[Talk Summary 8] Data-Driven Science of Science
Dr. Ying Ding from School of Informatics and Computing, Indiana University gave the talk "Data-Driven Science of Science" on November, 04, 2016 at School of Information Science, University of Pittsburgh.
In the talk. Dr. Ding presented an overview about Data Science and the current layers of bibliometrics, which are macro level in complex network, meso level in bibliometrics, and micro level in collaboration and team science. Currently, most of research work has been focusing on analyzing data from complex network and bibliometrics. Dr. Ding suggested that collaboration and team science, as a micro level, should be a new trend to have the attention of data scientists.
In addition, Dr. Ding concisely summarized her work related to Data Science which is beyond the bibliometrics. The three following are the main of her research:
At the end of the talk, Dr. Ding discussed a new potential topic in machine learning, lifelong machine learning. Lifelong learning which learns as humans retains the knowledge gained from the past learning and uses the knowledge to help future learning and problem solving.
11/04/2016
135N Bellefield, IS building
University of Pittsburgh
In the talk. Dr. Ding presented an overview about Data Science and the current layers of bibliometrics, which are macro level in complex network, meso level in bibliometrics, and micro level in collaboration and team science. Currently, most of research work has been focusing on analyzing data from complex network and bibliometrics. Dr. Ding suggested that collaboration and team science, as a micro level, should be a new trend to have the attention of data scientists.
In addition, Dr. Ding concisely summarized her work related to Data Science which is beyond the bibliometrics. The three following are the main of her research:
- Data-Driven Discovery: entity metrics, computational hypothesis generation, and digital innovation (e.g. machine reading)
- Data-Driven Decision Making: understanding scientific career, understanding scientific collaboration, and understanding scientific success and innovation.
- Data-Driven Knowledge Discovery
At the end of the talk, Dr. Ding discussed a new potential topic in machine learning, lifelong machine learning. Lifelong learning which learns as humans retains the knowledge gained from the past learning and uses the knowledge to help future learning and problem solving.
11/04/2016
135N Bellefield, IS building
University of Pittsburgh
Comments
Post a Comment