This presentation guides you through satellite data sources. It compares Landsat images to SPOT images.

*The distribution of patients flowing from nmCRPC to mCRPC that has not been treated with or not progressed on chemotherapy was determined based on Oudard et al 2009 [23].PSA, prostate-specific antigen; nmCRPC, non-metastatic castration-resistant prostate cancer; mCRPC, metastatic castration-resistant prostate cancer; NA, not applicable.Data sources used to determine the hazard rates for progression-free survival and overall survival associated with each clinical state, and the survival estimates derived from these publications for inclusion into the model.

This presentation examines the wealth of user guides and documentation available on the Statistics Canada web site. The metadata is not necessarily centralized and various locations for finding the documentation are explored.

Our gaming industry data solutions leverage data from major gaming platforms like Playstation, Xbox, and Steam to provide businesses with insights into the global gaming market. We collect data on the number of hours played for specific games, as well as monthly trends in player behavior and preferences.

Doorda's UK Health Data provides a comprehensive database covering 1.8M postcodes sourced from 20 data sources, offering unparalleled insights for local area health insights and analytics purposes.

Volume and stats: - 1.8M Postcodes - UK Coverage - Age and Gender bands

Our Health Data offers a multitude of use cases: - Market Analysis - Geodemographic Insights - Risk Management - Location Planning

The key benefits of leveraging our Health Data include: - Data Accuracy - Informed Decision-Making - Competitive Advantage - Efficiency - Single Source

Covering a wide range of industries and sectors, our data empowers organisations to make informed decisions, uncover market trends, and gain a competitive edge in the UK market.

Market Analysis of Internet Financial Data Terminal Services The global market for Internet financial data terminal services is projected to reach a valuation of XXX million by 2033, expanding at a CAGR of XX%. The surge in demand for real-time financial data, the proliferation of online trading platforms, and the growing adoption of cloud-based solutions drive market growth. The segment of institutional investors holds a dominant market share due to their need for comprehensive data for investment decision-making. Mobile versions of financial data terminals are gaining traction, providing investors with access to market information on the go. Key trends shaping the market include the integration of artificial intelligence (AI) for data analysis and visualization, the increasing adoption of open-source platforms, and the growing focus on data security. Major players in the market include Bloomberg, Refinitiv, FactSet, S&P, and Moody's Analytics. The Asia-Pacific region is expected to experience the fastest growth due to the rapid expansion of the financial industry in emerging economies like China and India. However, stringent data privacy regulations and competition from free data sources pose challenges to market players.

With our analytics tools, businesses can make data-driven decisions to enhance their operations and stay ahead of the competition. Our analytics tools help businesses gain insights into market trends, identify investment opportunities, and optimize their marketing efforts.

Whether you are a real estate developer, a property investor, or a financial institution, our real estate data sources and analytics can help you gain a competitive edge in the market.

Sources: Idealista IT KnightFrank rightmove Biura Inmuebles24 Sreality propestar habitaclia.com Iroda Findboliger.dk Immoweb Homegate Zimmo Funda.nl (RENT) fotocasa Funda.nl Comparis.ch Google Maps Flexioffices leboncoin idealista ES Remax.pl Realting Kyero parisattitude Finn.no immoscout24.de Immobilier.ch Jaap.nl Immo-vlan SeLoger Booli.se Immowelt Realla idealista (rent) Hemnet.se Home.ch Boliga.dk Instant Office UK idealista PT pisos.com domy.pl

— Europe-wide uniform web map — 14 predefined detailing levels — 2 different display areas: Europe-wide for small and medium scales, detail for Germany and neighbouring countries.

— generated in UTM32 (EPSG:25832) — also available in other common projections via the WMS interface

Combination of... ... free official geodata of the federal government ... free and non-free official geodata of the AdV

... free official geodata from EuroGeographics

... open or free non-official data sources (e.g. OSM, Natural Earth)

— Data sources see: http://sg.geodatenzentrum.de/web_public/Datenquellen_TopPlus_Open.pdf

— License: “Data License Germany — Attribution — Version 2.0”

Maximize your online sales potential with our e-commerce data and analytics solutions. Our comprehensive suite of data sources includes real-time information on market trends, consumer behavior, and product pricing. With our advanced analytics tools, you can unlock the power of data-driven insights to optimize your online sales strategy, improve customer engagement, and drive revenue growth.

Whether you want to identify new opportunities, streamline your operations, or stay ahead of the competition, our e-commerce data and analytics product can help you achieve your goals.

Sources: Cubus Official COS Boozt BIK BOK AS Royal Design Group Holding AB Bagaren och Kocken AB Rum21 Svenskt Tenn Kökets favoriter lannamobler.se KWA Garden furniture Confident Living Stalands Möbler Trendrum AB Svenssons Nordiska Galleriet Jotex Jollyroom Monki New Bubbleroom Sweden AB Wegot KitchenTime AB Lindex NA-KD.com Olsson & Gerthel Nordic Nest Bonprix Nederland Vero Moda Care of Carl Cervera Zoovillage ARKET Kappahl DesignTorget Mio AB Afound

The ever-increasing demand for electricity has presented a grave threat to traditional energy sources, which are finite, rapidly depleting, and have a detrimental environmental impact. These shortcomings of conventional energy resources have caused the globe to switch from traditional to renewable energy sources. Wind power significantly contributes to carbon-free energy because it is widely accessible, inexpensive, and produces no harmful emissions. Better and more efficient renewable wind power production relies on accurate wind speed predictions. Accurate short-term wind speed forecasting is essential for effectively handling unsteady wind power generation and ensuring that wind turbines operate safely. The significant stochastic nature of the wind speed and its dynamic unpredictability makes it difficult to forecast. This paper develops a hybrid model, L-LG-S, for precise short-term wind speed forecasting to address problems in wind speed forecasting. In this research, state-of-the-art machine learning and deep learning algorithms employed in wind speed forecasting are compared with the proposed approach. The effectiveness of the proposed hybrid model is tested using real-world wind speed data from a wind turbine located in the city of Karachi, Pakistan. Moreover, the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) are used as accuracy evaluation indices. Experimental results show that the proposed model outperforms the state-of-the-art legacy models in terms of accuracy for short-term wind speed in training, validation and test predictions by 98% respectively.

Databases of organismal traits that aggregate information from one or multiple sources can be leveraged for large-scale analyses in biology. Yet the differences among these data streams and how well they capture trait diversity have never been explored. We present the first analysis of the differences between phenotypes captured in free text of descriptive publications (‘monographs’) and those used in phylogenetic analyses (‘matrices’). We focus our analysis on osteological phenotypes of the limbs of four extinct vertebrate taxa critical to our understanding of the fin-to-limb transition. We find that there is low overlap between the anatomical entities used in these two sources of phenotype data, indicating that phenotypes represented in matrices are not simply a subset of those found in monographic descriptions. Perhaps as expected, compared to characters found in matrices, phenotypes in monographs tend to emphasize descriptive and positional morphology, be somewhat more complex, and relate to fewer additional taxa. While based on a small set of focal taxa, these qualitative and quantitative data suggest that either source of phenotypes alone will result in incomplete knowledge of variation for a given taxon. As a broader community develops to use and expand databases characterizing organismal trait diversity, it is important to recognize the limitations of the data sources and develop strategies to more fully characterize variation both within species and across the tree of life.

QGIS 3 map of Eaton County, Michigan, USA with:ParcelsBuilding FootprintsSite Address PointsPolling PlacesCounty DistrictsControl CornersTownshipsSectionsGeopolitical AreasRoadsFlowlinesCounty DrainsWaterbodiesCountyAerial 2015 map service * The data in the map is stored in a geopackage called "geodata.gpkg" which should be kept in the same folder as the map "OpenData.qgz" in order to maintain the map's connectivity to the data sources. You will need the free GIS software QGIS installed to view this map. It's available at https://qgis.org

This folder includes the shapefiles for the 10 validation countries included in the manuscript. Abstract: The study of population health through network science holds high promise, but data sources that allow complete representation of populations are limited in low- and middle-income settings. Large national health surveys designed to gather nationally representative health and development data in low- and middle-income countries are promising sources of such data. Although they provide researchers, healthcare providers, and policymakers with valuable information, they are not designed to produce small-area estimates of health indicators, and the methods for producing these tend to rely on diverse and imperfect covariate data sources, have high data input requirements and are computationally demanding, limiting their use for network representations of populations. To reduce the sources of measurement error and allow efficient multi-country representation of populations as networks of human settlements here, we present a covariate-free multi-country method to estimate small-area health indicators using standardized georeferenced surveys. The approach utilizes interpolation via local inverse distance weighting. The estimates are compared to those obtained using a Bayesian Geostatistical Model and have been cross-validated. The estimates are aggregated into population settlements and identified using the Global Human Settlement Layer database. The method is fully automated, requiring a single standard georeferenced survey data source for mapping populations, eliminating the need for indicator or country-specific covariate selection by investigators. Efficient estimation is achieved by only computing values for human-occupied areas and adopting a logical aggregation of estimates into the complete range of settlement sizes. An open-access library of standardized georeferenced settlement-level datasets for 15 indicators and 10 countries was validated in this paper, as well as the code used to identify settlements and estimate indicators. The datasets are intended to be used as the basis for population health studies, and the library will continue to be expanded. The novel aspects include using harmonized input sources and estimation procedures across countries and the adoption of real-world units for population data aggregation, creating a specialized library of nodes that serve as a basis for network representations of population health in low- and middle-income countries.

The prepared Longitudinal IntermediaPlus dataset 2014 to 2016 is a ´big data´, which is why the entire dataset will only be available in the form of a database (MySQL). In this database, the information of different variables of a respondent is organized in one column, one row per variable. The present data documentation shows the total database for online media use of the years 2014 to 2016. The data contains all variables of socio demography, free-time activities, additional information on a respondent and his household as well as the interview-specific variables and weights. Only the variables concerning the respondent´s media use are a selection: The online media use of all full online as well as their single entities for all genres whose business model is the provision of content is included - e-commerce, games, etc. were excluded. The media use of radio, print and TV is not included. Preparation for further years is possible, as is the preparation of cross-media media use for radio, press media and TV. Harmonization is available for radio and press media up to 2015 waiting to be applied. The digital process chain developed for data preparation and harmonization is published at GESIS and available for further projects updating the time series for further years. Recourse to these documents - Excel files, scripts, harmonization plans, etc. - is strongly recommended. The process and harmonization for the Longitudinal IntermediaPlus for 2014 to 2016 database was made available in accordance with the FAIR principles (Wilkinson et al. 2016). By harmonizing and pooling the cross-sectional datasets to one longitudinal dataset – which is being carried out by Inga Brentel and Céline Fabienne Kampes as part of the dissertation project ´Audience and Market Fragmentation online´ –, the aim is to make the data source of the media analysis, accessible for research on social and media change in Germany.

— uniform topographic presentation graphic on a scale of 1:10,000 — georeferenced square grid tiles in print resolution (200 pixels/cm, 508 dpi) — for printing and use in geoinformation systems

— generated in UTM32 (EPSG:25832)

— also available in other common projections via the WMS interface

Combination of...

... free official geodata of the federal government

... free official spatial data of the open data countries Berlin, Hamburg, North Rhine-Westphalia, Thuringia, Brandenburg, Saxony and Mecklenburg-Western Pomerania

... non-free official spatial data of Rhineland-Palatinate (within the framework of a cooperation agreement)

... open or free non-official data sources (e.g. OSM, Natural Earth)

— Data sources see: http://sg.geodatenzentrum.de/web_public/Datenquellen_TopPlus_Open.pdf

— License: “Data License Germany — Attribution — Version 2.0”

Presentation service (WMS) for a freely usable worldwide uniform web map based on free and official data sources.In the product, among other things, free official geodata of the federal government and the open data countries Berlin, Brandenburg, Hamburg, North Rhine-Westphalia, Saxony and Thuringia are presented. In addition, Mecklenburg-Western Pomerania and Rhineland-Palatinate provide their official spatial data for the TopPlusOpen within the framework of a cooperation agreement, so that these countries are also represented exclusively by official data.In the other federal states and abroad, OSM data is mainly used in the corresponding zoom levels, which from the point of view of the BKG meet all quality requirements and can be combined almost seamlessly with the official data.The web services of the TopPlusOpen are offered via the standardized interfaces WMS and WMTS and are high-performance.There are 4 different variants offered: - TopPlusOpen: Very detailed map display in solid colors - TopPlusOpen grayscale: Content identical to the full-tone version; Automatically generated grayscale - TopPlusOpen Light: Content reduced compared to the full-tone version; Subtle colour scheme - TopPlusOpen Light Grey: Content identical to the TopPlusOpen Light; Presentation in shades of grey and individual discreet colors (waters, borders)The TopPlusOpen web map is produced in two projections: - Pseudo-Mercator projection (EPSG:3857) - UTM32 (EPSG:25832)Pseudo-Mercator projection: The web map has 19 scale levels in this projection and is divided into three different display areas: - Worldwide representation for small scales - Europe-wide representation for medium scales - Detailed representation for Germany and the adjacent foreign countriesProjection UTM32: The web map has 14 scale levels in this projection and is divided into two display areas: - Europe-wide representation for medium scales - Detailed representation for Germany and neighbouring countries:Representation service (WMS) for a freely usable worldwide uniform web map based on free and official data sources.In the product, among other things, free official geodata of the federal government and the open data countries Berlin, Brandenburg, Hamburg, North Rhine-Westphalia, Saxony and Thuringia are presented. In addition, Mecklenburg-Western Pomerania and Rhineland-Palatinate provide their official spatial data for the TopPlusOpen within the framework of a cooperation agreement, so that these countries are also represented exclusively by official data.In the other federal states and abroad, OSM data is mainly used in the corresponding zoom levels, which from the point of view of the BKG meet all quality requirements and can be combined almost seamlessly with the official data.The web services of the TopPlusOpen are offered via the standardized interfaces WMS and WMTS and are high-performance.There are 4 different variants offered: - TopPlusOpen: Very detailed map display in solid colors - TopPlusOpen grayscale: Content identical to the full-tone version; Automatically generated grayscale - TopPlusOpen Light: Content reduced compared to the full-tone version; Subtle colour scheme - TopPlusOpen Light Grey: Content identical to the TopPlusOpen Light; Presentation in shades of grey and individual discreet colors (waters, borders)The TopPlusOpen web map is produced in two projections: - Pseudo-Mercator projection (EPSG:3857) - UTM32 (EPSG:25832)Pseudo-Mercator projection: The web map has 19 scale levels in this projection and is divided into three different display areas: - Worldwide representation for small scales - Europe-wide representation for medium scales - Detailed representation for Germany and the adjacent foreign countriesProjection UTM32: The web map has 14 scale levels in this projection and is divided into two display areas: - Europe-wide presentation for medium scales - detailed presentation for Germany and neighbouring countries

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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

Internal validation of the COVID-19 biosurveillance system relative to the aggregated UTOPIAN lab text, health condition diagnosis text and clinical note data sources (N = 6000 independent free text documents).