註釋 A series of interviews were performed with organizations with proven experience in identifying, aggregating, parsing, and analyzing large amounts of biologically focused data. The aim of the interviews was to generate an understanding of the best practices for creating and sustaining the knowledge engine which will become the backbone of the Global Burden for Animal Diseases program, which is focused on evaluating the disease burden in production animals to improve our relationship with these animals and build a more sustainable world. Five interviews were conducted across a range of topics from basic organizational structure, the types of data used in the system, methods for achieving a sustainable program, to how the organizations might do things differently if given the opportunity. While no “one size fits all” approach was identified which would serve as a structure for any new data aggregation system, several key factors emerged as key to a successful program. A core group of people with the proper motivation and a multidisciplinary set of skills was universally promoted as a key to success and longevity, along with embracing “open data” standards, and being flexible with program goals as they related to the ongoing need for sustained funding. 