Dataset Discovery: Finding Data You Didn't Know Existed

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-research-dataset-discovery-creators

1. What is the core idea behind "Dataset Discovery: Finding Data You Didn't Know Existed"?

   1. For any research question, the bottleneck is often data. AI can map the dataset landscape in ways Google never could.
   2. abstract screening
   3. Compute a timing plan with Q&A buffer and overrun policy
   4. Address AI-specific ethics (re-identification, hallucination)

2. Which term best describes a foundational idea in "Dataset Discovery: Finding Data You Didn't Know Existed"?

   1. Zenodo
   2. FAIR
   3. Figshare
   4. ICPSR

3. A learner studying Dataset Discovery: Finding Data You Didn't Know Existed would need to understand which concept?

   1. FAIR
   2. Figshare
   3. Zenodo
   4. ICPSR

4. Which of these is directly relevant to Dataset Discovery: Finding Data You Didn't Know Existed?

   1. FAIR
   2. Zenodo
   3. ICPSR
   4. Figshare

5. Which of the following is a key point about Dataset Discovery: Finding Data You Didn't Know Existed?

   1. Always verify that the dataset actually exists — LLMs hallucinate datasets too
   2. Check the license — 'open' is not one thing; CC0, CC-BY, and research-only have different rules
   3. Read the codebook or data dictionary BEFORE you commit to the dataset
   4. Ask: who curates this? When was it last updated?

6. Which of these does NOT belong in a discussion of Dataset Discovery: Finding Data You Didn't Know Existed?

   1. abstract screening
   2. Read the codebook or data dictionary BEFORE you commit to the dataset
   3. Check the license — 'open' is not one thing; CC0, CC-BY, and research-only have different rules
   4. Always verify that the dataset actually exists — LLMs hallucinate datasets too

7. What is the key insight about "Prompt" in the context of Dataset Discovery: Finding Data You Didn't Know Existed?

   1. abstract screening
   2. Compute a timing plan with Q&A buffer and overrun policy
   3. I need data on [topic] with [properties: time range, geography, granularity, population].
   4. Address AI-specific ethics (re-identification, hallucination)

8. What is the key insight about "Supplementary materials are graveyards" in the context of Dataset Discovery: Finding Data You Didn't Know Existed?

   1. abstract screening
   2. Compute a timing plan with Q&A buffer and overrun policy
   3. Address AI-specific ethics (re-identification, hallucination)
   4. Tons of valuable data is buried in supplementary PDFs of 2008 papers, impossible to search and unmaintained.

9. What is the key warning about "Maintain methodological rigour" in the context of Dataset Discovery: Finding Data You Didn't Know Existed?

   1. AI-assisted research requires transparent disclosure of tools used, validation of outputs against primary sources, and p…
   2. abstract screening
   3. Compute a timing plan with Q&A buffer and overrun policy
   4. Address AI-specific ethics (re-identification, hallucination)

10. Which statement accurately describes an aspect of Dataset Discovery: Finding Data You Didn't Know Existed?

    1. abstract screening
    2. Research datasets live in a dozen different places: Zenodo, Figshare, OSF, ICPSR, domain-specific repositories, government data portals, sup…
    3. Compute a timing plan with Q&A buffer and overrun policy
    4. Address AI-specific ethics (re-identification, hallucination)

11. What does working with Dataset Discovery: Finding Data You Didn't Know Existed typically involve?

    1. abstract screening
    2. Compute a timing plan with Q&A buffer and overrun policy
    3. The FAIR principles (Findable, Accessible, Interoperable, Reusable) are increasingly required by funders.
    4. Address AI-specific ethics (re-identification, hallucination)

12. Which of the following is true about Dataset Discovery: Finding Data You Didn't Know Existed?

    1. abstract screening
    2. Compute a timing plan with Q&A buffer and overrun policy
    3. Address AI-specific ethics (re-identification, hallucination)
    4. The big idea: LLMs can map the dataset ecosystem faster than search engines, but every discovery needs verification.

13. Which best describes the scope of "Dataset Discovery: Finding Data You Didn't Know Existed"?

    1. It focuses on For any research question, the bottleneck is often data. AI can map the dataset landscape in ways Go
    2. It is unrelated to research workflows
    3. It applies only to the opposite beginner tier
    4. It was deprecated in 2024 and no longer relevant

14. Which section heading best belongs in a lesson about Dataset Discovery: Finding Data You Didn't Know Existed?

    1. abstract screening
    2. The dataset-discovery prompt
    3. Compute a timing plan with Q&A buffer and overrun policy
    4. Address AI-specific ethics (re-identification, hallucination)

15. Which section heading best belongs in a lesson about Dataset Discovery: Finding Data You Didn't Know Existed?

    1. abstract screening
    2. Compute a timing plan with Q&A buffer and overrun policy
    3. FAIR in practice
    4. Address AI-specific ethics (re-identification, hallucination)