JSTOR

Symplectic Elements is a highly configurable research management system designed to collect, analyze, showcase, and report on all scholarly activities within an organization. It ingests data from multiple sources to build a comprehensive picture of organizational data, structuring and connecting scholarly information throughout the research lifecycle to reduce administrative burden, derive powerful new insights, and showcase the real-world impact of research. It offers tools for research funding and awards management, equipment and technology profiles, and assessment exercises, all within one easily navigable, single source of truth. By bringing data from multiple sources together in one centralized space, Symplectic Elements provides access to all necessary research information while reducing the administrative burden of institutional data management. Data capture is automated in the background wherever possible, freeing up researchers’ time to complete important tasks.

Resea AI is a full-featured academic research assistant that autonomously plans, conducts, and writes in-depth academic tasks from literature review to report drafting. It connects seamlessly with major scholarly databases such as Google Scholar, PubMed, and arXiv to source trusted research, then employs its proprietary “Think and Research” engine to determine research direction, core concepts, and writing angles through multi-stage inquiry. Feeding into its AI writing editor, Resea AI is capable of generating documents of unlimited length (even up to 50,000 words), and offers interactive editing for fast refinements. It ensures academic rigor through support for dozens of citation formats with accurate source indexing. It evaluates performance with benchmarks like xBench‑DeepSearch that measure deep research capabilities. Additional use cases include systematic literature reviews, academic outlines, content synthesis, reviewer-perspective feedback, and more.