I really don’t want to spend time talking about how big social media has become. While some of us are still in denial, the impact of social media platforms is so profound. Thus, it’s not surprising when social media trends and statistics go in sync with societal changes. Understanding these...

China Google Search Trends: Online Shopping: Tmall data was reported at 3.000 Score in 01 May 2025. This records a decrease from the previous number of 5.000 Score for 30 Apr 2025. China Google Search Trends: Online Shopping: Tmall data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 01 May 2025, with 1248 observations. The data reached an all-time high of 70.000 Score in 22 Jan 2023 and a record low of 0.000 Score in 19 Apr 2025. China Google Search Trends: Online Shopping: Tmall data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s China – Table CN.Google.GT: Google Search Trends: by Categories.

The digitalization of the travel market has transformed how consumers research, book, and experience travel, ushering in a new era that blends technology with customer service. As the industry pivots toward digital solutions, travel agencies, airlines, and hospitality providers are leveraging advanced technologies t

The Health Information National Trends Survey (HINTS) is a biennial, cross-sectional survey of a nationally-representative sample of American adults that is used to assess the impact of the health information environment. The survey provides updates on changing patterns, needs, and information opportunities in health; Identifies changing communications trends and practices; Assesses cancer information access and usage; Provides information about how cancer risks are perceived; and Offers a testbed to researchers to test new theories in health communication.

The United States Data Center Power Market is Segmented by Component (Electrical Solutions, Services), Data Center Type (Hyperscaler/Cloud Service Providers, Colocation Providers, and More), Data Center Size (Small Size Data Centers, Medium Size Data Centers, Large Size Data Centers and More), Tier Type (Tier I and II, Tier III, Tier IV). The Market Forecasts are Provided in Terms of Value (USD)

In 2024, natural skincare was the top skin care trend online, with a total of over 13.3 million mentions across social media platforms TikTok, Pinterest and Instagram. The skin care trend, which demonstrates the huge consumer interest for natural ingredients and sustainable beauty routines, garnered the most attention on Instagram, which accounted for the majority of its mentions.

Introduction

Solana Statistics: Solana (SOL) emerged as a strong leader in 2024, setting the pace in the blockchain ecosystem across several key areas. Its high-performance network gained substantial traction in decentralised finance (DeFi), non-fungible tokens (NFTS), stablecoin transactions, and developer adoption. The Solana blockchain recorded over 200 million transactions per day by early 2024, supported by its lightning-fast throughput of 65,000 transactions per second.

The total value locked (TVL) in Solana's DeFi ecosystem surged past USD 2.5 billion, reflecting growing investor confidence. On the NFT front, Solana hosted over 33 million NFT mints with more than USD 3 billion in total sales volume. Additionally, Solana Pay processed over 1 million transactions in Q1 2024, indicating rising adoption in real-world commerce. The number of active developers contributing to the Solana GitHub repositories reached over 2,500 by March 2024, highlighting increasing interest from the global dev community.

As of April 2024, the Solana token (SOL) traded in the range of USD 130 to USD 160, driven by increased usage and network activity. This article explores key statistics from GitHub and the wider ecosystem that showcase Solana’s rapid growth and technical development. In this article, we deeply seek to address the relevant figures that indicate Solana statistics.

About this dataset

Context

The dataset tabulates the West Sparta town population by year. The dataset can be utilized to understand the population trend of West Sparta town.

Content

The dataset constitues the following datasets

• West Sparta, New York Population Dataset: Yearly Figures, Population Change, and Percent Change Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

The Tabletop Role-Playing Game (TTRPG) market has evolved dramatically over the past few decades, emerging as a vibrant community of storytellers and strategists engaging in immersive gameplay experiences. Initially characterized by traditional pen-and-paper formats, the industry has diversified to include a plethor

To help you get the biggest takeaways from all of these digital marketing stats, I want to share some trends in marketing that’s working for businesses right now.

This dataset contains data from the National Center for Education Statistics' Academic Library Survey, which was gathered every two years from 1996 - 2014, and annually in IPEDS starting in 2014 (this dataset has continued to only merge data every two years, following the original schedule). This data was merged, transformed, and used for research by Starr Hoffman and Samantha Godbey.This data was merged using R; R scripts for this merge can be made available upon request. Some variables changed names or definitions during this time; a view of these variables over time is provided in the related Figshare Project. Carnegie Classification changed several times during this period; all Carnegie classifications were crosswalked to the 2000 classification version; that information is also provided in the related Figshare Project. This data was used for research published in several articles, conference papers, and posters starting in 2018 (some of this research used an older version of the dataset which was deposited in the University of Nevada, Las Vegas's repository).SourcesAll data sources were downloaded from the National Center for Education Statistics website https://nces.ed.gov/. Individual datasets and years accessed are listed below.[dataset] U.S. Department of Education, National Center for Education Statistics, Academic Libraries component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Academic Libraries Survey (ALS) Public Use Data File, Library Statistics Program, (2012, 2010, 2008, 2006, 2004, 2002, 2000, 1998, 1996), https://nces.ed.gov/surveys/libraries/aca_data.asp[dataset] U.S. Department of Education, National Center for Education Statistics, Institutional Characteristics component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Fall Enrollment component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014, 2012, 2010, 2008, 2006, 2004, 2002, 2000, 1998, 1996), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Human Resources component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014, 2012, 2010, 2008, 2006), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Employees Assigned by Position component, Integrated Postsecondary Education Data System (IPEDS), (2004, 2002), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Fall Staff component, Integrated Postsecondary Education Data System (IPEDS), (1999, 1997, 1995), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7

Data Historian Market Outlook

The Data Historian market size is experiencing significant growth, driven by an increasing demand for efficient data management and analytics solutions across various industries. In 2023, the global Data Historian market was valued at approximately USD 1.2 billion and is anticipated to reach USD 2.5 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 8.5% over the forecast period. This growth is attributed to a myriad of factors, including the accelerating adoption of industrial IoT, the need for real-time data analysis, and the increasing focus on process optimization and predictive maintenance.

One of the primary growth factors for the Data Historian market is the burgeoning Industrial Internet of Things (IIoT) landscape. As industries such as oil and gas, chemicals, and pharmaceuticals increasingly integrate smart devices and sensors into their operations, the volume of data generated is rising exponentially. Data historians play a crucial role in capturing, storing, and analyzing this data, providing organizations with actionable insights that drive efficiency and innovation. Moreover, the rise in big data analytics has further underscored the importance of effective data management tools, positioning data historians as indispensable components in the digital transformation journey of industrial players.

Another significant driver of the market is the increasing demand for real-time data analysis. In today's fast-paced industrial environment, the ability to monitor processes and equipment performance in real-time is critical. Data historians provide this capability, allowing organizations to identify and address potential issues promptly, thereby reducing downtime and improving overall operational efficiency. Furthermore, real-time data insights empower companies to make data-driven decisions that enhance productivity and competitiveness, a trend that is expected to continue fueling the market's expansion over the coming years.

The shift towards predictive maintenance is also propelling the Data Historian market forward. With the goal of minimizing unexpected equipment failures and optimizing maintenance schedules, industries are increasingly leveraging data historians to analyze historical data and predict future performance trends. This proactive approach not only reduces maintenance costs but also extends the lifespan of critical assets. As predictive maintenance becomes a standard practice across various sectors, the demand for robust data historian solutions is expected to surge, contributing to the market's sustained growth.

From a regional perspective, the Data Historian market is witnessing diverse growth patterns. North America currently holds a significant share of the market, driven by the presence of established industrial sectors and the early adoption of advanced technologies. Meanwhile, the Asia Pacific region is anticipated to exhibit the highest growth rate over the forecast period. This can be attributed to the rapid industrialization in countries like China and India, coupled with increasing investments in infrastructure and technology. Europe is also expected to see steady growth, supported by stringent regulatory standards and a strong focus on sustainability and energy efficiency within its industrial landscape.

Component Analysis

The Data Historian market is segmented by components into software and services. The software segment constitutes the core of the market, representing the platforms and applications that facilitate data collection, storage, and analysis. As industrial processes become increasingly digitized, the demand for sophisticated software solutions that can handle vast amounts of data is on the rise. Data historian software is designed to efficiently capture and manage time-series data, providing users with the tools needed for detailed analysis and decision-making. The software's ability to integrate with various IT and OT systems further enhances its value, making it a critical asset for organizations looking to harness the power of data.

On the other hand, the services segment encompasses a range of offerings that support the deployment and optimization of data historian solutions. This includes implementation services, consulting, training, and support services. As businesses strive to maximize the value of their data historian investments, the demand for expert guidance and support is increasing. Service providers play a vital role in ensuring that data historian systems are effectively integrated into existing operations, tailored to meet the s

Introduction

Docker Statistics: Docker has solidified its position as a cornerstone in modern software development, offering developers a streamlined approach to building, deploying, and managing applications through containerization. As we navigate through 2024, Docker's shadow is spreading even further with cloud computing, AI, and microservices architectures coming into the frame. This article will talk about the most recent Docker statistics and trends and will portray a detailed analysis of the current state.

Data used in the Bureau of Transportation Statistics' new interactive version of Transportation Economic Trends (TET). TET highlights transportation's role in the economy and explores changes (trends) over time through a series of interactive charts. TET also explains related concepts and data sources for a general audience.

Interactive visualizations available at: https://data.transportation.gov/stories/s/28tb-cpjy

The Data Analytics in Retail Industry is segmented by Application (Merchandising and Supply Chain Analytics, Social Media Analytics, Customer Analytics, Operational Intelligence, Other Applications), by Business Type (Small and Medium Enterprises, Large-scale Organizations), and Geography. The market size and forecasts are provided in terms of value (USD billion) for all the above segments.

Google Search Trends: Online Training: Udemy data was reported at 0.000 Score in 14 May 2025. This stayed constant from the previous number of 0.000 Score for 13 May 2025. Google Search Trends: Online Training: Udemy data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 100.000 Score in 24 Dec 2024 and a record low of 0.000 Score in 14 May 2025. Google Search Trends: Online Training: Udemy data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Laos – Table LA.Google.GT: Google Search Trends: by Categories.

Data Modeling Software Market Outlook

The global data modeling software market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach around USD 6.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5% from 2024 to 2032. The market's robust growth can be attributed to the increasing adoption of data-driven decision-making processes across various industries, which necessitates advanced data modeling solutions to manage and analyze large volumes of data efficiently.

The proliferation of big data and the growing need for data governance are significant drivers for the data modeling software market. Organizations are increasingly recognizing the importance of structured and unstructured data in generating valuable insights. With data volumes exploding, data modeling software becomes essential for creating logical data models that represent business processes and information requirements accurately. This software is crucial for implementation in data warehouses, analytics, and business intelligence applications, further fueling market growth.

Technological advancements, particularly in artificial intelligence (AI) and machine learning (ML), are also propelling the data modeling software market forward. These technologies enable more sophisticated data models that can predict trends, optimize operations, and enhance decision-making processes. The integration of AI and ML with data modeling tools allows for automated data analysis, reducing the time and effort required for manual processes and improving the accuracy of the results. This technological synergy is a significant growth factor for the market.

The rise of cloud-based solutions is another critical factor contributing to the market's expansion. Cloud deployment offers numerous advantages, such as scalability, flexibility, and cost-effectiveness, making it an attractive option for businesses of all sizes. Cloud-based data modeling software allows for real-time collaboration and access to data from anywhere, enhancing productivity and efficiency. As more companies move their operations to the cloud, the demand for cloud-compatible data modeling solutions is expected to surge, driving market growth further.

In terms of regional outlook, North America currently holds the largest share of the data modeling software market. This dominance is due to the high concentration of technology-driven enterprises and a strong emphasis on data analytics and business intelligence in the region. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. Rapid digital transformation, increased cloud adoption, and the rising importance of data analytics in emerging economies like China and India are key factors contributing to this growth. Europe, Latin America, and the Middle East & Africa also present significant opportunities, albeit at varying growth rates.

Component Analysis

In the data modeling software market, the component segment is divided into software and services. The software component is the most significant contributor to the market, driven by the increasing need for advanced data modeling tools that can handle complex data structures and provide accurate insights. Data modeling software includes various tools and platforms that facilitate the creation, management, and optimization of data models. These tools are essential for database design, data architecture, and other data management tasks, making them indispensable for organizations aiming to leverage their data assets effectively.

Within the software segment, there is a growing trend towards integrating AI and ML capabilities to enhance the functionality of data modeling tools. This integration allows for more sophisticated data analysis, automated model generation, and improved accuracy in predictions and insights. As a result, organizations can achieve better data governance, streamline operations, and make more informed decisions. The demand for such advanced software solutions is expected to rise, contributing significantly to the market's growth.

The services component, although smaller in comparison to the software segment, plays a crucial role in the data modeling software market. Services include consulting, implementation, training, and support, which are essential for the successful deployment and utilization of data modeling tools. Many organizations lack the in-house expertise to effectively implement and manage data modeling software, leading to increased demand for professional services.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for statistics in the U.S.

About this dataset

Context

The dataset tabulates the Vandalia population by year. The dataset can be utilized to understand the population trend of Vandalia.

Content

The dataset constitues the following datasets

• Vandalia, MO Population Dataset: Yearly Figures, Population Change, and Percent Change Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Google Search Trends: Travel & Accommodations: Lufthansa data was reported at 0.000 Score in 14 May 2025. This stayed constant from the previous number of 0.000 Score for 13 May 2025. Google Search Trends: Travel & Accommodations: Lufthansa data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 63.000 Score in 18 Apr 2024 and a record low of 0.000 Score in 14 May 2025. Google Search Trends: Travel & Accommodations: Lufthansa data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Saint Lucia – Table LC.Google.GT: Google Search Trends: by Categories.