The problem: data and model interoperability
This page only contains a brief outline of its final contents, which will be completed in 2018.
As our world has grown more interconnected, society’s challenges have also become more complex – food, water, and energy security, human migration, natural disaster prediction and response, climate change, and beyond. These challenges demand scientific solutions that empower decision makers with models that draw on decades of scientific information, integrating diverse data in near real-time with high predictive power.
Such approaches could integrate the diverse measurements taken daily across our world – data from space-borne satellites, sensor networks spread across our cities and oceans, and social media and other crowdsourced data provided by people across the world.
Unstructured big-data approaches have been used with great success in fields ranging from internet search to advertising to shipping and health care. Yet the complexity of scientific data makes such approaches inappropriate for rigorous, predictive, multidisciplinary scientific models.
This leads to today’s status quo: low reusability of scientific knowledge, with knowledge growing too slowly to address urgent societal challenges.
Scientific models are frequently developed across diverse fields, but many are accessible only to their developers, and lack the transparency to be well understood by the public and decision makers.
Technical solutions exist for the problem of integrating scientific knowledge, but have often been developed and applied in piecemeal fashion. Innovative approaches, including open data repositories, collaborative modeling, and ontologies to address data interoperability challenges, are largely confined to narrow disciplines. Scientists have thus succeeded in improving knowledge reuse on a limited scale, but not in ways that will support predictive modeling of complex societal challenges.