FAIR and Science-Ready Data of Returned Samples

The study of extraterrestrial samples returned to Earth by space missions or as meteorites is an integral part of research in the planetary sciences. Chemical, physical, and textural properties of these samples provide fundamental insights into the history of the Solar System and natural processes beyond Earth. With an increasing number of global planetary missions and new opportunities  in data analytics, there is an urgent need to agree on consistent practices and technical standards for digitally curating returned samples and disseminating and archiving the data generated by laboratory analysis of the samples. Such practices and standards should to the degree possible align with existing  data standards for planetary data as well as for scientific samples across domains and with best practices for laboratory analytical data of geoscience samples.

 

The Astromaterials Data System (Astromat, https://www.astromat.org) is the primary NASA-sponsored data archive for laboratory analyses of returned samples from planetary missions (e.g., Apollo, OSIRIS-REx) and for meteorites. Astromat has been working with various international organizations, programs, and initiatives to establish data, metadata, and curation standards, best practices, and policies that optimize findability and reusability of the data hosted and archived at Astromat. Ongoing activities that we will report on include the implementation of the IGSN as a persistent identifier for samples in collaboration with the IGSN e.V. and DataCite; the development of TAPPs (Technique-aligned Protocol Profiles) jointly with the OneGeochemistry initiative and based on the OSIRIS-REx Sample Analysis Data Standard Documents; and collaboration with the Planetary Data System to develop PDS4-compliant Data Dictionaries for sample data. Laboratory analytical data for astromaterials samples are also integrated into a database that aggregates and harmonizes compositional data and provides APIs for machine-actionable access to large volumes of analytical data that have so far been highly fragmented and difficult to use in computational methodologies.