Ai+ Training

At the Rehab Neural Engineering Labs (RNEL), we collect multi-dimensional, multi-modal, multi-format data from a multitude of cutting-edge neuroscience and neural engineering experiments, such as developing brain-computer-interfaces to control robotic limbs in a subject with   spinal cord injury, and enhancing natural sensory feedback in neuroprosthetic limbs. A primary challenge is managing the collected data.
With a critical focus on the 4V’s of Big Data (volume, velocity, variety, and veracity), we have been investing considerable resources to lay the foundation for better management of these data and for the implementation of an efficient and accessible data management system.
A lack of transparency to the foundation of the data structure can prevent users from completely understanding the full range of data descriptors within the datasets, leading to large-scale data duplication or sub-optimal usage. These situations can degrade appropriate curation and effective use of the datasets. Users are often contended with a steep learning curve in familiarizing themselves with a large and constantly evolving data structure. The traditional data dictionary models are not helpful in such a dynamic landscape.
Data visualization techniques based on readily-available libraries like d3.js and plotly can provide a highly effective means for exploring this underlying structure and facilitating better understanding. In our lab, we have produced multiple proof-of-concept multi-dimensional visualizations of complex datasets that can address the issues mentioned above.
This talk will illustrate selected use-cases that describe the process from design to deployment. Additionally, the talk will highlight our efforts in exploring different data visualization tools and engineering the best tool to help our diverse group of data managers, data curators, and researchers validate and select the right data for their analyses to produce more insightful papers that are enhanced by informative interactive visualizations.