Funding Trend Forecaster

Predict funding trend shifts using NLP analysis of grant abstracts from NIH, NSF, and Horizon Europe

Skill: Funding Trend Forecaster

ID: 200
Version: 1.0.0
Author: OpenClaw Agent
License: MIT

---

Overview

Funding Trend Forecaster is an intelligent analysis tool that uses Natural Language Processing (NLP) technology to analyze awarded project abstracts from major global research funding agencies (NIH, NSF, Horizon Europe) and predict funding preference shift trends for the next 3-5 years.

Features

• Multi-source Data Collection: Automatically fetches awarded project data from NIH, NSF, Horizon Europe
• NLP Deep Analysis: Uses advanced text mining techniques to extract topics, keywords, and research trends
• Trend Prediction Model: Predicts funding direction changes based on time series analysis and topic modeling
• Visualized Reports: Generates charts and trend reports for intuitive display of analysis results
• Field Segmentation: Categorized analysis by medicine, engineering, natural sciences, and other fields

Installation

bash

# Enter skill directory
cd skills/funding-trend-forecaster

# Install dependencies
pip install -r requirements.txt

# Download NLTK data
python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords'); nltk.download('wordnet')"

Dependencies

text

requests>=2.28.0
beautifulsoup4>=4.11.0
pandas>=1.5.0
numpy>=1.23.0
scikit-learn>=1.1.0
textblob>=0.17.1
nltk>=3.7
matplotlib>=3.6.0
seaborn>=0.12.0
wordcloud>=1.8.0
python-dateutil>=2.8.0

Usage

Command Line Interface

bash

# Run full analysis workflow
python scripts/main.py --analyze-all --output report.json

# Analyze specific agency only
python scripts/main.py --source nih --months 6

# Generate visualization report
python scripts/main.py --visualize --input data.json --output charts/

# View trend forecast
python scripts/main.py --forecast --years 5 --output forecast.json

API Call

python

from scripts.main import FundingTrendForecaster

# Initialize forecaster
forecaster = FundingTrendForecaster()

# Collect data
forecaster.collect_data(sources=['nih', 'nsf', 'horizon_europe'], months=6)

# Execute analysis
results = forecaster.analyze_trends()

# Generate forecast
forecast = forecaster.predict_trends(years=5)

# Export report
forecaster.export_report(output_path='report.pdf', format='pdf')

Parameters

Parameter	Type	Default	Required	Description
--analyze-all	flag	false	No	Run full analysis workflow on all sources
--source	string	-	No	Specific agency to analyze (nih, nsf, horizon_europe)
--months	int	6	No	Number of months of historical data to analyze
--years	int	5	No	Years ahead for trend prediction
--visualize	flag	false	No	Generate visualization charts
--forecast	flag	false	No	Generate trend forecast
--input, -i	string	-	No	Input data file path (for visualization/forecast)
--output, -o	string	-	No	Output file path
--config	string	config.json	No	Path to configuration file

Data Sources

Agency	Data Source URL	Update Frequency
NIH	https://reporter.nih.gov/	Daily
NSF	https://www.nsf.gov/awardsearch/	Daily
Horizon Europe	https://ec.europa.eu/info/funding-tenders/opportunities/	Weekly

Configuration

Create config.json file to customize analysis parameters:

json

{
  "sources": {
    "nih": {
      "enabled": true,
      "base_url": "https://reporter.nih.gov/",
      "max_results": 1000
    },
    "nsf": {
      "enabled": true,
      "base_url": "https://www.nsf.gov/awardsearch/",
      "max_results": 1000
    },
    "horizon_europe": {
      "enabled": true,
      "base_url": "https://ec.europa.eu/info/funding-tenders/",
      "max_results": 500
    }
  },
  "nlp": {
    "language": "en",
    "min_word_length": 3,
    "max_topics": 20,
    "stop_words": ["research", "study", "project"]
  },
  "forecast": {
    "method": "lda_trend",
    "confidence_level": 0.95,
    "years_ahead": 5
  }
}

Output Format

JSON Report Structure

json

{
  "metadata": {
    "generated_at": "2024-01-15T10:30:00Z",
    "data_period": "2023-07-01 to 2024-01-01",
    "sources": ["nih", "nsf", "horizon_europe"],
    "total_projects": 15420
  },
  "trend_analysis": {
    "top_keywords": [
      {"term": "artificial intelligence", "frequency": 342, "growth": 0.45},
      {"term": "climate change", "frequency": 298, "growth": 0.32}
    ],
    "emerging_topics": [
      {"topic": "Large Language Models", "projects": 89, "trend": "rising"},
      {"topic": "Carbon Capture", "projects": 156, "trend": "stable"}
    ],
    "funding_shifts": {
      "increasing": ["AI/ML", "Climate Tech", "Quantum Computing"],
      "decreasing": ["Traditional Materials", "Fossil Fuels Research"]
    }
  },
  "forecast": {
    "2025": {
      "predicted_hot_topics": ["Generative AI", "Gene Editing", "Fusion Energy"],
      "confidence": 0.87
    },
    "2026-2029": {
      "long_term_trends": ["AGI Safety", "Personalized Medicine", "Space Mining"],
      "confidence": 0.72
    }
  }
}

Architecture

text

funding-trend-forecaster/
├── scripts/
│   ├── main.py              # Main entry
│   ├── collectors/          # Data collection module
│   │   ├── __init__.py
│   │   ├── nih_collector.py
│   │   ├── nsf_collector.py
│   │   └── horizon_collector.py
│   ├── analyzers/           # NLP analysis module
│   │   ├── __init__.py
│   │   ├── text_processor.py
│   │   ├── topic_modeler.py
│   │   └── trend_detector.py
│   ├── predictors/          # Prediction module
│   │   ├── __init__.py
│   │   └── trend_forecaster.py
│   └── utils/               # Utility module
│       ├── __init__.py
│       ├── config.py
│       └── visualizer.py
├── data/                    # Data storage
│   ├── raw/
│   └── processed/
├── output/                  # Output directory
├── config.json              # Configuration file
├── requirements.txt         # Python dependencies
└── SKILL.md                 # This document

Roadmap

• Basic architecture design
• Core analysis module
• More data source support (Wellcome Trust, JSPS, etc.)
• Real-time data stream processing
• Interactive web interface
• Machine learning model optimization

License

MIT License - See LICENSE file in project root directory

---

Generated by OpenClaw Agent | Skill ID: 200

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python scripts with tools	High
Network Access	External API calls	High
File System Access	Read/write data	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Data handled securely	Medium

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• API requests use HTTPS only
• Input validated against allowed patterns
• API timeout and retry mechanisms implemented
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no internal paths exposed)
• Dependencies audited
• No exposure of internal service architecture

Prerequisites

bash

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support