Scientific Research Hot Spot Analysis and Visualization System

The guide can be found in Scientific-research-hot-spot-analysis-and-visualization-system.pdf

Overview

The Scientific Research Hot Spot Analysis and Visualization System (V1.0) is designed to uncover and analyze hotspots in scientific research using data visualization and machine learning. The system features a full-stack web framework for the server-side and a client-side developed with HTML, JavaScript, and CSS, using visualization tools like D3.js and Echarts. Machine learning libraries such as Scikit-learn and Keras are integrated to perform deep analysis of scientific literature.

Features

• Data Visualization: Visual representation of research trends, including geographical, journal, timeline, and keyword analysis.
• Interactive Frontend: An interactive interface for users to explore and visualize scientific data.
• Machine Learning Integration: Personalized recommendations based on the analysis of scientific papers and trends.
• Cross-Platform Support: Compatible with Windows and Linux platforms.

System Components

• Backend: Built with a full-stack web framework using asynchronous network libraries.
• Frontend: Developed with HTML, JavaScript, and CSS.
• Data Visualization Tools: Uses D3.js and Echarts to visually represent the research data.
• Machine Learning Libraries: Incorporates Scikit-learn and Keras for advanced analysis of scientific literature.

Installation

Backend Setup

1. Deploy the server application to your local environment:<br> pip install -r requirements.txt<br> python server.py<br>
2. After the server starts, you can access the system via the following:
   • Local: http://127.0.0.1:8000
   • Internal Network: http://<internal_IP>:8000
   • External Network: http://<external_IP>:8000

Running the Software Package (will be announced soon)

1. Download and unzip the software package.
2. Run the server.exe to start the server on your machine.
3. Access the system using the provided URLs.

Usage Instructions

Register and Log In

1. Registration: New users must click the registration button on the home page to provide a username, email, and password.
2. Login: Existing users can log in using their credentials.
3. Password Recovery: Users who forget their password can reset it through the "Forgot Password" feature.

Data Graph Visualization

1. Download data from the Web of Science retrieval platform to analyze research hotspots.
2. Use the Data Graph Visualization button to enter the visualization module and perform operations.

Modules Available:

• Regional Analysis: Represents the number of documents from various regions, showing the geographical distribution of research.
• Journal Analysis: Analyzes publications across different journals.
• Timeline Analysis: Visualizes trends in publication over time.
• Keyword Analysis: Uses machine learning algorithms to extract and display research keywords.

Upload Data

1. Users can upload data for analysis.
2. Upload data through the "Upload Data" module and follow the instructions for selecting and visualizing the data.

Literature Recommendations

1. Personalized Recommendations: The system recommends literature based on user interests, using machine learning algorithms to predict publication trends and identify potentially highly-cited documents.
2. Access Recommendations:
   • From Data Graph Visualization: Click the recommendation button.
   • From the Home Page: Navigate to the recommendation page and select the relevant publication characteristics to get recommendations.

Contributing

We welcome contributions to improve the system. To contribute:

1. Fork the repository.
2. Clone your fork and create a branch.
3. Make your changes and submit a pull request.

License

This project is licensed under the MIT License.