OGC Standards – Driving Reproducibility of Scientific Workflows
Reproducibility is a current hot topic in science, international initiatives such as GEOSS (Global Earth Observation System of Systems), or production environments the Copernicus Climate Data Service or other public administration data distribution platforms. Recently the US National Science Foundation published a consensus study report on Reproducibility and Replicability in Science. In all cases, a once produced result needs to be reproducible at a later stage independently of new incoming, updated, or obsolete data, revised software libraries, or renovated data access protocols. Defined workflows shall operate despite changes in the underlying infrastructure. At the same time, the scientific understanding of our environment increases and results in adapted models and algorithms, data and service governance models change, and legal, commercial or scientific constraints and requirements evolve.
The FAIR principles, a set of guiding principles to make data Findable, Accessible, Interoperable, and Reusable, has gained momentum since their publication in 2016. Reusability in FAIR is closely related to reproducibility, as results in data-intensive science and their following relevance to society are often the result of knowledge discovery from appropriate scientific data, associated algorithms, and applied workflows. It is the combination of reusable data and re-applicable algorithms and processing workflows that form solid wisdom.
The Open Geospatial Consortium is the worldwide leading standardization body for geospatial data and services. OGC uses the FAIR principles as part of its mission to use location to connect people, communities, technology, and decision making for the greater good. The OGC Innovation Program is exploring in several research and development initiatives as well as Standards Program working groups how to enhance reproducibility through enhanced data representation, discovery, and access models. This session will highlight the latest achievements in this context, demonstrate how open standards boost interoperability and reproducibility using a series of Web APIs that provide stable state-of-the-art interfaces to data, services, and applications; cloud-based deployment and execution models for arbitrary applications that process (Big) data at their physical location; or metadata and discovery models that include data, services, and applications.
* Tomas Resnik: “(Semantic) Metadata as Keys for Reproducibility and Replicability of Geospatial Resources”.
* Tom Landry, David Byrns, Francis Charette-Migneault, David Caron, Mario Beaulieu and Samuel Foucher: “Packaging, deployment and interfacing of machine learning applications in scientific workflow environments”
* Heather McGrath, Laura Salisbury: “The Adaption of FAIR principles into the Government of Canada Open Water Data”
* Athina Trakas, Marie-Francoise Voidrot: “Reproducibility of Scientific Workflows through Standards. A field study.”
* Ingo Simonis: “Standards based software architecture components to facilitate reproducible scientific workflows”