In November 2017, the National Science Foundation’s Big Data Innovation Hubs sponsored a workshop in Versailles, France to discuss the formation of public-private partnerships in big data research among institutions in the United States and the European Union. Organized in conjunction with the Big Data Value Association, the PICASSO Project, and Inria, the workshop was the first of its kind to bring together international big data experts representing government, industry, and academia. Continue reading
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.
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.”
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.
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.
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
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?