collabraCode(); Team Products

Team Parse.ly

Anil PaisNachiCheung Liu
Team Members: Anil Pais, Nachiket Joshi, Cheng-Lun Li
We are working with Parse.ly on an interactive analytic feature which allows users to input and interact with big, complex data. Highlights of the feature includes comparing trends, filtering by subject/time range, and creating basic predictions.

Team Mad Libs

GiovanniLeninShanshan
Team Members: Giovanni de la Rosa, Lenin Simicich, Shanshan Yang
Our Mad Libs project strives to re-create the classic word game with a bit of modern flair. The objective is to build an interactive system from the ground up whereby “authors” provide a story and create the “tags” that must be filled in by a “player.” The team is focusing on features that improve the experience for both users while retaining all the characteristics that has made the game a popular pastime for decades. Some of these enhancements include allowing the author to create custom stories and tags, automatic word/phrase suggestions, reusable stories, and the resulting boundless replay value.

Team MantisBT

OmarKaiJudy
Team Members: Omar Stewart, Kai Mallea, Judy Panicker
Our team has been commissioned to convert the MantisBT interface design into a Web 2.0 design-and feel for the issue tracking system. Steps will be taken to integrate a rich, intuitive user experience while developing a refresh into the system design.

Team Mendeley

Dinyar MistryKennyByron requests that we do not show his real face
Team Members: Dinyar Mistry, Kenny Polanco, Byron Hamilton (requests not to be seen)
Mendeley is a reference manager and academic social network that helps researchers to organize research, facilitate collaboration and discover the latest research. collabraCode’s Team Mendeley is working with academics in the field of bibliometrics, as well as team members from Mendeley, to develop a tool using their API. Team Mendeley is constructing a new search page for academic papers that applies a custom algorithm and allows for dynamic “paper” ranking to facilitate the identification of relevant research.

Dynamic “paper” ranking will allow users to isolate relevant papers based on Mendeley’s user generated data. Mendeley users tag papers with key words in order to organize their research. These tags, when aggregated, provide insightful guidance on a paper that goes beyond term matching in titles, abstracts and bodies. Mendeley also collect readership data on papers that comes with geographic and academic status information. Team Mendeley is building a real-time, on page result sorting application controlled by tag manipulation and facilitated by visual displays of readership data on each paper to provide a robust solution for research discovery.

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