Envestnet®| Yodlee®'s Consumer Behavior 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

PATRON is a human ethics approved program of research incorporating an enduring de-identified repository of Primary Care data facilitating research and knowledge generation. PATRON is a part of the 'Data for Decisions' initiative of the Department of General Practice, University of Melbourne. 'Data for Decisions' is a research initiative in partnership with general practices. It is an exciting undertaking that makes possible primary care research projects to increase knowledge and improve healthcare practices and policy. Principal Researcher: Jon EmeryData Custodian: Lena SanciData Steward: Douglas BoyleManager: Rachel CanawayMore information about Data for Decisions and utilising PATRON data is available from the Data for Decisions website.

Bangladesh completed a primary series of COVID-19 vaccinations for about 86 individuals per 100 population as of 5 July 2023. However, ensuring higher coverage in vulnerable areas is challenging. We report on the COVID-19 vaccine uptake and associated factors among adults in two vulnerable areas in Bangladesh. We conducted a cross-sectional study between August and September 2022 in Duaripara, a slum in northeast Dhaka (in-migration site), and Tala, a disaster-prone sub-district in southwest Satkhira (out-migration site). We surveyed 1,239 adults in Duaripara and 1,263 adults in Tala from 625 and 596 randomly selected households, respectively. We reported coverage and examined associations between the uptake and demographic and socioeconomic characteristics using multilevel mixed-effects generalized linear regression models. We checked for spatial autocorrelation to assess geographical patterns in vaccine distribution. First- and second-dose coverage was about 91% and 80.4% in Duaripara and 96.6% and 92.2% in Tala, respectively. Individuals above 40 were more likely to be vaccinated (IRR: 1.12, p-value = 0.04 for Duaripara, and IRR: 1.14, p-value

The Antibiotic Resistance Microbiology Dataset (ARMD) is a structured and de-identified resource developed using electronic health records (EHR) from Stanford Healthcare. It provides a comprehensive overview of microbiological cultures including urine, respiratory, and blood cultures. This dataset includes 283,715 unique adult patients and features detailed information on culture results, identified organisms, antibiotic susceptibility, and associated demographic and clinical data. The dataset was meticulously constructed through a multi-step process designed to enhance data quality and relevance. By enabling the study of antimicrobial resistance patterns and supporting antimicrobial stewardship efforts, ARMD offers a valuable resource for researchers and clinicians seeking to improve the management of infectious diseases and combat the growing threat of antimicrobial resistance., Cohort Selection The ARMD was created using de-identified EHR data from Stanford Healthcare to address this need. This dataset provides microbiological cultures from adult patients (≥18 years old) and includes key clinical data points relevant to studying antimicrobial resistance. The cohort construction involved the following features and processes:

Culture Types: Microbiological cultures were included, specifically urine, respiratory, and blood cultures.

Temporal Adjustment: The timing of culture orders was adjusted for data privacy through jittering, ensuring patient confidentiality while retaining meaningful temporal relationships.

Culture Positivity: Each culture is flagged as either positive or negative, indicating whether an organism was identified. Cultures flagged as negative are represented by a null value in the susceptibility field.

Organism Identification and Susceptibility: For positive cultures, the identified organism and its antibiotic susceptibility are recorde..., , # Antibiotic Resistance Microbiology Dataset (ARMD)

Background

Antimicrobial resistance (AMR) represents a pressing global health challenge, exacerbated by the overuse and misuse of antibiotics. Efforts to mitigate AMR require high-quality datasets to analyze trends in microbial susceptibility, guide clinical decision-making, and inform stewardship programs. electronic health records (EHR) are a rich source of real-world data that can be leveraged to study antimicrobial use and resistance patterns. However, constructing meaningful datasets from EHR data requires rigorous curation and preprocessing to ensure accuracy, relevance, and usability. ARMD aims to facilitate research in antimicrobial stewardship, with applications in identifying resistance patterns, evaluating treatment practices, and informing public health interventions. By leveraging de-identified EHR data from Stanford Healthcare, this dataset provides a unique opportunity to generate insights that can help improve infec...

The advent of large, open access text databases has driven advances in state-of-the-art model performance in natural language processing (NLP). The relatively limited amount of clinical data available for NLP has been cited as a significant barrier to the field's progress. Here we describe MIMIC-IV-Note: a collection of deidentified free-text clinical notes for patients included in the MIMIC-IV clinical database. MIMIC-IV-Note contains 331,794 deidentified discharge summaries from 145,915 patients admitted to the hospital and emergency department at the Beth Israel Deaconess Medical Center in Boston, MA, USA. The database also contains 2,321,355 deidentified radiology reports for 237,427 patients. All notes have had protected health information removed in accordance with the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provision. All notes are linkable to MIMIC-IV providing important context to the clinical data therein. The database is intended to stimulate research in clinical natural language processing and associated areas.

To generate the evidence needed to understand, improve and share what works to help refugee children learn and succeed in school, the International Rescue Committee (IRC) and NYU Global TIES for Children (TIES/NYU) established a strategic partnership, the Evidence for Action: Education in Emergencies (3EA) initiative. In Lebanon, this program was designed and delivered to complement the Lebanese public education system and enhance learning and retention of Syrian refugee children through remedial tutoring programs infused with climate-targeted social-emotional learning (SEL) principles and practices (Tutoring in a Healing Classrooms - HCT) and skill-targeted SEL interventions (Mindfulness activities, Brain Games, 5-Component SEL Curriculum). An estimated 5000 Syrian refugee children enrolled in Lebanese public schools and teachers working with them participated in the program. These students attended 2.5-hour-long tutoring sessions per day, three times a week, with each session consisting of three lessons (Arabic, French/English, mathematics) and each lesson lasting between 30 to 40 minutes. A series of cluster randomized control trials over the course of two years were held to evaluate the effectiveness of the HCT and skill-targeted SEL programming. This dataset does not contain treatment indicators. Please contact the authors for access if interested in using those variables.

Data management platform that houses all data generated by the Human Connectome Project - image data, clinical evaluations, behavioral data and more. ConnectomeDB stores raw image data, as well as results of analysis and processing pipelines. Using the ConnectomeDB infrastructure, research centers will be also able to manage Connectome-like projects, including data upload and entry, quality control, processing pipelines, and data distribution. ConnectomeDB is designed to be a data-mining tool, that allows users to generate and test hypotheses based on groups of subjects. Using the ConnectomeDB interface, users can easily search, browse and filter large amounts of subject data, and download necessary files for many kinds of analysis. ConnectomeDB is designed to work seamlessly with Connectome Workbench, an interactive, multidimensional visualization platform designed specifically for handling connectivity data. De-identified data within ConnectomeDB is publicly accessible. Access to additional data may be available to qualified research investigators. ConnectomeDB is being hosted on a BlueArc storage platform housed at Washington University through the year 2020. This data platform is based on XNAT, an open-source image informatics software toolkit developed by the NRG at Washington University. ConnectomeDB itself is fully open source.

Envestnet®| Yodlee®'s Tourism 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