Lifting Industrial Ecology Modeling to a New Level of Quality and Transparency: A Call for More Transparent Publications and a Collaborative Open Source Software Framework. - Pauliuk, Stefan; Majeau‐Bettez, Guillaume; Mutel, Christopher L.; Steubing, Bernhard; Stadler, Konstantin (Journal of Industrial Ecology, 2015)
Industrial ecology (IE) is a maturing scientific discipline. The field is becoming more data and computation intensive, which requires IE researchers to develop scientific software to tackle novel research questions. We review the current state of software programming and use in our field and find challenges regarding transparency, reproducibility, reusability, and ease of collaboration. Our response to that problem is fourfold: First, we propose how existing general principles for the development of good scientific software could be implemented in IE and related fields. Second, we argue that collaborating on open source software could make IE research more productive and increase its quality, and we present guidelines for the development and distribution of such software. Third, we call for stricter requirements regarding general access to the source code used to produce research results and scientific claims published in the IE literature. Fourth, we describe a set of open source modules for standard IE modeling tasks that represent our first attempt at turning our recommendations into practice. We introduce a Python toolbox for IE that includes the life cycle assessment (LCA) framework Brightway2, the ecospold2matrix module that parses unallocated data in ecospold format, the pySUT and pymrio modules for building and analyzing multiregion input-output models and supply and use tables, and the dynamic_stock_model class for dynamic stock modeling. Widespread use of open access software can, at the same time, increase quality, transparency, and reproducibility of IE research.