Envestnet®| Yodlee®'s Online Shopping 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
Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)
Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence
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 file provides summary or aggregated measures for the 82 societies participating in the first four waves of the World Value Surveys. Thus, the society, rather than the individuals surveyed, are the unit of analysis.
"The World Values Survey is a worldwide investigation of sociocultural and political change. It is conducted by a network of social scientists at leading universities all around world.
Interviews have been carried out with nationally representative samples of the publics of more than 80 societies on all six inhabited continents. A total of four waves have been carried out since 1981 making it possible to carry out reliable global cross-cultural analyses and analysis of changes over time. The World Values Survey has produced evidence of gradual but pervasive changes in what people want out of life. Moreover, the survey shows that the basic direction of these changes is, to some extent, predictable.
This project is being carried out by an international network of social scientists, with local funding for each survey (though in some cases, it has been possible to raise supplementary funds from outside sources). In exchange for providing the data from interviews with a representative national sample of at least 1,000 people in their own society, each participating group gets immediate access to the data from all of the other participating societies. Thus, they are able to compare the basic values and beliefs of the people of their own society with those of more than 60 other societies. In addition, they are invited to international meetings at which they can compare findings and interpretations with other members of the WVS network."
MIT Licensehttps://opensource.org/licenses/MIT
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The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects Sign up for the gnomAD mailing list here. This dataset was derived from summary data from gnomAD release 3.1, available on the Registry of Open Data on AWS for ready enrollment into the Data Lake as Code.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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A practical aggregation method for heterogeneous log-linear functions is presented. Inequality measures are employed in the construction of a simple but exact aggregate representation of an economy. Three macroeconomic applications are discussed: the aggregation of the Lucas supply function, the time-inconsistent behaviour of an egalitarian social planner facing heterogeneous discount rates, and the case of a simple heterogeneous growth model. In the latter application, aggregate CPS data is used to show that the slowdown that followed the first oil shock is worse than usually thought, and that the new economy growth resurgence is not as strong as it appears. The reaction of one man could be forecast by no known mathematics; the reaction of a billion is something else again.?Foundation and Empire, Isaac Asimov (1952)
Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
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This repository contains the data related to the paper ** "Granulometry transformer: image-based granulometry of concrete aggregate for an automated concrete production control" ** where a deep learning based method is proposed for the image based determination of concrete aggregate grading curves (cf. video).
More specifically, the data set consists of images showing concrete aggregate particles and reference data of the particle size distribution (grading curves) associated to each image. It is distinguished between the CoarseAggregateData and the FineAggregateData.
The coarse data consists of aggregate samples with different particles sizes ranging from 0.1 mm to 32 mm. The grading curves are designed by linearly interpolation between a very fine and a very coarse distribution for three variants with maximum grain sizes of 8 mm, 16 mm, and 32 mm, respectively. For each variant, we designed eleven grading curves, resulting in a total number 33, which are shown in the figure below. For each sample, we acquired 50 images with a GSD of 0.125 mm, resulting in a data set of 1650 images in total. Example images for a subset of the grading curves of this data set are shown in the following figure.
https://data.uni-hannover.de/dataset/ecb0bf04-84c8-45b1-8a43-044f3f80d92c/resource/8cb30616-5b24-4028-9c1d-ea250ac8ac84/download/examplecoarse.png" alt="Example images and grading curves of the coarse data set" title=" ">
Similar to the previous data set, the fine data set contains grading curves for the fine
fraction of concrete aggregate of 0 to 2 mm with a GSD of 28.5 $\mu$m.
We defined two base distributions of different shapes for the upper and lower bound, respectively, resulting in two interpolated grading curve sets (Set A and Set B). In total, 1700
images of 34 different particle size distributions were acquired. Example images of the data set and the corresponding grading curves are shown in the figure below.
https://data.uni-hannover.de/dataset/ecb0bf04-84c8-45b1-8a43-044f3f80d92c/resource/c56f4298-9663-457f-aaa7-0ba113fec4c9/download/examplefine.png" alt="Example images and grading curves of the finedata set" title=" ">
If you make use of the proposed data, please cite.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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Sample output from the Aggregate_Builder_Constant_Population simulation. This data can be used to create the figures in the paper.
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.
Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:
See the Splitgraph documentation for more information.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Data and Inferred Networks accompanying the manuscript entitled - “Aggregation of recount3 RNA-seq data improves the inference of consensus and context-specific gene co-expression networks”
Authors: Prashanthi Ravichandran, Princy Parsana, Rebecca Keener, Kaspar Hansen, Alexis Battle
Affiliations: Johns Hopkins University School of Medicine, Johns Hopkins University Department of Computer Science, Johns Hopkins University Bloomberg School of Public Health
Description:
This folder includes data produced in the analysis contained in the manuscript and inferred consensus and context-specific networks from graphical lasso and WGCNA with varying numbers of edges. Contents include:
all_metadata.rds: File including meta-data columns of study accession ID, sample ID, assigned tissue category, cancer status and disease status obtained through manual curation for the 95,484 RNA-seq samples used in the study.
all_counts.rds: log2 transformed RPKM normalized read counts for 5999 genes and 95,484 RNA-seq samples which was utilized for dimensionality reduction and data exploration
precision_matrices.zip: Zipped folder including networks inferred by graphical lasso for different experiments presented in the paper using weighted covariance aggregation following PC correction.
The networks can be found as follows. First, select the folder corresponding to the network of interest - for example, Blood, this will then include two or more folders which indicate the data aggregation utilized, select the folder corresponding appropriate level of data aggregation - either all samples/ GTEx for blood-specific networks, this includes precision matrices inferred across a range of penalization parameters. To view the precision matrix inferred for a particular value of the penalization parameter X, select the file labeled lambda_X.rds
For select networks, we have included the computed centrality measures which can be accessed at centrality_X.rds for a particular value of the penalization parameter X.
We have also included .rds files that list the hub genes from the consensus networks inferred from non-cancerous samples at “normal_hubs.rds”, and the consensus networks inferred from cancerous samples at “cancer_hubs.rds”
The file “context_specific_selected_networks.csv” includes the networks that were selected for downstream biological interpretation based on the scale-free criterion which is also summarized in the Supplementary Tables.
WGCNA.zip: A zipped folder containing gene modules inferred from WGCNA for sequentially aggregated GTEx, SRA, and blood studies. Select the data aggregated, and the number of studies based on folder names. For example, blood networks inferred from 20 studies can be accessed at blood/consensus/net_20. The individual networks correspond to distinct cut heights, and include information on the cut height used, the genes that the network was inferred over merged module labels, and merged module colors.
Monthly report including total dispatched trips, total dispatched shared trips, and unique dispatched vehicles aggregated by FHV (For-Hire Vehicle) base. These have been tabulated from raw trip record submissions made by bases to the NYC Taxi and Limousine Commission (TLC). This dataset is typically updated monthly on a two-month lag, as bases have until the conclusion of the following month to submit a month of trip records to the TLC. In example, a base has until Feb 28 to submit complete trip records for January. Therefore, the January base aggregates will appear in March at the earliest. The TLC may elect to defer updates to the FHV Base Aggregate Report if a large number of bases have failed to submit trip records by the due date. Note: The TLC publishes base trip record data as submitted by the bases, and we cannot guarantee or confirm their accuracy or completeness. Therefore, this may not represent the total amount of trips dispatched by all TLC-licensed bases. The TLC performs routine reviews of the records and takes enforcement actions when necessary to ensure, to the extent possible, complete and accurate information.
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.
Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
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Concrete is one if the most used building materials worldwide. With up to 80% of volume, a large constituent of concrete consists of fine and coarse aggregate particles (normally, sizes of 0.1mm to 32 mm) which are dispersed in a cement paste matrix. The size distribution of the aggregates (i.e. the grading curve) substantially affects the properties and quality characteristics of concrete, such as e.g. its workability at the fresh state and the mechanical properties at the hardened state. In practice, usually the size distribution of small samples of the aggregate is determined by manual mechanical sieving and is considered as representative for a large amount of aggregate. However, the size distribution of the actual aggregate used for individual production batches of concrete varies, especially when e.g. recycled material is used as aggregate. As a consequence, the unknown variations of the particle size distribution have a negative effect on the robustness and the quality of the final concrete produced from the raw material.
Towards the goal of deriving precise knowledge about the actual particle size distribution of the aggregate, thus eliminating the unknown variations in the material’s properties, we propose a data set for the image based prediction of the size distribution of concrete aggregates. Incorporating such an approach into the production chain of concrete enables to react on detected variations in the size distribution of the aggregate in real-time by adapting the composition, i.e. the mixture design of the concrete accordingly, so that the desired concrete properties are reached.
https://data.uni-hannover.de/dataset/f00bdcc4-8b27-4dc4-b48d-a84d75694e18/resource/042abf8d-e87a-4940-8195-2459627f57b6/download/overview.png" alt="Classicial vs. image based granulometry" title=" ">
In the classification data, nine different grading curves are distinguished. In this context, the normative regulations of DIN 1045 are considered. The nine grading curves differ in their maximum particle size (8, 16, or 32 mm) and in the distribution of the particle size fractions allowing a categorisation of the curves to coarse-grained (A), medium-grained (B) and fine-grained (C) curves, respectively. A quantitative description of the grain size distribution of the nine curves distinguished is shown in the following figure, where the left side shows a histogram of the particle size fractions 0-2, 2-8, 8-16, and 16-32 mm and the right side shows the cumulative histograms of the grading curves (the vertical axes represent the mass-percentages of the material).
For each of the grading curves, two samples (S1 and S2) of aggregate particles were created. Each sample consists of a total mass of 5 kg of aggregate material and is carefully designed according to the grain size distribution shwon in the figure by sieving the raw material in order to separate the different grain size fractions first, and subsequently, by composing the samples according to the dedicated mass-percentages of the size distributions.
https://data.uni-hannover.de/dataset/f00bdcc4-8b27-4dc4-b48d-a84d75694e18/resource/17eb2a46-eb23-4ec2-9311-0f339e0330b4/download/statistics_classification-data.png" alt="Particle size distribution of the classification data">
For data acquisition, a static setup was used for which the samples are placed in a measurement vessel equipped with a set of calibrated reference markers whose object coordinates are known and which are assembled in a way that they form a common plane with the surface of the aggregate sample. We acquired the data by taking images of the aggregate samples (and the reference markers) which are filled in the the measurement vessel and whose constellation within the vessel is perturbed between the acquisition of each image in order to obtain variations in the sample’s visual appearance. This acquisition strategy allows to record multiple different images for the individual grading curves by reusing the same sample, consequently reducing the labour-intensive part of material sieving and sample generation. In this way, we acquired a data set of 900 images in total, consisting of 50 images of each of the two samples (S1 and S2) which were created for each of the nine grading curve definitions, respectively (50 x 2 x 9 = 900). For each image, we automatically detect the reference markers, thus receiving the image coordinates of each marker in addition to its known object coordinates. We make use of these correspondences for the computation of the homography which describes the perspective transformation of the reference marker’s plane in object space (which corresponds to the surface plane of the aggregate sample) to the image plane. Using the computed homography, we transform the image in order to obtain an perspectively rectified representation of the aggregate sample with a known, and especially a for the entire image consistent, ground sampling distance (GSD) of 8 px/mm. In the following figure, example images of our data set showing aggregate samples of each of the distinguished grading curve classes are depicted.
https://data.uni-hannover.de/dataset/f00bdcc4-8b27-4dc4-b48d-a84d75694e18/resource/59925f1d-3eef-4b50-986a-e8d2b0e14beb/download/examples_classification_data.png" alt="Example images of the classification data">
If you make use of the proposed data, please cite the publication listed below.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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The file set is a freely downloadable aggregation of information about Australian schools. The individual files represent a series of tables which, when considered together, form a relational database. The records cover the years 2008-2014 and include information on approximately 9500 primary and secondary school main-campuses and around 500 subcampuses. The records all relate to school-level data; no data about individuals is included. All the information has previously been published and is publicly available but it has not previously been released as a documented, useful aggregation. The information includes: (a) the names of schools (b) staffing levels, including full-time and part-time teaching and non-teaching staff (c) student enrolments, including the number of boys and girls (d) school financial information, including Commonwealth government, state government, and private funding (e) test data, potentially for school years 3, 5, 7 and 9, relating to an Australian national testing programme know by the trademark 'NAPLAN'
Documentation of this Edition 2016.1 is incomplete but the organization of the data should be readily understandable to most people. If you are a researcher, the simplest way to study the data is to make use of the SQLite3 database called 'school-data-2016-1.db'. If you are unsure how to use an SQLite database, ask a guru.
The database was constructed directly from the other included files by running the following command at a command-line prompt: sqlite3 school-data-2016-1.db < school-data-2016-1.sql Note that a few, non-consequential, errors will be reported if you run this command yourself. The reason for the errors is that the SQLite database is created by importing a series of '.csv' files. Each of the .csv files contains a header line with the names of the variable relevant to each column. The information is useful for many statistical packages but it is not what SQLite expects, so it complains about the header. Despite the complaint, the database will be created correctly.
Briefly, the data are organized as follows. (a) The .csv files ('comma separated values') do not actually use a comma as the field delimiter. Instead, the vertical bar character '|' (ASCII Octal 174 Decimal 124 Hex 7C) is used. If you read the .csv files using Microsoft Excel, Open Office, or Libre Office, you will need to set the field-separator to be '|'. Check your software documentation to understand how to do this. (b) Each school-related record is indexed by an identifer called 'ageid'. The ageid uniquely identifies each school and consequently serves as the appropriate variable for JOIN-ing records in different data files. For example, the first school-related record after the header line in file 'students-headed-bar.csv' shows the ageid of the school as 40000. The relevant school name can be found by looking in the file 'ageidtoname-headed-bar.csv' to discover that the the ageid of 40000 corresponds to a school called 'Corpus Christi Catholic School'. (3) In addition to the variable 'ageid' each record is also identified by one or two 'year' variables. The most important purpose of a year identifier will be to indicate the year that is relevant to the record. For example, if one turn again to file 'students-headed-bar.csv', one sees that the first seven school-related records after the header line all relate to the school Corpus Christi Catholic School with ageid of 40000. The variable that identifies the important differences between these seven records is the variable 'studentyear'. 'studentyear' shows the year to which the student data refer. One can see, for example, that in 2008, there were a total of 410 students enrolled, of whom 185 were girls and 225 were boys (look at the variable names in the header line). (4) The variables relating to years are given different names in each of the different files ('studentsyear' in the file 'students-headed-bar.csv', 'financesummaryyear' in the file 'financesummary-headed-bar.csv'). Despite the different names, the year variables provide the second-level means for joining information acrosss files. For example, if you wanted to relate the enrolments at a school in each year to its financial state, you might wish to JOIN records using 'ageid' in the two files and, secondarily, matching 'studentsyear' with 'financialsummaryyear'. (5) The manipulation of the data is most readily done using the SQL language with the SQLite database but it can also be done in a variety of statistical packages. (6) It is our intention for Edition 2016-2 to create large 'flat' files suitable for use by non-researchers who want to view the data with spreadsheet software. The disadvantage of such 'flat' files is that they contain vast amounts of redundant information and might not display the data in the form that the user most wants it. (7) Geocoding of the schools is not available in this edition. (8) Some files, such as 'sector-headed-bar.csv' are not used in the creation of the database but are provided as a convenience for researchers who might wish to recode some of the data to remove redundancy. (9) A detailed example of a suitable SQLite query can be found in the file 'school-data-sqlite-example.sql'. The same query, used in the context of analyses done with the excellent, freely available R statistical package (http://www.r-project.org) can be seen in the file 'school-data-with-sqlite.R'.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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This dataset encompasses an Excel datasheet containing aggregate data from thin section analyses of ice samples from the Fimbule Ice Shelf, carried out during the 2021/2022 Antarctic expedition. The research forms a crucial part of the Master's research project: "Development of a Multi-tier System for the Analysis of Ice Crystallography of Antarctic Shelf Ice", conducted by Steven McEwen. Each entry in the datasheet corresponds to a specific thin section or ice grain and includes the following parameters: Grid Number, A1 Axis Reading, A4 Reading, Corrected A4 values, number of readings, Mean C-axis Orientation, Grain Size, Date, Sample number, x-coordinate, y-coordinate, Degree of Orientation, and Spherical Aperture. These data points collectively facilitate a comprehensive understanding of the crystallography of the Fimbule Ice Shelf's ice samples. Data was collected and analyzed during the 2021/2022 Antarctic summer expedition, with additional analysis being performed in the Polar engineering Research Group's laboratory.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Datasets and results associated with "Sample, estimate, aggregate: A recipe for causal discovery foundation models"
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘FHV Base Aggregate Report’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7992e33a-6319-413c-b196-dec3f18dafd0 on 13 February 2022.
--- Dataset description provided by original source is as follows ---
Monthly report including total dispatched trips, total dispatched shared trips, and unique dispatched vehicles aggregated by FHV (For-Hire Vehicle) base. These have been tabulated from raw trip record submissions made by bases to the NYC Taxi and Limousine Commission (TLC).
This dataset is typically updated monthly on a two-month lag, as bases have until the conclusion of the following month to submit a month of trip records to the TLC. In example, a base has until Feb 28 to submit complete trip records for January. Therefore, the January base aggregates will appear in March at the earliest. The TLC may elect to defer updates to the FHV Base Aggregate Report if a large number of bases have failed to submit trip records by the due date.
Note: The TLC publishes base trip record data as submitted by the bases, and we cannot guarantee or confirm their accuracy or completeness. Therefore, this may not represent the total amount of trips dispatched by all TLC-licensed bases. The TLC performs routine reviews of the records and takes enforcement actions when necessary to ensure, to the extent possible, complete and accurate information.
--- Original source retains full ownership of the source dataset ---
Comprehensive dataset of 86 Aggregate suppliers in Illinois, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.
Comprehensive dataset of 62 Aggregate suppliers in Virginia, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.
https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html
Understanding how assemblages of species responded to past climate change is a central goal of comparative phylogeography and comparative population genomics, and an endeavor that has increasing potential to integrate with community ecology. New sequencing technology now provides the potential to gain complex demographic inference at unprecedented resolution across assemblages of non-model species. To this end, we introduce the aggregate site frequency spectrum (aSFS), an expansion of the site frequency spectrum to use single nucleotide polymorphism (SNP) datasets collected from multiple, co-distributed species for assemblage-level demographic inference. We describe how the aSFS is constructed over an arbitrary number of independent population samples and then demonstrate how the aSFS can differentiate various multi-species demographic histories under a wide range of sampling configurations while allowing effective population sizes and expansion magnitudes to vary independently. We subsequently couple the aSFS with a hierarchical approximate Bayesian computation (hABC) framework to estimate degree of temporal synchronicity in expansion times across taxa, including an empirical demonstration with a dataset consisting of five populations of the threespine stickleback (Gasterosteus aculeatus). Corroborating what is generally understood about the recent post-glacial origins of these populations, the joint aSFS/hABC analysis strongly suggests that the stickleback data are most consistent with synchronous expansion after the Last Glacial Maximum (posterior probability = 0.99). The aSFS will have general application for multi-level statistical frameworks to test models involving assemblages and/or communities and as large-scale SNP data from non-model species become routine, the aSFS expands the potential for powerful next-generation comparative population genomic inference.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Additional file 4. Example MM to check missing BP.
Envestnet®| Yodlee®'s Online Shopping 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
Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)
Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence
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