Marc S. Boxberg

Scientific software is essential for accelerating research and enabling transparent, reproducible results, but increasing adoption also increases support demands that can overwhelm small academic development teams. Since most scientists are not trained as software engineers, early-stage research software often lacks the resources and structure needed for broader use, making streamlined support workflows crucial for both users and developers. Addressing these issues is essential to ensure that researchers can focus on their core activities while streamlining processes that benefit both users and developers.Our project CAES3AR (Collaborative and Efficient Scientific Software Support Architecture) aims to provide researchers with a more open and efficient infrastructure for software support by developing a collaborative architecture. The framework is currently being developed and evaluated using pyGIMLi, an open-source library for modeling and inversion in geophysics (www.pygimli.org), while being designed to remain transferable to a broad range of open-source projects. Thanks to its practicality and gallery of existing examples, pyGIMLi has become widely adopted in the near-surface geophysical community. At the same time, its use across diverse user environments introduces recurring support challenges, since variations in operating systems and installed dependencies can make issue reproduction and debugging time-intensive, which often reduces the capacity for methodological and software innovation.To address these challenges efficiently, the CAES3AR framework aims to automate key aspects of user support through a generic toolchain that integrates seamlessly with existing infrastructures such as GitHub and Jupyter. It facilitates user engagement by allowing them to create GitHub or GitLab issues that include links to temporary code execution environments (e.g., JupyterLab) equipped with collaborative editing features—potentially integrated with existing JupyterHub and cloud-based infrastructures. Additionally, automated bots powered by GitHub Actions or GitLab jobs will provide real-time feedback on whether issues exist across all platforms and with the latest software versions. If a problem persists, supporters can directly modify the user's code within Jupyter without requiring any downloads or installations. Proposed changes will be presented as formatted code alterations (“diffs”) attributed to their authors in the Git issue for future reference, ensuring clarity and continuity even after the temporary JupyterHub instance is no longer available.We recently hosted a community workshop to assess developer and user needs, identify challenges in current support practices, and gather requirements for practical adoption. This presentation summarizes key findings from those discussions and introduces early CAES3AR prototypes developed for the pyGIMLi ecosystem. As CAES3AR remains in active development, we conclude by inviting community feedback on additional features and design priorities, with the broader aim of ensuring transferability and long-term utility across multiple open-source scientific software projects.Project website: https://caesar.pages.rwth-aachen.de/

Ice is omnipresent in our Solar System: on Earth, on different planetary bodies, and on icy moons in the outer Solar System. Quite a number of those icy bodies feature subglacial water reservoirs under global ice shells, some of which may even host cryo-habitats. In addition, on the moons Europa and Enceladus the ocean is thought to be in contact with the rocky interior leading to water-rock processes potentially similar to the ones at the ocean floor on the Earth and making these two bodies highly relevant targets for planetary exploration. Space exploration missions, such as JUICE and Europa Clipper missions which are currently on their way to the Jupiter system, will allow us to further characterize cryo-environmental conditions on icy moons. Lander missions are likely to follow should a high habitability potential be identified. In order to prepare for both the interpretation of data acquired by Europa Clipper and JUICE, and for the design of future lander missions, it is of crucial importance to exploit any possible synergy between the various cryosphere research communities. In the past, terrestrial and extraterrestrial cryosphere research mostly developed as independent research fields whereas synergies may shed light on both fields. In fact, close cooperation across different cryosphere research communities is a necessary prerequisite for designing future planetary exploration missions. An in-depth knowledge of similarities and differences between ice regimes on Earth and hypothesized physical regimes on icy moons will pave the way for optimized information retrieval from mission data and allow to effectively orchestrate terrestrial analogue field test, lab experiments, and model-based design for lander technology development. An accessible database that provides information on available datasets, e.g., regarding activities at terrestrial analogue sites, dedicated lab experiments or ice properties is not available to date or maintained by the community.The International Space Science Institute (ISSI) team ‘Bridging the gap: From terrestrial to icy moons cryospheres’ [1] started its work in 2023 and brings together scientists and engineers with different terrestrial and extra-terrestrial cryosphere expertise. The overall goal of the project is to make knowledge hidden in the vast amounts of existing data from different cryosphere research groups accessible to the community. This should be achieved by consolidating information from existing data sets into comprehensive, moderated open access compilations. More specifically, the team focusses on two types of data compilations, namelya collection of experimental and theoretical work regarding ice properties along with their implicit assumptions and ranges of applicability, and a compilation of published work conducted at terrestrial analogues sites along with their relevance for icy moons exploration. Here, we will introduce the project and its rationale, and describe our approach to selecting and compiling the data. Most importantly, we will show how the community can contribute to and benefit from the data collection.Acknowledgement: This research was supported by the International Space Science Institute (ISSI) in Bern, through ISSI International Team project #23-589 Bridging the gap: From terrestrial to icy moons cryospheres.References: [1] https://teams.issibern.ch/icymoonscryospheres/

Germany is currently conducting a site selection procedure with the quest for an optimal repository site for high-level radioactive waste in geological subsurface. The site selection procedure must be done in accordance with the Final Repository Safety Requirements Ordinance, which restricts the maximum allowable exposure for high-level radioactive waste released from the final repository site. One of the potential risks associated with the repository site is the release of radionuclides through groundwater flow. Therefore, a risk assessment regarding the environmental impact of different hazard scenarios is crucial to carefully select and ensure long-term safety of the repository site.To assess the risk of radioactive contamination in the subsurface, physics-based process models are implemented to predict the spatial-temporal evolution of the radionuclide concentration associated with a given hazard scenario. The resulting radionuclide concentration provides the basis for impact modelling, namely estimating accumulated dose and subsequently quantifying potential radioactive contamination. Simulations are implemented through the OpenGeoSys software. A supporting Python package, Yaml2Solver, is developed to orchestrate process and impact modelling along with relevant parameters. The package centralizes simulation and material information in YAML files to define and adjust model parameters, and it enables simulating different coupled-level process models.These data-integrated models, however, are built in the presence of uncertainties in material properties, including permeability of rock and groundwater flow. Accounting for uncertainties in physics-based simulations calls for an effective and reliable uncertainty management tool. We therefore developed an analysis-ready and actionable data-hub. The data-hub consists of a database integrated with a graphic user interface (GUI). The database provides material properties along with their uncertainty margins and sensible defaults in YAML files for analysis readiness of simulation models. The material properties are associated with synthetic, reference, and candidate sites, enabling the compilation of site-specific scenarios for simulations. The GUI provides detailed visualization for each site, including a three-dimensional geostructural model, a chronostratigraphic chart indicating the geological formation time of each stratum, and a table providing information on rock properties and attributes of sensible defaults. The data-hub framework supports for systemic and uncertainty-informed model-based assessment as well as subsequent model-based decision-making tasks. We further integrated the data-hub with Yaml2Solver for efficient uncertainty management across various scenarios. Data-hub integerated process and impact modelling offers benefits for managing long-term uncertainties and improving reproducibility, and thereby increasing the transparency and reliability of decision-making. Depending on the material properties with their marginal values sourced from different sites, we construct various site-specific process models. Subsequently, the process models are extended to impact models, describing spatial-temporal evolutions of radiation. The resulting uncertainty-informed impact models enable us to quantify potential radioactive contamination in specific sites and offer valuable insights in repository site selection and safety assessments.

Given the importance of ensuring the safe disposal of radioactive waste, it is vital to understand the targeted subsurface systems and to build physics-based models to predict their dynamic responses to human interventions. Constructing robust predictive models, however, is very challenging due to the systems’ complexity as well as the scarcity and cost of geophysical data acquisition. Optimal matching of data acquisition and predictive simulations is therefore necessary and can be achieved via integrating predictive process modeling, Bayesian parameter estimation, and optimal experimental design into a modular workflow. This allows to quantify the information content of measurement data and therefore enables optimal planning of data acquisition and monitoring strategies. Conducting such data-integrated simulation studies, however, requires a robust workflow management that ensures reproducibility, error management, and transparency. To meet this demand, we established a data-centric approach to workflow control combining error-managed simulations with a functional data hub, providing simulations with direct access to a database of essential material properties. The latter are being made available as site specific scenario compilations along with uncertainty margins and meta information. The data hub serves as an interface facilitating seamless data and simulation exchange to support subsequent model-driven decision-making processes and guarantees that simulations are conducted using manageable, comparable, and reproducible test cases. Furthermore, it ensures that the simulation results can be readily transferred to a designated repository allowing for real-time updates of the model. The implementation of the data hub is based on a Python-based framework for two different use cases:1) GUI-based use case: The graphical user interface (GUI) facilitates data import, export, and visualization, featuring distinct sections for geographic data representation, structured table organization, and comprehensive visualization of physical properties in varying dimensions.2) Module-based use case: Built on the YAML-based data-hub framework, it enables direct integration of simulation modules storing measurements and model parameters in the YAML data format.The data is systematically organized to furnish a versatile data selection framework that allows information to be extracted from a variety of references, including specific on-site measurements, laboratory measurements and other references, thereby enabling a comprehensive exploration of different reference-oriented scenarios.This study showcases the data hub as a management infrastructure for executing a modular workflow. Multiple models—such as process and impact models as well as their surrogates and geophysical inverse models—are generated within this workflow utilizing scenarios provided by the data hub. Our study shows that adopting a data-centric approach to control the simulation workflow proves the feasibility of conducting different data-integrated simulations and enhances the interchangeability of information across different stages within the workflow. The paradigm of sustainable model development ensures reproducibility and transparency of our results, while also offering the possibility of synergetic exchange with other research areas.

Interpreting independent geophysical data sets can be challenging due to ambiguity and non-uniqueness. To address this, joint inversion techniques have been developed to produce less ambiguous multi-physical subsurface images. Recently, a novel cooperative inversion approach that uses minimum entropy constraints has been proposed. The major feature of this approach is that it can produce sharper boundaries inside the model domain. We implemented this approach in an open-source software framework and systematically investigated its capabilities and applicability on electrical resistivity tomography (ERT), seismic refraction tomography (SRT), and magnetic data.First, we conducted a synthetic 2D ERT and SRT data study to demonstrate the approach and investigate the influence of the equations’ parameters that must be calibrated as well as to justify extensions of the method. The results show that the use of the joint minimum entropy (JME) stabilizer outclasses separate, conventional smoothness-constrained inversions and provides improved images.Next, we used the method to analyze 3D ERT and magnetic field data from Rockeskyller Kopf, Germany. Independent inversion of the magnetic field data already suggested a subsurface volcanic diatreme structure, but the joint inversion using JME not only confirmed the expected structure, but also provided improved details in the subsurface image. The multi-physical images of both methods are consistent in many regions of the model as they produce similar boundaries. Due to the sensitivity of the ERT measurements to hydrogeological conditions in the subsurface, some structures are only visible in the ERT data. These features seem not to be enforced on the magnetic susceptibility model, which highlights another advantage and the flexibility of the approach.However, the results of both the synthetic and field data use cases suggest that careful parameter tests are required prior to cooperative inversion to obtain a suitable hyperparameters and reference model. Our work implies that minimum entropy constrained cooperative inversion is a promising tool for geophysical imaging provided that proper settings are chosen while it also identifies some objectives for future research to improve the approach.

Ice-covered ocean worlds, such as the Jovian moon Europa, are some of the prime targets for planetary exploration due to their high astrobiological potential. While upcoming space exploration missions, such as the Europa Clipper and JUICE missions, will give us further insight into the local cryoenvironment, any conclusive life detection investigation requires the capability to penetrate and transit the icy shell and access the subglacial ocean directly. Developing robust, autonomous cryorobotic technology for such a mission constitutes an extremely demanding multistakeholder challenge and requires a concentrated interdisciplinary effort between engineers, geoscientists, and astrobiologists. An important tool with which to foster cross-disciplinary work at an early stage of mission preparation is the virtual testbed. In this article, we report on recent progress in the development of an ice transit and performance model for later integration in such a virtual testbed. We introduce a trajectory model that, for the first time, allows for the evaluation of mission-critical parameters, such as transit time and average/overall power supply. Our workflow is applied to selected, existing cryobot designs while taking into consideration different terrestrial, as well as extraterrestrial, deployment scenarios. Specific analyses presented in this study show the tradeoff minimum transit time and maximum efficiency of a cryobot and allow for quantification of different sources of uncertainty to cryobot's trajectory models.

The exploration of ocean worlds in the outer Solar System, for example, the Jovian moon Europa and the Saturnian moon Enceladus, are of particular interest for the search for extraterrestrial life. Direct in situ exploration of moons harbouring significant amounts of liquid water beneath their ice surface poses many challenges and requires a sophisticated technological approach. The TRIPLE project (Technologies for Rapid Ice Penetration and Subglacial Lake Exploration) initiated by the German Space Agency at DLR forms a national consortium to work on robotic technologies for sub-ice exploration. The planned system consists of the fully autonomous, untethered miniature submersible robot, called nanoAUV, the IceCraft, a melting probe for penetrating the ice with the nanoAUV as payload, and an astrobiology in-situ laboratory, the AstroBioLab, to study fluid and sediment samples.Beneath a several kilometre-thick ice-shell of the moons considered here, global oceans are well hidden and not easily accessible, posing extreme challenges for any robotic exploration as it is addressed in the TRIPLE project. Therefore, ice drilling and state-of-the-art technologies need to be developed to meet the manifold requirements. In view of future missions to icy moons, in TRIPLE, an analogue terrestrial demonstration is intended for first time exploration of a subglacial lake at the Dome-C region in Antarctica. The Dome-C mission requires a retrievable melting probe that can penetrate a 4-kilometre-thick layer of ice. It is essential for the mission that the melting probe is able to detect and avoid obstacles along its trajectory and to anchor itself at the ice-water interface for release and support of the nanoAUV into the water. The AstroBioLab concept provides an automated sample analysis laboratory for habitability investigations. It shall not only be able to detect various biosignatures in samples taken from the subglacial habitats, but shall also provide unequivocal evidence of life. For the field test in a terrestrial analogue setting, portable and robust devices using fast analysis methods are particularly suitable, which, as far as possible, should not require time-consuming sample preparation. In this contribution, we give an overview of the TRIPLE project and report on its current status.

It is widely recognised that the icy moons of our solar system are interesting candidates for the search for habitable environments beyond Earth. While upcoming space missions such as the Europa Clipper and JUICE missions will give us further insight into the local cryo-environment of Jupiter’s moon Europa, any conclusive survey to detect life will require the ability to penetrate and traverse the ice shell and access the subglacial ocean directly. Developing a robust, autonomous cryobot for such a mission is an extremely demanding challenge and requires a concentrated interdisciplinary effort by engineers, geoscientists and astrobiologists.We report on recent progress in developing ice transit and performance models as a first step towards a modular virtual testbed. The modularity of the virtual testbed allows easy exchange of the trajectory model used, the environmental conditions, such as ice parameters, and the description of the cryobot. We introduce a trajectory model that allows the evaluation of mission-critical parameters such as transit time and energy demand for different cryobot designs and deployment scenarios both on Earth and on icy moons.Specific analyses presented in this study highlight the trade-off between minimum transit time and maximum efficiency of a cryobot, and allow quantification of different sources of uncertainty for cryobot trajectory models. Based on the terrestrial scenarios, our results show that the fastest transit time for the TRIPLE IceCraft cryobot is consistently achieved at all deployment sites, while its average energy consumption is rather high. The most energy efficient cryobot considered in our work is the EnEx-RANGE APU, that is, however, not designed for carrying large payloads.While we have focused on idealized models that, for example, assume a planar melting head and a laterally isolated probe, future extensions of the virtual testbed will include more detailed models and take into account non-uniform distributions of salt concentration observed in terrestrial ice drilling. Our models are a first major step forward in estimating the efficiency of melting probes and can help develop and improve robust, autonomous cryorobotic technologies for extraterrestrial missions that can ultimately shed light on the potential for life to exist in the alien oceans of Europa and other icy moons.

Several icy moons of our Solar System like Jupiter’s moon Europa have a global ocean of liquid water below their icy crust. These ocean worlds are possible targets for space missions that aim to assess their potential for habitability or even to search for life. Cryobots (or ice melting probes) are suitable tools to reach the subglacial oceans for in-situ investigations. The necessary ice shell transit provides an excellent opportunity to investigate structure and composition of the ice itself by means of geophysical and other in-situ measurements. This will allow us to better understand the evolution of icy moons and their role in our solar system.We present current ideas as well as first results from terrestrial analogue studies. Acoustic data obtained during a field test on Langenferner Glacier, Italy was used to conduct a travel time tomography, which yields insight into heterogeneities in the local acoustic wave propagation speed through the ice. The acoustic sensor set-up was originally designed for localization of the melting probe rather than an investigation of the ice structure. However, we can still show that such opportunity data can be used to obtain a wave velocity distribution which can be further interpreted with respect to ice properties like porosity.While we already investigated the acoustic data, we evaluate the potential of other measurements. For example, Radar measurements in combination with the acoustics can be used to identify the ice-water boundary and, in addition, cracks and inclusions in the ice. Conductivity measurements provide information on the salinity. At ice-water interface regions, the salinity is in thermochemical equilibrium with the temperature and porosity of the ice. We present our concept for on-board electrical conductivity measurements and analyze its potential, for example, to constrain ice properties and to predict ice-water interfaces based on existing terrestrial field data and process models. Furthermore, some of the cryobot’s housekeeping data might be of interest for investigating the ambiance, too. For example, the temperature and the density of the ice affect the melting velocity of the cryobot, which constitutes an inverse problem to get further information on the ice.