Introduction:  SPICE is an information system the purpose of which is to provide scientists the observation geometry needed to plan scientific observations and to analyze the data returned from those observations. SPICE is comprised of a suite of data files, usually called kernels, and software -mostly subroutines [1]. The user incorporates a few of the subroutines into his/her own program that is built to read SPICE data and compute needed geometry parameters for whatever task is at hand. Some examples of geometry parameters typically computed are range or altitude, latitude and longitude, illuminations angles (phase, incidence and emission), instrument pointing and field-of-view calculations, reference frame transformations, and coordinate system conversions. SPICE is also very adept at time conversions.

The ESA SPICE Service:  The ESA SPICE Service (ESS) leads the SPICE operations for ESA missions. The group generates the SPICE Kernel Datasets (SKDs) for missions in development (ExoMars Rover, Hera, Comet-Interceptor, EnVision, M-Matisse), missions in operations (Mars Express, ExoMars 2016, BepiColombo, Solar Orbiter, INTEGRAL and JUICE) and legacy missions (Venus Express, Rosetta and SMART-1). Moreover, ESS provides SPICE support Kernels for Gaia and James Webb Space Telescope. The generation of SKDs includes the development and operation of software to convert ESA orbit, attitude, payload telemetry and spacecraft clock correlation data into the corresponding SPICE format. ESS also provides consultancy and support to the Science Ground Segments of the planetary missions, the Instrument Teams and the science community. The access point for the ESS activities, data and latest news can be found at the following site https://www.cosmos.esa.int/web/spice. ESS works in partnership with NAIF.

Providing the best data: The quality of the data contained on a SKD is paramount. Bad SPICE data can lead to the computation of wrong geometric quantities which can jeopardize science results. ESS, in collaboration with NAIF, is focused on providing the best SKDs possible. Kernels can be classified as Setup Kernels (FK kernels defining Reference Frames of a given S/C, IK kernels describing all parameters defining a given instrument, PCK or Planetary Constants Kernels, LSK or Leapseconds Kernels, and DSK kernels defining Digital Shape models of S/C components and celestial bodies) and Time-varying Kernels (SPK and CK kernels providing Trajectory and Attitude data, SCLK providing Time Correlation Data, and MK or Meta-kernel). Setup kernels are iterated with the different stakeholders involved in the determination of the data contained in those kernels (Instrument Teams, Science Ground Segments, etc.) while Time-varying kernels are automatically generated by the ESS SPICE Operational Pipeline to produce the Operational kernels that are used in the day-to-day work of the missions in operations (planning and data analysis). Moreover, a validation step has been integrated with the pipeline to perform a series of Quality, Consistency and Validity checks before the new kernels are released. These Time-varying kernels are also peer-reviewed a posteriori for the consolidation of SKDs that are archived in the PSA and PDS.

Figure 1: SPICE Validation reports home and example of JUICE tests results.

Status of the Kernel Datasets: The current status and latest developments of the SKDs for the before mentioned missions will be described in this contribution. In general, the ESS is reviewing the legacy and operational datasets and developing the ones for future missions. It is worth mentioning the launch of JUICE in April 2023 and the forthcoming launch of Hera in October 2024.

SPICE Kernels Archived in the PSA. ESS is also responsible for the generation of PDS3 and PDS4 formatted SPICE Archives that are published by the PSA. ESS in close collaboration with NAIF, peer-reviews the operational kernels for the PSA [2] in order to publish being compliant with the Planetary Data System (PDS) standards and uses them in the processes that require geometry computations. The latest PDS4 SPICE Bundles are produced using the NAIF PDS4 Bundler tool [3].

Extended Services: ESS offers other services beyond the generation and maintenance of SPICE Kernel Datasets, such as instances and configuration for WebGeocalc and Cosmographia for the ESA missions, and additional software packages for geometric data exploitation.

SPICE-Enhanced Cosmographia. NAIF offers for public use a SPICE-enhanced version of the open source visualization tool named Cosmographia. This is an interactive tool devoted to 3D visualizations of celestial bodies ephemerides and shape models, spacecraft trajectories and orientations, movable parts position, and instrument field-of-views and footprints. ESS provides the framework and configuration required to load the ESA missions in Cosmographia, this contribution will demonstrate its usage for the ESA Planetary missions.

Figure 2: Hera spacecraft, docked Milani and Juventas, and its instruments in Cosmographia.

WebGeocalc. The WebGeocalc tool (WGC) provides a web-based graphical user interface to many of the observation geometry computations available from the SPICE APIs. A WGC user can perform SPICE computations without the need to write a program; just a web browser is required. WGC is provided to the ESS by NAIF. This contribution will outline the WGC instances for ESA missions.

References: [1] Acton C. (1996) Planet. And Space Sci., 44, 65-70. [2] Bessel, S. et al., (2017) Planet. And Space Sci. [3] Sitja, M. C. (2022) Journal of Open Source Software

How to cite: Escalante Lopez, A., Valles Blanco, R., Andres Blasco, R., and Arviset, C.: Updates on SPICE for ESA Missions, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-90, https://doi.org/10.5194/epsc2024-90, 2024.

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• Introduction

In the framework of the Virtual European Solar and Planetary Access (VESPA) of Europlanet, we have developed two new data services for planetary moons: MoonsProp and VOccDB. Both services are encoded using the Europlanet-Table Access Protocol (EPN-TAP) and can be accessed from the VESPA portal (https://vespa.obspm.fr/). The portal enables interoperability between planetary science data services in the Virtual Observatory context, but other access modes are available (see Erard et al 2024, "Virtual European Solar & Planetary Access (VESPA) 2024: Legacy", EPSC2024-355, this conference).

• 1. MoonsProp: physical and dynamical properties of natural satellites

Considering that it is difficult to access the physical and dynamical properties of natural satellites or moons for direct use in workflows, we have developed a specific database. This database, MoonsProp, provides up-to-date physical and dynamic characteristics of natural satellites. It concerns 288 satellites in total (Earth’s Moon, 2 satellites of Mars, 95 of Jupiter, 146 of Saturn, 28 of Uranus, 16 of Neptune). Users can access the size, mass, rotational properties, magnitude, albedo and orbital elements of all planetary satellites (see https://epn.imcce.fr/moonsprop.html for more details). These quantities are extracted from various publications, peer-reviewed papers and articles, books, or possibly web pages, all cited in a list of references (database parameter: bib_reference).

All quantities can hence be extracted for use by other VESPA planetary service necessitating reference value on global physical parameters. One can also derive graphs on statistical properties as shown in Fig. 1.

Figure 1. Distribution of some of the orbital elements distribution of satellites of giant planets in MoonsProp database. Circles and squares are proportional to the square root of the diameter.

• 2. VOccDB: prediction of stellar occultations by natural satellites

The VOccDB database provides the predictions of stellar occultations of bright GaiaDR3 stars by natural satellites of Jupiter, Saturn and Uranus over the period 2024-2033. All events up to visual magnitude 12 for the four biggest Galilean satellites, and magnitude 15 for other satellites are described: circumstances and observational data, including visibility maps (see Fig. 2), date and timing of the occultation, star position and magnitude, duration, etc. (see https://epn.imcce.fr/voccdb.html for more details) and additional information on the proximity of the Moon and the planet. All general circumstances were computed with the planetary and satellites ephemerides available at IMCCE. These data are given in TDB time scale and with present (2024) TDB-UTC; they will be updated as soon as new theories of natural satellites are published.

Figure 2.  Example of graphic showing the path of the satellite umbra (here Carme) during a stellar occultation with a Gaia DR3 star on 13 Nov. 2024.

Currently, 4485 events are published in the database. The EPN-TAP format can provide a global view of these predictions. The occurrence of these events naturally increases with the density of the star fields crossed by the satellites. We can therefore see this effect in this database when we plot the histogram of these events showing the transits of Jupiter, Saturn and Uranus in the Milky Way (see Fig.3).

Figure 3.  Histogramme of events showing the increase of events during the cross of the Milky Way by the different planets.

• Acknowledgements: The Europlanet-2024 Research Infrastructure project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 871149.

How to cite: Desmars, J., Hestroffer, D., Thuillot, W., David, P., Erard, S., and Azria, C.: Two new Europlanet/VESPA services about planetary moons, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-346, https://doi.org/10.5194/epsc2024-346, 2024.

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Introduction

The ExoMars Trace Gas Orbiter (TGO) was launched in 2016 and Science Phase started in April 2018. Apart from the failure of one sub-instrument, the spacecraft and payload remain healthy and in normal operations today. The mission approach to archiving the data had two significant differences from previous ESA missions archiving in the Planetary Science Archive (PSA): ExoMars was the first mission to archive the PDS4 standard data and it was the first to actively process and archive the mission raw data daily.

During the pre-launch phase the planning for the ExoMars archiving was done in close coordination with the BepiColombo mission team who would also take the active archiving approach and use the PDS4 standard, hence we were able to achieve a common data structure and set of rules for our data providers. This set of PSA rules has been recorded the PSA Archiving Guide, which remains a living document used by all new ESA missions archiving in the PSA.

Data Structures

One of the most impactful decisions made for the PSA PDS4 archives was to create a single bundle for each instrument then separate collections by data type, such as document, schema, data etc. Science data is also subdivided by processing level, hence all raw data for a mission accumulates in one collection inside one bundle. This naturally has led to large accumulating collections and while data volumes have not presented any significant issues, sheer numbers of files, especially if stored in a flat structure, have led to the need to consider carefully the physical structure adopted.

Another issue, was that early in the mission, the directory structures were not settled and to change the structure required the data products to be deleted then re-ingested with the new path. This has since been improved with internal tools to change a path internally. It's important to note that the storage layout adheres to a distinct schema from what the end user sees. Data offered to users (via ftp, web pages, or custom applications) is regulated by the distribution path value stored in a database, eliminating the need for physical file movement in case of future rearrangements.

Bundle and Collection Versions:

A major difficulty with the single accumulating bundle/collection approach faced early on, has been versioning. Initially the bundle/collection versions were incremented on every daily delivery, but it quickly became evident that maintaining a full record of each version was going to be more effort that it made sense to expend. The approach was modified to artificially increment on a monthly basis but this approach has also now been dropped with the current approach to only increment if there has been a significant change such as the ingestion of reprocessed data from the whole mission.

Product Design:

One choice which is made for all science products is whether to group data files together into a single product, or even a single file, or whether to produce smaller separate products. For the CaSSIS instrument on the TGO it was decided that the framelets making up the push broom image should be stored as separate images. However a CaSSIS observation typically has around 600 PDS4 framelet products at Raw level. Added to the fact that in a typical day CaSSIS typically has around 30 observations, and data gets re-processed, we now have an archive with tens of millions of CaSSIS products. This has caused 2 main issues. The first has been difficulty in maintaining database performance. The other issue has been the discovery and tracking of missing data due to issues with the initial data downlink, the data processing, validation and/or transfer and ingestion into the PSA. Putting all the framelets for each filter into one product per observation would reduce number of products but at the cost of a very long label e.g. including geometry information for each framelet, which potentially has different downsides.

Filenames:

One of the successful decisions made was to standardise file naming and at least for the science products to include filenames as part of the LID. The use of mission/instrument the bundle name plus the filename for the products has created a convention which keeps the LIDs in the PSA unique.

A different issue encountered was in the use of time in the filename. As data for most instruments did not have an observation ID, it is necessary to use date/UTC to create a unique filename/LID. However, the spacecraft clock drifted by up to a few seconds, corrected later in SPICE, so when the Raw data underwent a full re-processing the filename and hence the LID also changed meaning we had two different LIDs associated with a single observation.

PSA Dictionary:

Another success, has been to develop a dictionary for the PSA, allowing the addition of cross-mission attributes which are not included in the PDS4 core model. For some attributes, such as mission phase, we additionally include schematron in individual mission dictionaries and this standardisation in the schema has aided the development of the PSA tables and UI.

Summary:

Design choices made before the launch of ExoMars in 2016 remain in place, and the active archives now include the BepiColombo and JUICE missions. The structure and conventions adopted are easy to follow and have generally been successful. Some PDS conventions such as versioning have not been compatible with an active archive approach hence the way PSA deals with these has evolved.

How to cite: Lim, T., Coia, D., Bentley, M., Oside, J., Rancero, E., Giordano, F., and Docasal, R.: Six Years of ExoMars TGO Active Archiving – Lessons Learned, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-848, https://doi.org/10.5194/epsc2024-848, 2024.

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Introduction: The TRR170-DB data repository (https://planetary-data-portal.org/) manages the research data from the collaborative research center ‘Late Accretion onto Terrestrial Planets’ (TRR 170). The published data are provided in open machine-readable formats to the planetary science community. They have been generated in various fields of planetology such as astromaterials science, experimental studies, remote sensing, and geophysical modeling. Our data policy and practices are aligned with the principles of Open Science [1] and the FAIR principles [2] fostering openness and transparency in scientific research. Since TRR170-DB is permanently hosted at Freie Universität Berlin (FUB) and interlinked with FUB’s Central Library System the data are on a long term scale preserved and accessible. The TRR170-DB is referenced by Re3data, a global registry of research data repositories [3].

The TRR170-DB system: The repository is operated on the open source data management software Dataverse [4]. Users access the repository directly through its interface that connects to the storage environment of the datasets. Alternatively, a web portal allows for repository access while also guiding users how to use TRR170-DB.

Data access, storage, and publication: While the TRR170-DB serves as a hub to exchange data for the TRR 170 user community through a password-guarded member area, published data are also accessible for other interested researchers. The repository interface provides search tools to allow users to conduct general searches or specify queries for published data via filters. Filters can direct searches to specific published data types (i.e., planetary materials/geophysics, planetary surface data, astronomy) via their metadata information. Specified searches result in compilations of data and metadata information available in the repository that can be directly viewed, saved, and downloaded via the advanced search tool Data Explorer (Fig.1).  The Data Explorer complements the TRR170-DB and is an integrated part of the repository.

Fig. 1: TRR170-DB Data Explorer query results. 

We support TRR 170 members and other users to meet journals’ and funding agencies’ requirements for FAIR data by providing storage for replication datasets (Fig. 2). Replication datasets make published data more easily findable and usable for other researchers.  At present, most replication datasets were provided and used by TRR 170 authors of articles appearing in international journals since 2016. These replication datasets are freely available, and no special permission is required for reuse and verification of a study without having to contact the study's authors.

Fig 2: Replication datasets in TRR170-DB linked to published research articles.

Any data supplier receives a permanent archive and a digital object identifier (DOI, [5]) to make the dataset unique and findable. Data suppliers are requested to apply standardized ways to annotate, structure and organize data. This metadata information on the content, quality, origin, and other characteristics of the datasets ensure reliable data quality for future use in other research projects. In this way, data from different projects can be easier exchanged and be understood when used in different contexts. Metadata information links a dataset to its related published peer-reviewed paper.

The metadata model: TRR170-DB has a flexible data-driven metadata system that uses tailored “metadata blocks” for specific data communities. The metadata blocks are based on standards that are compliant with several international metadata schemata (i.e. DDI, DataCite, Dublin Core, VOResource Schema, etc.) and controlled vocabularies. Once a dataset has been published, its metadata and files can be exported in various other open metadata standards and file formats (Fig. 3) to allow for easy data transfer among various international external databases and repositories. 

Fig. 3: Extract of TRR170-DB metadata and controlled vocabulary to metadata formats, upper right corner.

TRR170-DB integration into the FUB Central Library System: FUB Central Library System houses the institutional open access repository, Refubium that contains research data collections. When TRR 170 members contribute datasets to the TRR 170-DB repository, the published version of this datasets gets automatically mirrored by the Refubium. This ensures a global visibility and accessibility through FUB's institutional repository.

Within the TRR 170-DB repository, rigorous curation standards have been upheld to maintain the quality and integrity of the research data to ensure their reliability and suitability for scholarly use. To enhance data discovery and citability, published datasets are assigned an additional DOI through the FUB Refubium. The FUB Refubium is using the OAI-PMH (Open Archives Initiative Protocol for Metadata Harvesting) protocol [6] to harvest metadata in the TRR170-DB repository. The integration of the TRR170-DB repository with FUB's Central Library System extends its accessibility to users worldwide. Through Primo, FUB's discovery and access platform interested users gain seamless access to the diverse range of datasets available within Refubium.

Future Work:  When the TRR 170 research program closes at the end of 2024, all data stored in the TRR170-DB repository will be further accessible to the interested user through three different avenues: via (1) the TRR170-DB repository, (2) the FUB Refubium, and (3) the TRR 170 webpage. As a result, the TRR 170-DB repository facilitates the dissemination and discovery of valuable research data, fostering a culture of knowledge sharing and academic excellence.

Acknowledgements: The TRR170-DB repository is managed by subproject TRR 170-INF (German Research Foundation (DFG), 263649064–TRR 170). Freie Universität Berlin provides server infrastructure and a web content management system for managing the TRR170-DB website. Dataverse software is maintained by the IQSS Dataverse team, Harvard University. We thank A. Balduin and archium® GmbH for technical support.

References:

[1] Open Science, https://www.dfg.de/en/research_funding/programmes/nfdi/index.html

[2] FAIR principles, Wilkinson et al., https://doi.org/10.1038/sdata.2016.18

[3] r3data, www.re3data.org.

[4] Dataverse, dataverse.org.

[5] DataCite, datacite.org.

[6] Open Archives Initiative Protocol for Metadata Harvesting, https://www.openarchives.org/pmh

How to cite: Lehmann, E., Becker, H., Fritz, T., Wille, F., Sabisch, A., Sievers, D., and Schlegel, B.: Globally Findable Planetary Data: The Interdisciplinary TRR170-DB Repository, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-1173, https://doi.org/10.5194/epsc2024-1173, 2024.

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NASA’s Planetary Data System (PDS) is an active archive of science data from NASA planetary missions. PDS archives and distributes peer-reviewed planetary science data to the world-wide planetary science community.  The Radio Science Sub-Node (RSSN) in collaboration with the Ring Moon Systems (RMS) Discipline Node aims to maintain a long-term radio science capability within PDS. Members of the RSSN team have strong connections to the NASA Deep Space Network (DSN), a long history of participation in radio science observations, and close relationships with the radio science community. 

RSSN plans to develop software tools and templates to assist data providers in the creation and ingestion of consistent PDS4-compliant data products as well as preparation and maintenance of mission-independent ancillary products from the DSN. RSSN will also develop software tools to assist data users in visualization and quick-look analysis of radio science data, enabling users to assess quality and usability early in their projects. With the constantly growing volume of data and the large variety of observation types, observing conditions, and object types, the development of metadata and quick-look tools will be critical in connecting scientists and other users to data of interest before committing to detailed analyses.

In this presentation, we will describe the RSSN objectives, overview of recent developments, near-term plans, and engagement with the radio science community.

How to cite: Majid, W., Kahan, D., Buccino, D., Lee, C., Barbinis, E., Yang, O., Tiscareno, M., and Simpson, R.: The Planetary Data System Radio Science Sub-Node, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-1182, https://doi.org/10.5194/epsc2024-1182, 2024.

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1.    Introduction:
The Republic of Korea inaugurated its space exploration era by launching Danuri lunar orbiter on August 5, 2022. As of now, Danuri is conducting scientific missions in a lunar polar orbit at an altitude of 100 km. Initially scheduled for a one-year mission (nominal mission) until December 2023, its mission has been extended until December 2025. These scientific missions are conducted by five scientific instruments onboard Danuri, and the scientific data obtained from these instruments have been accessible to the public since January 2024. 
The science mission payload of Danuri includes four Korean-developed instruments such as a Gamma-Ray Spectrometer (KGRS), a Wide-angle Polarimetric Camera (PolCam), a Magnetometer (KMAG), and a Lunar Terrain Imager (LUTI), as well as an internationally collaborative instrument, ShadowCam, developed by Arizona State University with funding from NASA.

2.    KARI Planetary Data System:
The KARI Planetary Data System (KPDS) comprises functionalities designed to deliver scientific data to the developers of Danuri’s scientific instruments, which are research institutes and universities in Korea, to collect processed data from these developers, and to manage these datasets. Furthermore, KPDS includes functionalities to make the scientific data and supplementary data accessible to the public via its website(Figure 1). 
All scientific data released to the public and managed for archiving in KPDS are compliant with the NASA PDS4 standard. To ensure compliance, all data uploaded to KPDS undergo PDS4 standard validation immediately upon upload. 
KPDS, developed through collaboration between the Korea Aerospace Research Institute (KARI), one of the Korean government-funded research institutes, and HANCOM InSpace Co. Ltd., one of the space companies in the Republic of Korea, fulfills these roles. Specifically, KPDS is the Republic of Korea's inaugural public release and management system for scientific data in space exploration. 
KPDS website offers users convenient access to scientific data through filter-based and keyword-based search functionalities (Figure 2). Additionally, users can directly access desired data through directories on the KPDS website without the need for separate search functions. Also, KPDS website features a Thumbnail Viewer for scientific datasets, providing users with thumbnail images. Users can perform simple image modifications such as brightness, gamma, hue, contrast, sharpness, and additional adjustments of the thumbnail images, as well as compare among selected thumbnails, if thumbnails are available. Additionally, it offers a Metadata Viewer for accessing metadata of scientific datasets compliant with the PDS4 standard. Furthermore, KPDS provides various information regarding Korea's space exploration and the utilization of scientific data, enabling general users to easily and efficiently utilize the scientific data obtained from Korea's space exploration missions.

Figure 1. KPDS Website Main Page

Figure 2. KPDS Website Search Page

How to cite: Kim, J. H. and Kim, D.: How to access and download the scientific data of Korean space exploration using KARI Planetary Data System, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-163, https://doi.org/10.5194/epsc2024-163, 2024.

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