The data.zip dataset contains metadata and total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen concentration data and associated daily mean streamflow data for the White River at Muncie, near Nora, and near Centerton, Indiana, 1991-2017

Envestnet®| Yodlee®'s Bank Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

This Excel based tool was developed to analyze means-end chain data. The tool consists of a user manual, a data input file to correctly organise your MEC data, a calculator file to analyse your data, and instructional videos. The purpose of this tool is to aggregate laddering data into hierarchical value maps showing means-end chains. The summarized results consist of (1) a summary overview, (2) a matrix, and (3) output for copy/pasting into NodeXL to generate hierarchal value maps (HVMs). To use this tool, you must have collected data via laddering interviews. Ladders are codes linked together consisting of attributes, consequences and values (ACVs).

The Uniform Appraisal Dataset (UAD) Aggregate Statistics Data File and Dashboards are the nation’s first publicly available datasets of aggregate statistics on appraisal records, giving the public new access to a broad set of data points and trends found in appraisal reports. The UAD Aggregate Statistics for Enterprise Single-Family, Enterprise Condominium, and Federal Housing Administration (FHA) Single-Family appraisals may be grouped by neighborhood characteristics, property characteristics and different geographic levels.DocumentationOverview (10/28/2024)Data Dictionary (10/28/2024)Data File Version History and Suppression Rates (12/18/2024)Dashboard Guide (2/3/2025)UAD Aggregate Statistics DashboardsThe UAD Aggregate Statistics Dashboards are the visual front end of the UAD Aggregate Statistics Data File. The Dashboards are designed to provide easy access to customized maps and charts for all levels of users. Access the UAD Aggregate Statistics Dashboards here.UAD Aggregate Statistics DatasetsNotes:Some of the data files are relatively large in size and will not open correctly in certain software packages, such as Microsoft Excel. All the files can be opened and used in data analytics software such as SAS, Python, or R.All CSV files are zipped.

The Measurable AI Amazon Consumer Transaction Dataset is a leading source of email receipts and consumer transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan) - EMEA (Spain, United Arab Emirates) - Continental Europe - USA

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

The IUC ADC became the official source of IUC statistics in April 2021, when the NHS 111 Minimum Dataset (NHS 111 MDS) was merged into a revised version of the IUC ADC. Since then, a provisional subset of the IUC ADC data is published in the month after the collection end date (eg, April data published in May), with the complete monthly IUC ADC published as Official Statistics the following month (eg, April data published in June). The IUC ADC specification is reviewed and updated annually which means not all data items will be directly comparable with the same data items collected in the previous year. The IUC ADC is used to monitor the IUC KPIs. This data is published on the NHS England website. Please follow the link below.

The Measurable AI Rappi alternative Dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our alternative data to produce actionable consumer insights for use cases such as: - User overlap between players - Market share analysis - User behavioral traits (e.g. retention rates, spending patterns) - Average order values - Promotional strategies used by the key players - Items ordered (SKU level data) Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - LATAM (Brazil, Mexico, Argentina, Colombia, Chile)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more - MAIDs

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from the Rappi food delivery app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact michelle@measurable.ai for a data dictionary and to find out our volume in each country.

Topographic databases normally contain areas of different land cover classes, commonly defining a planar partition, that is, gaps and overlaps are not allowed. When reducing the scale of such a database, some areas become too small for representation and need to be aggregated. This unintentionally but unavoidably results in changes of classes. In this article we present an optimisation method for the aggregation problem. This method aims to minimise changes of classes and to create compact shapes, subject to hard constraints ensuring aggregates of sufficient size for the target scale. To quantify class changes we apply a semantic distance measure. We give a graph theoretical problem formulation and prove that the problem is NP-hard, meaning that we cannot hope to find an efficient algorithm. Instead, we present a solution by mixed-integer programming that can be used to optimally solve small instances with existing optimisation software. In order to process large datasets, we introduce specialised heuristics that allow certain variables to be eliminated in advance and a problem instance to be decomposed into independent sub-instances. We tested our method for a dataset of the official German topographic database ATKIS with input scale 1:50,000 and output scale 1:250,000. For small instances, we compare results of this approach with optimal solutions that were obtained without heuristics. We compare results for large instances with those of an existing iterative algorithm and an alternative optimisation approach by simulated annealing. These tests allow us to conclude that, with the defined heuristics, our optimisation method yields high-quality results for large datasets in modest time.

The 2006 Second Edition TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER database. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on the latest available governmental unit boundaries. The Census TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The 2006 Second Edition TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. This shapefile represents the current State House Districts for New Mexico as posted on the Census Bureau website for 2006.

Political scientists use expert surveys to assess latent features of political actors. Experts, though, are unlikely to be equally informed and assess all actors equally well. The literature acknowledges variance in measurement quality, but pays little attention to the implications of uncertainty for aggregating responses. We discuss the nature of the measurement problem in expert surveys. We then propose methods to assess the ability of experts to judge where actors stand and to aggregate expert responses. We examine the effects of aggregation for a prominent survey in the literature on party politics and EU integration. Using a Monte Carlo simulation, we demonstrate that it is better to aggregate expert responses using the median or modal response, rather than the mean.

The Measurable AI UberEats E-Receipt Dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Taiwan, Japan, Australia) - Americas (United States, Mexico, Chile) - EMEA (United Kingdom, France, Italy, United Arab Emirates, AE, South Africa)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from the UberEats food delivery app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

The 2006 Second Edition TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER database. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on the latest available governmental unit boundaries. The Census TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The 2006 Second Edition TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. This shapefile represents the current State House Districts for New Mexico as posted on the Census Bureau website for 2006.

Abstract Background Clinical prediction models (CPMs) are increasingly deployed to support healthcare decisions but they are derived inconsistently, in part due to limited data. An emerging alternative is to aggregate existing CPMs developed for similar settings and outcomes. This simulation study aimed to investigate the impact of between-population-heterogeneity and sample size on aggregating existing CPMs in a defined population, compared with developing a model de novo. Methods Simulations were designed to mimic a scenario in which multiple CPMs for a binary outcome had been derived in distinct, heterogeneous populations, with potentially different predictors available in each. We then generated a new â localâ population and compared the performance of CPMs developed for this population by aggregation, using stacked regression, principal component analysis or partial least squares, with redevelopment from scratch using backwards selection and penalised regression. Results While redevelopment approaches resulted in models that were miscalibrated for local datasets of less than 500 observations, model aggregation methods were well calibrated across all simulation scenarios. When the size of local data was less than 1000 observations and between-population-heterogeneity was small, aggregating existing CPMs gave better discrimination and had the lowest mean square error in the predicted risks compared with deriving a new model. Conversely, given greater than 1000 observations and significant between-population-heterogeneity, then redevelopment outperformed the aggregation approaches. In all other scenarios, both aggregation and de novo derivation resulted in similar predictive performance. Conclusion This study demonstrates a pragmatic approach to contextualising CPMs to defined populations. When aiming to develop models in defined populations, modellers should consider existing CPMs, with aggregation approaches being a suitable modelling strategy particularly with sparse data on the local population.

This dataset consists of Particle Size Distribution (PSD) measurements made on 419 archived topsoil samples and derived aggregate stability metrics from arable and grassland habitats across Great Britain in 2007. Laser granulometry was used to measure PSD of 1–2 mm aggregates before and after sonication and the difference in their Mean Weight Diameter (MWD) used to indicate aggregate stability. The samples were collected as part of the Countryside Survey monitoring programme, a unique study or ‘audit’ of the natural resources of the UK’s countryside. The analyses were conducted as part of study aiming to quantify how soil quality indicators change across a gradient of agricultural land management and to identify conditions that determine the ability of different soils to resist and recover from perturbations.

Romania Other MFIs: Assets: Aggregate: Domestic: Cash & Other Payment Means data was reported at 10,590.851 RON mn in Sep 2018. This records a decrease from the previous number of 10,869.273 RON mn for Aug 2018. Romania Other MFIs: Assets: Aggregate: Domestic: Cash & Other Payment Means data is updated monthly, averaging 3,905.093 RON mn from Jan 2007 (Median) to Sep 2018, with 141 observations. The data reached an all-time high of 11,274.998 RON mn in Jan 2018 and a record low of 2,477.494 RON mn in Feb 2007. Romania Other MFIs: Assets: Aggregate: Domestic: Cash & Other Payment Means data remains active status in CEIC and is reported by National Bank of Romania. The data is categorized under Global Database’s Romania – Table RO.KB017: Balance Sheet: Other Monetary Financial Institutions.

Pay-Per-Use Lounge Aggregator Platform Market Outlook

According to our latest research, the global pay-per-use lounge aggregator platform market size reached USD 1.26 billion in 2024, reflecting a strong demand for flexible and accessible premium travel experiences. The market is advancing at a robust CAGR of 12.7% and is forecasted to attain USD 3.75 billion by 2033. This impressive growth trajectory is propelled by evolving traveler expectations, technological advancements, and the increasing penetration of digital platforms in the travel and hospitality industry. The rising preference for on-demand services and the shift toward digitalized travel experiences are significant contributors to the expansion of the pay-per-use lounge aggregator platform market.

One of the primary growth factors driving the pay-per-use lounge aggregator platform market is the transformation of consumer behavior in the travel sector. Modern travelers, both leisure and business, are increasingly seeking flexibility, convenience, and premium experiences without the constraints of traditional lounge memberships or elite status requirements. The proliferation of aggregator platforms has democratized lounge access, enabling a broader segment of travelers to purchase lounge services on an as-needed basis. Additionally, the integration of these platforms with airline and travel agency systems has streamlined the booking process, further enhancing user adoption. The rising frequency of global travel, coupled with the growing middle-class population in emerging markets, is also fueling demand for accessible and comfortable pre-boarding environments.

Technological innovation plays a pivotal role in the expansion of the pay-per-use lounge aggregator platform market. The widespread adoption of smartphones and the increasing reliance on mobile applications for travel-related services have significantly contributed to the market’s growth. Aggregator platforms leverage advanced technologies such as artificial intelligence, real-time data analytics, and personalized recommendations to deliver seamless user experiences. These platforms not only simplify the process of locating and booking lounges but also offer value-added services such as digital payments, loyalty integration, and tailored promotions. As digital transformation continues to reshape the travel industry, aggregator platforms are well-positioned to capitalize on evolving consumer expectations and drive sustained market growth.

Another key driver of market growth is the strategic partnerships and collaborations between lounge operators, airlines, and technology providers. By forming alliances with a diverse range of stakeholders, aggregator platforms can expand their service offerings and geographic reach, ensuring that travelers have access to a wide selection of lounges across airports, railway stations, and bus terminals. These collaborations also enable platforms to offer exclusive deals and bundled services, enhancing customer loyalty and retention. Furthermore, the increasing focus on enhancing customer experience and differentiating offerings in a competitive market landscape is prompting lounge operators to embrace aggregator platforms as a means to attract new customer segments and optimize capacity utilization.

From a regional perspective, Asia Pacific is emerging as a dominant force in the pay-per-use lounge aggregator platform market, driven by rapid urbanization, rising disposable incomes, and a burgeoning travel industry. North America and Europe continue to be significant markets, characterized by high levels of business and leisure travel and a mature digital ecosystem. Meanwhile, the Middle East & Africa and Latin America are witnessing steady growth, supported by infrastructure development and increasing international connectivity. The regional dynamics are influenced by factors such as travel frequency, digital adoption rates, and the presence of established lounge networks, shaping the competitive landscape and growth opportunities for aggregator platforms globally.

Service Type Analysis

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Data set presented in publication 'Assessment of aggregate mixture reactivity in concrete at 60°C' Structure and Environment 2024, vol. 16, (3), pp. 153-157'Data_set_expansion': Length changes measurements (expansion) of concrete beams in Miniature Concrete Prism Test (AASHTO T 380). Tests were performed on 6 aggregate mixtures (SB, SW, ST, AB, AX, AW) stored in molar sodium hydroxide solution at 60°C for 84 days. Measurements were performed on dilatometer design in IPPT PAN (patent WUP 08/2022). Measurements were made on 3 samples, the length change was calculated in acc. to AASHTO T 380. The expansion results (mean value of the length change) are presented in Fig. 1 in the above publication.‘Data_set_compressive_strength': Compressive strength measurements on 50 mm cubic samples cut from the concrete beams. The samples stored in 1 molar sodium hydroxide solution (1 M NaOH) at 60°C and water at 20°C for 84 days. Compressive strength was performed on at least 3 samples (with load rate 2400 N/s) using concrete compression machine Controls AUTOMAX MULTITEST.‘Data_set_modulus’: The resonant modulus of elasticity of the concrete was measured with the impulse excitation technique using a GrindoSonic MK5 device with a piezoelectric detector on the concrete beams stored in 1 molar sodium hydroxide solution (1 M NaOH) at 60°C and water at 20°C for 84 days. Presented data contains mean values for a concrete beam calculated in software WINEMOD - Version 2.05.

Symbionts tend to be aggregated on their hosts, such that few hosts harbor the majority of symbionts. This ubiquitous pattern can result from stochastic processes, but aggregation patterns may also depend on the type of host-symbiont interaction, plus traits that affect host exposure and susceptibility to symbionts. Untangling how aggregation patterns both within and among populations depend on stochastic processes, interaction type and host traits remains an outstanding challenge. Here, we address this challenge by using null models to compare aggregation patterns in a neutral system of Balanomorpha barnacles attached to patellid limpets and a host-parasite system of Trinidadian guppies (Poecilia reticulata) and their Gyrodactylus spp. monogeneans. We first used a model to predict patterns of symbiont-host aggregation due to random partitioning of symbionts to hosts. This null model accurately predicted the aggregation of barnacles on limpets, but the degree of aggregation varied across 303 quadrats. Quadrats with larger limpets had less aggregated barnacles, whereas aggregation increased with variation in limpet size. Across 84 guppy populations, Gyrodactylus spp. parasites were significantly less aggregated than predicted by the null model. As in the neutral limpet-barnacle system, aggregation decreased with mean host size. Parasites were also significantly less aggregated on males than females because male guppies tended to have higher prevalence and lower parasite burdens than predicted by the null model. Together, these results suggest stochastic processes can explain aggregation patterns in neutral but not parasitic systems, though in both systems host traits affect aggregation patterns. Because the distribution of symbionts on hosts can affect symbiont evolution via intraspecific interactions, and reciprocally host behavior and evolution via host-symbiont interactions, identifying the drivers of aggregation enriches our understanding of host-symbiont interactions.

Simulated rimed aggregates generated with https://github.com/jleinonen/aggregation in setting "aggregation followed by riming".

Aggregates were built from between 10 to 700 monomer crystals of columns, dendrites, needles, plates or rosettes with mean sizes of 100 or 200 micrometer. Then they were exposed to ELWP = 2.0 kg m⁻². Monomer crystals are composed of cubical elements with resolution 20 micrometer. Frozen rime droplets are also represented by 20 micrometer cubes.

The data set contains folders with evolution (evol) and shape files for each monomer crystal type. For each particle one evolution and one corresponding shape file exists. The evolution (evol) file contains particle mass, rime mass, area, size, fall speed (Heymsfield&Westbrook, 2010), fall speed (Khvorostyanov&Curry, 2005) for each step during the aggregation and riming process. The corresponding shape file contains the x,y,z positions of the cubical elements that compose the particle for each step. For further documentation see readme.