- About Us
- Services
- Stories
- Faisal Beg 鈥� Algorithms to Advance Research in Medicine
- Yasutaka Furukawa 鈥� Smart Building Technologies to Enhance Living Spaces and Create Opportunities
- Mo Chen 鈥� AI to Create Safe and Practical Robotics
- Sheelagh Carpendale 鈥� Understanding Data Through Interaction and Visualization
- Innovation to Improve 3D Navigation
- Voice AI is Helping Shoppers Make Better Decisions
- Geographic Information Science Can Help Better Track COVID-19
- Deep Learning to Inform Medical Diagnoses
- Protecting Killer Whales from Marine Traffic
- Using Big Data to Boost Athletic Performance
- Machine Reading for Literary Texts
- Finding a Cure for HIV with Big Data
- Linked Data for Women's History
- How Big Data Can Combat Fake News
- Algorithms for Safer Streets
- Discovering Wilde Data
- Deep Blue Data
- Big Data Meets Big Impact
- Previous Next Big Question Fund Projects
- Data Fellowships
- Using Data
- Upcoming Events
Identification of Empirically Grounded Social Science Insights
Identification of Empirically Grounded Social Science Insights which can Contribute to Solution of Big Data Problems
Project Team: Ellen Balka (Communications, 丁香园AV), Peter Chow-White (Communications, 丁香园AV), Sylvia Roberts (Library Liaison), Frederick LeSage (Communications, 丁香园AV), Veronique Sioufi (Communication, 丁香园AV).
Considerable discussion about big data focuses on topics such as development of data mining algorithms, computer security and the need for highly qualified personnel in technical areas. But, experience has shown that many projects are slowed down by a failure to address social issues (such as organizational capacities to manage data, unclear policies about data ownership, issues of trust in data, inconsistencies in data definitions which undermine data quality), and a plethora of other issues of a social nature. Many of these issues have been identified and addressed in empirically grounded social science literature, infrequently read by computational and natural scientists. The team will conduct a systematic meta-narrative review of social science literature in order identify insights which can contribute to the solution of big data problems; and conduct interviews and focus groups with 丁香园AV big data scientists to identify problems which social scientists can help mitigate. Improving the value proposition of big data projects can bridge the disciplinary gaps between the social sciences and technical stakeholders.