Research Guides: Research Data Management (RDM): Generative Artificial Intelligence (AI) and Research Data Management (RDM)

Introduction & Applications

Generative AI is revolutionizing Research Data Management (RDM) by automating time-consuming tasks like data cleaning, metadata generation, and organization. It allows for the creation of synthetic datasets, which preserve privacy while reflecting real-world patterns, streamlining processes like data sharing and analysis. This can lead to increased efficiency, improved data integrity, and fewer human errors.

However, there are challenges, including bias in AI models, which can lead to skewed data, and concerns around privacy and re-identification when using synthetic data. Additionally, AI models often act as "black boxes," lacking transparency, which can complicate reproducibility in research. Data quality also plays a crucial role—poor input data can result in unreliable AI outputs.

In the coming years, generative AI will likely transform RDM, but its success will depend on addressing these challenges while leveraging its strengths to enhance efficiency and accuracy in data management. This transformation requires careful consideration of ethical, legal, and technical aspects to ensure responsible and effective use in research.

Applications of Generative AI in RDM

CEDAR
The CEDAR (Center for Expanded Data Annotation and Retrieval) platform helps researchers create, manage, and share standardized metadata for datasets. It simplifies the process of ensuring metadata consistency and completeness, making data more discoverable and reusable. By providing templates and integrating with repositories, CEDAR streamlines metadata annotation, improving research data management and compliance with community standards.
DataSynthesizer
DataSynthesizer is a Python library that generates synthetic datasets for data privacy preservation. It simulates data that maintains the statistical properties of real datasets without exposing sensitive information. The tool supports three data synthesizing modes: random mode, independent attribute mode, and correlated attribute mode—allowing users to generate synthetic data that mirrors the original dataset in varying levels of detail. This resource is especially useful for researchers who need to share data while protecting sensitive information.
OpenRefine
OpenRefine is a powerful open-source tool for data cleaning and transformation. It helps researchers work with messy data, offering features like detecting duplicates, filtering out errors, and reformatting datasets for consistency. It supports various data formats, including CSV, JSON, and XML, and can even be used for extending data by fetching information from web services. While it's highly versatile, the main limitations are its learning curve and limited capabilities for large-scale datasets.

Guides

UHasselt guidelines on Generative Artificial Intelligence (GenAI)
In development. Visit site for more information.
Responsible use of GenAI in Research
The Deakin University Generative AI in Research Guide provides an overview of how generative AI tools can be applied in academic research while addressing ethical considerations. It highlights practical uses like data analysis, text generation, and image creation, as well as the challenges posed by bias, transparency, and intellectual property rights. The guide also offers recommendations on responsible AI use and further resources to help researchers understand the potential and limitations of generative AI in research.
Berkeley Lab | Generative AI Tools in Research
The Lawrence Berkeley National Lab's Generative AI Tools in Research guide addresses the opportunities and challenges of using generative AI in scientific research. It emphasizes ethical considerations, including the risks of bias, intellectual property issues, and transparency in AI-generated content. The guide encourages responsible AI use while providing resources to help researchers navigate these concerns.

Ethical Considerations

The use of Generative AI in research introduces significant ethical considerations. Key issues include the potential for bias in AI-generated outputs, the need for transparency in disclosing AI use, and the importance of respecting intellectual property rights. Researchers must also consider the accuracy and reliability of AI-generated data or content and ensure that their methodologies are clearly documented. Ethical AI use is essential for maintaining the integrity, honesty, and fairness of research.

Generative AI in research can unintentionally perpetuate bias and racism, particularly if the AI models are trained on biased datasets. AI systems may reinforce historical inequalities by replicating patterns of discrimination present in the training data, leading to skewed research outcomes. To mitigate this, researchers must ensure diverse and representative data is used to train AI models, actively monitor for biases, and remain transparent about the limitations of AI-generated results.

Tools & Guides

AI Ethics Lab
AI Ethics Lab offers guidance on integrating ethics into AI projects. The lab collaborates with organizations to tackle ethical challenges posed by AI development and implementation, focusing on areas such as fairness, transparency, accountability, and bias mitigation. They provide consulting, workshops, and resources to help developers and researchers navigate the ethical implications of AI systems across industries.
AI Fairness 360
A comprehensive toolkit that includes algorithms and metrics to detect and mitigate bias in AI models. It offers tutorials and code examples for assessing fairness across different datasets and scenarios.
What-If Tool
The What-If Tool is an interactive visual interface that helps researchers understand how machine learning models behave with different data inputs. It enables users to explore model performance, identify biases, and experiment with counterfactuals, making it easier to detect and address potential fairness issues. Integrated with TensorFlow, the tool supports a wide range of models and provides intuitive visualizations to help the researcher analyze results without needing to write code.

Research Data Management (RDM)

National Institute on Artificial Intelligence in Society

Articles

Introduction & Applications

Ethical Considerations

Further Reading & Resources