Academic Researcher
Instantly find scientific consensus, extract key data from papers, and verify citation integrity to accelerate your literature review and publication process by 10x.
ABOUT THIS STACK
This tool stack revolutionizes the academic research process by automating the most time-consuming steps: literature review, data extraction, and citation verification. It begins by using AI tools like Consensus to quickly establish scientific consensus on a topic. Key papers are then funneled into tools like Elicit for systematic extraction of specific data points (e.g., sample size, effect size) into comparison matrices. Finally, the stack integrates quality checks with services like Scite to ensure all sources are supported and not contrasted or retracted, leading to a robust, evidence-based draft in Google NotebookLM.
WORKFLOW INTEGRATION
This workflow is designed to move an academic researcher from a broad research question to a well-cited, high-quality draft of a literature review in a fraction of the traditional time. The tools integrate to automate discovery, data extraction, quality assurance, and synthesis, ensuring the final output is rigorously evidence-based.
1. Broad Discovery and Consensus Finding (Consensus & Perplexity)
Start with your research question in Consensus (e.g., "Does caffeine improve short-term memory?"). Consensus scans millions of papers to provide a summarized answer with direct citations. Use Perplexity for a broader contextual search to ensure a comprehensive view. Filter the results by "Meta-Analysis" to quickly gauge the scientific consensus and identify the highest-quality initial evidence.
2. Deep Dive and Systematic Data Extraction (Elicit)
Select the key papers identified in Step 1 and import them into Elicit. Initiate a "Systematic Review" workflow and prompt Elicit to extract specific, comparative data points (e.g., "What was the sample size?", "What was the dosage?"). Elicit automatically builds a comparison matrix, moving you from paper-reading to structured data in minutes.
3. Citation Quality and Integrity Check (Scite)
Before citing any paper from the extraction matrix, run its DOI through Scite. Use Scite's "Smart Citations" feature to check if the paper has been supported, contrasted, or retracted by subsequent studies. Reject any source that has been heavily contrasted or retracted to ensure your research relies on solid, reproducible foundations.
4. Draft Synthesis and Source Linking (Google NotebookLM & Google Gemini)
Upload the collected, verified papers and the data matrix to Google NotebookLM. Use the AI's features to synthesize the literature review, identify key thematic gaps, and draft sections of your paper. Use Google Gemini for refining complex arguments or brainstorming structure. The model will generate content only based on the verified source material, with sources automatically linked, moving you immediately to a high-quality, fully-cited draft.