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.
Applications were due August 16 and in November, Microsoft announced that eight researchers at institutions within the South Hub region will receive Azure credits. The researchers will also be able to take advantage of Azure services and tools to efficiently drive insights from data-driven research.
Research projects that will take advantage of the Azure credits include an effort to use Twitter data to develop a model that predicts public sentiment toward police over time (Graham MacDonald, Urban Institute); development of a compute-intensive graph mining system that divides graph mining jobs into individual tasks (Da Yan, University of Alabama at Birmingham); a project to build and test deep learning models for analysis of mobile text that measures covariates related health outcomes, such as trust, literacy, and anxiety (Ahmed Abbasi, University of Virginia), and an effort to develop a cloud-agnostic, large-scale data analytics framework (Claris Castillo, RENCI).
Projects from any discipline were considered for the awards, as long as the submission clearly articulated the data that the proposed research relied on, and the Azure-based analytics services that would be used.
Congratulations to all the South Big Data Hub Awardees:
- Prashanti Manda, assistant professor, department of computer science, University of North Carolina at Greensboro.
- Lisa Singh, professor, department of computer science, Georgetown University.
- Graham MacDonald, data scientist and senior manager for data technology and innovation, The Urban Institute.
- Ragib Hasan, associate professor, department of computer and information sciences, University of Alabama at Birmingham.
- Ahmed Abbasi, Murray Research Professor and director, Center for Business Analytics, University of Virginia.
- Da Yan, assistant professor, department of computer and information sciences, University of Alabama at Birmingham.
- John Craft, research assistant professor, department of biology and biochemistry, University of Houston.
- Claris Castillo, senior computational and networked systems researcher, RENCI, University of North Carolina at Chapel Hill.
Citizen Science Workshop photo courtesy of Secure World Foundation.
The proliferation of mobile devices and low-cost sensors has enabled citizens to collect timely geospatial information and contribute to scientific research and field work that addresses locally relevant, global environmental issues, including disaster management, food security and climate change. This collaborative exchange, in which citizens as well as scientists and policymakers, actively participate in the creation of new scientific knowledge, is called citizen science to contribute, together with scientists and policy makers, to address locally relevant, global environmental issues, including disaster management, food security and climate change. This collaborative exchange, in which citizens are active participants in the co-creation of new scientific knowledge, is known as Citizen Science.
Negotiating the Digital and Data Divide Workshop builds momentum for the series “Keeping Data Science Broad.”
Participants of the Negotiating the Digital and Data Divide Workshop, in front of the wall of challenges and visions used to collect ideas on the future of data science education.
This month, participants from universities across the nation, community colleges, tribal colleges, minority-serving institutions, nonprofits, and industry joined forces with the South Big Data Hub and Georgia Tech to confront the challenges of building data science capacity through traditional and alternative educational practices. Organized by Dr. Renata Rawlings-Goss, a co-executive director of the South Big Data Hub, the two-day workshop, sponsored by multiple directorates within the National Science Foundation, brought together a diverse mix of participants to navigate the complex issues of reforming data science education to prepare for the data-driven workforce of the future.
NSF’s Wendy Nilsen speaking at a South Big Data Hub Roundtable.
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.
Participants in the Orlando Smart Cities Hackathon take time out for a group photo.
By Dan Ellen
On August 26 and 27, programmers and software engineers convened in Orlando to push the boundaries of creativity, innovation, reality, and technology to build solutions and concepts that have the potential to make a difference in the Orlando community.
Called the Orlando Smart Cities Hackathon, the event aimed to support the city of Orlando in its efforts to become a smart city and also to demonstrate the city’s capabilities as it works to earn the title of “The Smartest City.” Orlando received two smart cities grant awards and is pursuing a variety of additional funding opportunities for smart cities initiatives that would help to enhance transportation citywide and beyond. In these pursuits, the city continues to move forward with building a data-driven infrastructure that will support safer, cleaner, and more efficient travel and an improved quality of life. Continue reading
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 Sahar Tavakoli
Our brains do an expert job of classification; it happens when we recognize people from their faces, categorize an object that we see, or predict the future state of an event. Proportional to the complexity of an input pattern, the classification can be easy (for example recognizing the difference between a cat and a dog) or difficult, such as predicting the probability of two people becoming friends in a social network. Continue reading
Panelists discuss data visualization at a recent workshop sponsored by the South and West Big Data Hubs.
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?
by Wenbin Zhang
As a first-year PhD student in information systems, I have been working on mobile health (mHealth) related research since the start of my PhD program. The growth of mHealth has facilitated better and instantaneous health communication, which was not previously possible. The capabilities of mHealth platforms promise to enhance healthcare quality and assist people in achieving healthy lifestyles at reduced costs. Attending the mHealth workshop organized by the South Big Data Hub and the National Consortium for Data Science (NCDS), located at the Renaissance Computing Institute (RENCI) deepened my understanding of mHealth, simply by having the chance to listen to and participate in intense discussions with an interdisciplinary group of mHealth and technology experts. Continue reading