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Practice Guide

This section demonstrates how to chain multiple platform modules together to complete a full research workflow through real-world academic scenarios. Each case includes background, pain point analysis, platform solutions, and step-by-step instructions to help you understand how to use UniResearch efficiently in different research contexts.

Case 1: Graduate Student Literature Review Writing​

Background​

Xiao Zhang is a first-year master's student in astronomy. His advisor assigned the first research task: write a literature review on "All-Sky Surveys and Multi-Wavelength Monitoring Techniques," requiring an overview of major all-sky survey projects in recent years, their observation methods, scientific objectives, and technical characteristics, a comparison of different survey projects, and an outlook on the next generation of survey facilities.

Challenges Xiao Zhang faces:

  • Time-consuming literature collection: Need to repeatedly search across arXiv, NASA ADS, Google Scholar and other platforms, manually download PDFs, and switch between platforms with very low efficiency
  • Heavy literature interpretation workload: 9 full-length English papers covering X-ray, gamma-ray, infrared, radio, and other wavelength bands; reading each one thoroughly and extracting key information is extremely time-consuming
  • Lack of structured analysis capability: Manually organizing comparison dimensions such as observation bands, instrument platforms, survey strategies, and scientific objectives for each survey project is prone to missing key information
  • Knowledge not reusable: Reading notes are scattered everywhere; when preparing presentations or proposals later, they need to be searched through again
  • Difficulty organizing the review: Facing multiple cross-band papers, it's hard to organize a framework, and repeated revisions are time-consuming

Under traditional methods, completing this task would require repeated searching, reading each paper in detail, and manual organization, taking a very long time. With UniResearch's multi-module integration, it can be completed in 1 day with high quality.

Platform Solution​

UniResearch provides a complete toolchain for the literature review scenario: after searching and downloading literature from external academic platforms such as arXiv and NASA ADS, users upload to Literature Management for unified archiving and study β†’ Research Assistant for AI batch interpretation and multi-literature horizontal comparison β†’ Cloud Docs for online writing and formatting β†’ Scientific Drawing for AI-generated survey project comparison charts. The full pipeline lets Xiao Zhang focus on understanding and thinking rather than repetitive labor.

Step-by-Step Guide​

Step 1: Search and Upload Literature to Literature Management

  1. Search for relevant literature on arXiv, NASA ADS, etc., and download PDFs
  2. Click "Literature Management" in the left navigation bar
  3. Click [+] in the upper left β†’ [New Group], create a group named "All-Sky Surveys & Monitoring Techniques"
  4. Click [+] β†’ [Upload Literature] β†’ [PDF], upload all PDF files
  5. The system automatically parses each paper's title, authors, abstract, keywords, and other metadata

You can also search for supplementary literature in the platform's built-in "Literature Library," but the library is still being continuously expanded. It is recommended to rely primarily on external collection.

Step 2: AI Batch Interpretation and Multi-Literature Horizontal Comparison

  1. Click "Research Assistant" on the left, switch to "Literature Interpretation" mode
  2. Batch upload all 9 PDF papers (supports Ctrl multi-select or drag-and-drop upload)

  1. Enter the comparison request in the input box, for example: "Please compare these 9 papers across four dimensions: observation band (X-ray/gamma-ray/infrared/radio/neutrino, etc.), instrument platform and device name, survey strategy (monitoring mode/coverage/sensitivity), and core scientific objectives"
  2. Select an appropriate AI model and click Send
  3. AI automatically generates a multi-dimensional structured comparative analysis report

Step 3: Follow-Up Queries to Generate Review Framework

  1. Continue asking follow-up questions in the same literature interpretation session from Step 2 β€” no need to switch modes. Refine specific sections, for example: "For the X-ray survey section, please focus on comparing the observation strategy differences between MAXI and ROSAT"
  2. AI generates a structured review draft based on the context of all interpreted literature

  1. Continue with follow-up questions for other sections, for example: "For the future outlook section, please incorporate the plans for SPICA and CTA," and AI automatically expands the report content

  1. Click "Add to Document Library" in the upper right corner of the generated document, select the target document library, and save to your personal cloud documents. The content added to cloud documents automatically retains original table styles, heading levels, and other rich-text formatting β€” no need to reformat

  1. Open the document in "Cloud Docs" and use the online editor to further refine it:
    • Review and polish the text in each section
    • Add missing comparison parameters to existing tables
    • Select text to add annotations, noting research ideas and to-do items

Step 4: AI Drawing to Generate Survey Project Comparison Chart

  1. Click "Scientific Drawing" β†’ "My Works" β†’ "New Drawing"
  2. Click the "AI" button at the top, enter the drawing description, for example: "Draw a three-column horizontal comparison chart with a deep blue cool-tone academic color scheme; top title: Next-Generation All-Sky Survey Facilities SPICA vs. CTA Performance Comparison; three side-by-side rectangular modules: AKARI (existing baseline), SPICA (next-gen infrared), CTA (gamma-ray optical array); each module has 3 sub-blocks: aperture, sensitivity improvement, core survey strategy; left side with corresponding telescope minimalist monochrome icons, white background flat vector graphics, journal-standard scientific illustration"
  3. AI automatically generates the complete chart layout; Xiao Zhang fine-tunes text and node positions on the canvas
  4. Export as PNG format

  1. Insert into the review document

Summary​

Core capabilities demonstrated by UniResearch in this case:

  • Centralized Literature Management: Batch upload with automatic metadata parsing and unified archiving
  • AI Multi-Literature Comparison: Complete structured parsing and multi-dimensional horizontal comparison of all literature in minutes
  • Review Framework Generation: Generate structured drafts based on all literature context, with support for targeted section refinement
  • AI Scientific Drawing: Generate academic-standard charts from natural language descriptions
  • Cloud Docs Collaboration: Online editing, annotations, multi-format export, supporting review refinement and optimization

Deliverables​

OutputDescription
Literature Collection9 core all-sky survey papers, categorized and archived by topic
AI Comparative Analysis ReportMulti-band survey project multi-dimensional horizontal comparison generated by Research Assistant
Review Draft DocumentComplete review report saved to cloud docs, supporting online editing and export
Survey Project Comparison ChartAI-generated multi-band survey classification chart, PNG/SVG format

Efficiency Comparison: Traditional method takes over one week β†’ with UniResearch, completed in 1 day, with core time spent on understanding and thinking rather than repetitive labor.

Case 2: Research Group Collaborative Project​

Background​

Professor Li leads a 5-member research group (2 PhD students, 2 master's students, 1 research assistant) working on a provincial-level project on "Intelligent Recommendation Systems and Fairness Optimization." The project spans 12 months and involves multiple parallel workflows including recommendation algorithm research and literature review, fairness mechanism design, multi-agent system modeling, system prototype development, and paper writing.

Challenges faced by Professor Li's research group:

  • Scattered literature resources: PDFs collected by 5 members are scattered across personal computers, WeChat groups, and email attachments, with significant duplicate downloads and no unified team literature asset
  • Progress tracking difficulty: The project involves multiple parallel research lines; traditional progress sync via WeChat messages is prone to information loss, with no visual tracking of each member's task completion
  • Chaotic paper collaboration: Multiple people editing paper drafts simultaneously leads to version chaos ("paper_v3_final_really_final.docx"), with no way to track modification history
  • Low communication efficiency: Research progress, literature findings, and experimental conclusions are scattered in WeChat chat history, important information gets buried, and new members cannot quickly understand the project background
  • Insufficient output archiving: Project milestones lack unified archiving, making end-of-project material compilation extremely laborious

Under traditional methods, the research group needs frequent meetings to sync progress, repeatedly transfer files, and manually organize materials, with significant time consumed on communication and coordination. With UniResearch's team collaboration capabilities, communication and coordination costs can be reduced by over 60%, allowing each member to focus on core research.

Platform Solution​

UniResearch provides a one-stop team collaboration workbench for research groups: create a Team Space for unified literature resource management β†’ use Task Management kanban to assign and track research progress β†’ use Cloud Docs for multi-person collaborative paper writing β†’ leverage Group Chat for real-time project progress updates. Full-pipeline collaboration, upgrading the research group from the fragmented "WeChat + Email + Cloud Drive" mode to a structured research collaboration workflow.

Step-by-Step Guide​

Step 1: Create Team Space and Invite Members

  1. Click the dropdown menu in the lower left corner of the page, switch to "Team Space"
  2. Click "Create Team," fill in team name, cover image, team description, and other basic information

  1. Click "Confirm" to complete creation

  2. Click "Invite Members," choose link invitation or password join, and send to the research group chat

  3. Configure literature and workspace permissions for each member

Step 2: Unified Team Literature Resource Archiving

  1. Each member uploads their individually collected project-related PDFs to the team literature library, organized by research direction
  2. Create groups in the team literature library, categorized by research direction

Step 3: Task Assignment and Progress Management

  1. Enter the "Task Management" module in the team space
  2. Create task lists divided by project phase (e.g., literature research, algorithm design, system development, paper writing, etc.)
  3. Create specific tasks for each phase and assign responsible persons and deadlines
  4. Each member updates task status in the task board (To Do β†’ In Progress β†’ Completed); the advisor can check overall progress at any time

Step 4: Cloud Docs Collaborative Paper Writing

  1. Enter the "Cloud Docs" module in the team space

  2. Create a team document library with sub-categories by document type (e.g., paper drafts, technical reports, meeting minutes, etc.)

  3. Create a review paper document in the document library

  4. Click "Share," set document permissions to "Editable," and share with collaborating members

  1. Multiple people write their respective sections in the document simultaneously
  2. The advisor reviews the document, selects paragraphs to add annotations with specific revision suggestions
  3. Members receive annotation notifications, reply and revise directly in the document; other members can also leave cross-section annotations for discussion

  1. Documents automatically save all members' modifications in real time, with support for viewing version history and reverting to any previous state

Step 5: Group Chat for Real-Time Collaborative Communication

  1. Click "Messages" in the left navigation bar to enter the chat module
  2. Click [+ New Chat] at the top, enter the group chat name, select all research group members, click [Create Group Chat]
  3. The advisor shares project progress and next steps in the group chat
  4. Members share literature findings in the group chat, attaching literature links from Literature Management for quick access by other members
  5. Create topic-specific group chats for specific directions (e.g., "Algorithm Discussion," "Experiment Issues") for organized communication
  6. After new members join the group chat, they can quickly browse message history to understand the full project context and recent progress

Summary​

Core capabilities demonstrated by UniResearch in this case:

  • Team Space Centralized Management: Unified management of all members' literature resources with permission-based access control
  • Unified Literature Archiving: All members share literature resources based on permissions; new members can quickly understand the project's literature foundation
  • Visual Task Kanban: Visual progress tracking to prevent task omissions and duplicate work
  • Cloud Docs Multi-Person Collaboration: Multi-person real-time collaborative editing with auto-save and traceable version history
  • Group Chat Real-Time Communication: Centralized project progress updates so important information is no longer buried in chat records

Deliverables​

OutputDescription
Team Literature LibraryProject-related literature, grouped by research direction, shared across all members
Task KanbanMulti-phase task lists with clear responsibilities and deadlines, visual progress tracking
Collaborative Paper DraftReview paper with multi-person real-time editing in cloud docs, with version history rollback
Group ChatProject communication group with real-time progress updates, enabling new members to quickly get up to speed

Efficiency Comparison: Traditional method consumes significant time on WeChat communication, file transfer, and progress sync β†’ with UniResearch team collaboration, communication and coordination costs are reduced by over 60%, with each member focused on core research for more efficient project progress.