Categories: SAGE Posts
SAGE Publishing believes that experimenting with cutting-edge technologies could lead to a range of innovative services and opportunities to build bridges to scholarly knowledge. SAGE Publishing Innovation Lab attempts to do that by harnessing new tech in short sprints, then publishing the results quickly so others can see, adopt, learn from or be inspired by the results.
“We aim,” said Ian Mulvany, SAGE’s head of product innovation, “for this site to be a window into the innovation that is happening across all of SAGE” for other organizations, researchers, librarians, students and societies.
Several completed experiments are already on view, ranging from a custom chatbot and work with Amazon’s chatty Alexa to several projects which specifically unlock potential in existing SAGE projects. Here are brief descriptions of those initial efforts:
- Tony the Study Coach Chatbot | We built a simple proof of concept chatbot using Google DialogFlowto understand whether it would be useful as an alternative way to search through the SAGE resources.
- Data Visualization in Virtual Reality | Our aim with this experiment was to find an inexpensive and quick way to try out a data visualization in VR. We wanted to learn more about the companies out there working on this, and to work through what some of the use cases might be for SAGE
- Using Keywords to Explore SAGE Research Methods | We explored the relationship between the key terms from SAGE Research Methodsusing visualization and classification tools to see if we could extract meaningful relationships between SRM in a given discipline, allowing users to understand their field better, find new methods, or find methods that are typically applied together in their discipline
- Alexa ‘Ask SAGE’ | We built a simple tool that listened to the phrase ‘ask SAGE to define [insert term]’, then retrieving the definition from the SAGE Research Methods ontology and read it out to the user.
- Computer Vision to Tag Images | We wanted to see what kind of information we can get out of our images by using some commercially available machine learning tools to do object recognition in images.