A Step-by-Step Tutorial: Extracting Data with Elicit for Systematic Reviews

If you are conducting a systematic literature review or a large-scale meta-analysis in 2026, you know that the most painful step is data extraction. Reading through a hundred papers just to find the sample size and exact methodology of each study can take weeks.

Thankfully, AI research assistants have evolved to solve this specific problem. While there are many AI tools available, Elicit has cemented itself as the gold standard for structured evidence synthesis.

In this tutorial, we will walk you through how to use Elicit to build a customized extraction table and then show you how to manage that data within your team's ResearchDock workspace.

Step 1: Define Your Research Query

When you first open Elicit, you will see a search bar. Unlike traditional keyword databases, Elicit uses semantic search. This means you should ask your research question as a complete sentence.

For example, instead of typing "sleep memory consolidation," type: "How does sleep deprivation affect memory consolidation in adults?"

Elicit will scan millions of peer-reviewed papers and return the most relevant results. It will also generate a short summary paragraph at the top of the page, aggregating the findings from the top four papers.

Step 2: Build Your Custom Extraction Table

This is where Elicit truly shines. Once you have your list of relevant papers, you can add custom columns to the results table.

Click the "Add Column" button. You can choose from pre-defined variables like "Number of participants," "Study design," or "Main findings." If your variable is not listed, you can type a custom prompt. For example, you might add a column for "Specific cognitive tests used."

Elicit will process the PDFs of the selected papers and extract the requested data directly into your table. Crucially, Elicit provides sentence-level citations. If you click on a generated cell, it will highlight the exact sentence in the original paper where it found the information. This level of transparency is essential for rigorous academic work.

Step 3: Export and Verify

Once you have screened your papers and filled your extraction table, you can export the data as a CSV file.

However, AI extraction is never flawless. You must verify the generated data before including it in your manuscript. This verification step is usually where team collaboration breaks down if you are relying on chaotic email threads.

Step 4: Synthesize in ResearchDock

To make this process seamless for your entire lab, you should integrate your Elicit export into your project management hub.

Once you have your CSV file, upload it directly into your project space in ResearchDock. You can then assign specific tasks to your co-authors or PhD students to verify sections of the table. For example, you can create a milestone called "Verify Extraction Matrix" and attach the CSV file.

By linking your Elicit data directly to your ResearchDock milestones, you ensure that your team is tracking the verification process accurately. You never have to wonder if a specific paper was checked because the entire decision history is documented in one central location.

Final Thoughts

Using niche tools like Elicit can save you hundreds of hours of manual extraction. However, the technology is only as good as the workflow that surrounds it. By pairing the extraction power of Elicit with the structured project management of ResearchDock, you can conduct systematic reviews that are both incredibly fast and undeniably rigorous.