This dataset contains 500 synthetic records of technology usage behavior, capturing how different devices are utilized for various purposes across age groups. It aims to provide insights into consumer technology preferences, usage habits, and engagement levels. The data is fully artificial and generated for educational and analytical purposes.

Dataset Features The dataset includes the following columns:

• Date: The date when the usage was recorded, spanning a simulated timeline in 2024.
• Technology Category: The type of device being used (e.g., Smartphones, Laptops, Tablets).
• Usage Purpose: The primary purpose of using the device (e.g., Work, Entertainment, Education, Social Media).
• Brand: The brand of the device being used (e.g., Apple, Samsung, Google, Microsoft).
• Age Group: The age group of the user (e.g., Under 18, 18-24, 25-34, etc.).
• Daily Usage (Hours): The approximate number of hours the device was used per day.

Potential Use Cases 1. Market Analysis: - Understand brand preferences and usage purposes for different devices. - Identify trends in technology adoption by age group. 2. Behavioral Insights: - Explore correlations between daily usage and device categories. - Analyze which purposes dominate technology usage (e.g., work vs. entertainment). 3. Data Visualization Projects: - Create charts or dashboards to visualize technology engagement trends. 4. Machine Learning Models: - Use the data to predict device usage patterns or preferences.

Key Highlights - Includes a diverse range of device types, purposes, and brands. - Simulates realistic daily usage habits across six distinct age groups. - Useful for practicing data cleaning, visualization, and predictive analytics.

Acknowledgments This dataset is fully synthetic and was generated using Python. It does not contain any real-world user data and is intended solely for educational and research purposes.

This graph depicts the rate of daily use for wearable technology among adults aged 50 years and older in the U.S. as of 2019, by age group. Among adults aged 50 to 59 years, 87 percent reported using their wearable technology daily, compared to 79 percent of adults over the age of 70 years.

The graph of routers comprising the Internet can be organized into sub-graphs called Autonomous Systems (AS). Each AS exchanges traffic flows with some
neighbors (peers). We can construct a communication network of who-talks-to-
whom from the BGP (Border Gateway Protocol) logs.

The data was collected from University of Oregon Route Views Project
(http://www.routeviews.org/) - Online data and reports. The dataset contains
735 daily instances which span an interval of 785 days from November 8 1997 to January 2 2000. In contrast to citation networks, where nodes and edges only
get added (not deleted) over time, the AS dataset also exhibits both the
addition and deletion of the nodes and edges over time.

Dataset statistics are calculated for the graph with the highest number of
nodes and edges (dataset from January 02 2000):

Dataset statistics
Nodes 6474
Edges 13233
Nodes in largest WCC 6474 (1.000)
Edges in largest WCC 13233 (1.000)
Nodes in largest SCC 6474 (1.000)
Edges in largest SCC 13233 (1.000)
Average clustering coefficient 0.3913
Number of triangles 6584
Fraction of closed triangles 0.009591
Diameter (longest shortest path) 9
90-percentile effective diameter 4.6

Source (citation)

J. Leskovec, J. Kleinberg and C. Faloutsos. Graphs over Time: Densification
Laws, Shrinking Diameters and Possible Explanations. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2005.

Files
File Description
as20000102.txt.gz Autonomous Systems graph from January 02 2000
as.tar.gz 735 Autonomous Systems graphs from November 8 1997 to
January 02 2000

NOTE: In the UF collection, the primary matrix (Problem.A) is the
as20000102 matrix from January 02 2000 (the last graph in the sequence).

The nodes are uniform across all graphs in the sequence in the UF collection. That is, nodes do not come and go. A node that is "gone" simply has no edges. This is to allow comparisons across each node in the graphs.
Problem.aux.nodenames gives the node numbers of the original problem. So
row/column i in the matrix is always node number Problem.aux.nodenames(i) in
all the graphs.

Problem.aux.G{k} is the kth graph in the sequence.
Problem.aux.Gname(k,:) is the name of the kth graph.

Graph and download economic data for Nasdaq US Benchmark Technology Hardware and Equipment NTR Index (NASDAQNQUSB101020N) from 2012-12-03 to 2025-11-07 about hardware, NASDAQ, equipment, indexes, and USA.

Graph and download economic data for NASDAQ-100 Technology Sector (NASDAQNDXT) from 2006-02-23 to 2025-11-14 about NASDAQ, sector, indexes, and USA.

This is an analysis on TCS Share Price where you can select the time period as per your requirements. This analysis uses the online dataset from yahoo finance. The graphs were created using ggplot library. Here you will get the graph of daily changing price. It also shows a graph of 10 day and 30 days moving average. You can also checkout Daily, Weekly, Monthly, Quarterly and Yearly returns.

Research-and-Development Time Series for Suntront Tech. Suntront Technology Co., Ltd. develops, manufactures, and sells smart meters in China. The company offers smart water, gas, heat, and energy meters; and remote devices and other products. It also provides prepayment, automatic meter reading, and technique related solutions, as well as smart management software with various functions, such as file creation, account opening, daily business dealing, sale and multi inquiry, report summary, read/write card, input/collect data, bill printing, and blacklist checking. It serves electricity, gas, and water utility and distribution industries. Suntront Technology Co., Ltd. was founded in 2000 and is headquartered in Zhengzhou, China.

Household Graph Platforms Market Outlook

According to our latest research, the Global Household Graph Platforms market size was valued at $1.2 billion in 2024 and is projected to reach $6.8 billion by 2033, expanding at a CAGR of 21.3% during the forecast period of 2025–2033. The primary driver propelling this robust growth is the increasing reliance on big data analytics and artificial intelligence within household environments, which is fostering the adoption of graph platforms for advanced data relationship mapping and personalized service delivery. As households become more digitally interconnected, the demand for platforms capable of efficiently processing complex, interrelated data sets has surged, enabling smarter home automation, enhanced security, and more intuitive user experiences. This trend is further bolstered by the proliferation of IoT devices and the growing need for real-time, context-aware insights to optimize daily living.

Regional Outlook

North America commands the largest share of the Household Graph Platforms market, accounting for over 38% of the global market value in 2024. This dominance is underpinned by the region’s mature technology infrastructure, widespread adoption of smart home devices, and a high concentration of leading tech companies investing heavily in graph database and analytics solutions. The presence of progressive regulatory frameworks supporting data-driven innovation has further accelerated market penetration, especially in the United States and Canada. Additionally, early adoption of AI-powered household platforms and a strong consumer appetite for personalized, connected experiences have positioned North America as the epicenter of market growth and innovation. The region’s established ecosystem of software vendors, service providers, and cloud infrastructure also supports seamless integration and scalability, making it an attractive market for both established players and new entrants.

The Asia Pacific region is anticipated to be the fastest-growing market, with a projected CAGR of 25.7% from 2025 to 2033. This rapid expansion is driven by increasing investments in smart home technologies, rising disposable incomes, and the digital transformation of urban households across China, Japan, South Korea, and India. Governments in the region are actively promoting smart city initiatives and digital infrastructure upgrades, which in turn are fueling demand for advanced data analytics and graph platform capabilities at the household level. The proliferation of affordable IoT devices and growing awareness of data-driven home automation solutions are further catalyzing adoption. Regional tech giants and startups alike are introducing innovative graph-based applications tailored to local market needs, accelerating market growth and fostering a competitive environment.

Emerging economies in Latin America, the Middle East, and Africa are witnessing a gradual uptick in the adoption of Household Graph Platforms, albeit from a lower base. These regions face unique challenges such as limited digital infrastructure, lower household penetration of smart devices, and regulatory hurdles related to data privacy and cross-border data flows. Nevertheless, localized demand for improved home security, energy management, and personalized services is slowly gaining momentum, supported by government-led digitalization programs and an expanding middle class. As connectivity improves and awareness of the benefits of graph platforms grows, these markets are expected to play an increasingly important role in the global landscape, especially as vendors tailor solutions to address region-specific challenges and opportunities.

Report Scope

Attributes	Details
Report Title	Household Graph Platforms Market Research Report 2033
By Component	Software, Services
By Deployment Mode	On-Premises, Cloud
By Application	Recommendation Engines, Fraud Detecti

According to a 2023 survey conducted in the United States, Gen Z respondents were spending the most time using their phones, over *** hours a day. By contrast, Baby Boomers recorded a daily screen time of roughly ***** hours and ** minutes.  Many users feel addicted to smartphones As technology’s role in our everyday life increases, consumers tend to spend more and more time using electronic devices, whether it is for working and studying on laptops and tablets, watching TV or scrolling social media on smartphones. As a consequence, many users across all generations feel somewhat addicted to smartphones. According to a 2023 survey conducted in the United States, Gen Z users felt addicted to such devices the most, followed by Millennials. Taking a step back and nostalgia for early 2000s How can we combat the overwhelming urge to stay connected and take a step back from our always-on reality? In an effort to reduce screen time, many users, especially those in Gen Z, are expressing a sense of nostalgia for early 2000s technology, particularly dumb phones and wired headphones. For instance, during a 2024 survey in the United States, ** percent of Gen Z respondents stated they would be interested in purchasing dumb phones, followed by ** percent of Millennials - a trend that might involve more users in the future.

Cash-and-Short-Term-Investments Time Series for Longshine Technology Co Ltd. Longshine Technology Group Co., Ltd. operates technology company in China. The company offers living payment platform, an easy-to-operate daily living payment and service experiences, including running water, electric power charge, gas, community property management and broadcasting, and TV broadband fee payment for various people, traditional public utilities, and enterprises; OTTTV solution, a household intelligent internet solution; sports management service platform, a basic information technology application support system for sports management; and intelligent travel solution, a transport solution for information guiding, scheduling, operation, supervision, and extensive publicity. It also provides digital intelligence class which analyses teachers and students pain points; city operation center, a central system of operation and commanding center; intelligent community, a property management service platform; Beijing all-in-one system, an ecology system serving citizens, governments, and enterprise; and All-In-One System for governments and enterprises that provides one-stop SME policy information services for SMEs. In addition, the company offers Marketing Audit and Monitoring System 2.0; Marketing application system; Marketing, Power Distribution and Scheduling Integration application; new electricity approach, an urban compound charging service platform; HanClouds industrial internet platform, a service system based on data acquisition, aggregation, and analyses; digital finance service platform; Intelligent Irrigation solution for enhanced the water and fertilizer utilization rate; and Single Window Standard Edition, foreign trade service platform. Further, it provides technology and business research, as well as information, management, and business consulting services. Longshine Technology Group Co., Ltd was formerly known as LongShine Technology Co., Ltd. Longshine Technology Group Co., Ltd. was founded in 1996 and is headquartered in Wuxi City, China.

Outline

This dataset is a multimodal knowledge graph (MMKG) of daily activity videos.

This dataset integrates a KG with embedded multi-view videos created by VirtualHome-AIST, an extended version of the VirtualHome simulator, and an event-centric KG generated by VirtualHome2KG.

We named this dataset VHAKG (VirtualHome-AIST-KG).

Details

VHAKG describes 2D bounding boxes of objects every five frames, compositional activities, primitive actions, target objects, object states, 3D bounding boxes, and their time-series changes.

The videos are encoded in base64 and embedded as a literal value.

VHAKG consists of 706 daily activity scenarios (e.g., clean desk, cook fried bread, and relax on sofa) and 3,530 videos captured by five synchronized cameras per scenario.

The file format is RDF (Turtle), which can be loaded into various Triplestores.

VHAKG's vocabularies are defined as an ontology and can be found in vh2kg_schema_v2.0.0.ttl.

Contents

vh2kg_video_base64.tar.gz

{activity name}{scene}_{camera}_2dbbox.ttl: KG with video embedded in base64 format, including 2D bounding box data every 5 frames.

To learn more about {scene}, check here.

To learn more about {camera}, check here.

vh2kg_event.tar.gz

{activity name}_{scene}.ttl: Event-centric KGs representing video content as sequences of events.

vh2kg_schema_v2.0.0.ttl: The ontology file of this dataset.

affordance.ttl: The affordance data of objects that were created by crowdsourcing.

Please see Section III.B of this paper for more information.

add_places.ttl: Events in which agents moved from one room to another.

Tools

A set of tools for searching and extracting videos from VHAKG is available.

From the project data sheet https://github.com/github/innovationgraph/blob/main/docs/datasheet.md:

The dataset is composed of 8 CSV files of GitHub metrics, aggregated by economy and reported quarterly. Each metric is reported quarterly dating back to January 2020. Metrics for economies are only reported when there are 100 or more unique developers performing the relevant activity within the time period.

Metrics of activity are assigned to a location based on the relevant user as determined by their IP address when interacting with GitHub. If a user changes locations in the time period, the location for all user-relevant activity would be determined by the mode of location sampled daily in the period. Concretely, if a developer were contributing to open source projects in the United States for two months, but also made contributions while traveling in India, all activity from that developer during that quarter would be assigned to the United States.

Additionally, the last known location of the developer is carried forward on a daily basis even if no activities were performed by the developer that day. For example, if a developer performed activities within the United States and then became inactive for 6 days, that developer would be considered to be in the United States for that 7-day span.

We report on the following metrics:

• Git pushes: the number of times developers in a given economy uploaded code to GitHub. See the documentation for git push for a description of the git push command. Changes to files made through GitHub’s online platform automatically result in a push. Note that a single git push may contain multiple commits.
• Repositories: the number of software projects in a given economy based on the mode location of all repository members with triage and above access. See our documentation for Repositories for more information.
• Developers: the number of developer accounts located in a given economy based on mode daily location. This count excludes users that are bots or otherwise flagged as “spammy” within internal systems. See our documentation for personal accounts for more information.
• Organizations: the number of developer groups in a given economy, including companies, academic groups, nonprofits, and informal collectives that organize activity on GitHub. Location is assigned based on the mode location of all organization members. See our documentation for Organizations for more information.
• Programming languages: the number of unique developers in each economy who made at least one git push to a repository with a given programming language. See our documentation for repository languages for more information about how we detect programming languages.
• Licenses. the number of unique developers in each economy who made at least one git push to a repository with a given license. See our documentation for Licenses for more information about how we classify repositories by license. Note that NOASSERTION in the data or Other (displayed) means a license file was found but could not be identified with high confidence, or multiple licenses were present in a repository.
• Topics: the number of unique developers who made at least one git push to a repository with a given topic. See our documentation for Topics for more information about how developers assign topics to repositories.
• Economy collaborators: the volume of collaboration on software projects based on the sum of git pushes sent and pull requests opened by a developer to a repository owned by another developer or organization. See the documentation for git push for a description of the git push command. See our documentation for Pull Requests and Repositories for more information about supported functionality.

Graph and download economic data for OMX Copenhagen Technology Hardware and Equipment GI (NASDAQCX101020GI) from 2000-01-03 to 2025-11-28 about hardware, NASDAQ, equipment, indexes, and USA.

As of March 2025, the share of time spent using the internet on smartphone devices among users in the United Kingdom (UK) was approximately 96 percent. Internet usage via smartphone was the highest amongst UK users aged between the ages 25 and 54 years, 98 percent. Furthermore, tablet devices had the largest engagement among users aged between 65 and over, while the share of time spent accessing the internet via PC or laptop devices was highest among UK users aged 75 and over.

🏡 Household Energy Consumption - April 2025 (90,000 Records)

📌 Overview

This dataset presents detailed energy consumption records from various households over the month. With 90,000 rows and multiple features such as temperature, household size, air conditioning usage, and peak hour consumption, this dataset is perfect for performing time-series analysis, machine learning, and sustainability research.

Column Name	Data Type Category	Description
Household_ID	Categorical (Nominal)	Unique identifier for each household
Date	Datetime	The date of the energy usage record
Energy_Consumption_kWh	Numerical (Continuous)	Total energy consumed by the household in kWh
Household_Size	Numerical (Discrete)	Number of individuals living in the household
Avg_Temperature_C	Numerical (Continuous)	Average daily temperature in degrees Celsius
Has_AC	Categorical (Binary)	Indicates if the household has air conditioning (Yes/No)
Peak_Hours_Usage_kWh	Numerical (Continuous)	Energy consumed during peak hours in kWh

📂 Dataset Summary

• Rows: 90,000
• Time Range: April 1, 2025 – April 30, 2025
• Data Granularity: Daily per household
• Location: Simulated global coverage
• Format: CSV (Comma-Separated Values)

📚 Libraries Used for Working with household_energy_consumption_2025.csv

🔍 1. Data Manipulation & Analysis

Library	Purpose
pandas	Reading, cleaning, and transforming tabular data
numpy	Numerical operations, working with arrays

📊 2. Data Visualization

Library	Purpose
matplotlib	Creating static plots (line, bar, histograms, etc.)
seaborn	Statistical visualizations, heatmaps, boxplots, etc.
plotly	Interactive charts (time series, pie, bar, scatter, etc.)

📈 3. Machine Learning / Modeling

Library	Purpose
scikit-learn	Preprocessing, regression, classification, clustering
xgboost / lightgbm	Gradient boosting models for better accuracy

🧹 4. Data Preprocessing

Library	Purpose
sklearn.preprocessing	Encoding categorical features, scaling, normalization
datetime / pandas	Date-time conversion and manipulation

🧪 5. Model Evaluation

Library	Purpose
sklearn.metrics	Accuracy, MAE, RMSE, R² score, confusion matrix, etc.

✅ These libraries provide a complete toolkit for performing data analysis, modeling, and visualization tasks efficiently.

📈 Potential Use Cases

This dataset is ideal for a wide variety of analytics and machine learning projects:

🔮 Forecasting & Time Series Analysis

• Predict future household energy consumption based on previous trends and weather conditions.
• Identify seasonal and daily consumption patterns.

💡 Energy Efficiency Analysis

• Analyze differences in energy consumption between households with and without air conditioning.
• Compare energy usage efficiency across varying household sizes.

🌡️ Climate Impact Studies

• Investigate how temperature affects electricity usage across households.
• Model the potential impact of climate change on residential energy demand.

🔌 Peak Load Management

• Build models to predict and manage energy demand during peak hours.
• Support research on smart grid technologies and dynamic pricing.

🧠 Machine Learning Projects

• Supervised learning (regression/classification) to predict energy consumption.
• Clustering households by usage patterns for targeted energy programs.
• Anomaly detection in energy usage for fault detection.

🛠️ Example Starter Projects

• Time-series forecasting using Facebook Prophet or ARIMA
• Regression modeling using XGBoost or LightGBM
• Classification of AC vs. non-AC household behavior
• Energy-saving recommendation systems
• Heatmaps of temperature vs. energy usage

Selling-General-and-Administrative Time Series for Suntront Tech. Suntront Technology Co., Ltd. develops, manufactures, and sells smart meters in China. The company offers smart water, gas, heat, and energy meters; and remote devices and other products. It also provides prepayment, automatic meter reading, and technique related solutions, as well as smart management software with various functions, such as file creation, account opening, daily business dealing, sale and multi inquiry, report summary, read/write card, input/collect data, bill printing, and blacklist checking. It serves electricity, gas, and water utility and distribution industries. Suntront Technology Co., Ltd. was founded in 2000 and is headquartered in Zhengzhou, China.

Time Series of Staked ETH

Columns: - datetime: This column contains the date and time in the format YYYY-MM-DD. It represents the timestamp of each data point. - staked_eth: This column contains the number of staked ETH at the corresponding datetime.

Data Points: - Each row represents a data entry with the datetime and staked_eth amount

Source: beaconcha.in Purpose: For analysis of Ether value and network health

Behavior Chart Magnets for Kids Market Outlook

According to our latest research, the global Behavior Chart Magnets for Kids market size reached USD 1.17 billion in 2024, with a robust year-on-year growth supported by increasing parental and educational focus on positive reinforcement tools. The market is anticipated to expand at a CAGR of 7.5% from 2025 to 2033, leading to a projected value of USD 2.23 billion by 2033. This growth is primarily driven by the rising adoption of innovative educational aids, the increasing prevalence of behavioral management strategies in schools and homes, and the surge in demand for customizable and interactive learning solutions worldwide.

One of the most significant growth factors for the Behavior Chart Magnets for Kids market is the increasing awareness among parents and educators about the importance of early childhood behavioral development. As more research highlights the effectiveness of positive reinforcement in shaping desirable behaviors, parents and teachers are actively seeking tools that facilitate structured and engaging behavioral management. Magnetic behavior charts, with their visual appeal and interactive nature, have proven to be highly effective in encouraging children to participate in daily routines, complete chores, and achieve academic goals. The trend is further reinforced by the growing emphasis on social-emotional learning (SEL) in educational curricula, which has prompted schools and daycare centers to invest in practical tools that support the development of key life skills in young children.

Another key driver fueling the expansion of the Behavior Chart Magnets for Kids market is the proliferation of e-commerce platforms and digital retail channels. The widespread availability of these products online has significantly broadened their reach, making it easier for parents, teachers, and caregivers to access a diverse range of behavior chart magnets tailored to various needs and preferences. Furthermore, the ability to customize charts according to individual child requirements, routines, or specific behavioral goals has enhanced product appeal and adoption rates. The integration of educational technology and gamification elements into behavior chart magnets is also contributing to market growth, as these features make the products more engaging and effective for children, thereby increasing repeat purchases and word-of-mouth recommendations.

The increasing investments in early childhood education infrastructure, especially in emerging economies, are also playing a pivotal role in driving market growth. Governments and private sector stakeholders are recognizing the long-term benefits of fostering positive behaviors in children, leading to increased funding for educational resources and classroom management tools. As a result, there is a rising demand for behavior chart magnets in both public and private educational institutions. Additionally, the growing trend of dual-income households has led to a heightened focus on efficient home management solutions, further boosting the adoption of behavior chart magnets for use in domestic settings. This confluence of factors is expected to sustain the upward trajectory of the market over the forecast period.

Reward Sticker Charts for Kids are increasingly becoming a popular complement to behavior chart magnets, offering an additional layer of motivation and engagement for children. These charts utilize the allure of stickers as a tangible reward for achieving specific goals or exhibiting positive behavior. Parents and educators find them particularly effective in reinforcing desired actions, as children can visually track their progress and feel a sense of accomplishment with each sticker earned. The versatility of reward sticker charts allows them to be easily integrated into various routines, whether at home or in educational settings, making them a valuable tool in promoting consistency and discipline. As the market for behavior management tools continues to expand, the demand for innovative and customizable reward systems like sticker charts is expected to rise, providing new opportunities for manufacturers to cater to diverse consumer needs.

Regionally, North America continues to dominate the Behavior Chart Magnets for Kids market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The high market penetration in the United State

Our US Technographic data is meticulously gathered and aggregated from surveys, digital services, and public data sources. Utilizing advanced profiling algorithms, we ensure the collection and ingestion of only the freshest and most reliable data points.

Our comprehensive data enrichment solution offers an extensive array of data sets that can bridge gaps in your Company data, provide deeper insights into your customers, and enhance client experiences.

Technographic Data Attributes:

Firmographics: Industry, Company Size, Revenue, Employee Count, Headquarters Location, Subsidiaries, etc. Technology Stack: Software Used, Hardware Used, Cloud Services, IT Infrastructure, CRM Systems, Marketing Automation Tools, etc. Digital Transformation: Digital Maturity, Technology Initiatives, Innovation Projects, etc. Purchase Behavior: Technology Purchasing Cycle, Vendor Preferences, Procurement Processes, etc. Online Presence: Website Technologies, E-commerce Platforms, Social Media Presence, etc. Communication Channels: Preferred Communication Tools, Collaboration Platforms, etc. Employee Skills: Certifications, Technical Expertise, Training Programs, etc. Technographic Graph Schema & Reach:

Our data reach represents the total counts available within various categories, including attributes such as country location, MAU (Monthly Active Users), DAU (Daily Active Users), and Monthly Location Pings.

Data Export Methodology: We dynamically collect data to provide the most up-to-date insights. Data and insights are delivered via the most suitable method at optimal intervals (daily/weekly/monthly).

Technographic Graph Use Cases:

360-Degree Customer View: Gain a comprehensive image of customers by aggregating internal and external data.

Data Enrichment: Build holistic audience segments to improve campaign targeting using enriched user profiles.

Fraud Detection: Verify real users and detect anomalies or fraudulent activities using multiple digital (web and mobile) identities.

Advertising & Marketing: Understand audience demographics, interests, lifestyles, hobbies, and behaviors to create targeted marketing campaigns.

Index Time Series for ProShares Ultra Technology. The frequency of the observation is daily. Moving average series are also typically included. The fund invests in financial instruments that ProShare Advisors believes, in combination, should produce daily returns consistent with the Daily Target. The index is designed to measure the performance of information technology companies included in the S&P 500 Index. Under normal circumstances, the fund will obtain leveraged exposure to at least 80% of its total assets in components of the index or in instruments with similar economic characteristics. The fund is non-diversified.