AI in Research Data Management

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-research-AI-and-research-data-management-creators

1. In the context of research data management, what does the acronym FAIR stand for?

   1. Functional, Automated, Indexed, Repository
   2. Findable, Accessible, Interoperable, Reusable
   3. Formatted, Analyzed, Interpreted, Reported
   4. Fast, Accurate, Integrated, Regulated

2. Which task would be most appropriate to automate using AI in research data management?

   1. Evaluating whether data sharing complies with funding mandates
   2. Deciding whether a dataset contains ethically sensitive information
   3. Determining which research questions a dataset could answer
   4. Generating metadata to make datasets findable and reusable

3. What is data lineage in research data management?

   1. A type of metadata describing research funding sources
   2. The complete history of how data was collected, processed, and transformed
   3. A requirement that data be stored in a single location
   4. The process of permanently deleting outdated datasets

4. Why must researchers maintain authority over substantive data choices when using AI tools?

   1. Institutional review boards only approve research with human data decision-makers
   2. AI tools cannot interpret findings within the broader scientific context
   3. Regulations require human signatures on all data-related decisions
   4. AI lacks the domain expertise to make decisions about research significance

5. A researcher wants to use AI to manage their laboratory's data. Which capability represents an appropriate use of AI?

   1. Generating standardized documentation describing dataset contents
   2. Automatically classifying which experiments should be conducted next
   3. Determining whether data sharing would compromise competitive advantage
   4. Evaluating if collected data supports or refutes a hypothesis

6. What aspect of research data management would NOT change even with extensive AI automation?

   1. The ability to search across multiple datasets
   2. The need to track where data originated and how it changed
   3. The requirement to create machine-readable metadata
   4. The effort required to organize and document data

7. When designing an AI system for research data management, which component should be prioritized?

   1. Eliminating the need for data documentation standards
   2. Building mechanisms to maintain researcher control over key decisions
   3. Replacing institutional repositories with cloud storage
   4. Ensuring the AI can independently determine data sharing permissions

8. What does it mean for AI to 'surface' storage and sharing requirements?

   1. The AI automatically deletes data that violates sharing policies
   2. The AI identifies and presents relevant requirements to researchers
   3. The AI generates new requirements based on data analysis
   4. The AI stores data in compliance-mandated locations

9. A university wants to integrate AI data management tools with existing systems. Which consideration is most important?

   1. The AI should operate independently from existing systems to avoid conflicts
   2. Integration should preserve data governance policies and access controls
   3. Existing institutional systems should be replaced entirely
   4. The AI should automatically override outdated policies

10. What responsibility cannot be delegated to AI in research data management?

    1. Creating data backup schedules
    2. Reviewing data for personally identifiable information
    3. Generating metadata for new datasets
    4. Completing ethical review processes for sensitive data

11. Which statement best describes the relationship between AI and researchers in data management?

    1. AI handles routine tasks while researchers focus on substantive decisions
    2. AI should manage data independently with minimal researcher oversight
    3. Researchers should delegate all technical decisions to AI
    4. AI and researchers share equal authority over all data management aspects

12. Why is compliance support important in AI-powered research data management systems?

    1. To eliminate the need for institutional review boards
    2. To allow AI to make legal decisions without researcher input
    3. To automatically approve all data sharing requests
    4. To ensure systems help researchers meet regulatory and policy requirements

13. What type of documentation would an AI most likely generate in research data management?

    1. Narrative interpretations of research findings
    2. Metadata describing data structure, variables, and collection methods
    3. Conclusions about whether data supports hypotheses
    4. Recommendations for future research directions

14. A research team collects data from human participants about their health behaviors. What must happen before AI can help manage this data?

    1. The research institution must adopt cloud storage
    2. The data must receive ethical review and approval
    3. Participants must sign contracts transferring ownership to the AI
    4. The AI must be trained on similar health datasets

15. What distinguishes routine data management tasks from substantive data choices?

    1. Routine tasks can only be performed by AI
    2. Substantive choices are less important than routine tasks
    3. Routine tasks take longer to complete than substantive choices
    4. Substantive choices require domain expertise and judgment