NSF Award
2411034
Forecasting how our environment will change into the future requires the scientific community to understand the processes that shape Earth's surface environments through time. To do this, geoscientists collect images of Earth with satellites, run simulations, and test hypotheses with laboratory experiments. All of these methods improve our understanding of landscape change, but scientists using each of these tools struggle to bring their research together to make new insights. This project establishes a framework of interoperable hardware and software tools, called sandpiper, that enables research products from different teams and approaches to integrate with one another more easily than ever before. Major efforts of the project team include (1) designing and implementing an affordable open-source hardware-firmware system for data acquisition, (2) forging a community-backed data standard, (3) developing a flexible and interoperable data-analysis software library, and (4) establishing a sustainable community of practice. The project team is also advancing science and technology education by creating science museum exhibits that demonstrate fundamental principles in geomorphology and reach a wide audience through an interactive web interface.<br/><br/>Recent strides in geomorphology have been fueled by widely available satellite imagery, powerful numerical modeling toolkits, and decades of physical laboratory experiments. Customized algorithms lie at the heart of the discipline because raster data—e.g., photographs, topography—form a fundamental bridge between these complementary modes of inquiry. Transformative insights can arise when researchers apply tools from one mode of inquiry to data from another. However, most innovation at the forefront of geomorphology currently proceeds in silos via ad-hoc algorithms that accumulate “mutations” as they traverse laboratories and graduate-student generations. The problem is particularly acute for experimental geomorphology, where technological barriers have prevented FAIR (Findable, Accessible, Interoperable, Reusable) and OS (open-source) principles from integration into the research process. At present, there is no unifying framework to support collaboration between modelers, observationalists, and experimentalists. The team for this project is creating such a cyberinfrastructure framework and solving these problems at every level. (1) To break down experimental silos, the project team is designing and implementing a modular and extensible open-source hardware–firmware system to affordably and uniformly make measurements and generate reproducible data products in labs across the world. (2) To promote and simplify data sharing, the project team is organizing a community effort to forge a data standard. (3) To mitigate algorithm drift, the project team is developing a flexible analysis library that integrates with this data standard. (4) To establish a community of practice, the project leaders are engaging researchers in their own laboratories and computing environments to facilitate reusing and contributing algorithms to the library. This acquisition-to-analysis toolchain, called sandpiper, will enable the next generation of collaborative research in geomorphology, sedimentology, and stratigraphy; advances could also influence seemingly unrelated fields like dendrochronology, hydrology, and seismology.<br/><br/>This award by the Office of Advanced Cyberinfrastructure is jointly supported by the National Discovery Cloud for Climate initiative within the Directorate for Computer and Information Science and Engineering and by the Geosciences Directorate’s Research, Innovation, Synergies, and Education and Earth Sciences divisions.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
