SESAR: AN ONLINE SOLID EARTH SAMPLE REGISTRY FOR UNIQUE SAMPLE IDENTIFICATION

The study of solid earth samples is fundamental to our understanding of Earth’s dynamical systems and evolution. Many different data types of geological samples contribute to our knowledge of the Earth’s history: age determinations, chemical compositions and isotope ratios of rocks and their mineral components, descriptions of lithology, texture, mineral or fossil content, and physical property measurements. The usefulness of these data is critically dependent on their integration and increases exponentially when the various data types can be analyzed as a coherent data set.

As the Geoscience community is now taking advantage of the rapid progress in information technology to build a digital data and knowledge system that will allow the sharing and integration of data across disciplines, it becomes essential, even unavoidable to use unique identifiers to reference specimens unambiguously. Currently, different samples are often given identical names, and different names are given to the sample splits, as they are passed from investigator to investigator to undergo new analyses.

We will present the Solid Earth SAmple Registry (SESAR), which addresses the urgent need for unique sample identifiers that are crucial for the development of a Geoscience Cyberinfrastructure. The project will build a web-based digital registry for solid earth samples that will provide for the first time a way to uniquely name and identify samples on a global scale, along with the generation of barcodes for sample labeling. We will show the prototype of the registry, demonstrating its structure, user-friendly web interface, and functionality, and outline future plans for further enhancement of the system, pertaining to interoperability within the Geoscience Cyber-infrastructure.

The unique identifiers will dramatically advance interoperability among existing and emerging data and information systems for sample-based data such as CHRONOS, EarthChem, SedDB, PaleoStrat - to name a few, which are currently seriously hampered in their potential to link disparate data on samples by the inability to uniquely identify them. SESAR will allow wide ranges of sample-based data types to be linked and integrated, opening an extensive range of new opportunities for discovery and for interdisciplinary approaches in research.