Access to millions of peer-reviewed articles and book chapters across multiple scientific disciplines.
Boolean search, filters by date, author, journal, and article type for precise literature discovery.
Export citations in multiple formats and integrate with reference managers like Mendeley.
Save articles, set up alerts, and create personal libraries for ongoing research management.
Access to open access articles without subscription, supporting open science initiatives.
Optimized for mobile devices with responsive design for research on-the-go.
View article metrics such as citations and downloads to gauge research impact.
Gathering and synthesizing existing research for academic papers, theses, or dissertations.
Citing relevant studies to support research proposals and secure funding.
Instructors accessing materials for lectures, assignments, and educational resources.
Healthcare professionals using medical research for evidence-based practice and patient care.
Companies staying updated with technological advancements for innovation and product development.
Students finding sources for essays, projects, and academic coursework.
Conducting comprehensive reviews using advanced search and filters to analyze research trends.
Tracking citations to measure research impact and identify key publications in a field.
Individuals exploring topics of interest for self-education and professional development.
Policymakers accessing scientific evidence for informed decisions and public policy formulation.
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Arxiv-sanity is an innovative web platform that enhances the arXiv repository by integrating machine learning for personalized paper recommendations. Created by AI researcher Andrej Karpathy, it helps academics and researchers efficiently discover and manage scientific publications. The tool uses algorithms like TF-IDF and collaborative filtering to analyze user preferences and suggest relevant papers from arXiv's vast database. Key functionalities include a personal library for saving papers, advanced search with filters by date, category, and citations, trend visualization to track popular topics, and similarity search to find related works. It is particularly valuable in fast-paced fields such as artificial intelligence, computer science, and physics, enabling users to stay updated with the latest research without manual browsing. The interface is designed for ease of use, allowing quick access to abstracts, PDFs, and metadata. By automating paper discovery, Arxiv-sanity saves time and improves research productivity, making it an essential tool for students, professionals, and enthusiasts in the scientific community.
CiteSeerX is a free, open-access digital library and search engine specifically designed for scientific publications in the fields of computer and information science. Developed and maintained by the College of Information Sciences and Technology at Pennsylvania State University, it provides researchers, students, and academics with access to millions of documents, including research papers, theses, and technical reports. The platform leverages advanced algorithms for citation indexing, allowing users to track citation networks and analyze the impact of publications. It features full-text search capabilities, metadata extraction, document clustering, and author disambiguation tools. CiteSeerX aims to enhance the discoverability and accessibility of scientific knowledge, supporting literature reviews, bibliometric studies, and research trend analysis. Its user-friendly interface and comprehensive database make it an essential resource for the computer science community, promoting open science and collaborative research.
Connected Papers is an AI-powered tool designed to assist researchers, academics, and students in visualizing and exploring connections between academic papers. By inputting a seed paper via title, DOI, or author, the tool generates an interactive graph based on citation networks, highlighting related works and seminal references. This visualization aids in comprehensive literature reviews, discovery of new research areas, identification of trends, and gap analysis. Utilizing advanced algorithms to analyze bibliographic data, Connected Papers streamlines the research process, saving time and enhancing productivity. It supports features like filtering, exporting, and customizable views, making it a valuable resource for evidence-based inquiry across various disciplines.
