Context

This data set contains some basic statistics about user count and user growth as well as crash count for a real mobile app. The dataset contains a basic timeseries of 1 hour resolution for a period of one week.

Content

The data set contains columns for total concurrent user count, new users acquired in that period of time, number of sessions and crash count.

Acknowledgements

This data set would not be available without the Real User Monitoring capabilities of Dynatrace and its flexibility to export and expose this data for scientific experiments.

Inspiration

The data set was intended to play around with seasonality, trend and prediction of timeseries.

At MFour, our Behavioral Data stands out for its uniqueness and depth of insights. What makes our data genuinely exceptional is the combination of several key factors:

• First-Party Opt-In Data: Our data is sourced directly from our opt-in panel of consumers who willingly participate in research and provide observed behaviors. This ensures the highest data quality and eliminates privacy concerns. CCPA compliant.

• Unparalleled Data Coverage: With access to 3B+ billion events, we have an extensive pool of participants who allow us to observe their brick + mortar location visitation, app + web smartphone usage, or both. This large-scale coverage provides robust and reliable insights.

• Our data is generally sourced through our Surveys On The Go (SOTG) mobile research app, where consumers are incentivized with cash rewards to participate in surveys and share their observed behaviors. This incentivized approach ensures a willing and engaged panel, leading to the highest-quality data.

The primary use cases and verticals of our Behavioral Data Product are diverse and varied. Some key applications include:

• Data Acquisition and Modeling: Our data helps businesses acquire valuable insights into consumer behavior and enables modeling for various research objectives.

• Shopper Data Analysis: By understanding purchase behavior and patterns, businesses can optimize their strategies, improve targeting, and enhance customer experiences.

• Media Consumption Insights: Our data provides a deep understanding of viewer behavior and patterns across popular platforms like YouTube, Amazon Prime, Netflix, and Disney+, enabling effective media planning and content optimization.

• App Performance Optimization: Analyzing app behavior allows businesses to monitor usage patterns, track key performance indicators (KPIs), and optimize app experiences to drive user engagement and retention.

• Location-Based Targeting: With our detailed location data, businesses can map out consumer visits to physical venues and combine them with web and app behavior to create predictive ad targeting strategies.

• Audience Creation for Ad Placement: Our data enables the creation of highly targeted audiences for ad campaigns, ensuring better reach and engagement with relevant consumer segments.

The Behavioral Data Product complements our comprehensive suite of data solutions in the broader context of our data offering. It provides granular and event-level insights into consumer behaviors, which can be combined with other data sets such as survey responses, demographics, or custom profiling questions to offer a holistic understanding of consumer preferences, motivations, and actions.

MFour's Behavioral Data empowers businesses with unparalleled consumer insights, allowing them to make data-driven decisions, uncover new opportunities, and stay ahead in today's dynamic market landscape.

App Data Statistics Tool Market Outlook

The global app data statistics tool market size was valued at approximately USD 5.3 billion in 2023 and is projected to reach USD 11.9 billion by 2032, growing at a CAGR of 9.2% during the forecast period. Several growth factors, including the escalating demand for data-driven decision-making and the rise in mobile app usage, are driving this market. As organizations increasingly recognize the value of data analytics in enhancing user engagement and optimizing app performance, the adoption of app data statistics tools is expected to surge significantly.

The growth of the app data statistics tool market is primarily fueled by the exponential increase in mobile app usage worldwide. With billions of smartphone users generating vast amounts of data daily, companies are leveraging app data statistics tools to gain actionable insights. These tools help in understanding user behavior, tracking app performance, and identifying areas for improvement. Furthermore, the growing emphasis on personalized user experiences has led to an increased demand for sophisticated analytics tools, thereby driving market growth.

Another critical growth factor is the rising importance of data-driven decision-making in various industries. Organizations across sectors such as BFSI, healthcare, retail, and media are increasingly relying on app data statistics tools to make informed decisions. These tools enable businesses to analyze large datasets, uncover trends, and optimize their strategies. The adoption of analytics tools is also propelled by the need to improve customer satisfaction and loyalty, as companies strive to offer tailored experiences to their users. The integration of artificial intelligence and machine learning in analytics tools further enhances their efficiency and accuracy, contributing to market growth.

Moreover, the market is benefitting from technological advancements and the increasing availability of advanced analytics tools. Innovations such as real-time analytics, predictive analytics, and big data analytics are enhancing the capabilities of app data statistics tools. These advancements enable organizations to gain deeper insights and make faster, more accurate decisions. Additionally, the proliferation of cloud-based solutions is making analytics tools more accessible and affordable for businesses of all sizes. Cloud deployment offers scalability, flexibility, and cost-efficiency, which are particularly attractive to small and medium enterprises (SMEs).

The role of Product Analytics Software is becoming increasingly significant in the realm of app data statistics tools. These software solutions are designed to help businesses understand how users interact with their products, providing insights that are crucial for enhancing user experience and driving product development. By analyzing user data, companies can identify trends and patterns that inform strategic decisions, such as feature enhancements and marketing strategies. The integration of Product Analytics Software with app data statistics tools enables businesses to gain a comprehensive view of user behavior, facilitating more informed decision-making and ultimately leading to improved product offerings.

Regionally, North America holds the largest market share, driven by the presence of numerous tech giants and a high adoption rate of advanced technologies. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period. The rapid digitization, increasing smartphone penetration, and the rising number of app developers in countries like China and India are driving the demand for app data statistics tools. Europe also presents significant growth opportunities, with increasing investments in technology and data analytics across various industries. Latin America and the Middle East & Africa are emerging markets with growing awareness and adoption of analytics tools.

Component Analysis

The app data statistics tool market is segmented by components into software and services. Software components dominate the market, driven by the demand for sophisticated analytics solutions that can process vast amounts of data. These software tools are designed to collect, analyze, and visualize data, enabling organizations to derive meaningful insights. The growing adoption of artificial intelligence and machine learning technologies in software solutions further enhances their capabilities, making them indispensable for

The app analytics market, valued at $7.29 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 21.09% from 2025 to 2033. This surge is driven by several key factors. The increasing adoption of mobile applications across diverse industries, coupled with the rising need for businesses to understand user behavior and optimize app performance, fuels the demand for sophisticated analytics solutions. Furthermore, advancements in data analytics technologies, including artificial intelligence (AI) and machine learning (ML), are enabling more insightful and actionable data analysis, further propelling market expansion. The diverse application of app analytics across marketing/advertising, revenue generation, and in-app performance monitoring across various sectors like BFSI, e-commerce, media, travel and tourism, and IT and telecom significantly contributes to this growth. The market is segmented by deployment (mobile apps and website/desktop apps) and end-user industry, with mobile app analytics currently dominating due to the widespread adoption of smartphones. The competitive landscape is characterized by a mix of established technology giants like Google and Amazon alongside specialized app analytics providers like AppsFlyer and Mixpanel. These companies are continuously innovating, integrating new technologies, and expanding their product offerings to cater to the evolving needs of businesses. While the North American market currently holds a significant share, the Asia-Pacific region is expected to witness substantial growth in the coming years driven by increasing smartphone penetration and digitalization initiatives. However, factors like data privacy concerns and the rising complexity of integrating various analytics tools could pose challenges to market growth. Nonetheless, the overall outlook for the app analytics market remains positive, indicating substantial opportunities for players across the value chain. Recent developments include: June 2024 - Comscore and Kochava unveiled an innovative performance media measurement solution, providing marketers with enhanced insights. This cutting-edge cross-screen solution empowers marketers to understand better how linear TV ad campaigns impact both online and offline actions. By integrating Comscore’s Exact Commercial Ratings (ECR) data with Kochava’s sophisticated marketing mix modeling, the solution facilitates the measurement of crucial metrics, including mobile app activities (such as installs and in-app purchases) and website interactions., June 2024 - AppsFlyer announced its integration of the Data Collaboration Platform with Start.io, an omnichannel advertising platform that focuses on real-time mobile audiences for publishers. Through this collaboration, businesses leveraging the AppsFlyer Data Collaboration Platform can merge their Start.io data with campaign metrics and audience insights, creating a more comprehensive dataset for precise audience targeting.. Key drivers for this market are: Increasing Usage of Mobile/Web Apps Across Various End-user Industries, Increasing Adoption of Technologies like 5G Technology and Deeper Penetration of Smartphones; Increase in the Amount of Time Spent on Mobile Devices Coupled With the Increasing Focus on Enhancing Customer Experience. Potential restraints include: Increasing Usage of Mobile/Web Apps Across Various End-user Industries, Increasing Adoption of Technologies like 5G Technology and Deeper Penetration of Smartphones; Increase in the Amount of Time Spent on Mobile Devices Coupled With the Increasing Focus on Enhancing Customer Experience. Notable trends are: Media and Entertainment Industry Expected to Capture Significant Share.

App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

TagX Web Browsing Clickstream Data: Unveiling Digital Behavior Across North America and EU Unique Insights into Online User Behavior TagX Web Browsing clickstream Data offers an unparalleled window into the digital lives of 1 million users across North America and the European Union. This comprehensive dataset stands out in the market due to its breadth, depth, and stringent compliance with data protection regulations. What Makes Our Data Unique?

Extensive Geographic Coverage: Spanning two major markets, our data provides a holistic view of web browsing patterns in developed economies. Large User Base: With 300K active users, our dataset offers statistically significant insights across various demographics and user segments. GDPR and CCPA Compliance: We prioritize user privacy and data protection, ensuring that our data collection and processing methods adhere to the strictest regulatory standards. Real-time Updates: Our clickstream data is continuously refreshed, providing up-to-the-minute insights into evolving online trends and user behaviors. Granular Data Points: We capture a wide array of metrics, including time spent on websites, click patterns, search queries, and user journey flows.

Data Sourcing: Ethical and Transparent Our web browsing clickstream data is sourced through a network of partnered websites and applications. Users explicitly opt-in to data collection, ensuring transparency and consent. We employ advanced anonymization techniques to protect individual privacy while maintaining the integrity and value of the aggregated data. Key aspects of our data sourcing process include:

Voluntary user participation through clear opt-in mechanisms Regular audits of data collection methods to ensure ongoing compliance Collaboration with privacy experts to implement best practices in data anonymization Continuous monitoring of regulatory landscapes to adapt our processes as needed

Primary Use Cases and Verticals TagX Web Browsing clickstream Data serves a multitude of industries and use cases, including but not limited to:

Digital Marketing and Advertising:

Audience segmentation and targeting Campaign performance optimization Competitor analysis and benchmarking

E-commerce and Retail:

Customer journey mapping Product recommendation enhancements Cart abandonment analysis

Media and Entertainment:

Content consumption trends Audience engagement metrics Cross-platform user behavior analysis

Financial Services:

Risk assessment based on online behavior Fraud detection through anomaly identification Investment trend analysis

Technology and Software:

User experience optimization Feature adoption tracking Competitive intelligence

Market Research and Consulting:

Consumer behavior studies Industry trend analysis Digital transformation strategies

Integration with Broader Data Offering TagX Web Browsing clickstream Data is a cornerstone of our comprehensive digital intelligence suite. It seamlessly integrates with our other data products to provide a 360-degree view of online user behavior:

Social Media Engagement Data: Combine clickstream insights with social media interactions for a holistic understanding of digital footprints. Mobile App Usage Data: Cross-reference web browsing patterns with mobile app usage to map the complete digital journey. Purchase Intent Signals: Enrich clickstream data with purchase intent indicators to power predictive analytics and targeted marketing efforts. Demographic Overlays: Enhance web browsing data with demographic information for more precise audience segmentation and targeting.

By leveraging these complementary datasets, businesses can unlock deeper insights and drive more impactful strategies across their digital initiatives. Data Quality and Scale We pride ourselves on delivering high-quality, reliable data at scale:

Rigorous Data Cleaning: Advanced algorithms filter out bot traffic, VPNs, and other non-human interactions. Regular Quality Checks: Our data science team conducts ongoing audits to ensure data accuracy and consistency. Scalable Infrastructure: Our robust data processing pipeline can handle billions of daily events, ensuring comprehensive coverage. Historical Data Availability: Access up to 24 months of historical data for trend analysis and longitudinal studies. Customizable Data Feeds: Tailor the data delivery to your specific needs, from raw clickstream events to aggregated insights.

Empowering Data-Driven Decision Making In today's digital-first world, understanding online user behavior is crucial for businesses across all sectors. TagX Web Browsing clickstream Data empowers organizations to make informed decisions, optimize their digital strategies, and stay ahead of the competition. Whether you're a marketer looking to refine your targeting, a product manager seeking to enhance user experience, or a researcher exploring digital trends, our cli...

Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

This dataset comprises two resources. The first resource contains a list of random people with their date and place of birth. This can be used for demographics and hypothetical scenario testing. The second resource includes user behavior data on various device models, detailing app usage, screen time, and other metrics, which is beneficial for analyzing mobile usage patterns.

This layer shows data on the number of establishments and revenue for select 2-digit North American Industry Classification System (NAICS) sectors and for NAICS 00, All Sectors. This is shown by county and state boundaries. The full NES data set (available at census.gov) is updated annually to contain the most currently released NES data, and contains estimates and measure of reliability. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Current Vintage: 2019CBP Table: NS1900NESData downloaded from: Census Bureau's API for Nonemployer StatisticsDate of API call: December 19, 2022National Figures: data.census.govThe United States Census Bureau's Nonemployer Statistics Program (NES):About this ProgramDataTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census Bureau and NES when using this data.Data Processing Notes:Boundaries come from the US Census Bureau TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census Bureau. These are Census Bureau boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 51 records - all US states, Washington D.C..Blank values represent industries where there either were no businesses in that industry and that geography OR industries where the data had to be withheld to avoid disclosing data for individual companies. Users should visit data.census.gov or Census Business Builder for more details on these withheld records.Data shown in thousands of dollars are indicated by '($1000)' in the field aliasing. Average and Totals include NAICS 11.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

Switzerland is leading the ranking by population share with mobile internet access , recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

Hello! I'm a French engineering student and I'm very interested in data analysis. I'm also a huge fan of football, and I wanted to mix both by studying the penalties of some european football championships.

In this dataset, I included data from Premier League, Ligue 1, Bundesliga, Serie A (in two separate tables) and Champions League (until the row of 8). I collected the data thanks to the mobile app "Match en Direct", going match by match to see if there was any penalty taken or not, and adding the data in an Excel sheet.

My goal is to see if it is possible to make some links between the succes in a penalty and some factors such as the moment of the game, the score of the game when the penalty is taken, home/away team, the player's main position on the pitch, if the penalty taker is a sub or not...

I started by studying all this in my Excel sheet and found out some interesting facts, but I want to improve my analysis by doing it with a notebook here on Kaggle.

I am very new in this data analysis field, so if you have any suggestion, i will be happy to listen!

This layer shows data on the number of establishments and revenue for 2-digit North American Industry Classification System (NAICS) sectors and for NAICS 00, All Sectors. This is shown by county and state boundaries. The full NES data set (available at census.gov) is updated annually to contain the most currently released NES data, and contains estimates and measure of reliability. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Current Vintage: 2018CBP Table: NS1800NESData downloaded from: Census Bureau's API for Nonemployer StatisticsDate of API call: June 1, 2020National Figures: data.census.govThe United States Census Bureau's Nonemployer Statistics Program (NES):About this ProgramDataTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census Bureau and CBP when using this data.Data Processing Notes:Boundaries come from the US Census Bureau TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census Bureau. These are Census Bureau boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoBlank values represent industries where there either were no businesses in that industry and that geography OR industries where the data had to be withheld to avoid disclosing data for individual companies. Users should visit data.census.gov or Census Business Builder for more details on these withheld records.

The global low code development market is approximated at a value of US$ 22.5 billion in 2024 and is calculated to increase at a CAGR of 26.8% to reach US$ 241.9 billion by the end of 2034.

Report Attribute	Detail
Low Code Development Market Size (2024E)	US$ 22.5 Billion
Forecasted Market Value (2034F)	US$ 241.9 Billion
Global Market Growth Rate (2024 to 2034)	26.8% CAGR
South Korea Market Value (2034F)	US$ 13.1 Billion
On-premise Demand Growth Rate (2024 to 2034)	24.9% CAGR
Key Companies Profiled	Mendix Technology BV; Zoho Corporation Pvt. Ltd.; Kintonne; Appian Corporation; Microsoft Corporation; Salesforce.com, Inc.; NewGen; AuraQuantic; Oracle Corporation; Pegasystems Inc.; ServiceNow Inc.; Creatio; Quick Base; Betty Blocks; TrackVia; OutSystems Inc.

Country-wise Analysis

Attribute	United States
Market Value (2024E)	US$ 2.5 Billion
Growth Rate (2024 to 2034)	26.7% CAGR
Projected Value (2034F)	US$ 26.7 Billion

Attribute	China
Market Value (2024E)	US$ 2.5 Billion
Growth Rate (2024 to 2034)	26.7% CAGR
Projected Value (2034F)	US$ 27 Billion

Category-wise Analysis

Attribute	BFSI
Segment Value (2024E)	US$ 4.5 Billion
Growth Rate (2024 to 2034)	27.8% CAGR
Projected Value (2034F)	US$ 52.2 Billion

Attribute	Cloud-based Low Code Development Platforms
Segment Value (2024E)	US$ 14.6 Billion
Growth Rate (2024 to 2034)	27.7% CAGR
Projected Value (2034F)	US$ 169.3 Billion

Description of the data and file structure

These directories contain the code, aggregated data, and preprocessing scripts to re-create the figures in "Architectural styles of curiosity in global Wikipedia readership"

Files and variables

File: Archive.zip

Figures: Publically usable illustrations are available here.

Description: Contains analysis, data, preprocessing, results, and utils folders. 14 directories, 25 files.

|-- analysis
| |-- KNOT_analysis.R <- analyzes laboratory data
| |-- analyze_1000-networks.ipynb <- analyzes naturalistic data
| |-- analyze_1000-networks_comparison-knot-rw_clean.ipynb <- compares datasets and nulls
| |-- analyze_KNOT_networks.ipynb <- analyzes laboratory data
| |-- analyze_forward_flow.ipynb <- calculates forward flow
| |-- forest_plots.R <- correlations wtih sociodemographic variables
| |-- topic_analysis.R <- analysis of topic and information diversity
| `-- worldmap.R <- visualization of geographical data sources
|-- data
| |-- laboratory_data <- variables for laboratory browsing and survey data
| |-- mobile_app_data <- aggregated data for network structure and topic (rows are individuals)
| |-- pretrained_embeddings <- fastText word embeddings
| |-- spatial_navigation <- data from Sea Hero Quest
| |-- surveys <- data from nationally aggregated sociodemographic surveys
| `-- wikispeedia <- data from WikiSpeedia game
|-- preprocessing
| |-- data_knowledge-networks_generate-subsample_clean.ipynb <- processes mobile app data
| |-- data_knowledge-networks_metrics-combined_clean.ipynb <- calculates network metrics
| |-- data_knowledge-networks_rw_get-data.ipynb <- calculates null networks
| `-- data_knowledge-networks_sessions-app_cleaned.ipynb <- processes individual browsing
|-- requirements.txt
|-- results
| |-- UMAP <- data used to generate network embedding (rows are individuals)
| `-- figs <- code for generated figure on forward flow
`-- utils
|-- plot_knowledge-networks_network-comparison.ipynb <- visualizations of network comparisons
|-- plot_knowledge-networks_network-metrics_distance.ipynb <- visualizations of distance between datasets
|-- plot_knowledge-networks_summary-stats.ipynb <- visualizations of summary stats
|-- utils_embedding.py <- get word and document embeddings
|-- utils_filtration_metrics.py <- higher-order topology functions (unused)
|-- utils_gt.py <- graph-tool functions
|-- utils_network.py <- functions to make networks from series of article IDs
|-- utils_network_metrics.py <- network metrics
|-- utils_networkx.py <- networkx functions
|-- utils_rw.py <- functions to generate random walks and null models
`-- utils_tokenizer.py <- functions for processing embeddings.

Code/software

See requirements.txt

Access information

Other publicly accessible locations of the data:

* https://gitlab.wikimedia.org/repos/research/curiosity

Additional data was derived from the following sources:

* [Human Development Index]
* [World Happiness Report]
* [WikiSpeedia]
* [FastText]
* [Sea Hero Quest]
* [Knowledge Networks Over Time Study]

Purpose: Cumulative Intervention Intensity (CII) is a proposed framework for conceptualizing and calculating dose that has been used to quantify intensity of speech-language therapy (SLT) in highly controlled laboratory studies and clinical trials. However, it is unknown whether CII can be applied to characterize the practice patterns of patients undertaking at-home, self-managed SLT. The current study leverages real-world mobile health data to investigate the applicability of CII parameters to self-managed SLT, including the interrelationships between individual CII parameters and their utility for identifying naturally occurring subgroups of patient users.Method: Anonymized data from 2,223 poststroke survivors who used the Constant Therapy application were analyzed. Four quantitative CII parameters—dose, session frequency, session duration, and total intervention duration—were calculated per user over a 3-month analysis period using raw session-level data. We conducted correlation analyses at the level of the individual and group to examine the degree of relatedness between each of the CII parameters. CII parameter measures were additionally used as inputs to a k-mean clustering analysis to identify practice pattern subgroups.Results: Results demonstrate the feasibility of calculating components of CII based on available usage statistics from a commercial app for self-managed SLT. Specifically, results suggest that, although CII parameters are related, session frequency offers complementary and nonoverlapping information (cf. dose, session duration, total intervention duration) about dosage. Clustering results show that practice patterns can be broadly differentiated according to the (a) amount and (b) frequency of practice.Conclusions: The calculation of CII may provide both users and clinicians with a fuller picture of at-home, self-managed practice habits than looking at any one dosage component alone. The study represents a first step toward more comprehensive and theoretically grounded dose reporting for self-managed SLT.Supplemental Material S1. CII and component quantitative parameters by individual task domain.Cordella, C., & Kiran, S. (2024). Quantifying dosage in self-managed speech-language therapy: Exploring components of Cumulative Intervention Intensity in a real-world mobile health data set. American Journal of Speech-Language Pathology, 33(3), 1513–1523. https://doi.org/10.1044/2024_AJSLP-23-00285

This layer shows data on the number of establishments and revenue for select 2-digit North American Industry Classification System (NAICS) sectors and for NAICS 00, All Sectors. This is shown by county and state boundaries. The full NES data set (available at census.gov) is updated annually to contain the most currently released NES data, and contains estimates and measure of reliability. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Current Vintage: 2022NES Table: NS2200NESData downloaded from: Census Bureau's API for Nonemployer StatisticsDate of API call: February 6, 2025National Figures: data.census.govThe United States Census Bureau's Nonemployer Statistics Program (NES):About this ProgramDataTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census Bureau and NES when using this data.Data Processing Notes:Boundaries come from the US Census Bureau TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census Bureau. These are Census Bureau boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 51 records - all US states, Washington D.C..Blank values represent industries where there either were no businesses in that industry and that geography OR industries where the data had to be withheld to avoid disclosing data for individual companies. Users should visit data.census.gov or Census Business Builder for more details on these withheld records.Data shown in thousands of dollars are indicated by '($1000)' in the field aliasing. Average and Totals include NAICS 11.