Online Research Collaboration System

Introduction

Jupyter is a powerful tool for data science, machine learning, and deep learning, integrating coding, visualization, documentation, and more to make scientific computing intuitive and efficient. Jupyter is an open-source web application that can be used to create and share code and documentation. It provides an environment where you can write your code, run code, view output, visualize data, and view results. Therefore, it is a convenient tool for executable end-to-end data science workflows for scientific computing tasks including data cleaning, statistical modeling, building and training machine learning models, visualizing data, and more.

With the continuous development of data science and artificial intelligence, Jupyter is constantly updating and refining its features and performance. In the future, we can expect to see more innovative features and tools added to Jupyter to further advance scientific computing and data analytics. As a scientific researcher, you should keep an eye on new technologies and new tools, and constantly improve your skills and competitiveness.

System Kernel

Currently, the system provides the following three kernels for running. In the future, the public kernel and the default class libraries in the kernel will be added according to the requirements.

• Python 3.11 [System]
• Python 3.12 [Conda]
• R 4.4 [Conda]

Note

• The system is built using Jupyter technology and authenticated with a technology cloud pass. The login authentication of the system uses the email of the research institute.
• The system is currently limited to internal network access of the research institute and cannot be accessed through external networks.
• Provide a certain amount of hard disk space storage for users to use. The current limit is 500M.