Context

This file contains 5 years of daily time series data for several measures of traffic on a statistical forecasting teaching notes website whose alias is statforecasting.com. The variables have complex seasonality that is keyed to the day of the week and to the academic calendar. The patterns you you see here are similar in principle to what you would see in other daily data with day-of-week and time-of-year effects. Some good exercises are to develop a 1-day-ahead forecasting model, a 7-day ahead forecasting model, and an entire-next-week forecasting model (i.e., next 7 days) for unique visitors.

Content

The variables are daily counts of page loads, unique visitors, first-time visitors, and returning visitors to an academic teaching notes website. There are 2167 rows of data spanning the date range from September 14, 2014, to August 19, 2020. A visit is defined as a stream of hits on one or more pages on the site on a given day by the same user, as identified by IP address. Multiple individuals with a shared IP address (e.g., in a computer lab) are considered as a single user, so real users may be undercounted to some extent. A visit is classified as "unique" if a hit from the same IP address has not come within the last 6 hours. Returning visitors are identified by cookies if those are accepted. All others are classified as first-time visitors, so the count of unique visitors is the sum of the counts of returning and first-time visitors by definition. The data was collected through a traffic monitoring service known as StatCounter.

Inspiration

This file and a number of other sample datasets can also be found on the website of RegressIt, a free Excel add-in for linear and logistic regression which I originally developed for use in the course whose website generated the traffic data given here. If you use Excel to some extent as well as Python or R, you might want to try it out on this dataset.

The dataset contains information, divided by month, on the accesses made to the online services offered by the citizen's file and provided by the municipality of Milan. The pageviews column represents the total number of web pages, which have been displayed within the time frame used. The visitors column represents the total number of unique visitors who have accessed the web pages. By unique visitor, we mean a visitor counted only once within the time frame used.

This competition involves advertisement data provided by BuzzCity Pte. Ltd. BuzzCity is a global mobile advertising network that has millions of consumers around the world on mobile phones and devices. In Q1 2012, over 45 billion ad banners were delivered across the BuzzCity network consisting of more than 10,000 publisher sites which reach an average of over 300 million unique users per month. The number of smartphones active on the network has also grown significantly. Smartphones now account for more than 32% phones that are served advertisements across the BuzzCity network. The "raw" data used in this competition has two types: publisher database and click database, both provided in CSV format. The publisher database records the publisher's (aka partner's) profile and comprises several fields:

publisherid - Unique identifier of a publisher. Bankaccount - Bank account associated with a publisher (may be empty) address - Mailing address of a publisher (obfuscated; may be empty) status - Label of a publisher, which can be the following: "OK" - Publishers whom BuzzCity deems as having healthy traffic (or those who slipped their detection mechanisms) "Observation" - Publishers who may have just started their traffic or their traffic statistics deviates from system wide average. BuzzCity does not have any conclusive stand with these publishers yet "Fraud" - Publishers who are deemed as fraudulent with clear proof. Buzzcity suspends their accounts and their earnings will not be paid

On the other hand, the click database records the click traffics and has several fields:

id - Unique identifier of a particular click numericip - Public IP address of a clicker/visitor deviceua - Phone model used by a clicker/visitor publisherid - Unique identifier of a publisher adscampaignid - Unique identifier of a given advertisement campaign usercountry - Country from which the surfer is clicktime - Timestamp of a given click (in YYYY-MM-DD format) publisherchannel - Publisher's channel type, which can be the following: ad - Adult sites co - Community es - Entertainment and lifestyle gd - Glamour and dating in - Information mc - Mobile content pp - Premium portal se - Search, portal, services referredurl - URL where the ad banners were clicked (obfuscated; may be empty). More details about the HTTP Referer protocol can be found in this article. Related Publication: R. J. Oentaryo, E.-P. Lim, M. Finegold, D. Lo, F.-D. Zhu, C. Phua, E.-Y. Cheu, G.-E. Yap, K. Sim, M. N. Nguyen, K. Perera, B. Neupane, M. Faisal, Z.-Y. Aung, W. L. Woon, W. Chen, D. Patel, and D. Berrar. (2014). Detecting click fraud in online advertising: A data mining approach, Journal of Machine Learning Research, 15, 99-140.

The dataset contains information, divided by month, on accesses made to the online services offered by the opendata portal and provided by the municipality of Milan. The pageviews column represents the total number of web pages that have been viewed within the time frame used. The visits column represents the total visits made, within the time frame used. The visitors column represents the total number of unique visitors who have accessed the web pages. By unique visitor, we mean a visitor counted only once within the time frame used.

Number of unique visitors who logged onto GCcampus/BrightSpace per month.

The All CMS Data Feeds dataset is an expansive resource offering access to 119 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system including nursing facility owners and accountable care organization participants contact data. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis too...

Individual visits to El Pueblo museums, per month. *The Museum of Social Justice is an independently operated museum, and reopened to the public May 2021. All El Pueblo-operated museums partially reopened June 10, 2021.

This Location Data & Foot traffic dataset available for all countries include enriched raw mobility data and visitation at POIs to answer questions such as:

-How often do people visit a location? (daily, monthly, absolute, and averages). -What type of places do they visit ? (parks, schools, hospitals, etc) -Which social characteristics do people have in a certain POI? - Breakdown by type: residents, workers, visitors. -What's their mobility like enduring night hours & day hours?
-What's the frequency of the visits partition by day of the week and hour of the day?

Extra insights -Visitors´ relative income Level. -Visitors´ preferences as derived by their visits to shopping, parks, sports facilities, churches, among others.

Overview & Key Concepts Each record corresponds to a ping from a mobile device, at a particular moment in time and at a particular latitude and longitude. We procure this data from reliable technology partners, which obtain it through partnerships with location-aware apps. All the process is compliant with applicable privacy laws.

We clean and process these massive datasets with a number of complex, computer-intensive calculations to make them easier to use in different data science and machine learning applications, especially those related to understanding customer behavior.

Featured attributes of the data Device speed: based on the distance between each observation and the previous one, we estimate the speed at which the device is moving. This is particularly useful to differentiate between vehicles, pedestrians, and stationery observations.

Night base of the device: we calculate the approximated location of where the device spends the night, which is usually their home neighborhood.

Day base of the device: we calculate the most common daylight location during weekdays, which is usually their work location.

Income level: we use the night neighborhood of the device, and intersect it with available socioeconomic data, to infer the device’s income level. Depending on the country, and the availability of good census data, this figure ranges from a relative wealth index to a currency-calculated income.

POI visited: we intersect each observation with a number of POI databases, to estimate check-ins to different locations. POI databases can vary significantly, in scope and depth, between countries.

Category of visited POI: for each observation that can be attributable to a POI, we also include a standardized location category (park, hospital, among others). Coverage: Worldwide.

Delivery schemas We can deliver the data in three different formats:

Full dataset: one record per mobile ping. These datasets are very large, and should only be consumed by experienced teams with large computing budgets.

Visitation stream: one record per attributable visit. This dataset is considerably smaller than the full one but retains most of the more valuable elements in the dataset. This helps understand who visited a specific POI, characterize and understand the consumer's behavior.

Audience profiles: one record per mobile device in a given period of time (usually monthly). All the visitation stream is aggregated by category. This is the most condensed version of the dataset and is very useful to quickly understand the types of consumers in a particular area and to create cohorts of users.

The dataset contains information, divided by month, on accesses made to the online services offered by the institutional portal and provided by the municipality of Milan. The pageviews column represents the total number of web pages that have been viewed within the time frame used. The visits column represents the total visits made, within the time frame used. The visitors column represents the total number of unique visitors who have accessed the web pages. By unique visitor, we mean a visitor counted only once within the time frame used.

Foreword

This users dataset is a preview of a much bigger dataset, with lots of related data (product listings of sellers, comments on listed products, etc...).

My Telegram bot will answer your queries and allow you to contact me.

Context

There are a lot of unknowns when running an E-commerce store, even when you have analytics to guide your decisions.

Users are an important factor in an e-commerce business. This is especially true in a C2C-oriented store, since they are both the suppliers (by uploading their products) AND the customers (by purchasing other user's articles).

This dataset aims to serve as a benchmark for an e-commerce fashion store. Using this dataset, you may want to try and understand what you can expect of your users and determine in advance how your grows may be.

• For instance, if you see that most of your users are not very active, you may look into this dataset to compare your store's performance.

If you think this kind of dataset may be useful or if you liked it, don't forget to show your support or appreciation with an upvote/comment. You may even include how you think this dataset might be of use to you. This way, I will be more aware of specific needs and be able to adapt my datasets to suits more your needs.

This dataset is part of a preview of a much larger dataset. Please contact me for more.

Content

The data was scraped from a successful online C2C fashion store with over 10M registered users. The store was first launched in Europe around 2009 then expanded worldwide.

Visitors vs Users: Visitors do not appear in this dataset. Only registered users are included. "Visitors" cannot purchase an article but can view the catalog.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Questions you might want to answer using this dataset:

• Are e-commerce users interested in social network feature ?
• Are my users active enough (compared to those of this dataset) ?
• How likely are people from other countries to sign up in a C2C website ?
• How many users are likely to drop off after years of using my service ?

Example works:

• Report(s) made using SQL queries can be found on the data.world page of the dataset.
• Notebooks may be found on the Kaggle page of the dataset.

License

CC-BY-NC-SA 4.0

For other licensing options, contact me.

Determining the Prospective Tariff for a Telecom Company

Project Description

You are an analyst at "Megaline," a federal mobile operator. The company offers two tariff plans to customers: "Smart" and "Ultra." To adjust the advertising budget, the commercial department wants to understand which tariff generates more revenue.

You need to conduct a preliminary analysis of the tariffs on a small sample of customers. You have data on 500 users of "Megaline": who they are, where they are from, which tariff they use, how many calls and messages they sent in 2018. You need to analyze customer behavior and conclude which tariff is better.

Tariff Descriptions

"Smart" Tariff: - Monthly fee: 550 rubles - Included: 500 minutes of calls, 50 messages, and 15 GB of internet traffic - Cost of services beyond the tariff package: 1. Call minute: 3 rubles (Megaline always rounds up minutes and megabytes. If the user talked for just 1 second, it counts as a whole minute); 2. Message: 3 rubles; 3. 1 GB of internet traffic: 200 rubles.

"Ultra" Tariff: - Monthly fee: 1950 rubles - Included: 3000 minutes of calls, 1000 messages, and 30 GB of internet traffic - Cost of services beyond the tariff package: 1. Call minute: 1 ruble; 2. Message: 1 ruble; 3. 1 GB of internet traffic: 150 rubles.

Note: Megaline always rounds up seconds to minutes and megabytes to gigabytes. Each call is rounded up individually: even if it lasted just 1 second, it is counted as 1 minute. For web traffic, separate sessions are not counted. Instead, the total amount for the month is rounded up. If a subscriber uses 1025 megabytes in a month, they are charged for 2 gigabytes.

Project Steps

Step 1: Open the file with data and study the general information File paths: - /datasets/calls.csv - /datasets/internet.csv - /datasets/messages.csv - /datasets/tariffs.csv - /datasets/users.csv

Step 2: Prepare the data - Convert data to the required types; - Find and fix errors in the data, if any. Explain what errors you found and how you fixed them. You will find calls with zero duration in the data. This is not an error: missed calls are indicated by zeros, so they do not need to be deleted.

For each user, calculate: - Number of calls made and minutes spent per month; - Number of messages sent per month; - Amount of internet traffic used per month; - Monthly revenue from each user (subtract the free limit from the total number of calls, messages, and internet traffic; multiply the remainder by the value from the tariff plan; add the corresponding tariff plan's subscription fee).

Step 3: Analyze the data Describe the behavior of the operator's customers based on the sample. How many minutes of calls, how many messages, and how much internet traffic do users of each tariff need per month? Calculate the average, variance, and standard deviation. Create histograms. Describe the distributions.

Step 4: Test hypotheses - The average revenue of users of the "Ultra" and "Smart" tariffs is different; - The average revenue of users from Moscow differs from the revenue of users from other regions. Moscow is written as 'Москва'. You can put it in your value, when check the hypothesis

Set the threshold alpha value yourself.

Explain: - How you formulated the null and alternative hypotheses; - Which criterion you used to test the hypotheses and why.

Step 5: Write a general conclusion

Formatting: Perform the task in Jupyter Notebook. Fill the program code in the cells of type code, and the textual explanations in the cells of type markdown. Apply formatting and headers.

Data Description

Table users (user information): - user_id: unique user identifier - first_name: user's first name - last_name: user's last name - age: user's age (years) - reg_date: date of tariff connection (day, month, year) - churn_date: date of tariff discontinuation (if the value is missing, the tariff was still active at the time of data extraction) - city: user's city of residence - tariff: name of the tariff plan

Table calls (call information): - id: unique call number - call_date: call date - duration: call duration in minutes - user_id: identifier of the user who made the call

Table messages (message information): - id: unique message number - message_date: message date - user_id: identifier of the user who sent the message

Table internet (internet session information): - id: unique session number - mb_used: amount of internet traffic used during the session (in megabytes) - session_date: internet session date - user_id: user identifier

Table tariffs (tariff information): - tariff_name: tariff name - rub_monthly_fee: monthly subscription fee in rubles - minutes_included: number of call minutes included per month - `messages_included...

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...

Our Activity dataset reveals real-world behavior through detailed foot traffic metrics around POIs in the US, Canada, and Mexico — all GDPR-compliant and non-PII.

By capturing total visits, unique visitors, and frequency of visits, our dataset enables a precise view of how consumers move, behave, and engage with locations over time — helping brands uncover demand, evaluate performance, and outpace competitors.

Key data points include: - Total visits, unique visitors, and visit frequency - Daily, weekly, monthly, and quarterly aggregation - Movement patterns around and within trade areas - Cleaned, normalized, and updated daily - Non-PII, GDPR-compliant location intelligence

Ideal for demand sensing, competitive benchmarking, and performance analysis, this dataset helps retail, real estate, and investment teams unlock powerful insights across North America.

This Mobility & Foot traffic dataset includes enriched mobility data and visitation at POIs to answer questions such as: -How often do people visit a location? (daily, monthly, absolute, and averages). -What type of places do they visit? (parks, schools, hospitals, etc) -Which social characteristics do people have in a certain POI? - Breakdown by type: residents, workers, visitors. -What's their mobility like during night hours & day hours?
-What's the frequency of the visits by day of the week and hour of the day? Extra insights -Visitors´ relative Income Level. -Visitors´ preferences as derived from their visits to shopping, parks, sports facilities, and churches, among others. - Footfall measurement in all types of establishments (shopping malls, stand-alone stores, etc). -Visitors´ preferences as derived from their visits to shopping, parks, sports facilities, and churches, among others. - Origin/Destiny matrix. - Vehicular traffic, measurement of speed, types of vehicles, among other insights. Overview & Key Concepts Each record corresponds to a ping from a mobile device, at a particular moment in time, and at a particular lat and long. We procure this data from reliable technology partners, which obtain it through partnerships with location-aware apps. All the process is compliant with applicable privacy laws. We clean, process and enrich these massive datasets with a number of complex, computer-intensive calculations to make them easier to use in different tailor-made solutions for companies and also data science and machine learning applications, especially those related to understanding customer behavior. Featured attributes of the data Device speed: based on the distance between each observation and the previous one, we estimate the speed at which the device is moving. This is particularly useful to differentiate between vehicles, pedestrians, and stationery observations. Night base of the device: we calculate the approximate location of where the device spends the night, which is usually its home neighborhood. Day base of the device: we calculate the most common daylight location during weekdays, which is usually their work location. Income level: we use the night neighborhood of the device, and intersect it with available socioeconomic data, to infer the device’s income level. Depending on the country, and the availability of good census data, this figure ranges from a relative wealth index to a currency-calculated income. POI visited: we intersect each observation with a number of POI databases, to estimate check-ins to different locations. POI databases can vary significantly, in scope and depth, between countries. Category of visited POI: for each observation that can be attributable to a POI, we also include a standardized location category (park, hospital, among others). Delivery schemas We can deliver the data in three different formats: Full dataset: one record per mobile ping. These datasets are very large, and should only be consumed by experienced teams with large computing budgets. Visitation stream: one record per attributable visit. This dataset is considerably smaller than the full one but retains most of the more valuable elements in the dataset. This helps understand who visited a specific POI, and characterize and understand the consumer's behavior. Audience profiles: one record per mobile device in a given period of time (usually monthly). All the visitation stream is aggregated by category. This is the most condensed version of the dataset and is very useful to quickly understand the types of consumers in a particular area and to create cohorts of users.

This dataset provides the number of visits to various cultural venues in Qatar. It includes data for museums, creative hubs, and heritage sites. The dataset is structured by venue type, with monthly visit counts for each venue, as well as the total number of visits for the year. This data can be used to analyze trends in visitor behavior, the popularity of specific venues, and overall cultural engagement.

The U.S. Monthly Climate Normals for 1981 to 2010 are 30-year averages of meteorological parameters for thousands of U.S. stations located across the 50 states, as well as U.S. territories, commonwealths, the Compact of Free Association nations, and one station in Canada. NOAA Climate Normals are a large suite of data products that provide users with many tools to understand typical climate conditions for thousands of locations across the United States. As many NWS stations as possible are used, including those from the NWS Cooperative Observer Program (COOP) Network as well as some additional stations that have a Weather Bureau Army-Navy (WBAN) station identification number, including stations from the Climate Reference Network (CRN). The comprehensive U.S. Climate Normals dataset includes various derived products including daily air temperature normals (including maximum and minimum temperature normal, heating and cooling degree day normal, and others), precipitation normals (including snowfall and snow depth, percentiles, frequencies and other), and hourly normals (all normal derived from hourly data including temperature, dew point, heat index, wind chill, wind, cloudiness, heating and cooling degree hours, pressure normals). In addition to the standard set of normals, users also can find "agricultural normals", which are used in many industries, including but not limited to construction, architecture, pest control, etc. These supplemental "agricultural normals" include frost-freeze date probabilities, growing degree day normals, probabilities of reaching minimum temperature thresholds, and growing season length normals. Users can access the data either by product or by station. Included in the dataset is extensive documentation to describe station metadata, filename descriptions, and methodology of producing the data. All data utilized in the computation of the 1981-2010 Climate Normals were taken from the ISD Lite (a subset of derived Integrated Surface Data), the Global Historical Climatology Network-Daily dataset, and standardized monthly temperature data (COOP). These source datasets (including intermediate datasets used in the computation of products) are also archived at the NOAA NCDC.

Social Media has become a part of our day-to-day routine, keeping users from across the world well-connected through digital platforms. With each passing year, social media is evolving at a rapid speed. With each passing year, the number of social media users is increasing at an immersive speed. Reports also suggest the number of social media users will reach a milestone of 5.85 billion in 2027.

In 2024, 62.6% of the world’s population will access social media, which clearly indicates the dominance of social media platforms in today’s world. In this article, we will examine social media statistics for 2024, uncovering monthly active users, daily time spent by users, most downloaded social media apps, etc.

This dataset comprises a corpus of 50 text contexts, each about 60 words in length, sourced from five distinct domains. Each context has been evaluated by multiple annotators who identified and ranked the most important words—up to 10% of each text—according to their perceived significance. The annotators followed specific guidelines to ensure consistency in word selection and ranking. For further details, please refer to the cited source.

rankings_task.csv - This csv contains information about the contexts which are to be annotated: - id: A unique identifier for each task.

- content: The context to be ranked.

rankings_ranking.csv - This csv includes ranking information for various assignments. It contains four columns: - id: A unique identifier for each ranking entry. - score: The score assigned to the entry. - word_order: A JSON detailing the order of words positions. It is essentially the selected word positions and their ordering from an annotator.

- assignment_id: A reference ID linking to the assignments.

rankings_assignment.csv - This csv tracks the completion status of tasks by users. It includes four columns: - id: A unique identifier for each assignment entry. - is_completed: A binary indicator (1 for completed, 0 for not completed). - task_id: A reference ID linking to the tasks.

- user_id: The identifier for the user who should complete the task (rank the words).

Known Issues:

Please note that each annotator was intended to rank each context only once. However, due to a bug in the deployment of the annotation tool, some entries may be duplicated. Users of this dataset should be cautious of this issue and verify the uniqueness of the annotations where necessary.

This dataset is a part of work from a bachelor thesis: OSUSKÝ, Adam. Predicting Word Importance Using Pre-Trained Language Models. Bachelor thesis, supervisor Javorský, Dávid. Prague: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics, 2024.

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.