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Scientific Writing with AI Literature Search and Data Extraction

Sowmiya RaniEnglishLiterature reviewExtract data and build a literature matrix

TL;DR

1-min read

Before scientific writing can begin, researchers often spend hours sorting irrelevant papers and scattered data. This hands-on tutorial with Dr. Sowmiya Rani, a scientific paper editor and founder of a research services startup incubated at IIM Bangalore, tackles that problem in three parts. It is aimed at researchers, scientific writers and students who want a stronger evidence base in place before drafting.

Key takeaways

  • AI tools can help identify, collect and organise relevant literature so you start with a focused set of papers.
  • Several scientific papers can be scanned and analysed quickly, cutting the time spent on first-pass reading.
  • Data extraction pulls consistent details from each study, such as design, sample, methodology, outcomes and key results, from article PDFs into a structured table.
  • AI performs well on clearly stated details but can misread tables, figures or unusual formats, so treat extraction as a first pass and spot-check values, especially numbers that feed your conclusions.
  • Summaries across multiple studies can highlight main findings and point to possible research gaps.
  • A synthesised, checked evidence base gives writers a stronger foundation for the paper itself.
Apply it

To apply this, collect your core papers in SciSpace, extract the key study details into a table, verify the important values against each PDF, and use the summary to plan your draft.

How to do it

  1. Collect your core papers

    Begin AI literature search and data extraction in SciSpace by identifying, collecting and organising relevant papers, so you start with a focused set.

  2. Scan multiple papers quickly

    Scan and analyse several papers at once to cut the time spent on first-pass reading.

  3. Extract study details into a table

    Pull design, sample, methodology, outcomes and key results from each PDF into a structured table, so studies are easy to compare.

  4. Spot-check the extracted values

    Verify important values against each PDF, especially numbers that feed your conclusions, since AI can misread tables, figures or unusual formats.

  5. Summarise findings and spot gaps

    Summarise findings across studies to highlight main results and possible research gaps, then use the summary to plan your draft.

Watch the tutorial1 h 56 min

Scientific Writing with AI Literature Search and Data ExtractionPlay video: Scientific Writing with AI Literature Search and Data Extraction

Chapters

  1. Introduction
  2. Using AI for literature review
  3. SciSpace Copilot
  4. Extracting data from multiple Papers
  5. Paraphrasing
  6. SciSpace literature search
  7. Narrowing your search with My Library
  8. Ethical dilemma
  9. Q&A

Try this on SciSpace

Frequently asked questions

What is data extraction in a literature review?

Data extraction is the process of pulling consistent details from each included study, such as design, sample, methods, outcomes and key results, into a structured table. It makes studies comparable and supports synthesis. AI tools can speed up extraction, but the extracted values should always be checked against the original papers for accuracy.

How accurate is AI data extraction from research papers?

Accuracy depends on the tool and on how clearly each paper reports its data. AI performs well on clearly stated details but can misread tables, figures or unusual reporting formats. Treat AI extraction as a first pass, and spot-check values against the source, especially numbers and outcomes that feed directly into your conclusions.

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