Traditionally, in biodiversity studies, the scientists who collected the data were also the people who analyzed it and published the associated scientific papers. The advent of DiGIR and Species Analyst software by Vieglais and collaborators, however, spawned a revolution in how biodiversity studies were undertaken. It became possible for scientists to electronically collate data from disparate museum collections and spurred the growth of niche modeling. This new approach helped create the Global Biodiversity Information Facility (GBIF) and programs such as iDigBio. The GBIF portal now serves as a directory to open source biodiversity data from museums, government agencies and non-governmental organizations (NGOs) around the world. In addition, it allows for the complete separation of data collection and data analysis. However, this separation of data collector and data analyst has generated a new concern. How can GBIF and data analysts have confidence in the data especially when it comes from a variety of sources? The traditional answer has been using data quality measures but studies in other fields such as social science, psychology, economics and computer science show that trust also plays a role. Here we develop a model that includes both elements of data quality and trust to understand the confidence one might have in biodiversity data. This modeling effort can lead to new policies that increase the perception of data quality and the knowledge among data suppliers, data aggregators such as GBIF data and data analysts. The purpose is find ways to increase the use of biodiversity data from aggregators to meet conservation goals.

DiGIR = Distributed Generic Information Retrieval