What Do I See in This Data? Visual Tools to Enhance Data Understanding
What Do I See in This Data? Visual Tools to Enhance Data Understanding


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.


Max Novelli is a Software Engineer, Data Architect, and Data Manager with a “Laurea” degree from Politecnico di Milano, Italy. He currently works as “Head of Informatics and Data” at Rehab Neural Engineering Lab at the University of Pittsburgh.
His latest interests are in Big Data and its management, structure, visualization, and curation applied to research data -- although he never lets go any opportunity to play with hardware and customized experimental equipment. In his current position, he is responsible for the entire lab’s IT infrastructure and the safety, integrity, validation, and curation of experimental data. He is also leading R&D projects spanning from data visualization to data analysis and translating them into viable production tools. His focus is in developing visual tools to explore data structure and to assess the integrity of complex experimental data as well as using neural networks to further study and prove specific experimental results. He has been heavily involved in publishing open-access large datasets into public domain under the Open Science initiative of National Institutes of Health.
When Max is not lost in ""computer land,"" he enjoys spending time with his family and friends, mountain–biking, hiking trails, swimming, walking (better on the beach), cross-country skiing, eating good food, sipping good wines, and drinking good espresso. He also invests a considerable amount of energy practicing, teaching, and experimenting with yoga and body movement.  Lately, he has discovered rock climbing and is trying to perfect his climbing skills.

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