Research Grant Analytics Platform (RGAP)

Milestone 2 Updates

Quick guide on how to run? In the terminal:

1. source setup_env.sh
2. ./setup_mysql.sh
3. ./setup_app.sh

Current features supported: All 5 basic features:

1. user creation/deletion
2. user login/management
3. user search history
4. bookmark
5. sorting

Table of Contents

• Research Grant Analytics Platform (RGAP)
  • Table of Contents
  • Project Overview
    • Key Focus Areas
  • Documentation
  • Technology Stack
    • Frontend
    • Backend
    • Development Tools
  • Quick Start
    • Prerequisites
    • Installation
  • Data Fetch and App Setup
  • Features
    • Basic Features:
    • Advanced Features:
  • Data Management
    • Sample Dataset:
    • Production Dataset:
    • Data Processing Pipeline:
  • Performance Optimization
    • Database Level:
    • Application Level:

Project Overview

RGAP is a comprehensive web-based analytics platform analyzing research funding data from Canada's three major research funding agencies: NSERC (Natural Sciences and Engineering Research Council), CIHR (Canadian Institutes of Health Research), and SSHRC (Social Sciences and Humanities Research Council). The platform leverages open data from Open Canada (https://search.open.canada.ca/grants), which provides detailed grant information from these agencies:

• NSERC: 136,034+ grant records
• CIHR: 39,768+ grant records
• SSHRC: 55,808+ grant records Total Dataset: 231,610+ grants

The platform will provide researchers, administrators, and the public with insights into funding patterns, institutional success rates, and research investment trends across Canadian academic institutions.

Key Focus Areas

• Integration of tri-agency funding data
• Cross-agency funding pattern analysis
• Geographical distribution of research funding
• Program-specific success metrics
• Institutional funding trajectories

Documentation

• Installation Guide
• User Guide
• Developer Guide
• API Reference
• Data Documentation
• Data Fetcher Guide
• MySQL for RGAP Guide
• Troubleshooting

Technology Stack

Frontend

• React 18.2.0+
• Tailwind CSS for styling
• Recharts/chart.js for data visualization
• React Query for data fetching

Backend

• Node.js + Express
• MySQL 8.0.36
• RESTful API design

Development Tools

• Git & GitHub
• npm for package management
• Docker for development environment

Quick Start

Prerequisites

• Node.js 22.14.0+
• MySQL 8.0.36+
• Python 3.12.7+ (for data fetching)
• 7zip (optional, for better data compression)

Installation

1. Clone the repository

   git clone https://github.com/jaxendutta/rgap.git
   cd rgap

2. Set up environment variables and packages

   source setup_env.sh

3. Run the MySQL setup script

   ./setup_mysql.sh

4. Start the application

   ./setup_app.sh

This will:

• Set up a local MySQL instance
• Install dependencies for server and client
• Create database schema and import sample data
• Start the server and client applications

The setup will display URLs for accessing the application, typically:

• Client: http://localhost:3000
• Server: http://localhost:4000

Data Fetch and App Setup

RGAP comes with sample data, but you can import the full dataset:

# Fetch data from the tri-agency sources
python fetcher.py --year-start 2019 --year-end 2023 --save

# Import the downloaded data
./setup_app.sh --full

Features

Basic Features:

1. Comprehensive Grant Search

   • Multi-agency search capability
   • Filter by program, date range, amount
   • Advanced filtering options (institution, location)

2. Institution Analytics

   • Funding history visualization
   • Success rate analysis
   • Program participation metrics

3. Program Analysis

   • Success rates by program
   • Funding distribution analysis
   • Year-over-year comparisons

4. Geographic Distribution

   • Provincial funding distribution
   • Institution concentration analysis
   • Regional success rates

5. Temporal Analysis

   • Funding trends over time
   • Seasonal pattern analysis
   • Multi-year grant tracking

Advanced Features:

1. Cross-Agency Analytics

   • Comparative funding analysis
   • Inter-agency program relationships
   • Combined success metrics

2. Machine Learning Insights

   • Funding success prediction
   • Pattern recognition
   • Anomaly detection

3. Custom Report Generation

   • Configurable metrics
   • Multiple export formats
   • Scheduled reports

4. Research Network Analysis

   • Institution collaboration patterns
   • Research field clustering
   • Geographic collaboration mapping

5. Program Impact Metrics

   • Outcome tracking
   • ROI analysis
   • Success indicator monitoring

Data Management

Sample Dataset:

• 100 grants per agency
• 20 institutions
• 5 years of historical data
• Complete program coverage

Production Dataset:

• Full tri-agency data (231K+ grants)
• All Canadian institutions
• 25+ years of historical data
• Complete program listings

Data Processing Pipeline:

1. Data extraction from Open Canada portal using their standardized grant data format
2. Agency-specific data parsing (NSERC, CIHR, SSHRC formats)
3. Data cleaning and normalization
4. Agency-specific transformations
5. Database loading and validation

Performance Optimization

Database Level:

• Indexed queries for common searches
• Materialized views for analytics
• Partitioning for historical data
• Query optimization for large datasets

Application Level:

• Client-side caching
• Server-side response caching
• Pagination for large result sets
• Lazy loading of components