P31A-2044
An integrated and accessible sample data library for Mars sample return science

Wednesday, 16 December 2015
Poster Hall (Moscone South)
Michael L Tuite Jr and Kenneth H Williford, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States
Abstract:
Over the course of the next decade or more, many thousands of geological samples will be collected and analyzed in a variety of ways by researchers at the Jet Propulsion Laboratory (California Institute of Technology) in order to facilitate discovery and contextualize observations made of Mars rocks both in situ and here on Earth if samples are eventually returned. Integration of data from multiple analyses of samples including petrography, thin section and SEM imaging, isotope and organic geochemistry, XRF, XRD, and Raman spectrometry is a challenge and a potential obstacle to discoveries that require supporting lines of evidence. We report the development of a web-accessible repository, the Sample Data Library (SDL) for the sample-based data that are generated by the laboratories and instruments that comprise JPL’s Center for Analysis of Returned Samples (CARS) in order to facilitate collaborative interpretation of potential biosignatures in Mars-analog geological samples.

The SDL is constructed using low-cost, open-standards-based Amazon Web Services (AWS), including web-accessible storage, relational data base services, and a virtual web server. The data structure is sample-centered with a shared registry for assigning unique identifiers to all samples including International Geo-Sample Numbers. Both raw and derived data produced by instruments and post-processing workflows are automatically uploaded to online storage and linked via the unique identifiers. Through the web interface, users are able to find all the analyses associated with a single sample or search across features shared by multiple samples, sample localities, and analysis types. Planned features include more sophisticated search and analytical interfaces as well as data discoverability through NSF's EarthCube program.