Abstract   One of the challenges of computational-centric research is to make the research undertaken reproducible in a form that others can repeat and re-use with minimal effort. In addition to the data and tools necessary to re-run analyses, execution environments play crucial roles because of the dependencies of the operating system and software version used. However, some of the challenges of reproducible science can be addressed using appropriate computational tools and cloud computing to provide an execution environment.  Here, we demonstrate the use of a Kepler scientific workflow for reproducible science that is sharable, reusable, and re-executable. These workflows reduce barriers to sharing and will save researchers time when undertaking similar research in the future.  To provide infrastructure that enables reproducible science, we have developed cloud-based Collaborative Environment for Ecosystem Science Research and Analysis (CoESRA) infrastructure to build, execute and share sophisticated computation-centric research. The CoESRA provides users with a storage and computational platform that is accessible from a web-browser in the form of a virtual desktop. Any registered user can access the virtual desktop to build, execute and share the Kepler workflows. This approach will enable computational scientists to share complete workflows in a pre-configured environment so that others can reproduce the computational research with minimal effort.  As a case study, we developed and shared a complete IUCN Red List of Ecosystems Assessment workflow that reproduces the assessments undertaken by Burns et al. ( 2015 ) on Mountain Ash forests in the Central Highlands of Victoria, Australia. This workflow provides an opportunity for other researchers and stakeholders to run this assessment with minimal supervision. The workflow also enables researchers to re-evaluate the assessment when additional data becomes available. The assessment can be run in a CoESRA virtual desktop by opening a workflow in a Kepler user interface and pressing a “start” button. The workflow is pre-configured with all the open access datasets and writes results to a pre-configured folder.
['Siddeswara Guru', 'Ivan Hanigan', 'Hoang Anh Nguyen', 'Emma Burns', 'John Stein', 'Wade Blanchard', 'David B. Lindenmayer', 'Tim Clancy']
Development of a cloud-based platform for reproducible science: A case study of an IUCN Red List of Ecosystems Assessment