This dataset provides a detailed analysis of emoji usage across various social media platforms. It captures how different emojis are used in different contexts, reflecting emotions, trends, and user demographics.

With emojis becoming a universal digital language, this dataset helps researchers, marketers, and data analysts explore how people express emotions online and identify patterns in social media communication.

📌 Key Features: 😊 Emoji Details: Emoji 🎭: The specific emoji used in a post, comment, or message. Context 💬: The meaning or emotion associated with the emoji (e.g., Happy, Love, Funny, Sad). Platform 🌐: The social media platform where the emoji was used (e.g., Facebook, Instagram, Twitter). 👤 User Demographics: User Age 🎂: Age of the user who posted the emoji (ranges from 13 to 65 years). User Gender 🚻: Gender of the user (Male/Female). 📈 Additional Insights: Emoji Popularity 🔥: Frequency of each emoji’s usage across platforms. Trends Over Time 📅: How emoji usage changes based on trends or events. Regional Usage Patterns 🌍: How different cultures and regions use emojis differently. 📊 Use Cases & Applications: 🔹 Understanding emoji trends across social media 🔹 Analyzing emotional expression through digital communication 🔹 Exploring demographic differences in emoji usage 🔹 Identifying platform-specific emoji preferences 🔹 Enhancing sentiment analysis models with emoji insights

⚠️ Important Note: This dataset is synthetically generated for educational and analytical purposes. It does not contain real user data but is designed to reflect real-world trends in emoji usage.

Context

The data was extracted to be used for my submission to the 2020 kaggle online survey. You can find the related submission here. I used it to analyze the searches related to Automated Machine learning.

Content

The data consists of google trends for AutoML /Automated machine learning. This data is Aggregated, **Anonymised **, Indexed and Normalized. The dataset consists of four files:

• Timeline Data(2015-2020) Timeline data shows interest over time. The Numbers represent search interest relative to the highest point on the chart for the given region and time. A value of 100 is the peak popularity for the term. A value of 50 means that the term is half as popular. A score of 0 means that there was not enough data for this term. I have used the search data from the year 2015 to 2020.

• GeoMap data for the year 2020 GeoMap data shows Interest by sub-region. It essentially highlights locations where the search term was most popular during the specified time frame. Values are calculated on a scale from 0 to 100, where 100 is the location with the most popularity as a fraction of total searches in that location, a value of 50 indicates a location that is half as popular. A value of 0 indicates a location where there was not enough data for this term.

• Related Entities data for the year 2020 Related entities mean users searching for your term also searched for these topics. Scoring is on a relative scale where a value of 100 is the most commonly searched topic and a value of 50 is a topic searched half as often as the most popular term, and so on

• Related Queries data Related Queries mean users searching for your term also searched for these queries. Scoring is on a relative scale where a value of 100 is the most commonly searched query, 50 is a query searched half as often as the most popular query, and so on.

The explanations have been taken from the Google trend official site.

Acknowledgements

• Banner Image: People vector created by pch.vector - www.freepik.com
• Google Trends

Useful links: - Google Trends - Google News Lab - @GoogleTrends

The High Definition Digital Video Capture Module market has gained significant traction in recent years, driven by the increasing demand for high-resolution video recording across various industries, including broadcasting, surveillance, entertainment, and education. These modules play a pivotal role in capturing re

The size of the Data Classification market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 24.00% during the forecast period.Sorting out data based on their levels of sensitivity, value, and other regulation requirements is referred to as classification. Sensitive information shall be identified, labeled, and protected for confidentiality, integrity, and availability. Encoded, access controls, DLP policies, and protection measure will be in place correctly based on the classification of data levels.Data classification is very important in several reasons. It means organizations meet most of the data protection regulations, including GDPR and HIPAA, that clearly specify how they must treat and store sensitive data.This means that it allows organizations to give priority to the most important data. Thus, an organization identifies its sensitive data and categorizes them appropriately so that proper resource allocation is done to avoid such risks and prevent a breach. Moreover, data classification improves data governance and informed decision-making by analyzing data usage patterns and potential vulnerabilities. Key drivers for this market are: , Government Regulations and Compliance for Privacy & Data Security; Concern for Data Theft due to Mismanagement; Surge in Analytics Applications with Stored Data. Potential restraints include: Limited Efficiency Compared to Shared Hosting, Dedicated Hosting, and Cloud Servers. Notable trends are: Surge in Data Security Solutions for Increased Malware Infection Rates in Computers.

The High Definition Electromagnetic Shielding Glass market has emerged as a pivotal segment in advanced technology industries, providing essential protection against electromagnetic interference (EMI) that can disrupt electronic devices and sensitive equipment. This specialized glass is designed to shield high-frequ

China Big Data Technology Investment Opportunities Market was valued at USD 45.2 Billion in 2023 and is projected to reach USD 95.6 Billion by 2031, growing at a CAGR of 9.8% from 2024 to 2031.

China Big Data Technology Investment Opportunities Market: Definition/Overview

Big data technology is defined as the complex ecosystem of tools, processes, and methodologies that are utilized to handle extremely large datasets. These technologies are designed to extract valuable insights from structured and unstructured data that is generated at unprecedented volumes. Furthermore, the applications of big data technology are seen across multiple sectors, where data is processed, analyzed, and transformed into actionable intelligence. Advanced analytics, artificial intelligence, and machine learning capabilities are integrated into these systems, through which deeper insights are enabled, and predictive capabilities are enhanced.

Monthly means data sets from satellite, ground-based and model records used in the article entitled: "Updated trends of the stratospheric ozone vertical distribution in the 60 S–60 N latitude range based on the LOTUS regression model".

Information about and the most recent versions of each dataset can be found at their individual source locations:

Merged satellite datasets

1. SBUV MOD – https://acd-ext.gsfc.nasa.gov/Data_services/merged/index.html (NASA GSFC, USA)
2. SBUV COH: https://ftp.cpc.ncep.noaa.gov/SBUV_CDR/ (NOAA, USA).
3. GOZCARDS: https://www.earthdata.nasa.gov/esds/competitive-programs/measures/gozcards (JPL, NASA, USA)
4. SWOOSH: https://csl.noaa.gov/groups/csl8/swoosh/ (NOAA, USA).
5. SAGE-CCI-OMPS and MEGRIDOP datasets are available through https://climate.esa.int/en/projects/ozone/data/ and ftp://cci_web@ftp-ae.oma.be/esacci (ESA Climate Office). They are provided by FMI, Finland
6. SAGE-SCIAMACHY-OMPS: data record is available upon registration via the following link: http://www.iup.uni-bremen.de/DataRequest/ (U. Bremen, Germany).
7. SAGE-OSIRIS-OMPS: downloading instructions can be found at https://research-groups.usask.ca/osiris/data-products.php#OSIRISLevel3andMergedDataProducts (U. Saskatchewan, Canada).

Ground-based records:

1. Umkehr – https://gml.noaa.gov/aftp/data/ozwv/Dobson/AC4/Umkehr/Monthly/ (NOAA, USA)
2. ozonesondes – https://hegiftom.meteo.be/datasets/ozonesondes (HEGIFTOM). Measurements at the various stations are provided by the following institutions:
   • Hohenpeissenberg: DWD, Germany
   • Payerne:MeteoSwiss, Switzerland
   • OHP, CNRS, France
   • Hilo, NOAA, USA
   • Lauder, NIWA, New Zealand
3. lidar: http://www.ndacc.org/ . Measurement at the various stations are provided by the following institutions:
   • Hohenpeissenberg: DWD, Germany
   • OHP: CNRS, France
   • MLO: JPL, NASA, USA
   • Lauder: NIWA, New Zealand
4. FTIR spectrometers – http://www.ndacc.org/ Three sites only provided quality checked measurements relevant for the article. For other ozone FTIR measurements, data in http://www.ndacc.org/ must be used. Measurement used in the article are provided by the following institutions:
   • Zugspitze: KIT, Germany
   • Jungfraujoch: ULiège, GIRPAS team, Belgium
   • Lauder: NIWA, New Zealand
5. Microwave spectrometers: http://www.ndacc.org/ Measurement at the various stations are provided by the following institutions:
   • Payerne: MeteoSwiss, Switzerland
   • Mauna Loa: NRL, USA
   • Lauder: NRL, USA

Chemistry Climate Model (CCM) CCMI simulations are avilable at https://blogs.reading.ac.uk/ccmi

As search engines have become the main information resources of our daily life, studies about search behavior on the internet have gained great popularity with the growing knowledge of how the search behavior itself can affect our daily decisions, e.g. what to purchase, where to travel and even how to define beauty. However, there is no consensus conclusion whether the search behavior itself or the linguistic meaning behind it that can affect their decision. After analyzing the linguistic meanings of 13,915 English words obtained from Google Trends and its profit gained from the US house market by automatic transactions. It is found that linguistic meanings can affect financial decision results as word clusters with supervised machine learning methods.

The High Definition Objective market has experienced significant growth in recent years, reflecting the increasing demand for precision and clarity in various applications, from surgical imaging to industrial inspections. High-definition objectives, which are critical components in optical systems, enhance the resol

In an effort to measure the effectiveness of crime deterrents and to estimate crime rates, calls for assistance placed to police in Oklahoma City over a two-year period were enumerated. This type of call was studied in order to circumvent problems such as "interviewer's effect" and sampling errors that occur with other methods. The telephone calls were stratified by police district, allowing for analysis on the neighborhood level to determine whether deterrence operates ecologically--that is, by neighbors informing one another about arrests which took place as a result of their calls to the police. In measuring deterrence, only the calls that concerned robbery were used. To estimate crime rates, calls were tallied on a monthly basis for 18 types of offenses: aggravated assault, robbery, rape, burglary, grand larceny, motor vehicle theft, simple assault, fraud, child molestation, other sex offenses, domestic disturbance, disorderly conduct, public drunkenness, vice and drugs, petty larceny, shoplifting, kidnapping/hostage taking, and suspicious activity.

Optical Data Storage Devices Market Outlook

The global optical data storage devices market size was valued at approximately USD 4.5 billion in 2023 and is forecasted to reach around USD 6.8 billion by 2032, expanding at a CAGR of 4.5% during the forecast period. The robust growth of this market is driven by the escalating demand for high-capacity storage solutions, along with the increasing adoption of optical data storage devices across various sectors such as consumer electronics, healthcare, and media and entertainment. Technological advancements, coupled with the need for long-term data preservation, are significant factors contributing to the market’s expansion.

One of the primary growth factors of the optical data storage devices market is the continuous technological innovation in data storage solutions. With the proliferation of data generation across industries, there is an increasing need for reliable and durable storage media. Optical storage devices, known for their longevity and durability, are gaining traction as a preferred storage solution. Innovations such as multi-layer discs and advancements in Blu-ray technology have expanded the storage capacity of optical devices, making them suitable for various high-capacity data storage needs, which is propelling the market growth.

Moreover, the rising popularity of 4K and 8K video content is driving the demand for optical data storage devices. As consumers and industries alike shift towards higher resolution content, the need for effective storage solutions that can handle large file sizes is becoming more pronounced. Optical storage devices, especially Blu-ray discs, are capable of storing vast amounts of data efficiently, making them ideal for storing high-definition video content. This trend is being further accelerated by the growing adoption of optical storage in gaming consoles and media players, which require high-capacity storage to support advanced graphics and features.

Additionally, the optical data storage devices market is witnessing growth due to the increasing emphasis on data security and integrity. Optical storage is considered a safer option for long-term data retention compared to magnetic storage solutions, as it is less susceptible to data corruption due to environmental factors. This characteristic is particularly important for industries such as healthcare and IT, where data integrity is paramount. Furthermore, initiatives towards digitalization in various sectors are also boosting the demand for optical data storage devices as they provide a secure, cost-effective, and eco-friendly solution for data archiving.

Regionally, the Asia Pacific holds a significant share in the optical data storage devices market, driven by the rapid growth of consumer electronics and IT sectors in countries like China, Japan, and India. The region's booming e-commerce industry is also contributing to the increased demand for data storage solutions. North America and Europe follow, with substantial market shares owing to the presence of major technology companies and a strong focus on research and development. In contrast, the Middle East & Africa and Latin America are expected to witness moderate growth due to the gradual adoption of advanced storage technologies.

Product Type Analysis

In the optical data storage devices market, the product type segment is categorized into CDs, DVDs, Blu-ray Discs, and others. Compact Discs (CDs) were among the earliest forms of optical storage and still hold a niche market for audio and limited data storage needs. Though their market share has diminished over time due to advancements in technology, CDs continue to be used in specific segments due to their cost-effectiveness and familiarity among users. They remain popular for audio content distribution and small data storage requirements in sectors where cutting-edge technology is not a priority.

DVDs have a larger storage capacity compared to CDs and have historically been the preferred medium for video content distribution. The standard DVD format continues to see usage in markets where high-definition content is not the primary focus. However, the introduction of Blu-ray technology, which offers significantly greater storage capacity, has overshadowed DVDs in terms of popularity, especially in applications requiring high-definition video storage. Despite this, DVDs maintain their relevance for certain consumer segments, particularly in markets with budget constraints.

Blu-ray Discs represent the most advanced form of optical storage in the current market, catering

South America Data Center Rack Market size was valued at USD 4.70 Billion in 2024 and is expected to reach USD 9.65 Billion by 2032, growing at a CAGR of 9.4% from 2026 to 2032.

South America Data Center Rack Market: Definition/Overview

A South America Data Center Rack is a physical structure for storing and organizing servers, networking equipment, and other IT hardware in a data center. These racks are normally standardized in size, with widths ranging from 19 to 23 inches and heights defined in rack units (Us). They provide structured cable management, airflow control, and power distribution to ensure the proper operation of key IT systems. Data center racks in South America are designed to accommodate high-density computer environments, with features such as flexible mounting rails, integrated cooling systems, and security enclosures to secure sensitive data.

*****Documentation Process***** 1. Data Preparation: - Upload the data into Power Query to assess quality and identify duplicate values, if any. - Verify data quality and types for each column, addressing any miswriting or inconsistencies. 2. Data Management: - Duplicate the original data sheet for future reference and label the new sheet as the "Working File" to preserve the integrity of the original dataset. 3. Understanding Metrics: - Clarify the meaning of column headers, particularly distinguishing between Impressions and Reach, and comprehend how Engagement Rate is calculated. - Engagement Rate formula: Total likes, comments, and shares divided by Reach. 4. Data Integrity Assurance: - Recognize that Impressions should outnumber Reach, reflecting total views versus unique audience size. - Investigate discrepancies between Reach and Impressions to ensure data integrity, identifying and resolving root causes for accurate reporting and analysis. 5. Data Correction: - Collaborate with the relevant team to rectify data inaccuracies, specifically addressing the discrepancy between Impressions and Reach. - Engage with the concerned team to understand the root cause of discrepancies between Impressions and Reach. - Identify instances where Impressions surpass Reach, potentially attributable to data transformation errors. - Following the rectification process, meticulously adjust the dataset to reflect the corrected Impressions and Reach values accurately. - Ensure diligent implementation of the corrections to maintain the integrity and reliability of the data. - Conduct a thorough recalculation of the Engagement Rate post-correction, adhering to rigorous data integrity standards to uphold the credibility of the analysis. 6. Data Enhancement: - Categorize Audience Age into three groups: "Senior Adults" (45+ years), "Mature Adults" (31-45 years), and "Adolescent Adults" (<30 years) within a new column named "Age Group." - Split date and time into separate columns using the text-to-columns option for improved analysis. 7. Temporal Analysis: - Introduce a new column for "Weekend and Weekday," renamed as "Weekday Type," to discern patterns and trends in engagement. - Define time periods by categorizing into "Morning," "Afternoon," "Evening," and "Night" based on time intervals. 8. Sentiment Analysis: - Populate blank cells in the Sentiment column with "Mixed Sentiment," denoting content containing both positive and negative sentiments or ambiguity. 9. Geographical Analysis: - Group countries and obtain additional continent data from an online source (e.g., https://statisticstimes.com/geography/countries-by-continents.php). - Add a new column for "Audience Continent" and utilize XLOOKUP function to retrieve corresponding continent data.

*****Drawing Conclusions and Providing a Summary*****

• The data is equally distributed across different categories, platforms, and over the years.
• Most of our audience comprises senior adults (aged 45 and above).
• Most of our audience exhibit mixed sentiments about our posts. However, an equal portion expresses consistent sentiments.
• The majority of our posts were located in Africa.
• The number of posts increased from the first year to the second year and remained relatively consistent for the third year.
• The optimal time for posting is during the night on weekdays.
• The highest engagement rates were observed in Croatia then Malawi.
• The number of posts targeting senior adults is significantly higher than the other two categories. However, the engagement rates for mature and adolescent adults are also noteworthy, based on the number of targeted posts.

WWF developed a global analysis of the world's most important deforestation areas or deforestation fronts in 2015. This assessment was revised in 2020 as part of the WWF Deforestation Fronts Report.Emerging Hotspots analysisThe goal of this analysis was to assess the presence of deforestation fronts: areas where deforestation is significantly increasing and is threatening remaining forests. We selected the emerging hotspots analysis to assess spatio-temporal trends of deforestation in the pan-tropics.Spatial UnitWe selected hexagons as the spatial unit for the hotspots analysis for several reasons. They have a low perimeter-to-area ratio, straightforward neighbor relationships, and reduced distortion due to curvature of the earth. For the hexagon size we decided on a unit of 1,000 ha, based on the resolution of the deforestation data (250m) meant that we could aggregate several deforestation events inside units over time. Hexagons that are closer to or equal to the size of a deforestation event means there could only be one event before the forest is gone and limit statistical analysis.We processed over 13 million hexagons for this analysis and limited the emerging hotspots analysis to only hexagons with at least 15% forest cover remaining (from the all-evidence forest map). This prevented including hotspots in agricultural areas or where all forest has been converted.OutputsThis analysis uses the Getis-Ord and Mann-Kendall statistics to identify spatial clusters of deforestation which have a non-parametric significant trend across a time series. The spatial clusters are defined by the spatial unit and a temporal neighborhood parameter. We use a neighborhood parameter of 5km to include spatial neighbors in the hotspots assessment and time slices for each country described below. Deforestation events are summarized by a spatial unit (hexagons described below) and the results comprise a trends assessment which defines increasing or decreasing deforestation in the units determined at 3 different confidence intervals (90%, 95% and 99%) and the spatio-temporal analysis classifying areas into 8 hot unique or cold spot categories. Our analysis identified 7 hotspot categories:Hotspot TypeDefinitionNewA location with a statistically significant increasing hotspots only in the final time stepConsecutiveAn uninterrupted run of statistically significant hotspot in the final time-steps IntensifyingA statistically significant hotspot for >90% of the bins, including the final time stepPersistentA statistically significant hotspot for >90% of the bins with no upward or downward trend in clustering intensityDiminishingA statistically significant hotspot for >90% of the time steps, with where the clustering is decreasing, or the most recent time step is not hot.SporadicA on-again then off-again hotspot where <90% of the time-step intervals have been statistically significant hot spots and none have been statistically significant cold spots.HistoricalAt least ninety percent of the time-step intervals have been statistically significant hot spots, with the exception of the final time steps..For the evaluation of spatio-temporal trends of tropical deforestation we selected the Terra-i deforestation dataset to define the temporal deforestation patterns. Terra-i is a freely available monitoring system derived from the analysis of MODIS (NVDI) and TRMM (rainfall) data which are used to assess forest cover changes due to anthropic interventions at a 250 m resolution [ref]. It was first developed for Latin American countries in 2012, and then expanded to pan-tropical countries around the world. Terra-i has generated maps of vegetation loss every 16 days, since January 2004. This relatively high temporal resolution of twice monthly observations allows for a more detailed emerging hotspots analysis, increasing the number of time steps or bins available for assessing spatio-temporal patterns relative to annual datasets. Next, the spatial resolution of 250m is more relevant for detecting forest loss than changes in individual tree cover or canopies and is better adapted to process trends on large scales. Finally, the added value of the Terra-i algorithm is that it employs an additional neural network machine learning to identify vegetation loss that is due to anthropic causes as opposed to natural events or other causes. Our dataset comprised all Terra-i deforestation events observed between 2004 and 2017. Temporal unitThe temporal unit or time slice was selected for each country according to the distribution of data. The deforestation data comprised 16-day periods between 2004 and 2017 for a total of 312 potential observation time periods. These were aggregated to time bins to overcome any seasonality in the detection of deforestation events (due to clouds). The temporal unit is combined with the spatial parameter (i.e. 5km) to create the space-time bins for hotspot analysis. For dense time series or countries with a lot of deforestation events (i.e. Brazil) a smaller time slice was used (i.e. 3 months, n=54) with a neighborhood interval of 8 months, meaning that the previous year and next year together were combined to assess statistical trends relative to the global variables together. The rule we employed was that the time slice x neighborhood interval was equal to 24 months, or 2 years, in order to look at general trends over the entire time period and prevent the hotspots analysis from being biased to short time intervals of a few months.Deforestation FrontsFinally, using trends and hotpots we identify 24 major deforestation fronts, areas of significantly increasing deforestation and the focus of WWF's call for action to slow deforestation.

1. Overview

This dataset provides monthly stock price data for the MAG7 over the past 20 years (2004–2024). The data includes key financial metrics such as opening price, closing price, highest and lowest prices, trading volume, and percentage change. The dataset is valuable for financial analysis, stock trend forecasting, and portfolio optimization.

2. What is MAG7 ?

MAG7 refers to the seven largest and most influential technology companies in the U.S. stock market : - Microsoft (MSFT) - Apple (AAPL) - Google (Alphabet, GOOGL) - Amazon (AMZN) - Nvidia (NVDA) - Meta (META) - Tesla (TSLA)

These companies are known for their market dominance, technological innovation, and significant impact on global stock indices such as the S&P 500 and Nasdaq-100.

3. Dataset Details

The dataset consists of historical monthly stock prices of MAG7, retrieved from Investing.com. It provides an overview of how these stocks have performed over two decades, reflecting market trends, economic cycles, and technological shifts.

4. Columns Descriptions

• Date The recorded month and year (DD-MM-YYYY)
• Price The closing price of the stock at the end of the month
• Open The price at which the stock opened at the beginning of the month
• High The highest stock price recorded in the month
• Low The lowest stock price recorded in the month
• Vol. The total trading volume for the month
• Change % The percentage change in stock price compared to the previous month # 5. Data Source & Format The dataset was obtained from Investing.com and downloaded in CSV format. The data has been processed to ensure consistency and accuracy, with date formats standardized for time-series analysis. # 6. Potential Use Cases This dataset can be used for :
• 📈 Stock price trend analysis over 20 years
• 📊 Building financial models for long-term investing
• 🔎 Machine learning applications in stock market prediction
• 📉 Evaluating market volatility and economic impact on MAG7 stocks

7. Limitations & Considerations

• ⚠️ The dataset is limited to monthly data, meaning short-term price fluctuations are not captured.
• ⚠️ Trading volume (Vol.) may vary in availability due to differences in reporting.
• ⚠️ External factors such as stock splits, dividends, and market crashes are not explicitly noted but may impact historical trends.

The High Definition Oscilloscopes market is experiencing significant growth as the demand for advanced diagnostic tools in various industries continues to rise. These sophisticated instruments are essential for engineers and technicians in fields such as telecommunications, automotive, and electronics, enabling them

The Ultra High Definition (UHD) 4K panel market has emerged as a pivotal segment within the electronics industry, driven by the demand for enhanced visual experiences across various sectors, including consumer electronics, entertainment, gaming, and professional applications. UHD 4K panels, renowned for their superi

The High-definition (HD) Voice market has emerged as a significant segment within the telecommunications industry, revolutionizing the way we communicate. Defined by its ability to enhance voice clarity through a wider frequency range, HD Voice transforms standard voice calls into high-quality audio experiences, ena

The High Definition Breast Tomosynthesis System market is experiencing significant growth, driven by the increasing demand for advanced diagnostic imaging technologies in the healthcare sector. This cutting-edge system offers a revolutionary approach to breast cancer detection, enabling radiologists to obtain high-r

The Ultra High Definition (UHD) Medical Video Recorder market is experiencing significant growth, driven by the rising demand for high-resolution imaging solutions in healthcare settings. As medical institutions increasingly prioritize the quality of video documentation for surgical procedures, education, and teleme