Think of AI data management as organising a digital library. Instead of cataloging digital books or files, you are cataloging prompts and models.
Effective AI data management is crucial for several reasons:
| Enhanced Research Quality | Good AI prompt management leads to better research outcomes, whether in traditional or AI-based studies. Both AI-assisted and traditional research benefit from clear insights and the ability to validate and understand findings. Properly managed data ensures that research is reproducible and verifiable |
| Building Trust and Credibility | Well-organised data builds trust and credibility within the research community. It ensures that research findings are consistent and impactful, which is essential for advancing knowledge and innovation |
| Efficiency and Focus | Effective data management allows researchers to focus on analysis and interpretation rather than spending time on data organisation. This efficiency supports more in-depth and meaningful research |
| Supporting Fairness and Integrity | Proper data management practices ensure that research is conducted fairly and ethically. It helps in maintaining the integrity of the research process by preventing data manipulation and ensuring transparency |
| Facilitating Collaboration | Organised data makes it easier for researchers to collaborate. Shared datasets and models can be easily accessed and understood by different team members, fostering a collaborative research environment |
| Compliance and Security | Managing AI data properly ensures compliance with data protection regulations and standards. It also enhances data security, protecting sensitive information from breaches and unauthorised access |
Best Practices for Effective AI Data Management:
|
Ensure Data Quality |
|
| Maintain Compliance |
|
| Lifelong Learning |
|
| Monitor and Evaluate |
|
AI prompt log template
|
Date |
Prompt version |
Purpose /context |
Instruction |
Input data |
Expected output |
Changes made |
Results |
Issues noted |
Next steps |
|---|---|---|---|---|---|---|---|---|---|
| 01/02/2025 |
v1.0 |
Initial analysis of early-stage research support services offered at ECU |
Categorise the types of support offered by ECU in early-stage research. |
Data from surveys of researchers (100 responses) |
Categorisation of support services into types: funding, mentorship, infrastructure, etc. |
None |
Clear categorisation of services offered by ECU |
Some overlap in mentorship and collaboration services |
Refine definitions for mentorship vs collaboration support |
|
02/02/2025 |
v1.1 |
Refined analysis to include satisfaction ratings from researchers |
Analyse the satisfaction levels of researchers with the support services. |
Survey data with researcher satisfaction ratings (100 responses) |
Sentiment analysis of satisfaction levels (positive, neutral, negative) |
Added a filter to focus on specific services mentioned in responses |
High satisfaction in funding, low satisfaction in infrastructure |
Clarify survey question related to "infrastructure" in next survey |
|
|
03/02/2025 |
v1.2 |
Assessment of gaps in research support based on analysis |
Identify gaps or unmet needs in early-stage research support services. |
Combined data from previous survey and usage analysis |
Report highlighting gaps in available services, with recommendations |
New analysis added comparing services against researcher needs |
Highlighted a lack of training workshops for researchers |
Plan new service offerings to address training gaps |
Finalise report and prepare for dissemination |
Edith Cowan University acknowledges and respects the Nyoongar people, who are
the traditional custodians of the land upon which its campuses stand and its programs
operate.
In particular ECU pays its respects to the Elders, past and present, of the Nyoongar
people, and embrace their culture, wisdom and knowledge.