Earlier this year, the South Big Data Hub partnered with Microsoft Research to offer researchers in the South Hub region the opportunity to apply for cloud credits on Azure, the comprehensive cloud services platform offered through Microsoft. The opportunity was designed to provide cloud computing resources to support data-intensive research projects.
Each day countless devices—from monitors in hospitals to diagnostic tests to Fitbits—capture huge amounts of health data. That data could change how patients and doctors interact, how diseases are diagnosed and treated, and the amount of control individuals have over their health outcomes.
But there’s a catch, says Wendy Nilsen, PhD, program director of the Smart and Connected Health Initiative at the National Science Foundation.
The data is plentiful, Nilsen acknowledged. The challenge, she said, is how to make that data easier to use, how to standardize it so it can be analyzed, how to scale it, keep it safe, and how to account for external factors such as the environment or a person’s genome.
Nilsen discussed these challenges and how to address them in a roundtable discussion hosted by the South Big Data Hub on October 14. Nilsen’s talk, titled “Smart Health and Our Future” provides an overview of the challenges that must be addressed as well as the ultimate goal: A system where patients use data to take more control of their health and where healthcare practitioners can use data from multiple sources to improve diagnoses and health outcomes.
To view the presentation slides, click here.
On August 28, Karl Schmitt, PhD, an assistant professor in the department of mathematics and statistics at Valparaiso University, attended the webinar Data Science Education in Traditional Contexts, hosted by the South Big Data Innovation Hub as part of its Keeping Data Science Broad: Bridging the Data Divide series. The webinar featured five speakers, including Schmitt, who is also the director of data sciences at Valparaiso. Each speaker talked about their own programs and experiences in data science education as well as some of the challenges involved in creating and implementing educational programs in a field that is still very new and in the process of being defined. Continue reading
By Eun Kyong Shin
The 2017 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2017) was held in Washington, DC, in July, and prominent fields applying social computing techniques include public health and healthcare. In early modern epidemiology, data collection processes relied heavily on painstaking manual labor. Data on a large scale was hard to obtain and resulted from careful observation and intensive recording. Since the introduction of the internet and advances in digital communication, massive amounts of dynamic data have accumulated exponentially. Along with the digitization of medical practices and other social data collection process, the nature of scientific discovery has been fundamentally changed. Continue reading
By Mark Schroeder
Throughout human history, stories have helped us make sense of sequences of events in our lives, infer cause and effect relationships, and share them with others. Just as our own memories are fallible and retelling stories can shape how we remember events, data can be fallible too. Its value is shaped by the process used to collect it and can be incomplete, incorrect, or biased in some fashion. How can we use data to gain true insights about the world and share them despite these challenges?
The American Association for Advancement of Science (AAAS) Science & Technology Policy Fellowship Big Data Affinity Group, in collaboration with the South Big Data Hub, West Big Data Hub, and The National Consortium for Data Science, are making this Friday’s data visualization and storytelling event available for virtual attendees. To learn more about the event, visit the website or read our earlier blog post announcing the event.
Data-Driven Storytelling: A Deep Dive into Visualization Techniques
July 14 | 9:00 AM – Noon ET | WebCast
Join the Webcast: http://bit.ly/datavizwebex
Event Number: 641 886 660 | Event password: dataviz
By Alex Cheng
I was honored to have the opportunity to attend the Mobile Health (mHealth) conference sponsored by the South Big Data Innovation Hub and the National Consortium for Data Science as a third-year graduate student in biomedical informatics at Vanderbilt University. My research focuses on using mHealth technology to improve the efficiency of outpatient clinic operations and the quality of care for patients. Continue reading
Reflections on the South BD Hub mHealth Workshop
By Chenzhang Bao
In recent years, mobile health (mHealth) has become one of the most popular health care movements for patients and providers. Consumers have embraced the use of mHealth applications in their daily lives through wearable devices, and use these apps to monitor their exercise routines, heartbeats, and sleep quality. The use of mHealth apps is critical for research into new mechanisms designed to improve the quality of patient engagement; a factor that has previously been hard to measure or even unobservable to providers. One important research question looks at the relationship between patients’ usage of mHealth devices, their engagement in their own health and the future health outcomes. Continue reading
When we launched the Big Data Innovation Hubs at the end of 2015, we could never have imagined that our mission of “breaking barriers, bridging solutions, and accelerating partnerships,” intense but rewarding work, would yield over 800 members—many of whom actively contribute to Hub communities of practice, dozens of productive partnerships, several funded new projects, and nearly 20 workshops. A year and a half later, on Friday, June 9, 2017, more than 75 people from across sectors and disciplines—academia, government, nonprofits, and industry—met at the Microsoft Chevy Chase Pavilion near Washington, DC, to assess the progress of the South Big Data Hub, and shape its future. It was a day of catching up on current efforts (some of which began at the first all-hands hub meeting), and sparking new collaborations.
Microsoft Research understands that taking full advantage of big data and new data technologies requires more than developing new tools and technologies. To paraphrase Vani Mandava, director of data science for the research arm of the tech giant, it requires cross-disciplinary research that extends well beyond computer science, and collaboration among domain science experts, computing and data science specialists, and industry leaders in technology and other verticals.