With OpenTeams, teams can visualize their network of interactions, and also, cross this data with information on a teams demographic, professional, and psychological characteristics.
The use of OpenTeams is completely voluntary. Users that opt-in can delete their data at any time by using the logout and delete option.
If you are interested on using OpenTeams in a secure setting, please use the opensource code to spin your own private instance of OpenTeams
How to use OpenTeams?
To use OpenTeams users need to login first to create their personal network.
This will allow them to visualize their own network of interactions, and answer surveys.
Unlike other online platforms, which provide a one-way street for personal data, users of OpenTeams can delete their data at any time by using the “Logout & Delete” option. OpenTeams also does not collect the content of emails, but only its headers (From, To, CC, and Timestamp).
After creating a personal network a user can create a team. Teams are organized around rooms, with unique user defined URLs.
To create a room, just define a room name and create a password for your room.
Then sign in to your room using the account you created to create your personal network.
Other users can then join the team by using the same room name and password in the join team option.
OpenTeams provides a variety of analytical capabilities.
Or you can visualize the network of the organizations that the team is interacting with, and see who is holding each relationship.
After completing multiple built-in surveys, users can click on the “Analytics” option to see a detailed analysis of the team’s demographics, psychology, and network characteristics.
The demographic panel presents information about a team’s gender and cultural diversity.
The cognitive and professional diversity panel shows information about a team’s academic background and experience.
The psychology tab visualizes information about the personality and moral psychology of team members (collected in the platform using validated psychological surveys).
The centrality tab shows information on the evolution of the network centrality of the team.
The scatter tab allows users to explore correlations among different network and personality characteristics.
The relationships tab shows the volume of communication between each pair of team members.
The response time tab shows the response times between pairs of members of a team.
The volume tab shows how much communication is done by each team member, as a function the time of the day, or day of the week.
Interests: Visualization, Data Analysis, Human-Computer Interaction
Jingxian is an engineer who is passionate about data. OpenTeams is Jingxian's master thesis at the MIT Media Lab. Prior to MIT, Jingxian studied at the University of Illinois at Urbana-Champaign and the University of Science and Technology of China.
Interests: UX design, Cognitive Science, Human-Computer Interaction, Data Visualization
Xiaojiao Chen is a user experience designer and user interface designer who studies visualization methods applied in the Big Data era. She is a Ph.D. candidate at School of Mechanical Engineering in Southeast University of China and a visiting student at the MIT Media Lab - Collective Learning Group.
Interests: Social Psychology, Collective Learning, Social Cognition, Personality
Diana is a social psychologist that studies how people perceive their social world. She has been mainly interested in how people form impressions about others and how variables such as power affect those impressions. She completed her PhD at University of Lisbon and is currently a postdoctoral associate at MIT Media Lab.
Interests: Collective Learning, Complexity, Knowledge Diffusion, Data Visualization
César Hidalgo is a scholar and entrepreneur who studies the complexity and diffusion of knowledge. He has lead the creation of multiple award winning visualization projects, such as DataUSA and DataChile. His latest book is Why Information Grows.