Research Assistant - Guided Workflow
In this release, we enhanced the existing "Understand a Topic" and "Find a Journal" guided workflows by leveraging Agentic AI technology and adding new features to improve research efficiency and user experience. These updates introduce more intuitive, conversational interactions, guided exploration, and insights. Researchers can now benefit from smarter topic discovery, journal recommendations, and deeper insights through interactive visualizations and contextual guidance, making it easier to navigate complex research landscapes.
Topic Explorer
The existing "Understand a Topic" guided workflow has now been renamed Topic Explorer. Integrating Agentic AI with the existing workflow delivers a more insightful and conversational experience. Topic Explorer offers topic, subtopic and related topic discovery, author and institutional insights, access to review articles, interactive visualizations and country-level mapping. Users can engage in deeper exploration by asking follow-up questions and insight about the visualizations, making the research journey more dynamic and intuitive.
When a user enters a research topic, the Topic Explorer provides a basic definition, suggests related topics, and displays the documents used to generate the response. It also offers further exploration options such as viewing top review articles, identifying leading authors and institutions, analyzing document trends over time, generating word clouds, and visualizing top publishing countries on a world map. Some features like topic maps and document-level exploration are currently under discussion. Topic Explorer is designed to help researchers gain a deeper understanding of their chosen domain by combining bibliometric insights with conversational guidance and visual tools.
Find A Journal
Find a Journal was designed to help researchers identify the most suitable journals for publishing their manuscripts. The feature combines the Manuscript Matcher (using MJL data) with the Web of Science search to generate tailored journal recommendations. Users begin by entering a research area, and are then prompted to provide a manuscript title and abstract to improve recommendation accuracy. If these details are available, the system uses the Manuscript Matcher to rank journals based on relevance. If not, it falls back to Web of Science data using publication title filter to suggest journals with a strong publication history in the specified area. Each recommended journal is presented in a redesigned journal card that includes rich metadata such as publisher, country, ISSN, publication frequency, indexing information, and citation metrics like JIF and JCI. Only users with JCR subscription will be able to view JCR Metrics.
Journal cards in RA with no JCR entitlement
Journal cards in RA with JCR entitlement
Users can further refine their results by specifying preferences such as open access status, impact factor, language, region, and publication frequency. This dual recommendation system, combined with interactive filtering and flexible input options, makes Find a Journal 2.0 a powerful and user-friendly tool for navigating the journal selection process.