SciSpace Workshop: Discover Research Papers Faster with SciSpace | By Dr. Lyndon Walker

SciSpace is positioned as a faster, more question-driven workflow for literature reviews—moving beyond keyword searches and manual reading toward AI-assisted discovery, filtering, summarization, and note-taking. The core shift is asking research questions with context, then using stronger ranking and filters to narrow results, followed by tools that summarize papers and support writing in the same workspace. That matters because the traditional path—searching across multiple databases, opening dozens of PDFs, taking notes separately, and then hunting citations again during writing—creates major time loss and fragmentation.

The session starts by tracing how literature search evolved: physical journals in libraries, then online databases that still required multiple searches, then Google Scholar, which improved breadth but relied heavily on keyword matching and produced long, noisy result lists with limited control. With large language models and AI search tools, the workflow becomes more efficient: users can provide a research question instead of just keywords, apply better filters (sorting, ranking, and narrowing), and generate summaries. The emphasis then turns to writing support—integrating “notebooks” where summaries and notes can be built while reviewing papers, rather than switching back and forth between reading and drafting.

From there, the walkthrough focuses on SciSpace’s main modules and how they fit into a literature review pipeline. The homepage centers on a search box where a research question triggers literature retrieval. A “Find Topics” tool helps brainstorm and niche down by suggesting topic phrasings and research angles, with results that include summaries and sources. For literature review, SciSpace offers different search modes: Standard (free tier), High Quality (more detailed output), and Deep Review (a premium mode that takes longer). The presenter demonstrates that Standard and High Quality follow a similar format but differ in how many papers they evaluate—High Quality considers more papers from a large corpus, producing longer, more detailed summaries.

Deep Review is treated as a different approach: it prompts for additional specificity, then runs multiple queries and uses citation/reference chaining—mirroring how researchers expand a reading list by following where relevant work is cited and what those papers cite. In the demo, a query about creatine and muscle development leads to a more extensive review with a table of contents, many more references, and the ability to summarize up to a larger set of top papers.

A major practical section explains how SciSpace handles access to full texts. When PDFs are available, they’re linked and viewable inside the tool. For paywalled items, a “request PDF” option is offered (described as sending an email request), and users can alternatively contact authors directly. Filters also allow restricting results to PDF-accessible or open-access papers.

The interface then supports deeper analysis and customization. Users can filter and sort results, add columns to extract specific information (including creating a new column via a prompt, such as “funding source” or “changes over time”), and open individual papers to chat with the PDF—asking about methods, limitations, datasets, or summarizing sections. For mathematical or tabular content, a selection tool lets users highlight a table or formula and receive plain-language explanations plus follow-up questions.

Finally, the session addresses workflow and responsibility: SciSpace provides export options for references (CSV, Excel, BibTeX, XML, RIS) and notebook-based writing that can be exported to docx, with citation formatting choices. Accuracy still requires manual verification—especially for references—and the presenter warns against copy-pasting AI-generated text into publications without human authorship and accountability. Discount codes are mentioned as time-limited, and the Q&A encourages users to share what they want to try next.