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RJS Graph VS DataMelt

Compare RJS Graph VS DataMelt and see what are their differences

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RJS Graph logo RJS Graph

RJS Graph is an artificial intelligence-based data management platform that allows users or developers to organize the data by manipulating the binaries, scientific, mathematical, and other insights with accurate results.

DataMelt logo DataMelt

DataMelt (DMelt), a free mathematics and data-analysis software for scientists, engineers and students.
  • RJS Graph Landing page
    Landing page //
    2021-09-01
  • DataMelt Landing page
    Landing page //
    2019-07-18

DataMelt is a Java program for statistics, general data analysis and data visualization. The program is often termed "computational platform" since it can be used with different programming languages (Java, Python, Groovy..). DataMelt is not limited to a single programming language. The program is used for numeric computation, statistics, analysis of large data volumes ("big data") and scientific visualization. Full description: https://handwiki.org/wiki/Software:DataMelt

RJS Graph features and specs

  • Interactive Visualizations
    RJS Graph provides highly interactive graphs and charts that allow users to engage with data in a dynamic way, enhancing understanding and presentation.
  • Customization
    The tool offers extensive customization options, enabling users to tailor visual elements to meet specific needs or preferences.
  • Ease of Integration
    RJS Graph can be easily integrated into existing web projects, making it suitable for developers looking for seamless incorporation into applications.
  • User-Friendly Interface
    The platform features an intuitive user interface that allows users, including those with limited technical skills, to create and manage their data visualizations effectively.
  • Responsive Design
    Charts and graphs created with RJS Graph are responsive, ensuring they look good on a variety of devices and screen sizes.

Possible disadvantages of RJS Graph

  • Limited Free Resources
    There might be limited free resources or templates available, potentially requiring users to create visualizations from scratch or invest in premium offerings.
  • Learning Curve
    While the interface is user-friendly, there might still be a learning curve for those unfamiliar with creating data visualizations or integrating them into websites.
  • Performance Limitations
    For very large datasets or highly complex visualizations, performance could suffer, potentially affecting the user experience.
  • Dependency on External Libraries
    RJS Graph may require dependencies on certain libraries, which could complicate integration and affect compatibility with other web technologies.

DataMelt features and specs

  • Versatility
    DataMelt supports a wide range of programming languages including Java, Jython, Groovy, and JRuby, making it versatile for users familiar with different coding environments.
  • Rich Libraries
    It offers a comprehensive set of scientific libraries for numerical computation, data analysis, and visualization, which can be beneficial for complex scientific research and data processing tasks.
  • Cross-Platform
    DataMelt is platform-independent, running on any operating system that supports Java, such as Windows, macOS, and Linux. This makes it accessible to a wide audience.
  • Integrated Development Environment
    DataMelt provides a powerful IDE that integrates coding, plotting, and visualization tools, streamlining the workflow for developers and researchers.
  • Free and Open Source
    The core functionality of DataMelt is available for free, which can be appealing to individuals and organizations looking for budget-friendly computational tools.

Category Popularity

0-100% (relative to RJS Graph and DataMelt)

RJS Graph

DataMelt

Technical Computing
47 47%
Technical Computing
53% 53
Office & Productivity
60 60%
Office & Productivity
40% 40
Numerical Computation
40 40%
Numerical Computation
60% 60
Tool
100 100%
Tool
0% 0

Questions and Answers

As answered by people managing RJS Graph and DataMelt.

How would you describe your primary audience?

DataMelt's answer:

students and data scientists

What's the story behind your product?

DataMelt's answer:

DataMelt has its roots in particle physics where data mining is a primary task. It was created as Software:jHepWork project in 2005 and it was initially written for data analysis for particle physics.

What makes your product unique?

DataMelt's answer:

Multiplatform. Supports multiple programming languages: Java, Python (Jython), Groovy, Ruby

Why should a person choose your product over its competitors?

DataMelt's answer:

Large database of examples and code snippets https://datamelt.org/code/

Who are some of the biggest customers of your product?

DataMelt's answer:

Students at universities and data scientists.

Which are the primary technologies used for building your product?

DataMelt's answer:

Java (JDK any new new release including JDK20)

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare RJS Graph and DataMelt

RJS Graph Reviews

We have no reviews of RJS Graph yet.
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DataMelt Reviews

  1. J.Mayear
    Great 3D graphics

    I like this DataMelt analysis program since it has many 2D/3D visualisation and a massive number of practical examples

What are some alternatives?

When comparing RJS Graph and DataMelt, you can also consider the following products

LabPlot - LabPlot is a KDE-application for interactive graphing and analysis of scientific data.

SciDaVis - SciDAVis is a free application for Scientific Data Analysis and Visualization.

Aveloy Graph - Aveloy Graph is an application for graph creation / data visualization

DPlot - DPlot graphing software lets scientists and engineers graph, plot, analyze, and manipulate data.

IGOR Pro - Technical graphing and data analysis for Macintosh and Windows.

OriginPro - OriginLab OriginPro is a comprehensive interface-based data management platform that allows users to calculate or visualize the data insights in various fields like engineering, scientific domain, or multi-sector industrial stats.

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