SciSpace Academy
Tutorial

AI for Research Data Analysis: Turning Data into Discovery

SciSpace AcademyEnglishData analysis

TL;DR

1-min read

This SciSpace tutorial shows how an AI agent can support the whole research data process in one place, from extraction and cleaning to analysis and presentation. It is aimed at researchers working with quantitative, qualitative or mixed-methods data in any field, with examples from medicine, engineering, social sciences, humanities and economics.

Key takeaways

  • Data can be extracted from PDFs, surveys, lab outputs, transcripts and spreadsheets, and messy datasets can be cleaned before analysis begins.
  • Quantitative work covers descriptive and inferential statistics, regression, time-series analysis and basic machine learning; you still need to choose methods, check assumptions and verify key numbers.
  • Qualitative support includes thematic coding, discourse analysis and sentiment analysis, which work best as a first pass that you review, refine and justify with an audit trail.
  • Charts, posters and dashboards can be created and exported to Word, LaTeX or PowerPoint, but every plotted value should be checked against your dataset and journal figure guidelines followed.
  • The tutorial takes an honest look at limits, noting where dedicated statistical software remains the better choice for complex or fully reproducible analyses.
  • Asking the tool to show the code or steps it used makes results easier to check and explain.
Apply it

To apply this, upload a dataset to SciSpace, run a first-pass analysis with the agent, and confirm the methods and key outputs before using them in your work.

How to do it

  1. Upload and clean your dataset

    Upload data from PDFs, surveys, lab outputs, transcripts or spreadsheets to SciSpace and clean messy datasets before analysis begins.

  2. Run a first-pass quantitative analysis

    Ask the agent for descriptive or inferential statistics, regression or time-series analysis. You still choose the methods, check assumptions and verify key numbers.

  3. Code qualitative data as a first pass

    Use thematic coding, discourse analysis or sentiment analysis as a first pass, then review, refine and justify it with an audit trail.

  4. Ask the agent to show its steps

    Request the code or steps used for each result. Visible steps make AI for research data analysis easier to check and explain.

  5. Create and export your charts

    Build charts, posters or dashboards and export them to Word, LaTeX or PowerPoint, checking every plotted value against your dataset and journal figure guidelines.

  6. Switch tools for complex analyses

    Use dedicated statistical software for complex or fully reproducible analyses, where it remains the better choice.

Watch the tutorial42 min

AI for Research Data Analysis: Quantitative and Qualitative WorkflowsPlay video: AI for Research Data Analysis: Quantitative and Qualitative Workflows

Chapters

  1. How to use AI for data analysis in research: three steps
  2. How to extract structured data from a PDF into JSON with AI
  3. How to audit messy CSV data quality with SciSpace Agent
  4. Why SciSpace Agent writes Python code to analyse your data
  5. How to clean a messy dataset with AI without writing code
  6. Quantitative analysis with AI: descriptive statistics on clinical data
  7. How to run a t-test on treatment vs control groups with AI
  8. How to build a multiple regression model with AI (R-squared, VIF)
  9. Publication-ready charts and a data visualization dashboard with AI
  10. How to do thematic analysis of interview transcripts with AI
  11. How to run sentiment analysis on qualitative interview data
  12. Turn research data into an interactive web dashboard with one prompt
  13. How to generate a LaTeX research report from your analysis with AI
  14. Is your data safe in SciSpace Agent? Key takeaways

Try this on SciSpace

Frequently asked questions

Can AI analyse qualitative research data?

AI can help with qualitative tasks such as suggesting initial codes, grouping excerpts into themes and summarising transcripts. It works best as a first pass that you then review, refine and justify. Keep an audit trail of coding decisions and check that themes reflect the data, as reflexivity and interpretation remain the researcher's job.

Is AI reliable for statistical analysis in research?

AI can run standard tests and explain outputs, but you still need to choose appropriate methods, check assumptions and interpret results in context. Ask the tool to show the code or steps it used, verify key numbers independently, and use dedicated statistical software when an analysis is complex or must be fully reproducible.

How do I make publication-ready figures from research data?

Choose a chart type that matches your data and message, label axes with units, keep colours readable in greyscale and follow the target journal's figure guidelines for size, resolution and file format. AI tools can generate and refine charts quickly, but check that every plotted value matches your dataset.

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