Discover how AI code interpreters are revolutionizing data visualization, reducing chart creation time from 20 to 5 minutes while simplifying complex statistical analysis.

Context: This flowchart helps data scientists and researchers choose the right statistical test based on data characteristics like normality and variance. It simplifies test selection and improves decision-making.

Sources: Inspired by common statistical guidelines and resources such as "Practical Statistics for Data Scientists" and widely used online platforms like Khan Academy and Coursera.

Inspiration: Created to address the challenges of selecting appropriate statistical tests, this flowchart offers a clear, easy-to-follow decision path for users at all levels.

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

The environmentally responsible behaviors of residents and tourists are great significance to the protection of natural resources and sustainable development of ecotourism. This paper takes China’s Qilian Mountains National Park as the case place. By constructing a theoretical model of perceived value on environmentally responsible behavior and studying the relationship between residents’ and tourists’ perceived value, satisfaction and environmentally responsible behavior from both subject and object perspectives, the study shows that. Educational level and occupational distribution have significant effects on residents’ and tourists’ perceptions of ecotourism environmentally responsible behaviors, but age only has a significant effect on residents’ perceptions of ecotourism environmentally responsible behaviors. Gender differences do not affect residents’ and tourists’ perceptions of ecotourism environmentally responsible behaviors. The theoretical model between residents’ perceptions of environmentally responsible behaviors, environmentally responsible behaviors, and satisfaction was basically confirmed. Perceived environmentally responsible behaviors of tourists does not affect satisfaction. Satisfaction has a positive effect on tourists’ environmentally responsible behaviors. Perceived environmental responsibility of tourists has a significant positive effect on tourists’ environmentally responsible behaviors. The overall level of residents’ perception of environmentally responsible behaviors in ecotourism is higher than tourists’ perception. Residents and tourists have a poor perception of ecological and environmental protection policies. This paper expects to strengthen residents’ and tourists’ perceptions of ecologically responsible behaviors. Establishing the sentiment of satisfaction and commitment to environmental protection motivates residents and tourists to implement environmentally responsible behaviors and contribute to the sustainable development of ecotourism.

Learn how to create professional data visualizations using R and ggplot2. A step-by-step guide for startup founders and analysts to build publication-quality charts.

Total income tax return statistical analysis chart for the top 10 percent of income units: Amounts (in thousands of dollars)

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Chart Recorder Market Size 2024-2028

The chart recorder market size is forecast to increase by USD 464.5 million at a CAGR of 5.9% between 2023 and 2028. The market is experiencing significant growth due to the increasing demand for multi-channel recording solutions, particularly in sectors like water purification systems where accurate data recording is essential for monitoring system performance. Strip chart recorders and circular chart recorders continue to be popular choices for continuous analog record keeping. However, the emergence of data loggers and automated data acquisition systems has introduced digital file storage as a viable alternative, offering enhanced efficiency and accuracy. Multi-pen recorders offer the advantage of recording multiple data streams on a single chart, which is beneficial in applications such as water quality monitoring. The market is also witnessing the introduction of web-based chart recorders, enabling remote monitoring and real-time data access. Despite these advancements, challenges such as the availability of substitutes and the need for calibration and maintenance persist. Overall, the market is expected to grow as industries, including those involved in water purification, continue to prioritize accurate and efficient data recording solutions.

What will the size of the market be during the forecast period?

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The market for chart recorders, a vital component of data acquisition systems (DAQ), continues to gain traction in various industries, including manufacturing, science, engineering labs, and power plants. These instruments are essential for capturing, recording, and analyzing electrical signals and process parameters such as temperature, pressure, flow, pH, humidity, vibration, movement, diagnostics, and statistical analysis. Chart recorders offer high resolution visualization tools that enable real-time monitoring and analysis of data. Their applications span across numerous sectors, including water purification, where they help monitor turbidity, dissolved oxygen, and sterilization processes. In environmental testing, they assist in tracking the effectiveness of various processes and maintaining optimal conditions. In the manufacturing sector, chart recorders play a crucial role in equipment maintenance and power plant operations.

Moreover, in the context of single-channel and multi-channel applications, chart recorders cater to diverse requirements, offering flexibility and scalability. The demand for chart recorders is driven by the increasing need for accurate and reliable data acquisition and analysis in various industries. As the importance of data-driven decision-making continues to grow, the market for these instruments is expected to expand. Furthermore, the integration of advanced features, such as statistical analysis and diagnostics, enhances their value proposition, making them indispensable tools for various applications.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Digital chart recorders
  Analog chart recorders


Application

  Food and beverage
  Pharmaceuticals
  Industrial applications
  Environmental monitoring


Geography

  North America

    Canada
    US


  APAC

    China
    India
    Japan
    South Korea


  Europe

    Germany
    UK
    France
    Italy


  South America



  Middle East and Africa

By Type Insights

The digital chart recorders segment is estimated to witness significant growth during the forecast period. Digital chart recorders are an essential component of the expanding market, providing sophisticated features and enhanced functionality for modern data recording applications. One notable example is the OMEGA RD8250 by Omega Engineering Inc., which caters to diverse industrial requirements. This advanced digital process recorder boasts dual-function keys and a clear, colored graph display, ensuring a user-friendly experience. The RD8250 offers the flexibility to display real-time data in both digital and trend formats, making it a versatile tool for monitoring temperatures and other vital parameters in dispersed systems, marine installations, and large engines. The front-panel USB port enables seamless data transfer to a PC, enabling efficient analysis and management.

Real-time data can be viewed in both digital and trend formats, ensuring versatility and accuracy in monitoring. The front-panel USB port enables seamless data transfer to a PC via a flash memory card, streamlining data management and analysis. Multi-channel strip chart recorders, circular chart recorders, data loggers, and automated data acquisition systems are other essential components of the digital chart recorder market.

Context

This dataset provides synthetically generated financial time series data, presented as OHLCV (Open-High-Low-Close-Volume) candlestick charts. A key feature of this dataset is the inclusion of technical analysis annotations (labels) meticulously created by a human analyst for each chart.

The primary goal is to offer a resource for training and evaluating machine learning models focused on automated technical analysis and chart pattern recognition. By providing synthetic data with high-quality human labels, this dataset aims to facilitate research and development in areas like algorithmic trading and financial visualization analysis.

This is an evolving dataset. It represents the initial phase of a larger labeling effort, and future updates are planned to incorporate a greater number and variety of labeled chart patterns.

Content

The dataset is provided entirely as a collection of JSON files. Each file represents a single 300-candle chart window and contains:

1. metadata: Contains basic information related to the generation of the file (e.g., generation timestamp, version).
2. ohlcv_data: A sequence of 300 data points. Each point is a dictionary representing one time candle and includes:
   • time: Timestamp string (ISO 8601 format). Note: These timestamps maintain realistic intra-day time progression (hours, minutes), but the specific dates (Day, Month, Year) are entirely synthetic and do not align with real-world calendar dates.
   • open, high, low, close: Numerical values representing the candle's price range. Note: These values are synthetic and are not tied to any real financial instrument's price.
   • volume: A numerical value representing activity during the candle's period. Note: This is also a synthetic value.
3. labels: A dictionary containing the human-provided technical analysis annotations for the corresponding chart window:
   • horizontal_lines: A list of structures, each containing a price key. These typically denote significant horizontal levels identified by the labeler, such as support or resistance.
   • ray_lines: A list of structures, each defining a line segment via start_date, start_price, end_date, and end_price. These are used to represent patterns like trendlines, channel boundaries, or other linear formations observed by the labeler.

Data Generation Approach

The dataset features synthetically generated candlestick patterns. The generation process focuses on creating structurally plausible chart sequences. Human analysts then carefully review these sequences and apply relevant technical analysis labels (support, resistance, trendlines).

While the patterns may resemble those seen in financial markets, the underlying numerical data (price, volume, and the associated timestamps) is artificial and intentionally detached from any real-world financial data. Users should focus on the relative structure of the candles and the associated human-provided labels, rather than interpreting the absolute values as representative of any specific market or time.

Acknowledgements

This dataset is made possible through ongoing human labeling efforts and custom data generation software.

Inspiration

• Train models (e.g., CNNs, Transformers) to recognize support/resistance levels and trendlines directly from chart data.
• Develop and benchmark algorithms for automated technical analysis pattern detection.
• Use as a basis for generating further augmented chart data for ML training.
• Explore novel approaches to financial time series analysis using labeled, synthetic data.

Excel workbook with included Read.Me sheet, including FW and DW biomass data derived from files linked elsewhere; a compilation of the rosette area and gas exchange data for every plant measured of the Col, lsf1 and prr7prr9 genotypes; statistical analysis across the experiments; and charts of the compiled data, some of which are presented as figure panels in the 2022 versions.

Additional file 1. Zip file containing the interactive supplement.

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!

According to Cognitive Market Research, the global Graph Analytics market size was USD 2522 million in 2024 and will expand at a compound annual growth rate (CAGR) of 34.0% from 2024 to 2031. Key Dynamics of Graph Analytics Market

Key Drivers of Graph Analytics Market

Increasing Demand for Immediate Big Data Insights: Organizations are progressively depending on graph analytics to handle extensive amounts of interconnected data for instantaneous insights. This is essential for applications such as fraud detection, recommendation systems, and customer behavior analysis, particularly within the finance, retail, and social media industries.

Rising Utilization in Fraud Detection and Cybersecurity: Graph analytics facilitates the discovery of intricate relationships within transactional data, aiding in the identification of anomalies, insider threats, and fraudulent patterns. Its capacity to analyze nodes and edges in real-time is leading to significant adoption in cybersecurity and banking sectors.

Progress in AI and Machine Learning Integration: Graph analytics platforms are progressively merging with AI and ML algorithms to improve predictive functionalities. This collaboration fosters enhanced pattern recognition, network analysis, and more precise forecasting across various sectors including healthcare, logistics, and telecommunications.

Key Restrains for Graph Analytics Market

High Implementation and Infrastructure Expenses: Establishing a graph analytics system necessitates sophisticated infrastructure, storage, and processing capabilities. These substantial expenses may discourage small and medium-sized enterprises from embracing graph-based solutions, particularly in the absence of a clear return on investment.

Challenges in Data Modeling and Querying: In contrast to conventional relational databases, graph databases demand specialized expertise for schema design, data modeling, and query languages such as Cypher or Gremlin. This significant learning curve hampers adoption in organizations lacking technical expertise.

Concerns Regarding Data Privacy and Security: Since graph analytics frequently involves the examination of sensitive personal and behavioral data, it presents regulatory and privacy challenges. Complying with data protection regulations like GDPR becomes increasingly difficult when handling large-scale, interconnected datasets.

Key Trends in Graph Analytics Market

Increased Utilization in Supply Chain and Logistics Optimization: Graph analytics is increasingly being adopted in logistics for the purpose of mapping routes, managing supplier relationships, and pinpointing bottlenecks. The implementation of real-time graph-based decision-making is enhancing both efficiency and resilience within global supply chains.

Growth of Cloud-Based Graph Analytics Platforms: Cloud service providers such as AWS, Azure, and Google Cloud are broadening their support for graph databases and analytics solutions. This shift minimizes initial infrastructure expenses and facilitates scalable deployments for enterprises of various sizes.

Advent of Explainable AI (XAI) in Graph Analytics: The need for explainability is becoming a significant priority in graph analytics. Organizations are pursuing transparency regarding how graph algorithms reach their conclusions, particularly in regulated sectors, which is increasing the demand for tools that offer inherent interpretability and traceability. Introduction of the Graph Analytics Market

The Graph Analytics Market is rapidly expanding, driven by the growing need for advanced data analysis techniques in various sectors. Graph analytics leverages graph structures to represent and analyze relationships and dependencies, providing deeper insights than traditional data analysis methods. Key factors propelling this market include the rise of big data, the increasing adoption of artificial intelligence and machine learning, and the demand for real-time data processing. Industries such as finance, healthcare, telecommunications, and retail are major contributors, utilizing graph analytics for fraud detection, personalized recommendations, network optimization, and more. Leading vendors are continually innovating to offer scalable, efficient solutions, incorporating advanced features like graph databases and visualization tools.

The code will run on an installation of R with the add on packages lattice, dplyr, lattice, and latticeExtra. The output is a graph (Fig. 2) and a table showing likelihood ratios of run chart rules for identification of non-random variation in simulated run charts of different length with or without a shift in process mean. (R)

Net income quintile tax declaration statistical analysis chart Unit: Amount (thousand yuan)

This dataset illustrates sales data from a company and its three product lines - boats, cars, and planes. It contains information such as historical and sales data. This is fictional data, created and used for data exploration and profit margin analysis.

The link for the Excel project to download can be found at this GitHub Repository. It includes the raw data, statistical analysis, Pivot Tables, and a dashboard with Pivot Charts for interaction.

Below is a screenshot of the charts for ease. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10624788%2Fc945ef4223f1b0b6c2dfe7ade798e34e%2FWeekly%20Revenue%20by%20Product%20Line.png?generation=1722385095875351&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10624788%2Fd3be2fd1f741b0899e79b9c50c7e29a0%2FRevenue%20and%20Profit%20by%20Quarter.png?generation=1722385108310009&alt=media" alt="">

Diagnostic properties of run chart rules based on the results from Table 2.

The study aims to determine the differences in miRNA expression, particularly miRNA-21 and miRNA-221/222, of acute ischemic stroke patients relative to controls and determine its relationship with inflammatory cytokines, clinical severity, and outcome

IntroductionA required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.MethodsThe system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality.ResultsThe system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.DiscussionMedical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

These data are based on the latest Veteran Population Projection Model, VetPop2020, provided by the National Center for Veterans Statistics and Analysis, published in 2023.

The AFCARS Trends Chart tracks children in Foster Care from FY 2002 through the most recent year. A table of data and a graphic depiction of trends are shown for children in care on the first day of the year, entries to foster care, exits, children waiting to be adopted, children adopted, children with terminations of parental rights, and total children served in foster care.