From adolescence to old age: who do we spend our time with?

To understand how social connections evolve throughout our lives, we can look at survey data on how much time people spend with others and who that time is spent with.

This dataset shows the amount of time people in the US report spending in the company of others, based on their age. The data comes from time-use surveys, where people are asked to list all the activities they perform over a full day and the people who were present during each activity. Currently, there is only data with this granularity for the US – time-use surveys are common across many countries, but what is special about the US is that respondents of the American Time Use Survey are asked to list everyone present for each activity.

The numbers in this chart are based on averages for a cross-section of US society – people are only interviewed once, but the dataset represents a decade of surveys, tabulating the average amount of time survey respondents of different ages report spending with other people.

Source

https://ourworldindata.org/time-with-others-lifetime by Esteban Ortiz-Ospina December 11, 2020

How Does Daily Yoga Impact Screen Time Habits

A Study of Daily Screen Time Behavior

By Taylor L Bailey [source]

About this dataset

This dataset contains data on daily minutes of screen time between April 17th and May 14th. With this dataset, you can gain insights into daily phone usage habits and determine the effect that regular yoga practice has on reducing phone use. By recording the amount of time spent using different types of apps -- such as social media, reading, productivity and entertainment -- you can understand how phone habits have changed over time. Moreover, this dataset captures my attempt to do at least 10 minutes of yoga every day for a period of 15 days from April 29th to May 13th. Did this experiment successfully reduce my screen time overall? Dive in deep and find out!

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How to use the dataset

How to use this dataset

This dataset contains information on daily minutes of screen time habits, categorized by type of usage, as well as the effect of yoga on those habits. This is useful for gaining insights into an individual's screen time habits and its variability with respect to doing yoga.

To start with, there are a few key columns to check out: Date (to keep track of the days in view), Week Day (to identify which day it is precisely), Social Networking/Reading and Reference/Other/Productivity/Health and Fitness (to determine how much time was spent in each category) and Yoga (whether or not any yoga was done that day).

You may find it helpful to analyze the daily data over a certain duration by creating separate datasets grouped by weeks or months. Additionally, tallying each person's total minutes per week or per month can show changes over long-term periods. As you will notice right away in viewing this dataset, consistency is important; if someone were tracking their smartphone use regularly but only measured twice during a month period or skipped days without setting aside any reference points prior, then this particular experiment would be somewhat difficult to draw conclusions from. It would be especially impactful if specific factors such as sleep hygiene were tracked along with practice evolution such us advanced yoga sequences tried out over time alongside different approaches at making screens off-limits during mealtime - all items that could bring interesting insight into our relationship with technology devices when looking at screentime fluctuations before and after our mediations become part of our daily routine

Research Ideas

• Track the impact of daily yoga on overall and category-specific screen time.
• Explore the relationship between day of the week and overall or category-specific screen time.
• Investigate how long it takes to establish a healthy habit, such as decreased phone usage, by looking at changes in average daily screen time over the period of a month or two months before and after beginning yoga practice, adjusting for weekly period effect

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Screen Time Data.csv | Column name | Description | |:--------------------------|:------------------------------------------------------------------------------------------| | Date | The date of the data entry. (Date) | | Week Day | The day of the week of the data entry. (String) | | Social Networking | The amount of time spent on social networking. (Integer) | | Reading and Reference | The amount of time spent on reading and reference activities. (Integer) | | Other ...

American Time Use Survey: Daily Activities

Americans' Daily Activities: Education, Employment, Gender, and Leisure Time

By Throwback Thursday [source]

About this dataset

The American Time Use Survey dataset provides comprehensive information on how individuals in America allocate their time throughout the day. It includes various aspects of daily activities such as education level, age, employment status, gender, number of children, weekly earnings and hours worked. The dataset also includes data on specific activities individuals engage in like sleeping, grooming, housework, food and drink preparation, caring for children, playing with children, job searching, shopping and eating and drinking. Additionally it captures time spent on leisure activities like socializing and relaxing as well as engaging in specific hobbies such as watching television or golfing. The dataset also records the amount of time spent volunteering or running for exercise purposes.

Each entry is organized based on categorical variables such as education level (ranging from lower levels to higher degrees), age (capturing different age brackets), employment status (including employed full-time or part-time), gender (male or female) and the number of children an individual has. Furthermore it provides information regarding an individual's weekly earnings and hours worked.

This extensive dataset aims to provide insights into how Americans prioritize their time across various aspects of their lives. Whether it be focusing on work-related tasks or indulging in recreational activities,it offers a comprehensive look at the allocation of time among different demographic groups within American society.

This dataset can be used for understanding trends in daily activity patterns across demographics groups over multiple years without directly referencing specific dates

How to use the dataset

How to use this dataset: American Time Use Survey - Daily Activities

Welcome to the American Time Use Survey dataset! This dataset provides valuable information on how Americans spend their time on a daily basis. Here's a guide on how to effectively utilize this dataset for your analysis:

• Familiarize yourself with the columns:

  • Education Level: The level of education attained by the individual.
  • Age: The age of the individual.
  • Age Range: The age range the individual falls into.
  • Employment Status: The employment status of the individual.
  • Gender: The gender of the individual.
  • Children: The number of children that an individual has.
  • Weekly Earnings: The amount of money earned by an individual on a weekly basis.
  • Year: The year in which the data was collected.
  • Weekly Hours Worked: The number of hours worked by an individual on a weekly basis.

• Identify variables related to daily activities: This dataset provides information about various daily activities undertaken by individuals. Some important variables related to daily activities include:

  • Sleeping
  • Grooming
  • Housework
  • Food & Drink Prep
  • Caring for Children
  • Playing with Children
  • Job Searching …and many more!

• Analyze time spent on different activities: This dataset includes numerical values representing time spent in minutes for specific activities such as sleeping, grooming, housework, food and drink preparation, etc. You can use this data to analyze and compare how different groups of individuals allocate their time throughout the day.

• Explore demographic factors: In addition to daily activities, this dataset also includes columns such as education level, age range, employment status, gender, and number of children. You can cross-reference these demographic factors with activity data to gain insights into how different population subgroups spend their time differently.

• Identify trends and patterns: You can use this dataset to identify trends and patterns in how Americans allocate their time over the years. By analyzing data from different years, you may discover changes in certain activities and how they relate to demographic factors or societal shifts.

• Visualize the data: Creating visualizations such as bar graphs, line plots, or pie charts can provide a clear representation of how time is allocated for different activities among various groups of individuals. Visualizations help in understanding the distribution of time spent on different activities and identifying any significant differences or similarities across demographics.

Remember that each column represents a specific variable, whi...

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

About Dataset

This dataset contains detailed information about phones listed on Amazon, including product specifications, user reviews, ratings, and pricing. The dataset can be useful for analyzing product trends, consumer preferences, pricing strategies, and technical features of smartphones sold on the platform. It includes both new and Amazon-renewed phones.

Description

The dataset includes the following key features:

• Color: The available color of the phone.
• Image Links: URLs to the images of the products.
• Descriptions: Detailed descriptions of the phone, including specifications.
• Kind Product: The type or category of the product (smartphones, accessories, etc.).
• Ratings: User ratings (out of 5 stars).
• Number of Ratings: Total count of ratings the product has received.
• Status: Availability status (e.g., In Stock, Out of Stock).
• Number of Buyers Last Month More Than: Approximate number of buyers in the previous month.
• Typical Price: The regular price with usd of the phone without any discounts.
• Price: The current price with usd of the phone.
• You Save: The amount saved if the phone is on discount.
• Discount: The percentage discount offered on the product.
• Brand: The brand name of the phone (e.g., Apple, Samsung).
• OS: The operating system of the phone (e.g., Android, iOS).
• CPU Model: The model of the processor used in the phone.
• Resolution: The screen resolution of the phone.
• Name: The product name as listed on Amazon.
• Wireless Carrier: The supported wireless carrier (e.g., Verizon, AT&T).
• Cellular Technology: The cellular network technology (e.g., 4G, 5G).
• Dimensions: Physical dimensions of the phone.
• ASIN: Amazon Standard Identification Number, a unique product identifier.
• Model: The model number of the phone.
• Amazon Renewed: Indicates whether the product is part of the Amazon Renewed program (refurbished).
• Renewed Smartphones: Additional flag indicating if the phone is renewed.
• Battery Capacity: The capacity of the phone’s battery (in mAh).
• Battery Power: The power rating of the battery.
• Charging Time: Time taken to charge the phone fully.
• RAM: The amount of RAM in the phone.
• Storage: Internal storage capacity of the phone.
• Screen Size: Size of the display (in inches).
• Connectivity Technologies: Wireless technologies supported by the phone (e.g., Bluetooth, Wi-Fi).
• Wireless Network: Type of wireless networks supported (e.g., Wi-Fi 6).
• CPU Speed: The speed of the phone’s CPU (in GHz).
• Reviews USA: User reviews originating from the USA.
• Reviews Other: User reviews from countries other than the USA.

Detail

This dataset includes a comprehensive range of variables, offering insight into both the technical aspects and customer perceptions of various smartphones sold on Amazon. The dataset allows for:

• Product Comparisons: Comparison of specifications like RAM, CPU, storage, battery life, screen size, etc.
• Pricing Analysis: Understanding pricing trends, discounts, and price fluctuations across different brands and models.
• Consumer Insights: Analysis of consumer behavior through ratings, reviews, and the number of buyers over time.
• Product Availability: Insights into stock availability and how often certain products are sold or renewed.

Usage

The dataset can be used for several purposes, including but not limited to:

1. Market Research: Analyze product popularity and trends in smartphone sales on Amazon.
2. Sentiment Analysis: Perform sentiment analysis on reviews (USA and other countries) to understand customer satisfaction.
3. Price Forecasting: Build models to forecast price changes or identify the best time to buy based on historical data.
4. Product Recommendations: Develop recommendation systems based on user reviews, ratings, and product features.
5. Competitive Analysis: Compare different brands and models to identify strengths and weaknesses of various smartphones.
6. Feature Engineering for ML Models: Use product specifications like RAM, CPU speed, and battery power to create features for predictive machine learning models.

Summary

This Amazon product phones dataset provides an in-depth look at smartphones sold on Amazon, covering everything from technical specifications to user reviews and pricing. It is ideal for anyone looking to analyze trends in the smartphone market, consumer preferences, or technical specifications. The data can be leveraged for a wide array of projects such as market analysis, machine learning, and competitive intelligence.

Bahamas number dataset provides contact information from trusted sources. We clarify this data by collecting phone numbers that come from reliable sources only. To ensure clearness, we provide source URLs. This shows where the data is gathered from. In addition, we offer 24/7 support. If you have any questions or need help, our team is always here. With List to Data, you can find phone numbers from different countries. However, we care about accuracy, so we collect the Bahamas number dataset carefully from trusted sources. So, you can rely on this data for business or personal use. With customer support, you never have to wait for help or more information. We also use opt-in data to respect privacy. This ensures you contact people who want to hear from you. Bahamas phone data gives you access to contacts in Bahamas. Also, you can filter the information by gender, age, and relationship status. However, this makes it easy to find exactly the people you want to connect with. We define this data by ensuring it follows all GDPR rules to keep it safe and legal. Our team works hard to remove invalid data. This way, you only get correct, useful numbers. In addition, our Bahamas phone data is perfect for businesses looking to target specific groups. Hence, you can easily filter your list to focus on certain types of customers. Besides, we remove invalid data regularly, so you will not have to deal with useless numbers. With regular updates, your phone data will always be ready when you need it. Bahamas phone number list is a collection of phone numbers from people in the Bahamas. We define this list by providing 95% correct and valid phone numbers that are ready to use. Also, we offer a replacement guarantee if you ever receive an invalid number. As a result, you will always have accurate data. We collect the phone numbers we provide based on customer permission. Moreover, we work hard to provide the best Bahamas phone number list for businesses and personal use. Also, we focus on gathering data correctly, so you don’t have to worry about getting incorrect information. Our replacement guarantee gives you peace of mind, knowing that you will always have valid numbers.

This dataset contains metadata (title, abstract, date of publication, field, etc) for around 1 million academic articles. Each record contains additional information on the country of study and whether the article makes use of data. Machine learning tools were used to classify the country of study and data use.

Our data source of academic articles is the Semantic Scholar Open Research Corpus (S2ORC) (Lo et al. 2020). The corpus contains more than 130 million English language academic papers across multiple disciplines. The papers included in the Semantic Scholar corpus are gathered directly from publishers, from open archives such as arXiv or PubMed, and crawled from the internet.

We placed some restrictions on the articles to make them usable and relevant for our purposes. First, only articles with an abstract and parsed PDF or latex file are included in the analysis. The full text of the abstract is necessary to classify the country of study and whether the article uses data. The parsed PDF and latex file are important for extracting important information like the date of publication and field of study. This restriction eliminated a large number of articles in the original corpus. Around 30 million articles remain after keeping only articles with a parsable (i.e., suitable for digital processing) PDF, and around 26% of those 30 million are eliminated when removing articles without an abstract. Second, only articles from the year 2000 to 2020 were considered. This restriction eliminated an additional 9% of the remaining articles. Finally, articles from the following fields of study were excluded, as we aim to focus on fields that are likely to use data produced by countries’ national statistical system: Biology, Chemistry, Engineering, Physics, Materials Science, Environmental Science, Geology, History, Philosophy, Math, Computer Science, and Art. Fields that are included are: Economics, Political Science, Business, Sociology, Medicine, and Psychology. This third restriction eliminated around 34% of the remaining articles. From an initial corpus of 136 million articles, this resulted in a final corpus of around 10 million articles.

Due to the intensive computer resources required, a set of 1,037,748 articles were randomly selected from the 10 million articles in our restricted corpus as a convenience sample.

The empirical approach employed in this project utilizes text mining with Natural Language Processing (NLP). The goal of NLP is to extract structured information from raw, unstructured text. In this project, NLP is used to extract the country of study and whether the paper makes use of data. We will discuss each of these in turn.

To determine the country or countries of study in each academic article, two approaches are employed based on information found in the title, abstract, or topic fields. The first approach uses regular expression searches based on the presence of ISO3166 country names. A defined set of country names is compiled, and the presence of these names is checked in the relevant fields. This approach is transparent, widely used in social science research, and easily extended to other languages. However, there is a potential for exclusion errors if a country’s name is spelled non-standardly.

The second approach is based on Named Entity Recognition (NER), which uses machine learning to identify objects from text, utilizing the spaCy Python library. The Named Entity Recognition algorithm splits text into named entities, and NER is used in this project to identify countries of study in the academic articles. SpaCy supports multiple languages and has been trained on multiple spellings of countries, overcoming some of the limitations of the regular expression approach. If a country is identified by either the regular expression search or NER, it is linked to the article. Note that one article can be linked to more than one country.

The second task is to classify whether the paper uses data. A supervised machine learning approach is employed, where 3500 publications were first randomly selected and manually labeled by human raters using the Mechanical Turk service (Paszke et al. 2019).[1] To make sure the human raters had a similar and appropriate definition of data in mind, they were given the following instructions before seeing their first paper:

Each of these documents is an academic article. The goal of this study is to measure whether a specific academic article is using data and from which country the data came.

There are two classification tasks in this exercise:

1. identifying whether an academic article is using data from any country

2. Identifying from which country that data came.

For task 1, we are looking specifically at the use of data. Data is any information that has been collected, observed, generated or created to produce research findings. As an example, a study that reports findings or analysis using a survey data, uses data. Some clues to indicate that a study does use data includes whether a survey or census is described, a statistical model estimated, or a table or means or summary statistics is reported.

After an article is classified as using data, please note the type of data used. The options are population or business census, survey data, administrative data, geospatial data, private sector data, and other data. If no data is used, then mark "Not applicable". In cases where multiple data types are used, please click multiple options.[2]

For task 2, we are looking at the country or countries that are studied in the article. In some cases, no country may be applicable. For instance, if the research is theoretical and has no specific country application. In some cases, the research article may involve multiple countries. In these cases, select all countries that are discussed in the paper.

We expect between 10 and 35 percent of all articles to use data.

The median amount of time that a worker spent on an article, measured as the time between when the article was accepted to be classified by the worker and when the classification was submitted was 25.4 minutes. If human raters were exclusively used rather than machine learning tools, then the corpus of 1,037,748 articles examined in this study would take around 50 years of human work time to review at a cost of $3,113,244, which assumes a cost of $3 per article as was paid to MTurk workers.

A model is next trained on the 3,500 labelled articles. We use a distilled version of the BERT (bidirectional Encoder Representations for transformers) model to encode raw text into a numeric format suitable for predictions (Devlin et al. (2018)). BERT is pre-trained on a large corpus comprising the Toronto Book Corpus and Wikipedia. The distilled version (DistilBERT) is a compressed model that is 60% the size of BERT and retains 97% of the language understanding capabilities and is 60% faster (Sanh, Debut, Chaumond, Wolf 2019). We use PyTorch to produce a model to classify articles based on the labeled data. Of the 3,500 articles that were hand coded by the MTurk workers, 900 are fed to the machine learning model. 900 articles were selected because of computational limitations in training the NLP model. A classification of “uses data” was assigned if the model predicted an article used data with at least 90% confidence.

The performance of the models classifying articles to countries and as using data or not can be compared to the classification by the human raters. We consider the human raters as giving us the ground truth. This may underestimate the model performance if the workers at times got the allocation wrong in a way that would not apply to the model. For instance, a human rater could mistake the Republic of Korea for the Democratic People’s Republic of Korea. If both humans and the model perform the same kind of errors, then the performance reported here will be overestimated.

The model was able to predict whether an article made use of data with 87% accuracy evaluated on the set of articles held out of the model training. The correlation between the number of articles written about each country using data estimated under the two approaches is given in the figure below. The number of articles represents an aggregate total of

Namibia number dataset benefits your marketing campaigns better. Moreover, this list gives you direct access to mobile numbers for both businesses and individuals across the country. Whether you are targeting customers or looking for new business leads, having the right contact information is crucial. With over 95% accuracy, you can rely on this list to connect with the right people. This results in more sales and better ROI. The list is easy to access. Also, our Namibia number dataset is accurate and regularly updated. It gives you the best data for your marketing campaigns. Also, it is available at an affordable price, so you can start using it immediately to grow your business. Our team keeps the list updated and follows privacy laws like GDPR, ensuring security. Namibia phone data is a helpful tool for growing your business. It allows you to reach the right people quickly, which can lead to more sales and new deals. Our list contains phone numbers of real customers in Namibia who may be interested in your sales. We collect these numbers from trusted sources and carefully check them, so you know they are accurate. Use the data website from the list to make your business successful. Using this Namibia phone data, you can reach people who want to buy your products or services. Namibia’s economy is booming and there are many people here who have an interest in technology, services, and products. Contact us today to get the best and most accurate phone number list for your marketing needs. Namibia phone number list can help you increase your sales through easy marketing. Also, this list allows you to reach people directly through calls and messages, which can further improve your business. This is a great way to get your business better known and grow faster. Our team of experts is available 24/7 to guide you in finding the best phone number for your business. Our Namibia phone number list provides you with real and valuable contacts to use for your marketing. Using this updated list, you can reach your business goals and grow faster. Our Namibia number database also offers custom packages to meet your needs. Whether you are looking for specific numbers or a larger list, we can help.

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

LINE number database is an extensive list of people who use the LINE app. Assume you have a list of everyone in your school, but you build a separate list of only the students who enjoy playing soccer. This is what a LINE Database User List looks like. It allows businesses to target a certain set of people who may be interested in their offer. LINE number database is useful as it may be updated regularly. Businesses, like teachers, may update their lists as new users join LINE or as their interests change. This keeps the list functional and allows firms to contact the relevant people. LINE number database may help businesses measure user engagement and improve their marketing campaigns over time. Businesses that keep the list updated and use it appropriately may develop greater ties with their audience and achieve better outcomes. Finally, the LINE Number Database is an effective tool for businesses to reach the appropriate individuals at the correct time. This valuable database is available on List To Data. LINE data is a valuable resource for businesses seeking to connect with potential customers. This dataset encompasses information about individuals who utilize the LINE messaging app. LINE is a popular messaging platform with over 90 million monthly active users worldwide. Users can seamlessly communicate through messages, voice and video calls, and share engaging stickers. This database has information about users, like their names, phone numbers, email addresses, and sometimes even what they like to do on the app. LINE data is a very useful tool. It helps to sell things or provide services. They use the LINE app user database to find people who might be interested in their offers. But businesses need to be careful with this information. People’s details, like their phone numbers and email addresses, are private. Businesses should always ask for permission before using this information. They also need to keep it safe so that no one else can see it. If businesses respect users’ privacy, people will trust them more and be happier to hear about what the business offers. This data is available on List To Data.

Afghanistan number dataset lets you filter by gender, age, and relationship status to find exactly what you’re looking for. We define this data by organizing it in a way that makes it easy to find the right contacts. Additionally, we follow strict GDPR rules to protect personal information. We update the information regularly to keep your contact lists fresh and useful. However, when you use our Afghanistan number dataset, you can easily search and filter the contacts you want to reach. You don’t have to worry about outdated information because we take care of removing invalid data for you. Our focus on following GDPR rules ensures that you handle data responsibly and legally. Afghanistan phone data offers 100% correct and valid information. We define the data by organizing phone numbers from Afghanistan that you can use right away. If any number turns out to be incorrect, we have a replacement guarantee in place. This helps you always have the right details. Additionally, we collect the data on a customer-permission basis. We also focus on opt-in data, so the people on this list have agreed to be contacted. Our replacement guarantee makes sure you get the right information, and if a number doesn’t work, we replace it. Businesses trust us because we provide data that they can use to connect with people who want to hear from them. We make it simple to reach customers while respecting their privacy. We collect opt-in data, so businesses can use numbers confidently and legally. Afghanistan phone number list provides mobile users’ contact information for Afghanistan. We define this list by gathering phone numbers of people living in Afghanistan. This list includes details like mobile numbers and more, all organized in one place for easy access. The data comes from trusted sources, ensuring its reliability. We provide source URLs to show where we collect the data from. However, we know that accurate phone data matters, especially for businesses looking to contact customers in Afghanistan. We collect the phone numbers in our list carefully and update them regularly. This way, we can provide you with the most current information available. With our reliable support, you can always reach out if you need help using the list.

Cyprus phone number database helps you save both time and money. When you use real data, you spend less time trying to find the right contacts. This number library helps you reach the right people. It makes your marketing efforts faster and more efficient. In Cyprus, many people use their phones every day, so you are sure to reach active users. The library is easy to operate, and you can start reaching out to potential customers right away. Moreover, there is no need to spend hours looking for contact numbers online. Cyprus mobile number data helps you connect with people across Cyprus quickly and easily. Whether you are doing cold calling or sending out SMS promotions, this database helps you reach people quickly. We even offer excellent customer support. If you ever have questions about using Cyprus mobile number data, our team is ready to help. We help you find the right numbers. If you need guidance using the database, we are here for you. Overall, this will help people reach more customers and grow their business faster. Indeed, simply visit our List to Data website, and you can get started right away.

Overview

The goal of this project was to create a structured dataset which can be used to train computer vision models to detect electronic waste devices, i.e., e-waste or Waste Electrical and Electronic Equipment (WEEE). Due to the often-subjective differences between e-waste and functioning electronic devices, a model trained on this dataset could also be used to detect electronic devices in general. However, it must be noted that for the purposes of e-waste recognition, this dataset does not differentiate between different brands or models of the same type of electronic devices, e.g. smartphones, and it also includes images of damaged equipment.

The structure of this dataset is based on the UNU-KEYS classification Wang et al., 2012, Forti et al., 2018. Each class in this dataset has a tag containing its corresponding UNU-KEY. This dataset structure has the following benefits: 1. It allows the user to easily classify e-waste devices regardless of which e-waste definition their country or organization uses, thanks to the correlation between the UNU-KEYS and other classifications such as the HS-codes or the EU-6 categories, defined in the WEEE directive; 2. It helps dataset contributors focus on adding e-waste devices with higher priority compared to arbitrarily chosen devices. This is because electronic devices in the same UNU-KEY category have similar function, average weight and life-time distribution as well as comparable material composition, both in terms of hazardous substances and valuable materials, and related end-of-life attributes Forti et al., 2018. 3. It gives dataset contributors a clear goal of which electronic devices still need to be added and a clear understanding of their progress in the seemingly endless task of creating an e-waste dataset.

This dataset contains annotated images of e-waste from every UNU-KEY category. According to Forti et al., 2018, there are a total of 54 UNU-KEY e-waste categories.

Description of Classes

At the time of writing, 22. Apr. 2024, the dataset has 19613 annotated images and 77 classes. The dataset has mixed bounding-box and polygon annotations. Each class of the dataset represents one type of electronic device. Different models of the same type of device belong to the same class. For example, different brands of smartphones are labelled as "Smartphone", regardless of their make or model. Many classes can belong to the same UNU-KEY category and therefore have the same tag. For example, the classes "Smartphone" and "Bar-Phone" both belong to the UNU-KEY category "0306 - Mobile Phones". The images in the dataset are anonymized, meaning that no people were annotated and images containing visible faces were removed.

The dataset was almost entirely built by cloning annotated images from the following open-source Roboflow datasets: [1]-[91]. Some of the images in the dataset were acquired from the Wikimedia Commons website. Those images were chosen to have an unrestrictive license, i.e., they belong to the public domain. They were manually annotated and added to the dataset.

Cite This Project

This work was done as part of the PhD of Dimitar Iliev, student at the Faculty of German Engineering and Industrial Management at the Technical University of Sofia, Bulgaria and in collaboration with the Faculty of Computer Science at Otto-von-Guericke-University Magdeburg, Germany.

If you use this dataset in a research paper, please cite it using the following BibTeX: @article{iliev2024EwasteDataset, author = "Iliev, Dimitar and Marinov, Marin and Ortmeier, Frank", title = "A proposal for a new e-waste image dataset based on the unu-keys classification", journal = "XXIII-rd International Symposium on Electrical Apparatus and Technologies SIELA 2024", year = 2024, volume = "23", number = "to appear", pages = {to appear} note = {under submission} }

Contribution Guidelines

Image Collection

1. Choose a specific electronic device type to add to the dataset and find its corresponding UNU-KEY. * The chosen type of device should have a characteristic design which an object detection model can learn. For example, CRT monitors look distinctly different than flat panel monitors and should therefore belong to a different class, regardless that they are both monitors. In contrast, LED monitors and LCD monitors look very similar and are therefore both labelled as Flat-Panel-Monitor in this dataset.
2. Collect images of this type of device. * Take note of the license of those images and their author/s to avoid copyright infringement. * Do not collect images with visible faces to protect personal data and comply w

The Michigan Public Policy Survey (MPPS) is a program of state-wide surveys of local government leaders in Michigan. The MPPS is designed to fill an important information gap in the policymaking process. While there are ongoing surveys of the business community and of the citizens of Michigan, before the MPPS there were no ongoing surveys of local government officials that were representative of all general purpose local governments in the state. Therefore, while we knew the policy priorities and views of the state's businesses and citizens, we knew very little about the views of the local officials who are so important to the economies and community life throughout Michigan.The MPPS was launched in 2009 by the Center for Local, State, and Urban Policy (CLOSUP) at the University of Michigan and is conducted in partnership with the Michigan Association of Counties, Michigan Municipal League, and Michigan Townships Association. The associations provide CLOSUP with contact information for the survey's respondents, and consult on survey topics. CLOSUP makes all decisions on survey design, data analysis, and reporting, and receives no funding support from the associations.The surveys investigate local officials' opinions and perspectives on a variety of important public policy issues and solicit factual information about their localities relevant to policymaking. Over time, the program has covered issues such as fiscal, budgetary and operational policy, fiscal health, public sector compensation, workforce development, local-state governmental relations, intergovernmental collaboration, economic development strategies and initiatives such as placemaking and economic gardening, the role of local government in environmental sustainability, energy topics such as hydraulic fracturing ("fracking") and wind power, trust in government, views on state policymaker performance, opinions on the impacts of the Federal Stimulus Program (ARRA), and more. The program will investigate many other issues relevant to local and state policy in the future. A searchable database of every question the MPPS has asked is available on CLOSUP's website. Results of MPPS surveys are currently available as reports, and via online data tables.Out of a commitment to promoting public knowledge of Michigan local governance, the Center for Local, State, and Urban Policy is releasing public use datasets. In order to protect respondent confidentiality, CLOSUP has divided the data collected in each wave of the survey into separate datasets focused on different topics that were covered in the survey. Each dataset contains only variables relevant to that subject, and the datasets cannot be linked together. Variables have also been omitted or recoded to further protect respondent confidentiality. For researchers looking for a more extensive release of the MPPS data, restricted datasets will be available through openICPSR's Virtual Data Enclave.Please note: additional waves of MPPS public use datasets are being prepared, and will be available as part of this project as soon as they are completed.

Notice: Due to funding limitations, this data set was recently changed to a “Basic” Level of Service. Learn more about what this means for users and how you can share your story here: Level of Service Update for Data Products. The Sea Ice Index provides a quick look at Arctic- and Antarctic-wide changes in sea ice. It is a source for consistent, up-to-date sea ice extent and concentration images, in PNG format, and data values, in GeoTIFF and ASCII text files, from November 1978 to the present. Sea Ice Index images also depict trends and anomalies in ice cover calculated using a 30-year reference period of 1981 through 2010.The images and data are produced in a consistent way that makes the Index time-series appropriate for use when looking at long-term trends in sea ice cover. Both monthly and daily products are available. However, monthly products are better to use for long-term trend analysis because errors in the daily product tend to be averaged out in the monthly product and because day-to-day variations are often the result of short-term weather.

Number of People Never Married By Year

Number of People Never Married By Year in the US

By Andy Kriebel [source]

About this dataset

This dataset provides a comprehensive look at the changing trends in marriage and divorce over the years in the United States. It includes data on gender, age range, and year for those who have never been married – examining who is deciding to forgo tying the knot in today’s society. Diving into this data may offer insight into how life-changing decisions are being made as customs shift along with our times. This could be especially interesting when examined by generation or other trends within our population. Are young adults embracing or avoiding marriage? Has divorce become more or less common within certain social groups? Can recent economic challenges be related to changes in marital status trends? Take a look at this dataset and let us know what stories you find!

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How to use the dataset

This dataset contains surveys which explore the number of never married people in the United States, separated by gender, age range and year. You can use this dataset to analyze the trends in never married people throughout the years and see how it is affected by different demographics.

To make the most out of this dataset you could start by exploring the changes on different ages ranges and genders. Plotting how they differ along time might unveil interesting patterns that can help you uncover why certain groups are more or less likely to remain single throughout time. Understanding these trends could also help people looking for a life-partner better understand their own context as compared to others around them enabling them to make informed decisions about when is a good time for them to find someone special.

In addition, this dataset can be used to examine what acts as an enabler or deterrent for staying single within different couples of age ranges and genders across states. Does marriage look more attractive in any particular state? Are there differences between genders? Knowing all these factors can inform us about economic or social insights within society as well as overall lifestyle choices that tend towards being single or married during one's life cycle in different regions around United States of America.

Finally, with this information policymakers can construct efficient policies that better fit our country's priorities by providing programs designed based on specific characteristics within each group helping ensure they match preferable relationships while having access concentrated resources such actions already taken towards promoting wellbeing our citizens regarding relationships like marriage counseling services or family support centers!

Research Ideas

• Examine the differences in trends of ever-married vs never married people across different age ranges and genders.
• Explore the correlation between life decision changes and economic conditions for ever-married and never married people over time.
• Analyze how marriage trends differ based on region, socio-economic status, or religious beliefs to understand how these influence decisions about marriage

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Never Married.csv | Column name | Description | |:------------------|:--------------------------------------------------------| | Gender | Gender of the individual. (String) | | Age Range | Age range of the individual. (String) | | Year | Year of the data. (Integer) | | Never Married | Number of people who have never been married. (Integer) |

Acknowledgements

If you use this dataset in your research, please ...

This dataset provides our current fiscal year's revenue budget and a transparent look at how we allocate public funds. There is also a total at the bottom of the dataset. Datasets will update every Friday by 11 p.m. (CST).

Dataset Description: VIP Actress and Models Service

Overview: The VIP Actress and Models Service dataset is a comprehensive collection of fictional data meticulously curated for the purpose of managing and optimizing a high-end service catering to the needs of clients seeking exclusive experiences with renowned actresses and models. This dataset serves as a valuable resource for service providers in this niche industry, offering insights into customer preferences, service utilization, and satisfaction levels.

Dataset Fields:

1. Customer_ID: A unique identifier for each customer.
2. Customer_Name: The name of the customer.
3. Customer_Age: The age of the customer.
4. Customer_Gender: The gender of the customer (e.g., Male, Female, Non-binary).
5. Customer_Location: The location of the customer (e.g., City, State, Country).
6. Customer_Email: The email address of the customer.
7. Customer_Phone: The phone number of the customer.
8. Service_Type: The type of service requested by the customer (e.g., Actress, Model).
9. Service_Date: The date on which the service was requested.
10. Service_Duration: The duration of the service in hours.
11. Service_Rate: The rate charged for the service.
12. Service_Requested_Actress_Model: The name of the requested actress or model.
13. Service_Description: Additional details or notes about the service request.
14. Payment_Status: The payment status (e.g., Paid, Pending, Refunded).
15. Customer_Review: A customer review or feedback on the service.
16. Customer_Rating: The rating provided by the customer (e.g., on a scale of 1 to 5).

Use Cases:

• Service Optimization: Service providers can use this dataset to analyze customer preferences and tailor offerings accordingly, ensuring a personalized experience.
• Financial Analysis: By examining payment statuses and rates, businesses can track revenue and financial performance.
• Customer Satisfaction: Customer reviews and ratings enable evaluation of service quality and areas for improvement.
• Market Insights: Geographical and demographic data can inform expansion strategies and target audience selection.

Important Notes:

• This dataset is entirely fictitious and intended for demonstration and analytical purposes only.
• Any resemblance to real individuals or entities is purely coincidental.
• Ensure compliance with all relevant privacy and data protection regulations when using this dataset.

The VIP Actress and Models Service dataset is a valuable asset for businesses looking to excel in the luxury entertainment industry by providing top-tier services tailored to the unique desires of their clientele.

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The Research on Open Educational Resources for Development (ROER4D) project was a four-year, large-scale networked project which set out to contribute to the body of research on Open Educational Resources (OER) and address the primary research question: Whether, how, for whom and under what circumstances can engagement with open educational practices and OER provide equitable access to relevant, high-quality, affordable and sustainable education in the Global South?The project engaged a total of 103 research team members in 18 sub-projects in 21 countries across South America, Sub-Saharan Africa and South and Southeast Asia, coordinated by a central Network Hub. The research work culminated in an edited volume, Adoption and Impact of OER in the Global South.A primary contribution of the edited volume was the metasynthesis of sub-project findings, published as Factors influencing Open Educational Practices and OER in the Global South: Meta-synthesis of the ROER4D project. The aim of this synthesis was to distill key themes emerging from sub-project research and to identify:● Educational challenges pertinent in the Global South context.● Claims about OER as a mechanism to address these challenges.● OER and related open educational practices observed in the sub-projects, as well as the structural, cultural and agential factors influencing those practices.● Any impact that could be attributed to OER adoption in addressing educational challenges.The 13 sub-project chapters from the edited volume comprised the primary data source for the synthesis, with final research reports being utilised in two instances where there were no associated sub-project chapters. Coding was performed before final book production using NVivo according to the analytical framework (Figure 1) developed by the principal investigator (PI) in collaboration with the deputy PI and other members of the Network Hub. The dataset is comprised of the primary codes and their associated sub-codes derived from the analytical framework, along with the corresponding coded excerpts. There was no de-identification undertaken on micro-data as the data objects were publication-ready narratives which had already been interrogated for disclosive information.The dataset makes a contribution by demonstrating the transparency of the ROER4D metasynthesis process. Its publication allows external researchers to examine the ROER4D coding process and draw their own conclusions from the data, or to apply different theoretical, conceptual and analytical models to extract findings more relevant to their research contexts. It will be of use to researchers looking for an empirical baseline of research on OER in the Global South, and those interested in performing latitudinal research studies. It is also of use to PIs and researchers undertaking metasynthesis work in open education research.This dataset was first published by DataFirst.

Financial Access and Usage

Global Comparative Ratios Across 189 Jurisdictions

By International Monetary Fund [source]

About this dataset

This dataset provides an unprecedented opportunity to explore global financial access and usage trends from 2004-2016 from 189 of the world's reporting jurisdictions—which cover over 99 percent of the total adult population. With 152 time series and 47 indicator ratios, this Financial Access Survey gives insight into ways that access to and usage of financial services differ by households vs small/medium enterprises, life vs non-life insurance, deposits & microfinance institutions as well as credit unions & financial cooperatives. Utilizing this data, we can gain a better understanding of how policies or shifts in the global economy may influence or relate to access or utilization of services in certain regions while having comparable cross-economy comparisons. The IMF Monetary and Financial Statistics Manual Compilation Guide is utilized for all methodologies used in accumulating these datasets, while all data is available “as-is” with no guarantee provided either express or implied. Are you looking for ways to implement insightful macroeconomic analyses? Download FAS 2004–2016 now!

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How to use the dataset

The Financial Access Survey provides global supply-side data on access to and usage of financial services by households and firms for 189 reporting jurisdictions, covering 99 percent of the world’s adult population. With a robust selection of time series in this dataset, users can make meaningful insights into trends over time or across countries concerning various indicators related to access and usage of financial services. To help users navigate this large dataset, the following guide explains how to use the data most effectively.

Understanding The Dataset Columns

The columns in the dataset provide information about each indicator such as country name, indicator name, code for that indicator, its attribute (i.e., rate/ratio), when data is available for that particular indicator. Once you have identified an interesting measure/indicator whether it be credit union density or life insurance penetration rate measure in a given country during a certain year period then you can look up those numbers from the rows provided in this dataset .

Understanding The Different Years Available & Comparing Numbers Over Time

It is useful for users to compare different indicators over time by looking at specific years within this dataset which will allow us to see if rates are increasing or decreasing worldwide patterns across these trends among different countries based on these various measures listed provided in this survey such as mortgage lending rate or ratio GDP per capita etc that have been collected . We can therefore make use of our knowledge off economic changes that have occurred over time within certain parts of world , no matter if they are longer term economic effects due increases certain capabilities within a geographical area or shorter term changes due taxation laws by governments etc driving some people either towards using or away from using certain kinds financial products .

#### Comparing Between Countries

This datasets allows us direct comparisons between different countries with regards how many people are currently making use particular types off finances services , we certainly be able analyse current international relationships between services providers as well customers where ever concerned about particular attributes mentioned above whether being deposit interest rates small business credits terms tenders so forth . Knowing more about related dynamics helps build better user experiences with providers who understand needs risks impacts generating larger customer bases globally which often beneficial both parties involved exchange relationship so not forget always keep cross border motif whenever eye process from afar !

Research Ideas

• Comparing the access to and usage of financial services in different countries to better inform research policy decisions.
• Analyzing trends in financial access and usage by jurisdiction over time, to identify areas needing improvement in order to promote financial inclusion and stability.
• Cross-referencing FAS data with macroeconomic indicators such as GDP information to measure the potential impact of changes in level of access on economic growth or other metrics specific to a country or region of interest

Acknowledgements

If you use this dataset in yo...