide standards. Companies that effectively navigate these dynamics can capitalize on the wealth of opportunities presented by alternative data, driving innovation and competitive advantage.

What will be the Size of the Alternative Data Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, with new applications and technologies shaping its dynamics. Predictive analytics and deep learning are increasingly being integrated into business intelligence systems, enabling more accurate risk management and sales forecasting. Data aggregation from various sources, including social media and web scraping, enriches datasets for more comprehensive quantitative analysis. Data governance and metadata management are crucial for maintaining data accuracy and ensuring data security. Real-time analytics and cloud computing facilitate decision support systems, while data lineage and data timeliness are essential for effective portfolio management. Unstructured data, such as sentiment analysis and natural language processing, provide valuable insights for various sectors. Machine learning algorithms and execution algorithms are revolutionizing trading strategies, from proprietary trading to high-frequency trading. Data cleansing and data validation are essential for maintaining data quality and relevance. Standard deviation and regression analysis are essential tools for financial modeling and risk management. Data enrichment and data warehousing are crucial for data consistency and completeness, allowing for more effective customer segmentation and sales forecasting. Data security and fraud detection are ongoing concerns, with advancements in technology continually addressing new threats. The market's continuous dynamism is reflected in its integration of various technologies and applications. From data mining and data visualization to supply chain optimization and pricing optimization, the market's evolution is driven by the ongoing unfolding of market activities and evolving patterns.

How is this Alternative Data Industry segmented?

The alternative data industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeCredit and debit card transactionsSocial mediaMobile application usageWeb scrapped dataOthersEnd-userBFSIIT and telecommunicationRetailOthersGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalyUKAPACChinaIndiaJapanRest of World (ROW)

By Type Insights

The credit and debit card transactions segment is estimated to witness significant growth during the forecast period.Alternative data derived from card and debit card transactions plays a pivotal role in business intelligence, offering valuable insights into consumer spending behaviors. This data is essential for market analysts, financial institutions, and businesses aiming to optimize strategies and enhance customer experiences. Two primary categories exist within this data segment: credit card transactions and debit card transactions. Credit card transactions reveal consumers' discretionary spending patterns, luxury purchases, and credit management abilities. By analyzing this data through quantitative methods, such as regression analysis and time series analysis, businesses can gain a deeper understanding of consumer preferences and trends. Debit card transactions, on the other hand, provide insights into essential spending habits, budgeting strategies, and daily expenses. This data is crucial for understanding consumers' practical needs and lifestyle choices. Machine learning algorithms, such as deep learning and predictive analytics, can be employed to uncover patterns and trends in debit card transactions, enabling businesses to tailor their offerings and services accordingly. Data governance, data security, and data accuracy are critical considerations when dealing with sensitive financial d

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Blockchain Technology In Healthcare Market Size 2024-2028

The blockchain technology in healthcare market size is forecast to increase by USD 8.03 billion at a CAGR of 58.96% between 2023 and 2028. Blockchain technology is revolutionizing the healthcare industry by offering enhanced security and transparency for medical records and patient histories. This technology enables secure data transactions through a decentralized system, ensuring that patient data is protected from financial losses due to theft or unauthorized access. The implementation of blockchain technology in healthcare can lead to revolutionary changes, including the creation of master patient indices for longitudinal records and an integrated workflow for seamless data exchange. The potential of the metaverse to create secure, great environments for healthcare applications is also being explored, enhancing patient engagement and data security. The merits of this technology extend beyond data security, as it also allows for informed consent and improved patient privacy management. The financial sector is also benefiting from blockchain technology through increased efficiency and reduced costs associated with traditional record-keeping methods. Overall, the adoption of blockchain technology in healthcare is a significant trend that is expected to continue, as the industry prioritizes data security and privacy while improving workflow and patient care.

What will be the Size of the Market During the Forecast Period?

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Blockchain technology, a digital ledger system, is revolutionizing various industries, including healthcare, by providing a secure and accountable method for recording and transferring data. This technology, which utilizes computers, blocks, and databases, operates on a network of interconnected nodes that maintain a permanent and unchangeable record of transactions. In the healthcare sector, blockchain technology offers significant advantages over traditional methods for managing data. With increasing concerns over healthcare data breaches and counterfeit drugs, the need for a secure and immutable system is paramount. Blockchain technology provides this security by creating a decentralized database that is resistant to data leaks and tampering.

One application of blockchain technology in healthcare is the secure exchange of sensitive information, such as vaccination certificates and medical records. By utilizing this technology, healthcare providers can ensure the authenticity and accuracy of these records, while patients maintain control over their data. Additionally, blockchain technology can be used to trace the origin of medications, preventing the distribution of counterfeit drugs and ensuring the integrity of the supply chain. Another area where blockchain technology can make a difference is in the management of medical devices and hospital equipment. By creating a digital ledger of device history, maintenance records, and ownership, healthcare facilities can ensure that all equipment is up-to-date and functioning properly.

Also, this not only improves patient safety but also reduces costs by eliminating the need for unnecessary repairs and replacements. Furthermore, blockchain technology can also be used to facilitate transactions with non-traditional suppliers, such as those in developing countries. By creating a secure and transparent system for recording and verifying transactions, blockchain technology can help to build trust and increase efficiency in global supply chains. In conclusion, blockchain technology is transforming the healthcare industry by providing a secure and accountable method for recording and transferring data. From preventing healthcare data breaches and drug counterfeiting to improving the management of medical devices and facilitating transactions with non-traditional suppliers, the benefits of this technology are vast.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Private
  Public
  Hybrid


End-user

  Pharmaceutical and medical device companies
  Healthcare payers
  Healthcare providers


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By Type Insights

The private segment is estimated to witness significant growth during the forecast period. The implementation of blockchain technology in the healthcare sector is gaining traction in the United States, as businesses seek to enhance data security and patient privacy. Blockchain technology, which utilizes cryptographic algorithms and independent computers, offers immutability and transparency, making it an attractive solution

Arctic Vegetation Archive - Canada: Canadian Western Arctic Vegetation Plots. The vegetation and soils of the Low Arctic Subalpine Zone of the Richardson and British Mountains of the Canadian Western Arctic were described by J. Lambert in 1968. The primary source document for this dataset is J. Lambert's Ph.D. thesis (1968) "The ecology and successional trends of tundra plant communities in the Low Arctic Subalpine Zone of the Richardson and British Mountains of the Canadian Western Arctic". Vegetation plot data were collected in June, July, and August of 1965 and July and August of 1966. Funding for the project was provided in part by President’s Committee on Arctic and Alpine Research from the University of British Columbia, and by a National Research Council Grant T-92 to V. J. Krajina. One hundred fifty-four plots were subjectively located in two main study areas, Canoe Lake and Divided Lake in the Richardson Mountains, Northwest Territories, and Trout Lake in the British Mountains, Yukon Territory. Plots were located in 14 broad plant communities and 6 broad habitat types including: a) Willow shrub vegetation of riparian areas and warm habitats (13 plots), 2) Sedge grass and dwarf shrub mire and fen vegetation (27 plots), 3) Bog vegetation, acidic mires including tussock tundra (32 plots), 4) Deep snowbed vegetation (20 plot), 5) Dry to moist dwarf-shrub heath and low shrub vegetation on acidic nutrient poor substrates (48 plots), and 6) Dry and mesic dwarf-shrub and graminoid vegetation on non-acidic substrates (14 plots). The cover of vascular and nonvascular plant species was originally recorded according to the cover abundance scale developed by Krajina (1933) but converted to the old Braun-Blanquet cover classes scale. Depending on the vegetation type, sample plots varied between 16, 25 and 100 square meters. Sample plots were not permanently marked and no specific location data were recorded. At each study plot, a soil pit was dug. Physical and chemical characteristics were determined for each recognizable soil horizon, however only summarized soil descriptions are available by community type. In addition, the depth of the active layer was recorded for most study plots. Photographs of the dominant plant associations are available but individual plot photos were not available. Other observations and data from his thesis are reported in J. Lambert (1972a) "Plant succession on tundra mudflows: preliminary observations" and J. Lambert (1972b) "Vegetation patterns in the Mackenzie Delta Area, Northwest Territories". References Lambert, J. D. H. 1968. The ecology and successional trends of tundra plant communities in the Low Arctic Subalpine Zone of the Richardson and British Mountains of the Canadian Western Arctic. Ph.D. thesis. University of British Columbia, Canada. Lambert, J. D. H. 1972a. Plant succession on tundra mudflows: preliminary observations. Arctic, 25: 99-106. Lambert, J. D. H. 1972b. Vegetation patterns in the Mackenzie Delta Area, Northwest Territories. In D. E. Kerfoot (Ed.), Mackenzie Delta Monograph, pp. 51-68. Brock University, St. Catharines, Ontario, Canada.