SciSpace Academy
Tutorial

From Data to Insight: Research Data Analysis with SciSpace AI

Marija DukicEnglishData analysis

TL;DR

1-min read

Working with research data means exploring it, spotting patterns, visualising results and reporting them clearly. This tutorial shows how SciSpace AI can support each of those steps while protecting scientific rigor and research integrity. It is meant for researchers, doctoral students and academics who want an analysis assistant without handing over their own expertise, and its central premise is that AI should support that expertise rather than replace it.

Key takeaways

  • SciSpace AI can help extract relevant information from both datasets and documents, giving you a faster starting point for analysis.
  • AI support is useful for identifying trends and highlighting areas that merit closer investigation, while you decide which leads are worth pursuing.
  • Charts, visualisations and infographics generated with AI can help communicate findings, but they should be inspected for mislabelled axes or misleading scales.
  • Verifying AI output is essential: re-run key calculations with a separate tool or script, confirm the methods suit your data type and question, and compare results with expected values or prior findings.
  • Keeping AI-assisted insights separate from your own interpretation, and noting what the AI did, makes the analysis traceable and reproducible.
  • Responsible use also means reporting AI assistance transparently and weighing the ethical questions it raises in research.
Apply it

To apply this, try SciSpace AI on one dataset or document set in your current project, check each output independently, and record its role in your methods notes.

How to do it

  1. Provide the dataset, data dictionary, study design, and primary research question.

    Provide a versioned dataset, data dictionary, study design, primary question, and planned unit of analysis. Record any access or privacy restrictions before upload.

  2. Ask for a structured data-quality report and document every cleaning decision.

    Request a structured quality report and keep a cleaning log containing the original value, change, reason, rule, and resulting dataset version. Avoid undocumented transformations.

  3. Request several plausible methods with assumptions, trade-offs, and decision criteria.

    Ask for multiple plausible methods with assumptions, trade-offs, and decision criteria. Choose the method because it fits the design, not because it produces the preferred result.

  4. Run the selected analysis and inspect diagnostics, sensitivity analyses, and visualization choices.

    Inspect diagnostics, sensitivity analyses, robustness checks, and visualization choices. Save parameters and random seeds where relevant so the analysis can be rerun.

  5. Export code and results, then verify key values independently and revise the narrative.

    Export code, exact results, figures, environment details, and a limitations-aware narrative. Independently verify key values and ensure the manuscript matches the reproducible output.

Watch the tutorial50 min

From Data to Insight: Research Data Analysis with SciSpace AIPlay video: From Data to Insight: Research Data Analysis with SciSpace AI

Chapters

  1. Welcome and agenda
  2. What is data analysis? The 4 types: descriptive, diagnostic, predictive, prescriptive
  3. What are the 5 steps of the data analysis process in research?
  4. Research data ethics: European Code of Conduct for Research Integrity
  5. GDPR, Data Governance Act and Trustworthy AI guidelines for researchers
  6. Can you share research data with ChatGPT and other generative AI tools?
  7. Which SciSpace AI agents help with data cleaning and data analysis?
  8. How to find open datasets for research with AI (basic prompt)
  9. Does a detailed prompt find better datasets? Survey data on GenAI use
  10. Choosing a synthetic Kaggle dataset on AI impact on students
  11. How to create a data cleaning plan with AI: validation, duplicates, missing data, outliers
  12. How to use AI for exploratory data analysis of a CSV and generate research hypotheses
  13. How AI picks the right charts and statistical tests for your research questions
  14. How to use AI for research data analysis responsibly
  15. Using a synthetic dataset to analyze private research data safely with AI

Try this on SciSpace

Frequently asked questions

Can AI be used for research data analysis?

Yes, AI can help explore data, detect patterns, generate visualisations and summarise results. It works best as an assistant under the researcher's direction. You remain responsible for choosing appropriate methods, checking assumptions and interpreting results. Every AI-produced output should be verified, and its use should be reported so others can assess and reproduce your analysis.

How do I verify AI-generated data analysis results?

Re-run key calculations with a separate tool or script, check that the methods applied suit your data type and research question, and compare outputs against expected values or prior findings. Inspect charts for mislabelled axes or misleading scales. Keep notes on what the AI did so the analysis can be traced and reproduced.

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