Summer 2008 Institute Home

This page contains information about the Summer 2008 Humanitarian FOSS Project Summer Institute.


Schedule and Activities



Abstract: VMOSS is a standalone version of the open source volunteer management module for the Sahana Disaster Management System. For certain volunteers who are participating in a disaster recovery effort, such as medical personnel, it is important that the system be able to validate their credentials. More generally, a VCS would be a useful feature for any volunteer management system. Working with a domain expert from the Institute for Crisis, Disaster and Risk Management (ICDRM) at George Washington University, this project will develop a customizable VCS that assists organizations that need to validate the professional credentials of their volunteers.

  • Credentialing
  • Wiki
  • ACL ?
  • Implement feedback from IBM workshop

Developers: Antonio (Team Leader), Dimitar, James, Eli.
Mentors: Frank Federich, Trishan de Lanerolle , Ralph Morelli

  • The deliverable for this project will be a deployable application with source code available on sourceforge.


Abstract:OpenMRS is an application which enables design of a customized medical records system with no programming knowledge (although medical and systems analysis knowledge is required). It is a common framework upon which medical informatics efforts in developing countries can be built. The system is based on a conceptual table structure which is not dependent on the actual types of medical information required to be collected or on particular data collection forms and so can be customized for different uses.

  • Project #1
  • Project #2

Developers: Rachel, Vinit
Mentor: Norman Danner, TBA from OpenMRS

  • Identify and make contribution to OpenMRS.


Abstract:Students and faculty from Trinity College and University of Hartford are building a prototype application tracking system for Literacy Volunteers of Greater Hartford (LVGH). This open source system monitors student use of various literacy tutorial applications, storing data in a common database that can be used by LVGH staff to analyze student performance and produce reports. The system will be designed to be usable by other LV organizations. The deliverable for this project will be a deployable application with source code available on sourceforge.

  • Develop pilot deployment for LVGH

Developers: Chris, Myles, Ernel, Sarah
Mentors: Khaiim Kelly, Carlos Espinosa, Ralph Morelli

  • Deploy application for LVGH Staff.


Abstract: POSIT is an Android application that assists rescue workers in searching for and identifying victims of a natural or man-made disaster. Using any handset that supports Android, rescue workers will be able map the locations of victims, store and transmit identifying information to rescue headquarters, photograph and tag victims, and perform other tasks that assist rescue and recovery missions. Another POSIT prototype that we are developing will help emergency medical technicians (EMTs) manage accident scenes. Here the need is to identify and classify the level of triage of accident victims and communicate this quickly to medical centers. During the summer the POSIT tool will also be designed to support scientific field research. Research projects in geology, botany, environmental science, and other areas would benefit from a hand-held device that helps field researchers identify, track, document and communicate findings. For example, an environmental scientist who studies environmental impacts on birds will help us design a tool that will assist in that application.

  • Stage 1: Prasanna (3-4 weeks) Prototype
  • Stage 2: Potential allocation after stage 1.

Developer: Prasanna(Initial)
Mentors: Vern Gillespie, Prof.Morison, Ralph Morelli
Deliverable: Prototype application to be deployed on the Android platform.

InSTEDD Summer 2008

Abstract:Participate in the design and implementation of methods and system to help professional groups in early warning identification, characterization, and response to health events from non-standard (unstructured) data streams (e.g., news reports, alert networks, blogs ,articles). You will use machine learning methods and techniques within social networking and collaboration context. You will work with the distributed InSTEDD team and a domain expert mentor using an agile methodology in the selection, implementation and trial of advanced algorithms to correlate, cluster, and predict characteristics of real-world events.

  • Machine Learning for early disease detection.

Developer: Juan Pablo, Qianqian
Mentors: Danny Krizanc, Nicholas, Ingrid Russell?
Deliverable: The results will include the design, software, and data – which will be piloted in a system for early detection used in South East Asia.