Why is DART important?

The growing array of tools – powerful high-level programming languages, distributed data storage and computation, visualization tools, statistical modeling, and machine learning – along with a staggering array of big data sources, has the potential to empower people to make better and more timely decisions in science, business, and society. However, there remain fundamental barriers to practical application and acceptance of data analytics, any one of which could derail or impede its full development and contributions.

DART research will systematically investigate key aspects of these three barriers and develop novel, integrated solutions to address them by operating as a true, multi-institution, multi-disciplinary data science research center, in which faculty and students from campuses across the state work together on targeted problems important to the research community and the economy of Arkansas.

The 3 Integrative Questions (Barriers)

Big Data Management

Before data streams and datasets can be used in the many kinds of learning models, they are often manually curated, or at the least, curated for a specific problem. We still rely on hosts of analysts to assess the content and quality of source data, engineer features, define and transform data models, annotate training data, and track data processes and movement.

Security and Privacy

Government agencies and private entities collect and integrate large amounts of data, process it in real-time, and deliver products or services based on these data to consumers and constituents. There are increasing worries that both the acquisition and subsequent application of big data analytics are not secure or well-managed. This can create a risk of privacy breaches, enable discrimination, and negatively impact diversity in our society.

Model Interpretability

Machine learning models often sacrifice interpretability for predictive power and are difficult to generalize beyond their training and test data. But interpretability and generalizability of trained models is critical in many decision-making systems and/or processes, especially when learning from multi-modal and heterogeneous big data sources. There is a continuing to need to better balance the predictive power of complex machine learning models with the strengths of statistical models to better configure deep learning models to allow humans to see the reasoning behind the predictions.


Broadening Participation

The Track-1 project DART has convened a large group of participants including undergraduate and graduate students, early career and tenured faculty, administrators, staff, and K12 educators. We firmly believe in the importance of diversity, equity, and inclusion, and acknowledge the difference in those three terms.

To remain updated on best practices in broadening participation and mentorship, DART’s leadership team and external evaluator will conduct quarterly reviews of leading organizations such as Advancing Research in Society (ARIS) and the Center for the Improvement of Mentored Experiences in Research (CIMER), as well as resources such as the National Academies Science of Effective Mentorship in STEMM guide and the Institute for Broadening Participation Mentor Manual. These resources were used in the development of this plan.

Broadening Participation Efforts

  1. Mentorship Program
  2. DART Research Seed Grant Program
  3. Career Development Workshops
  4. DART Summer Undergraduate Research Experiences (SURE) Program
  5. Arkansas Summer Research Institute (ASRI)
  6. Broadening Participation Seed Mini-Grants

Participating Institutions

Project Component: Education Research Education & Research