I’ve been building cyberinfrastructure and research software for computational social science and the study of complex adaptive systems at Arizona State University since 2006. Past and current projects include the Digital Archaeological Record, the Virtual Commons, the Social Ecological Systems Library, Synthesizing Knowledge of Past Environments (SKOPE), the Port of Mars, and CoMSES Net, where I serve as co-director and technical lead.

I also work to improve the state of open, transparent, reusable, and reproducible computational science as a Carpentries instructor and maintainer for the Plotting and Programming in Python and Good Enough Practices for Scientific Computing lessons, currently co-chair the Consortium of Scientific Software Registries and Repositories and Open Modeling Foundation Cyberinfrastructure Working Group, and serve on the DataCite Services and Technology Steering Group and CSDMS’s Basic Model Interface open source governance council.

As a data scientist, I employ a variety of ecoinformatic tools to understand and improve the sustainability of complex social-ecological systems.  I also apply Science and Technology Studies lenses to my modeling processes in order to see potential ways to make social-ecological system management more just.  I prefer to work collaboratively with communities on modeling: teaching mapping and modeling skills, collaboratively building data representations and models, and analyzing and synthesizing community-held data as appropriate. At the same time, I look for ways to create space for qualitative and other forms of knowledge to reside alongside quantitative analysis, using mixed and integrative methods.

Recent projects include: 1) Studying Californian forest dynamics using Bayesian statistical models and object-based image analysis (datasets included forest inventories and historical aerial photographs); 2) Indigenous mapping and community-based modeling of agro-pastoral systems in rural Zimbabwe (methods included GPS/GIS, agent-based modeling and social network analysis); 3) Supporting Tribal science and environmental management on the Klamath River in California using historical aerial image analysis of land use/land cover change and social networks analysis of water quality management processes; 4) Bayesian statistical modeling of community-collected data on human uses of Marine Protected Areas in California.

I am a marine environmental scientist by training (U Oldenburg, 2001) with a PhD in atmospheric physics (U Wuppertal, 2005) and a strong modeling focus throughout my career.

Archaological modeling

I have built models (C, C++) for understanding the regional transitions from hunting-gathering subsistence to agropastoral life styles throughout the world. The fundamental principle of these models is to consider aggregate traits of populations, such as the preference for a subsistence style. I applied these models to the European “Wave of Advance”, to the disintegration of the urban Indus civilisation and to the differential emergence of agropastoralism in the Americas versus Europe, but also globally. An interesting outcome of these models are global and reginoally resolved prehistoric CO2 emissions caused by the land use transitions.

Ecosystem modeling

I have built and applied models for understanding the ecological relations and biogeochemical flows through the North Sea ecosystem. Also for this research I apply trait-based models, looking at traits such as vertical positioning or energy allocation. As an outcome, I have, e.g., estimated the biomass of blue mussels in the North Sea and quantified the effect of Offshore Wind Farm biofouling on the sea’s produtivity.

Model coupling

I led the development of the Earth System coupler MOSSCO, leveraging ESMF technologies. I like to rip legacy models apart and reconstruct them with interoperability and reusability by design. I contribute to building the next-generation modular hurricane forecasting system.

Good scientific modeling practices

As a member of the Open Modeling Foundation (OMF), I am an evangelist of good scientific software practices, and educate and publish about improving underlying assumptions, stating clear purposes, keeping models simple and aquiring tools to further good practices.