Definition of usability patterns for software interfaces applied to biodiversity and climate change management activities and a case of study applicable to seasonal pandemics

• Grant number: 16/04827-5
• Support Opportunities: Scholarships abroad - Research
• Start date: September 01, 2016
• End date: August 31, 2017
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Marcelo Morandini
• Grantee: Marcelo Morandini
• Host Investigator: Suzanne L. Allard
• Host Institution: Escola de Artes, Ciências e Humanidades (EACH). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Institution abroad: University of Tennessee (UT), United States

Abstract

Currently, usability principles that can be used to ensure the quality of a product that meets user expectations and bring quality to the presented solution are being considered as meaningful. Thus, developing a software product that meets the user expectations is crucial in any application domains. In particular, the monitoring of biodiversity and climatic conditions is one of the most important tools for the management of protected areas. So, for the data obtained to be effective and produce reliable results, it is necessary that each step of the process - collection, structuring, scanning, storage and access to data - must be performed using specific tools that minimize loss or distortion. Within this context, the general objective of this proposal is to generate a set of usability patterns to help the development of systems that support this application domain and can also be used for analyzes of pandemics caused by seasonal transmitting agents that depend on weather conditions, such as Aedes Aegypts. Still, it is intended to create guidance documents that can be used as a guide for developers of similar systems. To achieve these goals, we intend to use strategies and support tools to evaluate the usability of systems that support these monitoring activities. Finally, it is emphasized that the activities to be carried out will be supported by researchers involved in areas related to Human-Computer Interaction (HCI) and big data at the University of Tennessee and the Oak Ridge National Laboratory.