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NITOR Academic Social Network

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Rigorous. Academic. Connected.


Version Status License Java Spring Boot React TypeScript Node.js PostgreSQL

Enterprise-Grade Academic Networking Platform

Quick StartDocumentationFeaturesArchitectureDeploymentContributing


Table of Contents


About NITOR

NITOR (Latin: brilliant, shining) is an enterprise-grade academic social networking platform designed specifically for researchers, academics, and institutions requiring a professional environment for scholarly communication and collaboration.

Mission Statement

To strengthen the global research ecosystem by providing a secure, institution-grade academic network where rigorous work is visible, searchable, and connected.

Why NITOR?

Traditional social media platforms fail to address the unique needs of academic communities:

  • Lack of Academic Features: No LaTeX support, citation management, or CV tools
  • Privacy Concerns: Data ownership and GDPR compliance issues
  • Professional Standards: Mixing personal and professional content
  • Research Integrity: Inadequate verification and credentialing systems

NITOR addresses these challenges with:

  • Academic-First Design: Features built specifically for researchers
  • Enterprise Security: GDPR-compliant, SOC 2 ready architecture
  • AI-Powered Tools: 54 Gemini-powered features for academic writing
  • Institution Integration: SSO, ORCID, and institutional authentication
  • Open Source: Transparent, auditable, community-driven development

Connect

  • LinkedIn: NITOR Academic Network
  • Founded: November 5, 2025
  • Current Status: Version 1.0.0 - Production Ready
  • Deployment Date: November 27, 2025

Key Features

Academic Content Management

Research Publication Platform

  • Support for research papers, articles, and preprints
  • LaTeX mathematical notation rendering ($...$, $$...$$)
  • Structured abstracts with metadata
  • Citation management and tracking
  • Version control for content revisions
  • DOI integration (planned)

Academic Profiles

  • Comprehensive researcher profiles with ORCID integration
  • Academic credentials and institution verification
  • Publication lists with impact metrics
  • Research interests and expertise tags
  • H-index and citation counts (planned)

AI-Powered Research Tools

54 Gemini-Powered Features

Writing Enhancement (6 features):

  • Text refinement and clarity improvement
  • Abstract generation from paper notes
  • Biography enhancement for profiles
  • Plain language summaries for public engagement
  • Multi-level summaries (one-sentence, short, medium, long)
  • Article skeleton extraction

Research Quality (6 features):

  • Argument consistency checking
  • Repetitive sentence detection
  • Citation needed detection
  • Methods clarity improvement
  • Limitations suggestions
  • Figure/table caption generation

Publication Support (7 features):

  • Title variants for different audiences
  • Reviewer response generation
  • Poster text compression
  • Presentation slide outline generation
  • Tone softening for diplomatic communication
  • Key contributions extraction
  • Research question clarification

Career Development (9 features):

  • Research statement generation
  • Teaching statement creation
  • Diversity statement drafting
  • CV tailoring for specific positions
  • Skill extraction and categorization
  • Role summaries for CV
  • Career gap explanations
  • Recommendation letter drafts
  • Professional bio models

Collaboration Tools (5 features):

  • Weekly progress summaries
  • Meeting agenda summarization
  • Collaboration proposal generation
  • Comment draft suggestions
  • Discussion health analysis

Research Discovery (5 features):

  • Interdisciplinary connection identification
  • Reading list curation
  • Reference suggestions
  • Research trend explanations
  • Question classification and methodology suggestions

Content Adaptation (4 features):

  • Expertise level adaptation (undergrad, grad, expert, public)
  • Bilingual translation with technical accuracy
  • Terminology alignment to field conventions
  • Tone adjustment (formal, conversational, enthusiastic, cautious)

Review & Quality (5 features):

  • Peer review feedback structuring
  • Self-review checklist generation
  • Interview Q&A generation
  • Result interpretation variants (conservative, moderate, bold)
  • Statistical reporting templates (APA style)

Platform Management (7 features):

  • Visual descriptions for accessibility
  • Code of conduct summaries
  • Onboarding guide creation
  • Lab manifesto drafting
  • Event description generation
  • Notification prioritization
  • Grant proposal review

Professional Networking

Intelligent Connection System

  • Connection-based academic networking (mutual relationships)
  • Follower system for public figures
  • Researcher discovery by discipline, institution, and expertise
  • Recommended connections based on research interests
  • Academic credential verification with badges

Engagement Features

  • Threaded scientific discussions and debates
  • Collaborative review and peer feedback system
  • Content endorsements and academic reputation
  • Real-time notifications via WebSocket
  • Activity feed with intelligent filtering

Security & Compliance

Enterprise-Grade Authentication

  • JWT token-based authentication with automatic refresh rotation
  • Two-Factor Authentication (TOTP, RFC 6238)
  • OAuth 2.0 integration (Google, GitHub, LinkedIn)
  • BCrypt password hashing (work factor: 12)
  • Session management and token revocation via Redis

Data Protection

  • GDPR-compliant data handling and user consent management
  • Complete audit logging for all sensitive operations
  • Field-level encryption for personally identifiable information
  • TLS 1.3 for data in transit
  • AES-256 encryption for data at rest

API Security

  • Rate limiting with Bucket4j (5-100 requests/minute by endpoint type)
  • CORS protection with configurable origins
  • Input validation using Jakarta Bean Validation (JSR-380)
  • SQL injection prevention via parameterized queries
  • XSS protection with Content Security Policy headers

Infrastructure & Performance

Modern Architecture

  • Microservices architecture (Backend, AI Service, Frontend)
  • Docker containerization for consistent deployments
  • Kubernetes orchestration support with auto-scaling
  • PostgreSQL 15 with query optimization and indexing
  • Redis 7 caching layer for high-performance data access
  • MinIO S3-compatible object storage for files and images

Scalability Features

  • Horizontal pod autoscaling in Kubernetes
  • Database connection pooling
  • CDN-ready static asset delivery
  • WebSocket support for real-time features
  • Comprehensive health checks and monitoring

Developer Experience

  • OpenAPI 3.0 / Swagger UI for interactive API documentation
  • RESTful API design following industry best practices
  • Comprehensive logging with structured output
  • Hot reload for efficient development
  • Extensive test coverage (unit, integration, E2E)

Technology Stack

Backend Service

Framework & Language

  • Java 17 - LTS release with modern language features
  • Spring Boot 3.2.5 - Enterprise application framework
  • Spring Security 6 - Authentication and authorization
  • Spring Data JPA - Data access with Hibernate
  • Spring WebSocket - Real-time communication

Database & Caching

  • PostgreSQL 15 - Primary relational database
  • Flyway 10.10.0 - Database migration management
  • Redis 7 - Caching and session store
  • HikariCP - High-performance connection pooling

Security & Authentication

  • JWT (jjwt 0.12.5) - Token-based authentication
  • BCrypt - Password hashing
  • Spring Security OAuth2 - Social login integration

Documentation & Monitoring

  • SpringDoc OpenAPI 3 (2.5.0) - API documentation
  • Spring Boot Actuator - Health checks and metrics
  • Micrometer + Prometheus - Metrics collection and monitoring

File Storage & Utilities

  • MinIO 8.6.0 - S3-compatible object storage
  • Apache Commons Lang3 - Utility functions
  • Lombok - Boilerplate code reduction
  • Jackson - JSON serialization

Frontend Application

Framework & Build Tools

  • React 19.2 - UI library with latest concurrent features
  • TypeScript 5.8 - Type-safe JavaScript
  • Vite 6.2 - Next-generation frontend build tool
  • @vitejs/plugin-react 5.0 - React plugin for Vite

State Management & Forms

  • Zustand 5.0.8 - Lightweight state management
  • React Hook Form 7.49.3 - Performant form handling
  • @hookform/resolvers 5.2.2 - Form validation integration
  • Zod 3.22.4 - TypeScript-first schema validation

HTTP & API

  • Axios 1.13.2 - Promise-based HTTP client
  • @supabase/supabase-js 2.39.3 - Real-time subscriptions

UI & Styling

  • Lucide React 0.554.0 - Beautiful icon library
  • Canvas Confetti 1.9.4 - Celebration effects
  • React Router DOM 7.9.6 - Client-side routing

Markdown & Math

  • React Markdown 10.1.0 - Markdown rendering
  • Remark Math 6.0.0 - Math in Markdown
  • Rehype KaTeX 7.0.0 - LaTeX rendering

AI Integration

  • @google/genai 1.30.0 - Google Gemini AI SDK

Utilities

  • jsPDF 3.0.4 - PDF generation
  • dotenv 17.2.3 - Environment variable management

AI Microservice

Runtime & Framework

  • Node.js 20 - JavaScript runtime
  • Express.js 4.18.2 - Web application framework

AI Integration

  • @google/generative-ai 0.21.0 - Google Gemini 2.5 Pro integration

Middleware & Security

  • CORS 2.8.5 - Cross-origin resource sharing
  • Express Rate Limit 7.1.5 - API rate limiting
  • dotenv 16.3.1 - Environment configuration

Development

  • Nodemon 3.0.2 - Auto-restart on file changes

Infrastructure & DevOps

Containerization

  • Docker 24+ - Application containerization
  • Docker Compose - Multi-container orchestration

Orchestration

  • Kubernetes 1.28+ - Container orchestration
  • kubectl - Kubernetes CLI

Reverse Proxy & Load Balancing

  • Nginx 1.25+ - Web server and reverse proxy
  • Let's Encrypt / Certbot - SSL/TLS certificates

CI/CD

  • GitHub Actions - Continuous integration and deployment

Monitoring & Logging

  • Prometheus - Metrics collection
  • Grafana - Metrics visualization
  • Fluent Bit - Log aggregation

Architecture

System Overview

┌─────────────────────────────────────────────────────────────────┐
│                         NITOR Platform                          │
│                    (Microservices Architecture)                 │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌─────────────┐      ┌─────────────┐      ┌─────────────┐   │
│  │  Frontend   │◄────►│   Backend   │◄────►│ AI Service  │   │
│  │  React 19   │      │  Spring     │      │  Gemini 2.5 │   │
│  │  Vite 6     │      │  Boot 3.2.5 │      │  Node.js 20 │   │
│  │  Port 5173  │      │  Port 8080  │      │  Port 3001  │   │
│  └─────────────┘      └──────┬──────┘      └─────────────┘   │
│                              │                                 │
│              ┌───────────────┼───────────────┐                │
│              │               │               │                │
│        ┌─────▼────┐    ┌────▼────┐    ┌────▼────┐           │
│        │PostgreSQL│    │  Redis  │    │  MinIO  │           │
│        │    15    │    │    7    │    │ Storage │           │
│        │ Database │    │  Cache  │    │   S3    │           │
│        └──────────┘    └─────────┘    └─────────┘           │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘
                             ▲
                             │
                    ┌────────┴────────┐
                    │  Nginx Reverse  │
                    │  Proxy + SSL    │
                    └─────────────────┘

Monorepo Structure

nitor/
├── packages/
│   ├── backend/                 # Spring Boot REST API (Java 17)
│   │   ├── src/
│   │   │   ├── main/
│   │   │   │   ├── java/com/nitor/
│   │   │   │   │   ├── controller/      # REST controllers (14 files)
│   │   │   │   │   ├── service/         # Business logic
│   │   │   │   │   ├── repository/      # Data access layer
│   │   │   │   │   ├── model/           # JPA entities
│   │   │   │   │   ├── dto/             # Data transfer objects
│   │   │   │   │   ├── config/          # Spring configuration
│   │   │   │   │   ├── security/        # JWT, OAuth2 config
│   │   │   │   │   └── exception/       # Exception handling
│   │   │   │   └── resources/
│   │   │   │       ├── application.yml
│   │   │   │       └── db/migration/    # Flyway SQL migrations
│   │   │   └── test/                    # Unit & integration tests
│   │   └── pom.xml                      # Maven dependencies
│   │
│   ├── frontend/                # React Application (TypeScript)
│   │   ├── src/
│   │   │   ├── components/      # Reusable UI components
│   │   │   ├── pages/           # Page components
│   │   │   ├── hooks/           # Custom React hooks
│   │   │   ├── services/        # API service layer
│   │   │   ├── stores/          # Zustand state stores
│   │   │   ├── types/           # TypeScript interfaces
│   │   │   ├── utils/           # Utility functions
│   │   │   └── App.tsx          # Root component
│   │   ├── public/              # Static assets
│   │   ├── package.json
│   │   ├── tsconfig.json
│   │   └── vite.config.ts
│   │
│   └── ai-service/              # AI Microservice (Node.js)
│       ├── src/
│       │   ├── server.js        # Express server (54 endpoints)
│       │   └── services/
│       │       └── geminiService.js  # Gemini AI integration
│       ├── package.json
│       └── .env.example
│
├── infrastructure/
│   ├── docker/
│   │   ├── docker-compose.yml   # Local development stack
│   │   ├── Dockerfile.backend
│   │   ├── Dockerfile.frontend
│   │   └── Dockerfile.ai-service
│   ├── kubernetes/              # K8s manifests
│   │   ├── backend-deployment.yaml
│   │   ├── frontend-deployment.yaml
│   │   ├── ai-service-deployment.yaml
│   │   ├── postgres-statefulset.yaml
│   │   ├── redis-deployment.yaml
│   │   └── ingress.yaml
│   └── nginx/                   # Nginx configurations
│       ├── nginx.conf
│       ├── nitor.conf
│       └── nitor-dev.conf
│
├── docs/                        # Comprehensive documentation
│   ├── API_DOCUMENTATION.md     # Complete API reference
│   ├── DEPLOYMENT_GUIDE.md      # Production deployment
│   ├── LOCAL_DEPLOYMENT.md      # Local development setup
│   ├── AI_SERVICE.md            # AI features documentation
│   └── USER_GUIDE.md            # End-user guide
│
├── scripts/                     # Utility scripts
│   ├── start-dev.sh
│   ├── stop-dev.sh
│   └── verify-deployment.sh
│
├── .github/                     # GitHub Actions CI/CD
│   └── workflows/
│
├── README.md                    # This file
├── CONTRIBUTING.md              # Contribution guidelines
├── CODE_OF_CONDUCT.md           # Community standards
├── SECURITY.md                  # Security policy
├── CONTRIBUTORS.md              # Contributors list
├── DEPLOYMENT_FIXES.md          # Troubleshooting guide
├── LICENSE                      # MIT License
└── .env.example                 # Environment variables template

Data Flow

Authentication Flow:

  1. User submits credentials to /api/auth/login
  2. Backend validates credentials against PostgreSQL
  3. JWT access token (24h) and refresh token (7d) generated
  4. Tokens stored in Redis for validation and revocation
  5. Frontend stores tokens securely
  6. Subsequent requests include JWT in Authorization header
  7. Backend validates token signature and expiration
  8. Token refresh handled automatically before expiration

Content Publishing Flow:

  1. User creates content in React editor (Markdown + LaTeX)
  2. Frontend validates and sends to /api/content endpoint
  3. Backend validates authorization and content
  4. Content saved to PostgreSQL with metadata
  5. Redis cache invalidated for relevant feeds
  6. WebSocket notification sent to followers
  7. Content appears in follower feeds immediately

AI Processing Flow:

  1. User requests AI feature (e.g., text refinement)
  2. Frontend sends request to Backend /api/ai/* proxy
  3. Backend forwards to AI Service with rate limit check
  4. AI Service calls Google Gemini 2.5 Pro API
  5. Response processed and formatted
  6. Result returned through Backend to Frontend
  7. User reviews and accepts/modifies AI suggestion

Quick Start

Prerequisites

  • Docker 24+ and Docker Compose
  • Git 2.30+
  • 8GB RAM minimum (16GB recommended)
  • 20GB free disk space
  • Gemini API Key (get from Google AI Studio)

Installation (5 Minutes)

# 1. Clone the repository
git clone https://github.com/olaflaitinen/nitor.git
cd nitor

# 2. Configure environment variables
cp .env.example .env
nano .env  # Add your GEMINI_API_KEY and other credentials

# 3. Start all services with Docker Compose
docker-compose -f infrastructure/docker/docker-compose.yml up -d

# 4. Verify deployment
docker-compose -f infrastructure/docker/docker-compose.yml ps

Access the Application

Service URL Description
Frontend http://localhost:5173 React application (development)
Backend API http://localhost:8080 REST API
API Documentation http://localhost:8080/swagger-ui.html Interactive API docs
AI Service http://localhost:3001 Gemini AI microservice
MinIO Console http://localhost:9001 Object storage UI
Health Check http://localhost:8080/actuator/health System health status
Metrics http://localhost:8080/actuator/prometheus Prometheus metrics

Default Credentials (Development Only)

Application Login:
  Email: test@example.com
  Password: password123

MinIO Console:
  Username: minioadmin
  Password: minioadmin

Next Steps

  1. Explore the Platform: Create an account, publish content, connect with researchers
  2. Review Documentation: See docs/ for detailed guides
  3. Try AI Features: Refine text, generate abstracts, create statements
  4. Customize: Edit .env for your configuration
  5. Develop: See CONTRIBUTING.md for development guidelines

Documentation

Comprehensive documentation is available in the /docs directory:

Document Description Audience
FREE Deployment (TR) ÜCRETSİZ deployment - Render, Railway, Vercel Herkes
Production Deployment Guide Complete production deployment with all credentials and API keys DevOps
API Documentation Complete REST API reference with 121 backend + 54 AI endpoints Developers
Local Deployment Guide Step-by-step local setup instructions Developers
Deployment Guide Production deployment with Kubernetes DevOps
AI Service Documentation All 54 Gemini-powered AI features Developers & Users
User Guide End-user manual for researchers Researchers

Additional Resources


Development

Local Development Setup

Backend Development:

cd packages/backend
mvn clean install
mvn spring-boot:run -Dspring-boot.run.profiles=dev
# Backend runs on http://localhost:8080

Frontend Development:

cd packages/frontend
npm install
npm run dev
# Frontend runs on http://localhost:5173 with hot reload

AI Service Development:

cd packages/ai-service
npm install
export GEMINI_API_KEY="your_api_key_here"
npm run dev
# AI Service runs on http://localhost:3001 with auto-restart

Running Tests

# Backend unit tests
cd packages/backend
mvn test

# Backend integration tests
mvn verify

# Frontend tests
cd packages/frontend
npm test

# Test coverage
npm run test:coverage

Building for Production

# Backend JAR
cd packages/backend
mvn clean package -DskipTests
# Output: target/nitor-backend-1.0.0.jar

# Frontend optimized build
cd packages/frontend
npm run build
# Output: dist/ directory

# AI Service
cd packages/ai-service
npm install --production
# Ready to deploy

Deployment

Docker Compose (Recommended for Testing)

# Start entire stack
docker-compose -f infrastructure/docker/docker-compose.yml up -d

# View logs
docker-compose -f infrastructure/docker/docker-compose.yml logs -f

# Stop services
docker-compose -f infrastructure/docker/docker-compose.yml down

Kubernetes (Production)

# Create namespace
kubectl create namespace nitor-production

# Apply configurations
kubectl apply -f infrastructure/kubernetes/

# Check status
kubectl get pods -n nitor-production

# Access logs
kubectl logs -f deployment/nitor-backend -n nitor-production

Production Checklist

  • Change all default passwords and secrets
  • Generate strong JWT secret (minimum 256 bits)
  • Configure production database with backups
  • Set up SSL/TLS certificates (Let's Encrypt or commercial CA)
  • Configure email service (SendGrid, AWS SES, or SMTP)
  • Set up OAuth credentials (Google, GitHub, LinkedIn)
  • Configure MinIO or AWS S3 for file storage
  • Enable HTTPS and configure Nginx reverse proxy
  • Set up monitoring (Prometheus + Grafana)
  • Configure backup strategy (database, files)
  • Set up log aggregation (ELK stack, Fluent Bit)
  • Enable rate limiting for API endpoints
  • Configure CORS for production domain only
  • Test disaster recovery procedures
  • Set up automated security scanning

For detailed deployment instructions, see DEPLOYMENT_GUIDE.md.


API Overview

Backend REST API (121 Endpoints)

Authentication (9 endpoints)

  • User registration, login, logout
  • Token refresh and revocation
  • Password reset and change
  • OAuth 2.0 integration (Google, GitHub, LinkedIn)
  • Account deletion

Profiles (8 endpoints)

  • Profile CRUD operations
  • Profile search and discovery
  • Academic credentials management
  • ORCID integration

Content Management (10 endpoints)

  • Create, read, update, delete content
  • Content feed with pagination
  • Search and filtering
  • Content reporting

Social Features (25 endpoints)

  • Follow/unfollow users
  • Connection requests
  • Comments and discussions
  • Likes, bookmarks, reposts

CV Management (18 endpoints)

  • Education, experience, publications
  • Skills and projects
  • Awards and honors
  • CV export (PDF, LaTeX, JSON)

Notifications (6 endpoints)

  • Real-time notifications
  • Notification preferences
  • Mark as read/unread
  • Notification filtering

File Upload (5 endpoints)

  • Avatar upload
  • File attachments
  • Image processing
  • MinIO integration

Search (4 endpoints)

  • Global search
  • User search
  • Content search
  • Advanced filtering

Admin (24 endpoints)

  • User management
  • Content moderation
  • Platform statistics
  • System configuration

WebSocket (12 endpoints)

  • Real-time notifications
  • Live updates
  • Chat system (planned)

AI Service API (54 Endpoints)

See AI_SERVICE.md for complete documentation of all 54 Gemini-powered AI features.

Categories:

  • Writing Enhancement (6 features)
  • Research Quality (6 features)
  • Publication Support (7 features)
  • Career Development (9 features)
  • Collaboration Tools (5 features)
  • Research Discovery (5 features)
  • Content Adaptation (4 features)
  • Review & Quality (5 features)
  • Platform Management (7 features)

Interactive API Documentation

Access Swagger UI at: http://localhost:8080/swagger-ui.html


Security

Authentication & Authorization

  • JWT Tokens: HS256 algorithm, 256-bit secrets
  • Token Expiration: Access (24h), Refresh (7d) with automatic rotation
  • Two-Factor Authentication: TOTP-based (RFC 6238) with backup codes
  • OAuth 2.0: Google, GitHub, LinkedIn integration
  • Password Security: BCrypt hashing (work factor: 12)

API Security

  • Rate Limiting: Bucket4j-based, configurable per endpoint type
    • Authentication: 5 requests/minute
    • General API: 100 requests/minute
    • File uploads: 20 requests/hour
  • CORS Protection: Configurable allowed origins
  • Input Validation: Jakarta Bean Validation (JSR-380)
  • SQL Injection Prevention: Parameterized queries with JPA
  • XSS Protection: Content Security Policy headers

Data Protection

  • Encryption in Transit: TLS 1.3 with modern cipher suites
  • Encryption at Rest: AES-256 for sensitive data
  • Data Classification: Public, Internal, Confidential, Restricted
  • Audit Logging: Complete activity logging for compliance
  • GDPR Compliance: User consent, data portability, right to erasure

Security Headers

Strict-Transport-Security: max-age=31536000; includeSubDomains
X-Frame-Options: SAMEORIGIN
X-Content-Type-Options: nosniff
X-XSS-Protection: 1; mode=block
Content-Security-Policy: default-src 'self'
Referrer-Policy: strict-origin-when-cross-origin

Vulnerability Reporting

DO NOT create public GitHub issues for security vulnerabilities.

Report to: security@nitor.io

See SECURITY.md for complete security policy.


Performance

Benchmarks

  • API Response Time: < 100ms (p95)
  • Page Load Time: < 2 seconds
  • Time to Interactive: < 3 seconds
  • Database Query Time: < 50ms (p95)
  • WebSocket Latency: < 100ms

Optimization Features

  • Database: Connection pooling (HikariCP), query optimization, indexing
  • Caching: Redis-based caching for frequent queries
  • CDN: Static asset delivery via CDN (production)
  • Compression: Gzip compression for text assets
  • HTTP/2: Multiplexing support in Nginx
  • Lazy Loading: Component and route-based code splitting
  • Image Optimization: WebP format, responsive images

Scalability

  • Horizontal Scaling: Kubernetes auto-scaling based on CPU/memory
  • Database Replication: PostgreSQL read replicas (planned)
  • Load Balancing: Nginx with round-robin algorithm
  • Caching Strategy: Redis cluster for distributed caching
  • Microservices: Independent scaling of backend, frontend, AI service

Contributing

We welcome contributions from the academic and developer communities!

How to Contribute

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature/amazing-feature
  3. Commit your changes: git commit -m 'feat: add amazing feature'
  4. Push to your branch: git push origin feature/amazing-feature
  5. Open a Pull Request

Contribution Areas

  • Code: Backend, Frontend, AI features
  • Documentation: Improve guides, add examples
  • Testing: Write tests, report bugs
  • Translation: Localization (coming soon)
  • Design: UI/UX improvements

Development Guidelines

  • Follow Conventional Commits specification
  • Write unit tests for new features (minimum 80% coverage)
  • Update documentation for API changes
  • Follow code style guides (Checkstyle for Java, ESLint for TypeScript)
  • Review CONTRIBUTING.md for detailed guidelines

Support

Documentation & Resources

Community

Professional Support


Roadmap

Version 1.1 (Q2 2025)

  • Mobile applications (iOS & Android with React Native)
  • Advanced analytics dashboard with visualizations
  • Citation network visualization and co-authorship graphs
  • Enhanced AI features (15+ new capabilities)
  • Multi-language support (Spanish, French, German, Chinese)

Version 1.2 (Q3 2025)

  • Institutional integrations and SSO
  • Conference management system
  • Grant collaboration tools
  • Research group features and lab pages
  • Enhanced search with Elasticsearch

Version 2.0 (Q4 2025)

  • Blockchain-based credential verification
  • Comprehensive peer review platform
  • Academic marketplace for services
  • Video conferencing integration
  • Advanced collaboration tools

License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2025 NITOR Development Team

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Acknowledgments

Technologies

  • Spring Framework - Comprehensive Java framework
  • React - Modern UI library
  • Google Gemini - Advanced AI capabilities
  • PostgreSQL - Robust relational database
  • Redis - High-performance caching
  • MinIO - S3-compatible object storage

Inspiration

Special thanks to the global academic community for inspiration and feedback. NITOR is built by researchers, for researchers.


Project Statistics

Metric Value
Total Lines of Code 30,000+
Backend Endpoints 121
AI Features 54
Database Tables 25+
Test Coverage 85%+
Documentation Pages 13
Docker Containers 6
Languages Java, TypeScript, JavaScript
Supported Platforms Linux, macOS, Windows
License MIT (Open Source)

NITORVersion 1.0.0Production ReadyNovember 27, 2025

Built with precision for the academic community.

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Nitor is an academic social networking platform designed for researchers, academics and institutions that require a professional, trusted environment to share and develop scientific work.

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