Analytical studies of astromaterials samples returned by NASA space missions generate unique and highly valuable data that contribute fundamentally to our knowledge and understanding of the origin and evolution of Earth, our solar system, and the universe. These data need to be openly accessible and curated in a manner that maximizes their reuse in and utility for future science and that ensures their quality and long-term preservation. In several recent strategic documents and reports, NASA recognizes this need [1] and is adjusting its science information policies [2]. In 2020, NASA charged the Planetary Data Ecosystem Independent Review Board (PDE-IRB) to conduct a review of the planetary data landscape and make recommendations for improving access to and use of planetary science data by the science community [3]. This presentation will highlight features and services of the Astromaterials Data System that align the IRB’s recommendations. The Astromaterials Data System (Astromat) is a data infrastructure that has been funded by NASA since 2018 to curate, archive, and publish analytical data that are generated from astromaterials samples collected by NASA missions and curated at the Johnson Space Center in the Astromaterials Research & Exploration Science Division. Astromat’s mission is to: preserve astromaterials data and ensure their long-term access and reusability for new science endeavors; restore legacy data of astromaterials samples acquired in the past; synthesize historic and new data into a comprehensive, analysis-ready data store that allows scientists to use new technologies such as Machine Learning and Artificial Intelligence to explore and mine these data in previously impossible ways. Astromat operates a data repository where researchers can deposit their data for archiving and publications, specifically to comply with new journal policies and guidelines for Open and FAIR data and Data Management Plans required by funders. The repository follows international best practices. Astromat also maintains the Astromat Synthesis, a relational database that integrates legacy and new data into a harmonized data collection that allows users to find and extract data at the granularity of individual analytical measurements and combine these into customized new compilations for advanced data analysis. [1] SMD’s Strategy for Data Management and Computing for Groundbreaking Science 2019-2024. [2] Scientific Information policy for the Science Mission Directorate, SMD Policy Document SPD-41 (August 2021). [3] Besse, S., et al. (2021). LPI Contributions 2549, 7070.

Titan’s surface icy shell is likely composed of water ice and methane clathrate [1, 2]. Methane clathrate may play a role in Titan’s methane cycle [3–5] affect Titan’s thermal profile [6] , and may affect the habitability of Titan’s ocean. Although the bulk properties of clathrates are similar to those of pure water ice, the thermal conductivity of methane clathrate is about 20% the value for pure water ice [7, 8]. The lower thermal conductivity acts to insulate Titan’s icy shell, changing the thermal profile of Titan. As seismic wave speeds [9, 10] and attenuation [11] are dependent on temperature, any changes to the thermal profile will result in changes to seismic waveforms recorded by seismic instrumentation. Here, we compare the seismic waveforms of model with a 100 km thick pure water ice shell, versus a model with a 10 km clathrate lid over 90 km of pure water ice. Our results have implications for the upcoming Dragonfly mission, which will carry seismic instrumentation as part of its payload [12]. Methods: We use PlanetProfile [13] to create interior structures models of a pure water ice shell and a model with a pure water ice shell with a 10 km clathrate lid. The interior structure models are used as inputs with AxiSEM [14] and Instaseis ([15] to generate seismic waveforms. We interpret the results to quantify the differences in seismic velocities, arrival times of seismic phases, and amplitudes of seismic waveforms at the surface of Titan. Results: The interior structure models show a clathrate lid will reduce the conductive lid thickness by ~ 2/3 compared to the pure water ice shell model. As a result, the clathrate lid model reaches higher temperatures at shallower depths (Figure 1a). The temperature profile affects the seismic velocity (Figure 1b), and the seismic quality factor (Q, Figure 1c) profiles. A clathrate lid creates a steeper negative gradient in seismic velocities and Q. The greatest difference in seismic velocities occurs at the base of the clathrate lid (Figure 2). Because of the change in seismic velocities, the arrival times and observable distances of seismic phases will be different between the two models. Using TauP [16], we calculate the differences for several seismic phases. We find that the change in seismic velocity profile results in a difference of a few seconds at most in arrival times. The range of observable distances will also vary by a few degrees. The small changes might be noticeable on waveforms, but would require high signal to noise ratios, and precise determinations of location and depth of the event. The changes in seismic velocities and Q will also impact the observed ground motion. Using AxiSEM and InstaSEIS, we create a database of seismic waveforms spaced 1 degree in epicentral distance. We compare the same event magnitude and distance between source and seismometer for the two models. For each waveform we calculate the root mean square (RMS) using ground acceleration.