As the amount of scientific data continues to grow at ever faster rates, the research community is increasingly in need of flexible computational infrastructure that can support the entirety of the data science lifecycle, including long-term data storage, data exploration and discovery services, and compute capabilities to support data analysis and re-analysis, as new data are added and as scientific pipelines are refined. We describe our experience developing data commons-- interoperable infrastructure that co-locates data, storage, and compute with common analysis tools--and present several cases studies. Across these case studies, several common requirements emerge, including the need for persistent digital identifier and metadata services, APIs, data portability, pay for compute capabilities, and data peering agreements between data commons. Though many challenges, including sustainability and developing appropriate standards remain, interoperable data commons bring us one step closer to effective Data Science as Service for the scientific research community.
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