Engagement Trends for OMGitsAliA's Tweets

Analyzing Interactions and Popularity

By Twitter [source]

About this dataset

This dataset contains popular tweets from the gaming powerhouse OMGitsAliA, an internet sensation with over 18 million devoted followers. Explore trends and engagement in the Twitterverse around her awe-inspiring posts and get a closer look at how this content is received by the public. Gain deep insights into what draws people in, from hashtag choice to tweet style and more. See which topics garner the most attention and learn where your own efforts have potential to succeed. Get ready: This dataset invites you to unravel just how powerful social media can be!

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How to use the dataset

1. Analyze tweet types: Get an idea of the types of tweets AliA posts and the average engagement each type of tweet receives. Check for trends in language, images, hashtags, fellow accounts mentioned and likes/retweets by account type (businesses versus individuals).

2. Monitor growth: Compare engagement over time to assess how popular AliA's tweets are becoming and identify areas for improvement if needed.

3. Examine competition: Observe other accounts and compare their engagement to get a sense of how much competition there is in the gaming industry from a social media perspective.

4. Measure Advertising ROI: If you're running paid advertising campaigns on Twitter, compare your return on investment against AliA's engagements to measure your success rate against her campaigns.

5. Identify Seasonal Trends & Timing Preferences: Use this dataset to understand what times of year or days FOMGitsAliA receives the highest engagement rates so you can plan out future content accordingly

6 Visualize Results: The columns included in this dataset provide great data points which can be displayed through pie charts and scatterplots - or whatever visualization works best for your desired result!

Research Ideas

• Analyzing Trends in Engagement – By analyzing the engagement metrics such as Likes, Retweets, and Replies, you can gain insight into which types of tweets are more popular amongst the followers of OMGitsAliA. This could help inform future marketing decisions to better capture public attention.

• Identifying Influencers – By studying which influencers (mentions) were used in certain tweets and their associated response rates, you can identify key industry influencers who have a high level of engagement across multiple platforms.

• Tracking Sentiment Analysis – By running sentiment analysis on the text of each tweet you can gain insight into how different topics are discussed by AliA’s followers and better understand their overall attitudes towards certain issues in gaming industry discussions overall

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

Acknowledgements

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

Top 20 Holiday Songs by Position

Top 20 Holiday Songs 2011-2018 by Chart Position

By Throwback Thursday [source]

About this dataset

This dataset, titled Top 20 Holiday Songs 2011-2018, is a comprehensive collection of the most popular holiday songs that appeared on the charts from 2011 to 2018. These songs have been ranked based on their position on the charts, providing valuable insights into their popularity and success during this time period. The dataset includes several key columns that provide detailed information about each song, such as the title of the holiday song, the artist or group who performed it, and the date on which it appeared on the charts. By examining this dataset, researchers can analyze trends and patterns in holiday music throughout these eight years, gaining a deeper understanding of the dynamics of this genre's popularity

How to use the dataset

• Familiarize yourself with the columns:

  • Year: The year in which the song appeared on the charts.
  • Position: The position of the song on the charts.
  • Song: The title of the holiday song.
  • Artist: The artist or group who performed the holiday song.
  • Chart Date: The date on which the song appeared on the charts.

• Exploring popular songs: If you're curious about which holiday songs were trending during a specific year, simply filter by that particular year in order to see which songs made it into top positions.

• Discovering rising stars: By analyzing how each song progressed through different positions over time, you can identify holiday songs that gained popularity gradually or those that quickly climbed up in rankings.

• Comparing artists' success: Suppose you're interested in observing an artist's influence within this genre throughout multiple years. In that case, you can search for their name and analyze how many times their songs reached top positions across various years.

• Analyzing chart patterns: With data spanning multiple years, patterns may emerge in terms of when certain types or styles of holiday songs tend to peak in popularity during different periods.

• Creating your own rankings and insights: Utilizing this dataset as a starting point is perfect if you want to create your own personalized rankings or develop unique insights into these holiday hits based on various criteria like chart performance and artist success.

Remember that music tastes can vary across time and regions, so it's important to consider those aspects while making any conclusions or interpretations from this dataset.

Now that you have a good understanding of what this dataset entails, start exploring and uncovering intriguing insights about the top holiday songs of the past decade!

Research Ideas

• Analyzing trends in the popularity of holiday songs: This dataset can be used to examine how the popularity of certain holiday songs has changed over time. By looking at the position of a song on the charts each year, we can identify which songs have remained popular consistently and which ones may have had a spike in popularity during specific years.
• Comparing artists' success with holiday songs: With this dataset, we can investigate which artists or groups have achieved consistent success with their holiday songs over the years. By analyzing their positions on the charts, we can see if certain artists consistently release chart-topping holiday hits or if they only have isolated successes.
• Predicting future popular holiday songs: By studying patterns and trends in this dataset, one could develop models to predict potential future popular holiday songs based on historical data. These predictions could be used by music producers or advertisers to select appropriate music for promotions during the holidays and increase audience engagement

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: Week 42 - Top 20 Holiday Songs 2011 - 2018.csv | Column name | Description | |:---------------|:-------------------------------------------------------------------------------------------------------------| | Year | The year in which a holiday song appeared on the charts. (Numeric) | | Position | The position of a holiday song on the charts during its respective year. (Numeric) | | Song | The title ...

The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

The value of the DJIA index amounted to ****** at the end of June 2025, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

China's main stock market index, the SHANGHAI, fell to 3898 points on December 2, 2025, losing 0.42% from the previous session. Over the past month, the index has declined 1.98%, though it remains 15.36% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

Inspiration Retail and e-commerce businesses thrive on data to optimize operations, inventory management, and customer satisfaction. Analyzing sales, shipping, and profitability can reveal crucial patterns that help businesses make informed decisions. This dataset, modeled after a typical "Superstore" environment, provides an opportunity to apply analytical skills to solve common challenges faced by retailers, such as identifying best-selling products, managing inventory, optimizing shipping times, and improving customer segmentation.

Context Superstores often deal with massive volumes of transactional data, which include sales, product categories, customer demographics, order quantities, discounts, and shipping details. Understanding and analyzing these data points can unlock valuable insights to drive efficiency and improve profitability. This dataset could be inspired by popular global retailers, providing a comprehensive look at how different regions, product lines, and customer profiles interact to affect overall performance.

Whether you're looking to understand which regions generate the highest sales, or which product categories are the most profitable, this dataset offers a rich source for various types of analysis. It is particularly useful for business analysts, data scientists, and decision-makers aiming to model the financial and operational aspects of retail management.

Dataset Description The Superstore dataset consists of transactional data over a specified period of time, typically including the following key attributes:

Order ID: Unique identifier for each transaction. Order Date & Ship Date: The dates when the order was placed and shipped. Customer ID: Unique identifier for each customer. Customer Name: The name of the customer (optional depending on privacy). Segment: Type of customer (e.g., Consumer, Corporate, Home Office). Country/Region/City: Geographic location of the sale. State: Specific state or province. Product ID: Unique identifier for each product. Product Category: High-level grouping (e.g., Office Supplies, Technology, Furniture). Sub-Category: More detailed product category. Product Name: Description of the product sold. Sales: Total revenue generated from the transaction. Quantity: Number of units sold. Discount: Applied discount rate for the transaction. Profit: Profit margin of the sale. Shipping Mode: The type of shipping selected (e.g., Standard, Express). Potential Uses of the Dataset Sales Analysis: Understanding the overall sales performance across different segments, regions, and product categories. Profitability Analysis: Identifying which products or regions are the most profitable and which are underperforming. Customer Segmentation: Exploring purchasing patterns of different customer types and using this information for targeted marketing. Inventory Management: Analyzing demand for specific products to optimize stock levels. Shipping Optimization: Assessing shipping modes and times to identify inefficiencies and improve delivery performance. Discount and Profit Correlation: Understanding the impact of discounts on profitability and sales volume. This dataset is a rich source for beginners and experienced data scientists alike to practice data manipulation, cleaning, visualization, and building predictive models that can provide actionable business insights.

Consumer Confidence in the United States decreased to 51 points in November from 53.60 points in October of 2025. This dataset provides the latest reported value for - United States Consumer Sentiment - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

ABSTRACT Active optical sensors have been widely used for the spatial and temporal monitoring of peanut culture because they are accurate, non-destructive methods for rapidly obtaining data. The objective of this study was to determine the optimal stage of crop growth for collecting sensor readings based on correlations between quality indicators. In addition, we compared vegetation indices (Normalized Difference Vegetation Index [NDVI], Normalized Difference Red-Edge Index, [NDRE], and Inverse Ratio Vegetation Index, [IRVI]) by monitoring temporal variability in the peanut crop in order to determine which of them obtained the best reading quality throughout the process. The experiment was performed on the 2016/17 crop in the agricultural area of the municipality of Dumont in the state of São Paulo, Brazil. The experimental design was based on the basic assumptions of statistical quality control and contained 63 sample points in a 30 × 30 m grid. The parameters were evaluated at 30, 45, 60, 75, and 119 days after sowing (DAS) using proximal sensing with GreenSeeker and OptRX sensors. We found that 45 and 60 DAS were the optimal times for monitoring peanut crop variability. For spatiotemporal monitoring of the culture with control charts, NDRE showed the best readings throughout the process when compared to NDVI and IRVI.

Consumer Price Index CPI in the United States increased to 324.80 points in September from 323.98 points in August of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Israel's main stock market index, the TA-125, rose to 3538 points on December 2, 2025, gaining 1.75% from the previous session. Over the past month, the index has climbed 4.40% and is up 50.06% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Israel. Israel Stock Market (TA-125) - values, historical data, forecasts and news - updated on December of 2025.

Russia's main stock market index, the MOEX, fell to 2681 points on December 2, 2025, losing 0.20% from the previous session. Over the past month, the index has climbed 4.30% and is up 5.58% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Russia. Russia Stock Market Index MOEX CFD - values, historical data, forecasts and news - updated on December of 2025.

Baltic Dry rose to 2,600 Index Points on December 2, 2025, up 0.66% from the previous day. Over the past month, Baltic Dry's price has risen 33.68%, and is up 110.19% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on December of 2025.

Key information about India Sensitive 30 (Sensex)

• India Sensitive 30 (Sensex) closed at 85,706.7 points in Nov 2025, compared with 83,938.7 points at the previous month end
• India Equity Market Index: Month End: BSE: Sensitive 30 (Sensex) data is updated monthly, available from Apr 1979 to Nov 2025, with an average number of 2,987.7 points
• The data reached an all-time high of 85,706.7 points in Nov 2025 and a record low of 115.6 points in Nov 1979

• In the latest reports, SENSEX recorded a daily P/E ratio of 23.2 in Dec 2025

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset contains the python scripts used to generate some of the figures in CLM 2.6.2.7 and 2.6.2.8 (Cui et al. 2016).

Dataset History

A set of python scripts to generate figures based on the results of the CLM uncertainty analysis in CLM 2.6.2.7 and CLM 2.6.2.8 (Cui et al. 2016). Each script is written by the BA modelling team and generates a single figure by loading the relevant spreadsheets from dataset CLM MODFLOW Uncertainty Analysis. The filename of the relevant spreadsheet(s) are hardcoded in the scripts. The workbooks associated with the report detail which python script is used for which figure.

Dataset Citation

Bioregional Assessment Programme (2016) CLM Py Charts v01. Bioregional Assessment Derived Dataset. Viewed 10 July 2017, http://data.bioregionalassessments.gov.au/dataset/8577113f-b984-428d-9258-95d6b8da32bc.

Dataset Ancestors

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204

• Derived From Qld 100K mapsheets - Mount Lindsay

• Derived From Qld 100K mapsheets - Helidon

• Derived From NSW Office of Water - National Groundwater Information System 20140701

• Derived From CLM - Woogaroo Subgroup extent

• Derived From CLM - Interpolated surfaces of Alluvium depth

• Derived From CLM - Extent of Logan and Albert river alluvial systems

• Derived From CLM - Bore allocations NSW v02

• Derived From CLM - Bore allocations NSW

• Derived From CLM - Bore assignments NSW and QLD summary tables

• Derived From CLM - Geology NSW & Qld combined v02

• Derived From CLM - Orara-Bungawalbin bedrock

• Derived From CLM16gwl NSW Office of Water_GW licence extract linked to spatial locations_CLM_v3_13032014

• Derived From CLM groundwater model hydraulic property data

• Derived From CLM16swg Surface water gauging station data within the Clarence Moreton Basin

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From Qld 100K mapsheets - Ipswich

• Derived From CLM - Gatton Sandstone extent

• Derived From CLM16gwl NSW Office of Water, GW licence extract linked to spatial locations in CLM v2 28022014

• Derived From Bioregional Assessment areas v03

• Derived From NSW Geological Survey - geological units DRAFT line work.

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From CLM Preliminary Assessment Extent Definition & Report( CLM PAE)

• Derived From Qld 100K mapsheets - Caboolture

• Derived From CLM - AWRA Calibration Gauges SubCatchments

• Derived From CLM - NSW Office of Water Gauge Data for Tweed, Richmond & Clarence rivers. Extract 20140901

• Derived From Qld 100k mapsheets - Murwillumbah

• Derived From AHGFContractedCatchment - V2.1 - Bremer-Warrill

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From QLD Current Exploration Permits for Minerals (EPM) in Queensland 6/3/2013

• Derived From QLD Department of Natural Resources and Mining Groundwater Database Extract 20131111

• Derived From CLM - Bore water level NSW

• Derived From Climate model 0.05x0.05 cells and cell centroids

• Derived From CLM - New South Wales Department of Trade and Investment 3D geological model layers

• Derived From CLM - Metgasco 3D geological model formation top grids

• Derived From R-scripts for uncertainty analysis v01

• Derived From State Transmissivity Estimates for Hydrogeology Cross-Cutting Project

• Derived From CLM - Extent of Bremer river and Warrill creek alluvial systems

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Pilot points for prediction interpolation of layer 1 in CLM groundwater model

• Derived From Qld 100K mapsheets - Esk

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores and NGIS v4 28072014

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From CLM - Qld Surface Geology Mapsheets

• Derived From NSW Office of Water Pump Test dataset

• Derived From CLM - NSW River Gauge pdf documents.

• Derived From CLM - New South Wales well completion reports

• Derived From Data for river stage interpolation in the CLM groundwater model

• Derived From CLM - Extent of Lockyer Creek alluvial system

• Derived From CLM - DEM in ascii format

• Derived From CLM - Grafton-Rapville bedrock

• Derived From CLM - Bore water level QLD

• Derived From QLD Coal Seam Gas well locations - 14/08/2014

• Derived From CLM - Orara-Kangaroo bedrock

• Derived From Qld 100k mapsheets - Warwick

• Derived From CLM - Walloon Coal Measures spatial extent

• Derived From Geofabric Surface Catchments - V2.1

• Derived From CLM - Stratigraphic wells in the QLD area of the Clarence-Moreton bioregion

• Derived From CLM - Koukandowie FM bedrock

• Derived From CLM - Queensland well completion reports

• Derived From [National Groundwater Information System (NGIS)

Business Confidence in the United States decreased to 48.20 points in November from 48.70 points in October of 2025. This dataset provides the latest reported value for - United States ISM Purchasing Managers Index (PMI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Vietnam Exports to United States was US$97.07 Billion during 2023, according to the United Nations COMTRADE database on international trade. Vietnam Exports to United States - data, historical chart and statistics - was last updated on October of 2025.

The National Bathymetric Source (NBS) project creates and maintains high-resolution bathymetry composed of the best available data. This project enables the creation of next-generation nautical charts while also providing support for modeling, industry, science, regulation, and public curiosity. Primary sources of bathymetry include NOAA and U.S. Army Corps of Engineers hydrographic surveys and topographic bathymetric (topo-bathy) lidar (light detection and ranging) data. Data submitted through the NOAA Office of Coast Survey’s external source data process are also included, with gaps in deep water filled through Global Multi-Resolution Topography, a merged model of bathymetry. Different vertical datums and file formats are made available to meet various uses. The BlueTopo folder includes multilayer floating point GeoTIFFs with associated Raster Attribute Tables (RAT) containing elevation, vertical uncertainty, with other quality metrics and source information. These files are arranged in a spatial tiling and resolution scheme corresponding to the Electronic Navigational Chart (ENC) but are not for navigation due to the inclusion of additional non-navigation data and non-navigation vertical datums. For navigational datasets please see the S-102 distribution portal. "nowCOAST" provides public access to BlueTopo through the nowCOAST viewer, web map tile services (WMTS), and links to individual datasets.

United Kingdom's main stock market index, the GB100, fell to 9690 points on December 2, 2025, losing 0.13% from the previous session. Over the past month, the index has declined 0.12%, though it remains 15.91% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on December of 2025.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.