In this project, we aimed to map the visualisation design space of visualisation embedded in right-to-left (RTL) scripts. We aimed to expand our knowledge of visualisation design beyond the dominance of research based on left-to-right (LTR) scripts. Through this project, we identify common design practices regarding the chart structure, the text, and the source. We also identify ambiguity, particularly regarding the axis position and direction, suggesting that the community may benefit from unified standards similar to those found on web design for RTL scripts. To achieve this goal, we curated a dataset that covered 128 visualisations found in Arabic news media and coded these visualisations based on the chart composition (e.g., chart type, x-axis direction, y-axis position, legend position, interaction, embellishment type), text (e.g., availability of text, availability of caption, annotation type), and source (source position, attribution to designer, ownership of the visualisation design). Links are also provided to the articles and the visualisations. This dataset is limited for stand-alone visualisations, whether they were single-panelled or included small multiples. We also did not consider infographics in this project, nor any visualisation that did not have an identifiable chart type (e.g., bar chart, line chart). The attached documents also include some graphs from our analysis of the dataset provided, where we illustrate common design patterns and their popularity within our sample.

The Data Visualization Corpus

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1430847%2F29f7950c3b7daf11175aab404725542c%2FGettyImages-1187621904-600x360.jpg?generation=1601115151722854&alt=media" alt="">

Data Visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions

The Data Visualizaion Copus

The Data Visualization corpus consists:

• 32 cheat sheets: This includes A-Z about the techniques and tricks that can be used for visualization, Python and R visualization cheat sheets, Types of charts, and their significance, Storytelling with data, etc..

• 32 Charts: The corpus also consists of a significant amount of data visualization charts information along with their python code, d3.js codes, and presentations relation to the respective charts explaining in a clear manner!

• Some recommended books for data visualization every data scientist's should read:

  1. Beautiful Visualization by Julie Steele and Noah Iliinsky
  2. Information Dashboard Design by Stephen Few
  3. Knowledge is beautiful by David McCandless (Short abstract)
  4. The Functional Art: An Introduction to Information Graphics and Visualization by Alberto Cairo
  5. The Visual Display of Quantitative Information by Edward R. Tufte
  6. storytelling with data: a data visualization guide for business professionals by cole Nussbaumer knaflic
  7. Research paper - Cheat Sheets for Data Visualization Techniques by Zezhong Wang, Lovisa Sundin, Dave Murray-Rust, Benjamin Bach

Suggestions:

In case, if you find any books, cheat sheets, or charts missing and if you would like to suggest some new documents please let me know in the discussion sections!

Resources:

• Charts: I personally recommend data viz catalogue, it's easy to understand with their explanation!
• Python codes: Plotly for python and Python graph gallery
• R codes for charts:Plotly for R
• d3 codes: Visualization codes using d3

Request to kaggle users:

• A kind request to kaggle users to create notebooks on different visualization charts as per their interest by choosing a dataset of their own as many beginners and other experts could find it useful!

• To create interactive EDA using animation with a combination of data visualization charts to give an idea about how to tackle data and extract the insights from the data

Suggestion and queries:

Feel free to use the discussion platform of this data set to ask questions or any queries related to the data visualization corpus and data visualization techniques

Kindly upvote the dataset if you find it useful or if you wish to appreciate the effort taken to gather this corpus! Thank you and have a great day!

Data Visualization Market Outlook

The global data visualization market size was valued at approximately USD 6.5 billion in 2023 and is projected to reach USD 19.8 billion by 2032, growing at a robust CAGR of 12.8% during the forecast period. This impressive growth can be attributed to the escalating need for organizations to make data-driven decisions, the proliferation of big data, and the increasing adoption of advanced analytics tools.

One of the primary growth factors driving the data visualization market is the exponential increase in data generation across various industries. With the advent of IoT, social media proliferation, and digital transformation, organizations are inundated with vast amounts of data. The need to interpret this data to derive meaningful insights has never been greater. Data visualization tools enable businesses to transform raw data into graphical representations, facilitating easier understanding and more informed decision-making.

Another significant growth driver is the increasing adoption of business intelligence (BI) and analytics solutions. Enterprises are progressively recognizing the value of BI tools in gaining competitive advantages. Data visualization is a critical component of these BI platforms, providing interactive and dynamic representations of data that can be manipulated to uncover trends, patterns, and correlations. This ability to visualize complex data sets enhances strategic planning and operational efficiencies.

The rising demand for personalized customer experiences is also contributing to market growth. In sectors like retail, BFSI, and healthcare, understanding customer behavior and preferences is paramount. Data visualization tools help organizations analyze customer data in real-time, enabling them to tailor offerings and improve customer engagement. The ability to visualize data in an intuitive manner accelerates the speed at which businesses can respond to market changes and customer needs.

Marketing Dashboards have become an essential tool for businesses seeking to optimize their marketing strategies through data visualization. These dashboards provide a comprehensive view of marketing performance by aggregating data from various sources such as social media, email campaigns, and web analytics. By presenting this data in an easily digestible format, marketing teams can quickly identify trends, track campaign effectiveness, and make informed decisions to enhance their marketing efforts. The ability to customize these dashboards allows organizations to focus on key performance indicators that are most relevant to their objectives, ultimately leading to more targeted and successful marketing initiatives.

From a regional perspective, North America holds a significant share of the data visualization market, driven by the presence of major technology providers and high adoption rates of advanced analytics tools. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth is fueled by the increasing digitalization initiatives, rising investments in IT infrastructure, and the growing awareness of data-driven decision-making in emerging economies such as India and China.

Component Analysis

The data visualization market comprises two primary components: software and services. The software segment is further categorized into standalone visualization tools and integrated data visualization solutions. Standalone visualization tools are designed specifically for data visualization purposes, offering features such as interactive dashboards, real-time analytics, and customizable visualizations. Integrated solutions, on the other hand, are part of larger business intelligence or analytics platforms, providing seamless integration with other data management and analysis tools.

The services segment includes consulting, implementation, and support services. Consulting services help organizations identify the right data visualization tools and strategies to meet their specific needs. Implementation services ensure the successful deployment and integration of visualization solutions within the existing IT infrastructure. Support services provide ongoing maintenance, updates, and troubleshooting to ensure the smooth functioning of the data visualization tools.

Within the software segment, the demand for cloud-based data visualization solutions is growing rapidly. Cloud

Data Visualization Tools Market Outlook

The global data visualization tools market size is expected to reach approximately USD 15.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.5% from 2024, up from an estimated USD 6.5 billion in 2023. This robust growth is primarily driven by the increasing demand for data-driven business decisions and the growing importance of data visualization in simplifying complex data sets for better understanding and analysis. As organizations worldwide recognize the value of visual data representation to enhance decision-making processes, the market is poised to expand significantly in the coming years.

One of the main factors propelling the growth of the data visualization tools market is the exponential increase in data generation across various industries. With the advent of technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics, vast amounts of data are being generated at an unprecedented rate. This data, when visualized effectively, can uncover patterns and insights that are critical for strategic planning and operational efficiency. As businesses strive to achieve data-driven growth, the demand for advanced visualization tools that can present data in an accessible and meaningful way is expected to rise.

Another growth factor is the increasing adoption of business intelligence (BI) tools across industries. BI tools, which often include robust data visualization capabilities, help organizations in not only understanding their data but also in making informed business decisions. The shift towards data-driven cultures in organizations is also supported by the growing trend of self-service analytics, where employees at all levels can access and analyze data without extensive technical expertise. This democratization of data access is helping organizations to remain agile and responsive to market changes, further driving the demand for intuitive and user-friendly visualization tools.

The integration of advanced technologies such as AI and machine learning within data visualization tools is also contributing to market growth. These technologies enhance the capability of visualization tools to automatically generate insights and predictions, allowing users to identify trends and patterns with greater ease and accuracy. As organizations increasingly rely on predictive analytics for future forecasting, the integration of AI-driven visualization tools is becoming a key component of their data strategy. This technological advancement is expected to foster the development of more sophisticated tools, thereby opening up new opportunities for market players.

In the realm of data visualization, the role of Data Analysis Tools cannot be overstated. These tools are pivotal in transforming raw data into meaningful insights, enabling organizations to make informed decisions. By leveraging data analysis tools, businesses can dissect complex datasets, identify trends, and uncover hidden patterns that may not be immediately apparent through visualization alone. These tools complement visualization software by providing the analytical backbone necessary for accurate data interpretation. As the demand for data-driven strategies continues to rise, the integration of robust data analysis tools with visualization platforms is becoming increasingly essential for organizations aiming to stay competitive in a data-centric world.

The regional outlook of the data visualization tools market reveals significant opportunities for growth across different parts of the world. North America, with its well-established IT infrastructure and high adoption rates of advanced technologies, currently holds the largest market share. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. Driven by rapid digital transformation, increasing investments in IT infrastructure, and a burgeoning number of data-centric startups, the demand for data visualization tools in this region is expected to surge. Meanwhile, Europe and Latin America are also expected to show substantial growth, fueled by the increasing focus on data-driven decision-making and technological advancements.

Tool Type Analysis

The data visualization tools market is segmented into standalone visualization software and integrated software. Standalone visualization software refers to specialized applications designed solely for data visualization purposes. These tools offer ad

The global designing data visualization services market is projected to grow from USD XXX million in 2025 to USD XXX million by 2033, at a CAGR of XX%. The increasing demand for data visualization services is driven by the growing need for businesses to make informed decisions based on data. Data visualization services help businesses to understand their data and identify trends and patterns that can be used to improve their operations. The market is segmented by application (large enterprises, SMEs), type (dashboard software, data mining software, mobile business intelligence software, predictive analytical software), and region (North America, South America, Europe, Middle East & Africa, Asia Pacific). The North American region is expected to dominate the market, followed by the Asia Pacific region. The growth in the Asia Pacific region is attributed to the increasing number of businesses in the region and the growing adoption of data visualization services.

Reddit is a social news, content rating and discussion website. It's one of the most popular sites on the internet. Reddit has 52 million daily active users and approximately 430 million users who use it once a month. Reddit has different subreddits and here We'll use the r/AskScience Subreddit.

The dataset is extracted from the subreddit /r/AskScience from Reddit. The data was collected between 01-01-2016 and 20-05-2022. It contains 612,668 Datapoints and 25 Columns. The database contains a number of information about the questions asked on the subreddit, the description of the submission, the flair of the question, NSFW or SFW status, the year of the submission, and more. The data is extracted using python and Pushshift's API. A little bit of cleaning is done using NumPy and pandas as well. (see the descriptions of individual columns below).

The dataset contains the following columns and descriptions: author - Redditor Name author_fullname - Redditor Full name contest_mode - Contest mode [implement obscured scores and randomized sorting]. created_utc - Time the submission was created, represented in Unix Time. domain - Domain of submission. edited - If the post is edited or not. full_link - Link of the post on the subreddit. id - ID of the submission. is_self - Whether or not the submission is a self post (text-only). link_flair_css_class - CSS Class used to identify the flair. link_flair_text - Flair on the post or The link flair’s text content. locked - Whether or not the submission has been locked. num_comments - The number of comments on the submission. over_18 - Whether or not the submission has been marked as NSFW. permalink - A permalink for the submission. retrieved_on - time ingested. score - The number of upvotes for the submission. description - Description of the Submission. spoiler - Whether or not the submission has been marked as a spoiler. stickied - Whether or not the submission is stickied. thumbnail - Thumbnail of Submission. question - Question Asked in the Submission. url - The URL the submission links to, or the permalink if a self post. year - Year of the Submission. banned - Banned by the moderator or not.

This dataset can be used for Flair Prediction, NSFW Classification, and different Text Mining/NLP tasks. Exploratory Data Analysis can also be done to get the insights and see the trend and patterns over the years.

Conducted an in-depth analysis of Cyclistic bike-share data to uncover customer usage patterns and trends. Cleaned and processed raw data using Python libraries such as pandas and NumPy to ensure data quality. Performed exploratory data analysis (EDA) to identify insights, including peak usage times, customer demographics, and trip duration patterns. Created visualizations using Matplotlib and Seaborn to effectively communicate findings. Delivered actionable recommendations to enhance customer engagement and optimize operational efficiency.

The Geospatial Imagery Analytics Marketsize was valued at USD 11.88 USD Billion in 2023 and is projected to reach USD 83.39 USD Billion by 2032, exhibiting a CAGR of 32.1 % during the forecast period.Geospatial analytics gathers, manipulates, and displays geographic information system (GIS) data and imagery including GPS and satellite photographs. Geospatial data analytics rely on geographic coordinates and specific identifiers such as street address and zip code. geospatial visualization enables businesses to better understand complex information and make informed decisions. They can quickly see patterns and trends and assess the impact of different variables by visualizing data in a spatial context. The field encompasses several techniques and algorithms, such as spatial interpolation, spatial regression, spatial clustering, and spatial autocorrelation analysis, which help extract insights from various geospatial data sources. The growing adoption of location-based services in various industries, including agriculture, defense, and urban planning, is driving the demand for geospatial imagery analytics. Recent developments include: August 2023: onX, a digital navigation company, partnered with Planet Labs PBC, a satellite imagery provider, to introduce a new feature called ‘Recent Imagery’. This feature offers onX app users updated satellite imagery maps every two weeks, enhancing the user experience across onX Hunt, onX Offroad, and onX Backcountry apps. This frequent data update helps outdoor enthusiasts access real-time information for safer and more informed outdoor activities., August 2023: Quant Data & Analytics, a provider of data products and enterprise solutions for real estate and retail, partnered with Satellogic Inc. to utilize Satellogic’s high-resolution satellite imagery to enhance property technology in Saudi Arabia and the Gulf region., April 2023: Astraea, a spatiotemporal data and analytics platform, introduced a new ordering service that grants customers scalable access to top-tier commercial satellite imagery from providers such as Planet Labs PBC and others., May 2022: Satellogic Inc. established a partnership with UP42. This geospatial developer platform enables direct access to Satellogic’s satellite tasking capabilities, including high-resolution multispectral and wide-area hyperspectral imagery, through the UP42 API-based platform., April 2022: TomTom International BV, a geolocation tech company, broadened its partnership with Maxar Technologies, a space solution provider. This expansion involves integrating high-resolution global satellite imagery from Maxar’s Vivid imagery base maps into TomTom’s product lineup, enhancing their visualization solutions for customers.. Key drivers for this market are: Growing Demand for Location-based Insights across Diverse Industries to Fuel Market Growth. Potential restraints include: Complexity and Cost Associated with Data Acquisition and Processing May Hamper Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

Clinical Trial Data Visualization Market Outlook

The global clinical trial data visualization market size is projected to grow from USD 0.75 billion in 2023 to USD 2.62 billion by 2032, reflecting a compound annual growth rate (CAGR) of 15.2% during the forecast period. This growth is driven by the increasing complexity of clinical trials, the need for enhanced data transparency, and the rising adoption of digital tools in the healthcare sector.

One of the key drivers for the growth of the clinical trial data visualization market is the escalating complexity and volume of data generated during clinical trials. The pharmaceutical and biotechnology sectors are witnessing a surge in clinical trials, which demand sophisticated data management and visualization tools to make sense of the vast amounts of data collected. These tools enable researchers to identify patterns, trends, and outliers more efficiently, thereby accelerating the decision-making process and improving clinical trial outcomes.

Another significant factor contributing to market growth is the increasing emphasis on data transparency and regulatory compliance. Regulatory bodies, such as the FDA and EMA, are mandating greater transparency in clinical trial data to ensure patient safety and data integrity. Data visualization tools facilitate the clear presentation of complex data, making it easier for regulatory bodies and stakeholders to review and approve clinical trial processes. This ensures that clinical trials are conducted in a more transparent and compliant manner, thus driving the adoption of these tools.

The advent of advanced technologies, such as artificial intelligence (AI) and machine learning (ML), is also playing a crucial role in the growth of the clinical trial data visualization market. These technologies are being increasingly integrated into data visualization tools to enhance their capabilities. AI and ML algorithms can analyze large datasets quickly and provide insights that were previously unattainable. This not only improves the efficiency of clinical trials but also enhances the accuracy and reliability of the data being presented.

As the clinical trial data visualization market continues to expand, the importance of Clinical Trial Data Security becomes increasingly paramount. With the vast amounts of data generated during trials, ensuring the confidentiality, integrity, and availability of this data is critical. Organizations must implement robust security measures to protect sensitive information from unauthorized access and breaches. This involves not only securing the data itself but also safeguarding the systems and networks that store and process this information. As regulatory bodies tighten their data protection requirements, companies are investing in advanced security technologies and practices to comply with these standards and maintain trust with stakeholders. The focus on Clinical Trial Data Security is not just about compliance; it is about ensuring the reliability and credibility of clinical trial outcomes, which ultimately impacts patient safety and the development of new therapies.

Regionally, North America is expected to dominate the clinical trial data visualization market due to the presence of a large number of pharmaceutical and biotechnology companies, a well-established healthcare infrastructure, and a strong focus on research and development. Europe is also expected to witness significant growth, driven by the increasing adoption of digital technologies in clinical trials and supportive regulatory frameworks. The Asia Pacific region is poised to grow at the fastest rate, fueled by the expanding pharmaceutical industry, growing investments in healthcare technology, and an increasing number of clinical trials being conducted in countries like China and India.

Component Analysis

The clinical trial data visualization market is segmented into software and services based on components. The software segment is expected to hold the largest market share during the forecast period. This can be attributed to the increasing demand for advanced software solutions that offer real-time data analysis and visualization capabilities. These software tools are designed to handle large volumes of data and provide intuitive visual representations that facilitate better understanding and decision-making.

Furthermore, the integration of AI and ML technologies into data visualization software is enhancing their capabilities, makin

High-throughput sequencing and single nucleotide polymorphism (SNP) genotyping can be used to infer complex population structures. Fine-scale population structure analysis tracing individual ancestry remains one of the major challenges. Based on network theory and recent advances in SNP chip technology, we investigated an unsupervised network clustering method called Super Paramagnetic Clustering (Spc). When applied to whole-genome marker data it identifies the natural divisions of groups of individuals into population clusters without use of prior ancestry information. Furthermore, we optimised an analysis pipeline called NetView, a high-definition network visualization, starting with computation of genetic distance, followed clustering using Spc and finally visualization of clusters with Cytoscape. We compared NetView against commonly used methodologies including Principal Component Analyses (PCA) and a model-based algorithm, Admixture, on whole-genome-wide SNP data derived from three previously described data sets: simulated (2.5 million SNPs, 5 populations), human (1.4 million SNPs, 11 populations) and cattle (32,653 SNPs, 19 populations). We demonstrate that individuals can be effectively allocated to their correct population whilst simultaneously revealing fine-scale structure within the populations. Analyzing the human HapMap populations, we identified unexpected genetic relatedness among individuals, and population stratification within the Indian, African and Mexican samples. In the cattle data set, we correctly assigned all individuals to their respective breeds and detected fine-scale population sub-structures reflecting different sample origins and phenotypes. The NetView pipeline is computationally extremely efficient and can be easily applied on large-scale genome-wide data sets to assign individuals to particular populations and to reproduce fine-scale population structures without prior knowledge of individual ancestry. NetView can be used on any data from which a genetic relationship/distance between individuals can be calculated.

This is a dataset contain all Wind turbine units in Germany at the time of June 2023.

This is the official data of the German Marktstammdatenregister by the Bundesnetzagentur. Some fields of the original data are cut out due to unimportance for this purpose.

The full + extra datasets can be downloaded here.

Applications

The main focus of this datatset is on the distribution and amount of wind turbine units in Germany. Interesting applications could be:

• using clustering methods to determine where are good locations for potentially high energy consuming building.
• using open street maps and see facilities are located near by and maybe find some patterns of the location of wind turbines.

• visualizing of the data using maps and plots

  This dataset can also be combined with other data.

License

Datenlizenz Deutschland – Namensnennung – Version 2.0" oder „dl-de/by-2-0" mit Verweis auf den Lizenztext unter www.govdata.de/dl-de/by-2-0

Analysis of ‘Immigration to Canada’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ammaraahmad/immigration-to-canada on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

The Main Purpose for uploading this dataset is to analyze the trends and hidden patterns of Immigrants from all over the world to Canada.

Content

This dataset consists of immigrants record from 150+ countries to Canada between 1980 to 2013.

Acknowledgements

Data Source

Inspiration

By using Data visualization , find out hidden Patterns .

--- Original source retains full ownership of the source dataset ---

Online Shopping Consumer Behavior Dataset

Consumer Buying Patterns in E-Commerce

By Weitong Li [source]

About this dataset

This dataset is a rich compilation of data that thoroughly guides us through consumers' behavior and their buying intentions while engaged in online shopping. It has been constructed with immense care to ensure it effectively examines an array of factors that influence customers' purchasing intentions in the increasingly significant realm of digital commerce.

The dataset is exhaustively composed with careful attention to collecting a diverse set of information, thus allowing a broad view into what affects online shopping behavior. Specific columns included cover customer's existing awareness about the website or source from where they are shopping, their information regarding the products they wish to purchase, and more importantly, their satisfaction level related to previous purchases.

Additionally, the dataset delves deep into investigating both objective and subjective aspects impacting customer behavior online. As such, it includes data on various webpage factors like loading speed, user-friendly interface design, webpage aesthetics, etc., which could significantly persuade the consumer's decision-making process during online shopping. The completion and submission convenience provided by those websites also form part of this database.

In order to fully understand consumer behavior within an online environment from multiple facets', individual consumers' subjective views are also captured in this dataset; it explores how consumers perceive their trust towards an e-commerce site or if they believe it’s convenient for them to shop via these platforms versus traditional methods? Do they feel relaxed when doing so?

In recognizing how crucial products competitiveness within such landscapes influences buyer intention - columns that provide details on critical characteristics like price comparisons against offline stores or similar product competitors across different websites have been included too.

Overall this comprehensive aggregated data collection aims not only at understanding fundamental consumer preferences but also towards predicting future buying behaviors hence forth enabling businesses capitalize on emerging trends within online retail spaces more efficiently & profitably

How to use the dataset

In an online-focused world, understanding consumer behavioral data is crucial. The 'Online Shopping Purchasing Intention Dataset' provides a comprehensive collection of consumer-based insights based on their behavior in virtual shopping environments. This dataset explores various factors that might affect a customer's decision to purchase. Here's how you can harness this dataset:

Defining the Problem

Identify a problem or question this data may answer. This might be: understanding what factors influence buying decisions, predicting whether a visit will result in a purchase based on user behavior, analyzing the impact of the month, operating system or traffic type on online purchasing intention etc.

Data Exploration

Understand the structure of the dataset by getting to know each variable and its meaning: - Administrative: Counting different types of pages visited by the user in that session. - Informational & Product Related: Measures how many informational/product related pages are viewed. - Bounce Rates, Exit Rate, Page Values: Assess these metrics as they provide significant insight about visitor activity. - Special Day: Explore correlation between proximity to special days (like Mother’s day and Valentine’s Day) with transactions. - Operating Systems / Browser / Region / Traffic Type: Uncover behavioral patterns associated with technical specs/geo location/ source of traffic.

Analysis and Visualization

Use appropriate statistical analysis techniques to scrutinize relationships between variables such as correlation analysis or chi-square tests for independence etc.

Visualize your findings using plots like bar graphs for categorical features comparison or scatter plots for multivariate relationships etc.

Model Building

Use machine learning algorithms (like logistic regression or decision tree models) potentially useful if your goal is predicting purchase intention based on given features.

This could also involve feature selection - choosing most relevant predictors; training & testing model and finally assessing model performance through metrics like accuracy score, precision-recall scores etc.

Remember to appropriately handle missing values if any before diving into predictive modeling

The comprehens...

Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.

Enterprise Data Visualization Platform Market Outlook

The global market size of the Enterprise Data Visualization Platform market is poised to grow significantly from $X billion in 2023 to an estimated $Y billion by 2032, at a compound annual growth rate (CAGR) of Z%. This remarkable growth is driven by the increasing demand for data-driven decision-making across various industries, a surge in the use of big data analytics, and the rising popularity of business intelligence tools. The market size is expected to expand due to the need for organizations to visualize complex data in a comprehensible format to drive strategic business decisions.

One of the core growth factors for the Enterprise Data Visualization Platform market is the exponential increase in data generation from various sources, including social media, IoT devices, and enterprise applications. Organizations across the globe are struggling to manage and interpret this vast amount of data. Data visualization platforms enable businesses to transform raw data into meaningful insights through graphical representations such as charts, graphs, dashboards, and maps. This capability is essential for gaining a competitive edge, enhancing operational efficiency, and making informed decisions.

Another significant growth factor is the rising adoption of business intelligence (BI) tools and big data analytics. Businesses are increasingly recognizing the value of leveraging data to understand market trends, customer preferences, and operational performance. BI tools, integrated with data visualization platforms, offer advanced analytical capabilities that help organizations identify patterns, correlations, and anomalies in data. This, in turn, aids in optimizing business processes, improving customer satisfaction, and driving revenue growth.

The growing emphasis on self-service data analytics and democratization of data analytics across organizations is also propelling market growth. Self-service data visualization platforms empower non-technical users to create customized visual reports and dashboards without the need for extensive IT support. This not only reduces the dependency on data scientists and IT personnel but also fosters a data-driven culture within the organization. The increasing trend of data democratization allows more employees to access and analyze data, leading to more agile and informed decision-making processes.

Visual Data Discovery has emerged as a crucial aspect of data visualization platforms, offering users the ability to interactively explore and analyze data. This approach allows users to uncover insights and patterns that might not be immediately apparent through traditional static reports. By enabling dynamic exploration of data, visual data discovery tools empower users to ask new questions and gain deeper insights. These tools often include features such as drag-and-drop interfaces, interactive dashboards, and real-time data updates, making data analysis more accessible to a broader audience. As organizations strive to become more data-driven, the demand for visual data discovery capabilities continues to grow, enhancing the overall value of data visualization platforms.

Regionally, North America holds a significant share of the Enterprise Data Visualization Platform market, driven by the presence of major technology companies, high adoption rates of advanced analytics solutions, and robust IT infrastructure. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid digital transformation, increasing investments in big data and analytics, and the growing number of SMEs in countries like China, India, and Japan are contributing to the market expansion in this region.

Component Analysis

The Enterprise Data Visualization Platform market can be segmented by component into software and services. The software segment is anticipated to hold the largest market share during the forecast period. This is primarily due to the widespread adoption of visualization tools that facilitate easy interpretation of complex data sets. These software solutions offer a range of functionalities, including interactive dashboards, real-time data analysis, and predictive analytics, which are essential for modern business operations. The continuous advancements in software capabilities, such as enhanced user interfaces and integration with other business applications, further drive the demand in this segment.

This dataset provides a detailed insight into the daily activities of citizens in a futuristic smart city. It covers various aspects such as:

Demographics (Age, Gender) Mobility (Mode of Transport, Walking Steps) Lifestyle & Social Engagement (Work, Shopping, Entertainment, Social Media) Health & Well-being (Calories Burned, Sleep Hours) Energy & Sustainability (Home Energy Consumption, Carbon Footprint, Charging Station Usage) With 1000 rows and 15 columns, this dataset is ideal for data analysis, machine learning, and visualization projects related to urban mobility, sustainability, health trends, and behavioral analytics.

This dataset can be used to:

✅ Analyze citizen behavior trends

✅ Understand sustainable urban mobility

✅ Predict energy consumption patterns

✅ Identify health and social media habits

The Graph extension for CKAN adds the ability to visualize data resources as graphs, providing users with a more intuitive understanding of the information contained within datasets. It currently supports temporal and categorical graph types, enabling the creation of count-based visualizations over time or across different categories. While the current version is primarily designed for use with an Elasticsearch backend within the Natural History Museum's infrastructure, it is built to be extensible for broader applicability. Key Features: Temporal Graphs: Generates line graphs that display counts of data points over time, based on a designated date field within the resource. This allows to visualize trends and patterns dynamically. Categorical Graphs: Creates bar charts that show the distribution of counts for various values found within a specified field in a resource, making it easier to understand data groupings. Extensible Backend Architecture: Designed to support multiple backend data storage options, with Elasticsearch currently implemented, paving the way for future integration with other systems like PostgreSQL. Template Customization: Includes a template (templates/graph/view.html) that can be extended to override or add custom content to the graph view, giving full control over the visualization design. Configuration Options: Backend selection through the .ini configuration file. Users can choose between Elasticsearch or SQL, allowing administrators to align the extension with their specific requirements. Technical Integration: The Graph extension integrates with CKAN by adding a new view option to resources. Once enabled, the graph view will appear as an available option alongside existing resource viewers. The configuration requires modifying the CKAN .ini file to add 'graph' to the list of enabled plugins and setting the desired backend. The template templates/graph/view.html allows for full customization of the view. Benefits & Impact: The Graph extension enhances the usability of CKAN-managed datasets by providing interactive visualizations of data. Temporal graphs help users identify time-based trends, while categorical graphs illustrate data distribution. The extensible architecture ensures that the extension can be adapted to different data storage systems, improving its versatility. By providing a graphical representation of data, this extension makes it easier to understand complex information, benefiting both data providers and consumers.

Standalone Data Visualization Tools Market Outlook

The global market size for standalone data visualization tools was valued at USD 4.2 billion in 2023 and is projected to reach USD 9.1 billion by 2032, growing at a CAGR of 8.9% during the forecast period. The market's growth trajectory is primarily fueled by the increasing demand for data-driven business decision-making processes across various industries.

One of the major growth factors for this market is the exponential increase in data generation. The proliferation of IoT devices, social media, and other digital platforms has led to vast amounts of data being generated daily. Organizations are increasingly relying on data visualization tools to transform this raw data into meaningful insights that can drive strategic decision-making. The ability to visualize complex data sets in an easily interpretable format is becoming indispensable for businesses aiming to maintain a competitive edge.

Another significant driver for the growth of standalone data visualization tools is the increasing adoption of advanced analytics and business intelligence (BI) solutions. As companies strive to become more data-centric, there is a heightened demand for tools that can offer real-time data analysis and visualization. This trend is particularly prominent in sectors such as BFSI, healthcare, and retail, where timely decision-making is crucial. The integration of AI and machine learning technologies with data visualization tools is further enhancing their capabilities, thereby boosting their adoption across various industries.

Moreover, the rise of cloud computing has also played a crucial role in the market's expansion. Cloud-based data visualization tools offer several advantages, including scalability, cost-effectiveness, and ease of access. These benefits are particularly appealing to small and medium enterprises (SMEs) that may not have the resources to invest in on-premises solutions. The flexibility provided by cloud-based tools is enabling organizations to democratize data access and empower employees at all levels to make data-driven decisions.

In today's data-driven world, the role of a Data Discovery and Visualization Platform is becoming increasingly crucial. These platforms empower organizations to uncover hidden patterns and insights within their vast datasets, facilitating more informed decision-making. By integrating data from various sources, these platforms provide a comprehensive view of business operations, enabling users to identify trends and anomalies that might otherwise go unnoticed. As businesses continue to generate and collect data at an unprecedented rate, the need for robust data discovery and visualization solutions is more pressing than ever. These platforms not only enhance the ability to interpret complex data but also democratize data access, allowing employees at all levels to engage with data insights and contribute to strategic initiatives.

In terms of regional outlook, North America currently holds the largest market share due to the high adoption rate of advanced technologies and the presence of major market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. Rapid industrialization, increasing internet penetration, and a growing focus on digital transformation are some of the factors contributing to the market's growth in this region. Countries like China, India, and Japan are emerging as significant markets for standalone data visualization tools.

Component Analysis

The component segment of the standalone data visualization tools market is divided into software and services. Software solutions dominate this segment due to their critical role in data interpretation, enabling businesses to extract actionable insights from vast datasets. Advanced software solutions offer various features such as real-time analytics, interactive dashboards, and predictive analytics, which significantly enhance decision-making processes. The ongoing advancements in AI and machine learning are continually upgrading the functionalities of these software solutions, thus driving their adoption across different industry verticals.

Service components, although a smaller portion of the market, play a vital role in the effective implementation and utilization of data visualization tools. Services include consulting, implementation, and support &

You are invited to learn a new online tool for exploring streamflow in the Skagit River watershed. The tool provides historical and future streamflows based on hydrologic modeling by University of Washington (UW). The visualization and streamflow data can be used in long-term planning as well as in designs for long-lived infrastructure and resource projects. This training includes slides for a presentation and interactive run exercises using the visualization tool and explore how to use the tool to discover interesting patterns based on CMIP5 climate changes.

As the climate warms, people want information on what to consider as they plan for potential changes in streamflows. The following visualizations show a large set of outputs from a modeling study conducted by researchers at the University of Washington Civil and Environmental Engineering Department and supported by several organizations with a common interest in understanding a potential range of future conditions (Seattle City Light, Swinomish Indian Tribal Community, and the Sauk-Suiattle Indian Tribe in partnership with the Skagit Climate Science Consortium). The study is available at: https://www.hydroshare.org/resource/e5ad2935979647d6af5f1a9f6bdecdea/. The study modeled projected changes in streamflows at 20 locations in the Skagit River Watershed.

Specific locations modeled include: Red Cabin Creek, Finney Creek, Jackman Creek, Illabot Creek, Cascade River, Jordan Creek, Bacon Creek, Marblemount to Newhalem, Gorge, Diablo,Thunder Creek, Ross, Sauk River near Sauk, Big Creek, Sauk River at Darrington, Sauk River above Clear Creek, Sauk River above White Chuck, White Chuck, North Fork Sauk River, South Fork Sauk River,

Visualizations include Monthly Averages and Extremes within multiple dashboard page viewers with embedded maps, charts, and figures, with a tab on Definitions & Documentation used in the visualizations also provided.

Direct link to the tool - http://www.skagitclimatescience.org/projected-changes-in-streamflow/

Time: 1.5 hours

These files were originally developed for the Skagit Streamflow Visualization Online Tool Training on February 13, 2020 with Seattle City Light staff.

Attached files include: Help Guide, Training slideshow (with links to more data/info), Exercise with answers

eBird data is surveyed per Caterpillars Count circle so it is easy to visualize patterns with the arthropod data. More birds were found near trees without arthropods on this particular day. It would be interesting to see if this pattern is consistent over the season or if this date may be an outlier because it is the last day of the season. Are you completing Caterpillars Count with your organization or community group? Try this method with eBird and see what patterns you find on your site! Send an email to info@ecospark.ca if you are interested in creating maps or learning more about Caterpillars Count and eBird.Caterpillars Count: https://caterpillarscount.unc.edu/ eBird: https://ebird.org/home