Experience the Research Paper Downloader in action!
Launch Application 🚀The Research Paper Downloader is a sophisticated web-based application designed to streamline the academic research process by providing researchers, students, and professionals with an efficient platform for discovering and accessing scholarly publications. This Flask-based application leverages the Google Scholar API through the `scholarly` library to perform comprehensive searches across multiple parameters including datasets, authors, and topics, ultimately generating downloadable reports containing highly-cited research papers relevant to user queries.
The application addresses a critical need in the academic community by automating the time-consuming process of literature review and paper discovery, offering advanced search capabilities that support complex query combinations and providing structured output formats for easy reference and citation management.
def build_search_queries(search_type, datasets, topic, author):
query_parts = []
if 'Dataset' in search_type and datasets:
combinations = []
for r in range(1, len(datasets) + 1):
combinations.extend(itertools.combinations(datasets, r))
query_parts = [(" AND ".join(combination)) for combination in combinations]
if 'Topic' in search_type and topic:
query_parts.append(f"topic:{topic}")
if 'Author' in search_type and author:
query_parts.append(f"author:{author}")
return query_partsSearch by dataset, author, topic, or any combination. Supports combinatorial queries and advanced boolean logic for precise results.
Ranks papers by citation count, h-index, and venue impact. Extracts and validates comprehensive metadata for each result.
Generates structured reports with standardized formatting, complete bibliographic information, and citation metrics.
Optimized for fast search, secure file handling, and robust error management. Designed for scalability and reliability.
Responsive, accessible interface with real-time validation and dynamic form management.
def get_top_cited_papers(query, num_results=10):
results = []
for i, paper in enumerate(query):
if i >= num_results:
break
title = paper['bib']['title']
citations = paper['num_citations']
url = paper.get('url', 'URL not available')
author = paper['bib'].get('author', 'Author not available')
results.append((title, citations, url, author))
return sorted(results, key=lambda x: x[1], reverse=True)def generate_report_file(search_results, filename):
file_path = os.path.join(app.config['DOWNLOAD_FOLDER'], filename)
with open(file_path, 'w', encoding='utf-8') as file:
write_header(file, search_parameters)
for query_result in search_results:
write_query_section(file, query_result)
write_papers(file, query_result.papers)
write_footer(file, generation_timestamp)
return file_pathAdvanced query operators, metadata enrichment, UI/UX improvements, performance optimizations, and new export formats.
Analytics dashboard, multi-database integration (PubMed, IEEE Xplore), collaboration features, and advanced analytics.
AI-powered research assistant, enterprise integration, mobile apps, and predictive analytics for research trends.
In the contemporary academic landscape, researchers face an overwhelming volume of scholarly publications across various disciplines. The exponential growth of research output, with millions of papers published annually, creates significant challenges for academics attempting to conduct comprehensive literature reviews or stay current with developments in their field.
Traditional approaches to academic search often require researchers to:
Academic searches often require complex combinations of parameters that are difficult to manage manually
Researchers spend disproportionate amounts of time on paper discovery rather than analysis
Organizing and formatting citation information from multiple sources is labor-intensive
Identifying high-impact papers based on citation metrics requires additional manual research
Inconsistent output formats across different academic platforms create organizational challenges
Minimum: 1 vCPU, 512MB RAM, 1GB storage
Recommended: 2+ vCPUs, 2GB RAM, 5GB storage
# Python Environment Setup
python -m venv research_paper_env
research_paper_env\Scripts\activate # Windows
# Dependencies Installation
pip install Flask==2.0.1
pip install scholarly==1.5.0
pip install pytest==6.2.4
pip install black==21.6b0
pip install flake8==3.9.2# Nginx Configuration
server {
listen 80;
server_name your-domain.com;
location / {
proxy_pass http://127.0.0.1:8000;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
}
location /static {
alias /path/to/app/static;
expires 1y;
}
}