A critical missing database for the astrophysics and planetary science community using spectroscopy data is a compilation of band parameters (e.g., their position, width, intensity) of solids, for comparison with laboratory and field spectra, and observations of extraterrestrial objects. While many databases exist for gases [1], there's a scarcity for solids and liquids (mostly as tables in a few books and review papers), and the mode attributions of bands is not always clear.

The Solid Spectroscopy Hosting Architecture of Databases and Expertise (SSHADE) (https://www.sshade.eu/; [2]) is hosting data from 30 research groups in spectroscopy of solids across 15 countries. It provides spectra of solids relevant to astrophysics and planetary science (ices, minerals, carbonaceous matters, meteorites…) and over a wide range of wavelengths (mostly X-ray and VUV to sub-mm). Initial compilation of the “BandList” database [3], which is hosted in SSHADE, was publicly released in October 2021. It is an ongoing effort to provide the parameters (position, width, intensity, and their accuracy, shape) associated with electronic, vibration, and phonon bands of simple solids (ices, simple organics, minerals), in absorption and in Raman emission, and for different pressure and temperature. It also includes the solid composition and isotopic species involved, as well as the mode assignment (Fig. 1). The database is compilated from exhaustive review of the literature and laboratory measurements on well-characterized species, and as of early 2024, it consisted of over 1240 bands associated with 60 different band lists, including minerals and ices in different phases. An online search tool allows users to find specific bands or lists. Results can be displayed graphically using a spectra simulator with various unit and display options (Fig. 1), and data can be exported for further analysis. 

Figure 1. Absorption band list of natural calcite [4] from the SSHADE-BandList interface. (a) Bands displayed individually. (b) Sum of bands of whole band list.

Development of the SSHADE-BandList database interface and content will most likely last many years. This tool is expected to be crucial, aiding in the identification of unknown absorption bands in astrophysical and solar system objects, of best spectra to use in radiative transfer models, and in guiding the conception of new instruments.

References 

[1] Albert et al. (2020), Atoms 8(4)

[2] Schmitt et al. (2014), EPSC 2014

[3] Schmitt et al. (2022), EPSC 2022

[4] Leclef and Schmitt (2022), SSHADE/BANDLIST (OSUG Data Center)

How to cite: Mandon, L., Schmitt, B., Albert, D., Furrer, M., Bollard, P., Gorbacheva, M., Bonal, L., and Poch, O.: SSHADE-BandList, a novel database of absorption and Raman bands of solids , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4067, https://doi.org/10.5194/egusphere-egu24-4067, 2024.

Analysing individual hazards and the associated risk is a challenging task in its own right. It requires a lot of expertise built up over dozens of years. Unfortunately, there are situations where that a single hazard can trigger following - often horrific - consequences. The history of international catastrophes is full of examples: The fires in San Francisco after the 1906 earthquake due to destroyed gas pipelines; the tsunami that destroyed the Fukushima nuclear power plant after the Tohoku earthquake, or the climatic effects of the Krakatau eruption in 1883.

In our RIESGOS project we have been working on an demonstrator app to analyse multi-risk-scenarios - with a strong focus on the earthquake-tsunami combination. This is an use case that is very relevant in our partner countries Ecuador, Peru and Chile - and the knowledge is provided here by the partner institutions of the RIESGOS consortium.

The technical approach is strongly focused to be standard based using OGC Web Processing Services, as well as to be distributed. This allows to use the specific expertise of each of the partner institution to be taken into account, to share the involved data & algorithms that have been built up and refined over years.

What we focus in this presentation is a deeper insight into the implementation perspective, with the benefits as well as strategies to overcome challenging
aspects that we encounted when working with the distributed risk analysis framework. These include requirements on interoperability, deployments of bundled versions for testing & transfer, monitoring and others.

How to cite: Brinckmann, N., Langbein, M., Proß, B., Vogt, A., and Schöpfer, E.: Implementing a Distributed Processing Framework for Multi-Risk Analysis - A Lessons Learned Perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15031, https://doi.org/10.5194/egusphere-egu24-15031, 2024.

Robust infrastructures for managing and accessing high volume data are an essential foundation for unraveling complex spatiotemporal processes in the earth system sciences. Addressing multifaceted research questions demands data from diverse sources; however, isolated solutions hinder effective collaboration and knowledge advancement.

We present a novel digital ecosystem for FAIR time series data management, deeply rooted in contemporary software engineering and developed at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. Designed to flexibly address discipline-specific requirements and workflows, the system emphasizes user-centric accessibility, ensuring the reliability, efficiency, and sustainability of time series data across different domains and scales.

Our time series ecosystem includes a user-centric web-based frontend for (real-time) data flow and metadata management, a versatile data integration layer, a robust time series database, efficient object storage, near real-time quality control, and comprehensive data visualization capabilities. Supporting modern and classical data transfer protocols, the system ensures compliance with OGC standards for data access, facilitating efficient progress in the data lifecycle through high-performance computing. This fully integrated and containerized solution enables swift deployment and seamless integration with existing services.

Illustrating the practical application of the system, we showcase its success in managing Cosmic Ray Neutron Sensing data from the TERENO project. This success story underscores the system's effectiveness in addressing challenges associated with time series data management in earth system sciences, fostering more efficient research and facilitating informed decision-making processes.

This contribution aligns seamlessly with the session's focus on connecting RDIs. We aim to promote transferable approaches, use existing standards, and facilitate collaborations transcending barriers among RDI providers, developers, and researchers. By presenting our experiences and best practices, this presentation invites engagement and discussions to collectively address the challenges in bringing research data infrastructures together.

How to cite: Hemmen, J., Schäfer, D., Abbrent, M., Gransee, F., Kuhnert, T., Palm, B., Schaldach, M., Schulz, C., Schrön, M., Schnicke, T., and Bumberger, J.: Advancing Data Management: A Novel Digital Ecosystem for FAIR Time Series Data Management in Earth System Sciences, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12978, https://doi.org/10.5194/egusphere-egu24-12978, 2024.

The rapid growth of environmental data and the complexity of data pre-processing tasks poses significant challenges to environmental scientists. Repetitive and error-prone manual data preparation methods not only consume valuable research time but also introduce potential data quality issues. Also, individually pre-processed datasets are hardly reproducible. The V-FOR-WaTer virtual research environment (VRE) addresses these challenges as a powerful tool that seamlessly integrates data access, data pre-processing, and data exploration capabilities.

V-FOR-WaTer has an automated data pre-processing workflow to improve data preparation by eliminating the need for manual data cleaning, standardization, harmonization, and formatting. This approach significantly reduces the risk of human error while freeing up researchers to focus on their actual research questions without being hampered by data preparation. The pre-processing tools integrated in the virtual research environment are designed to handle a wide range of data formats, ensuring consistent and reliable data preparation across diverse disciplines. This empowers researchers to seamlessly integrate data from various sources in a standardized manner.

The web portal's user-centric design facilitates data exploration and selection through map operations and filtering options, empowering researchers to efficiently identify and focus on relevant data for their analyses. The scalability and extensibility of the V-FOR-WaTer web portal ensures that it can accommodate the ever-growing volume of environmental data and adapt to the evolving research landscape. Its ability to integrate user-developed tools reflects the dynamic nature of environmental research and ensures that the virtual research environment stays up-to-date with the latest advancements in data processing. The comprehensive features and user-friendly interface position it as a valuable tool for environmental scientists, fostering collaboration, streamlining data analysis, and accelerating the advancement of knowledge in the field of hydrology.

How to cite: Strobl, M., Azmi, E., Balazs, B., Bouguezzi, S., Dolich, A., Hassler, S. K., Mälicke, M., Manoj J, A., Meyer, J., Streit, A., and Zehe, E.: Streamlining Data Pre-processing and Analysis through the V-FOR-WaTer Web Portal, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10364, https://doi.org/10.5194/egusphere-egu24-10364, 2024.

Data-driven methods, such as machine learning (ML) and deep learning (DL), play a pivotal role in advancing the field of snow hydrology. These techniques harness the power of algorithms to analyze and interpret vast datasets, allowing researchers to uncover intricate patterns and relationships within the complex processes of snow dynamics. In snow hydrology, where traditional models may struggle to capture the nonlinear and dynamic nature of snow-related phenomena, data-driven methods provide a valuable alternative. Using data-driven methods (ML and DL) requires advanced skills in various fields, such as programming and hydrological modeling. In response to these challenges, we have developed an open-source Python package named MorSnowAI that streamlines the process of building, training and testing artificial intelligence models based on machine learning and deep learning methods. MorSnowAI not only automates the building, training, and testing of artificial intelligence models but also significantly simplifies the collection of data from various sources and formats, such as reanalyzing datasets (ERA5-Land) from Copernicus Climate Data and remote sensing data from Modis, Landsat, and Sentinel datasets to calculate Normalized Difference Snow Index (NDSI). It can also utilize local datasets as inputs for the model. Among other features available in the MorSnowAI package, it provides pre-processing and post-processing methods that users can choose, along with visualization and analysis of the available time series. The scripts developed in the MorSnowAI package have already undergone evaluation and testing in various snow hydrology applications. For instance, these applications include predicting snow depth, streamflow, snow cover, snow water equivalent, and groundwater levels in mountainous areas of Morocco. The automated processes within MorSnowAI contribute to advancing the field, enabling researchers to focus on refining model inputs, interpreting results, and improving the overall understanding of complex hydrological systems. By bridging the gap between hydrology and advanced data-driven techniques, MorSnowAI fosters advancements in research, offering valuable insights for resource management in regions heavily influenced by snow dynamics. 

How to cite: Elyoussfi, H., Boudhar, A., Belaqziz, S., Bousbaa, M., Nifa, K., Bargam, B., Karaoui, I., Bouihrouchane, A., Benmira, T., and Chehbouni, A.: MorSnowAI v1.0 : An Open-Source Python Package for Empowering Artificial Intelligence in Snow Hydrology - A Comprehensive Toolbox, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13159, https://doi.org/10.5194/egusphere-egu24-13159, 2024.

Research data infrastructures (RDIs) like the Coupled Model Intercomparison Project (CMIP) exemplify geoscientific dataset archive organization and applied informatics. The CMIP metadata and data policies have continuously co-evolved with mature and FAIR technologies (e.g., CF, OpenDAP, ESGF) that are, in turn, often adopted by other RDIs. Improved lossy and lossless compression support in the standard netCDF/HDF5 scientific software stack merit consideration for adoption in upcoming MIPs and RDIs like CMIP7. We have proposed a three point plan to CMIP7 to utilize modern lossy and lossless compression to reduce its storage and power requirements (and associated greenhouse gas emissions). The plan will boost the compression ratio of CMIP-like datasets by a factor of about three relative to CMIP6, preserve all scientifically meaningful data, and retain CF-compliance. We will present the plan, and discuss why and how to implement it in CMIP7 and other MIPs and RDIs.

How to cite: Zender, C.: Why and How to Increase Dataset Compression in RDIs and MIPs like CMIP7, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13651, https://doi.org/10.5194/egusphere-egu24-13651, 2024.

As the High Performance Computing (HPC) marches into the exascale era, earth system models have transformed into a numerical regime wherein simulations with a 1 km spatial resolution on a global scale are a reality and are currently being performed at various HPC centers across the globe. In this contribution, we provide an overview of the strategy  and plans to adapt the data handling services and workflows available at the German Climate Computing Center (DKRZ) and the Jülich Supercomputing Center (JSC) to enable efficient data access, processing and sharing of output from such simulations using current and next generation Earth System Models. These activities are carried out in the framework of projects funded on an EU as well as national level, such as NextGEMS, WarmWorld and EERIE. 

With the increase in spatial resolution comes the inevitable jump in the volume of the output data. In particular, the throughput due to the enhanced computing power always surpasses the capacity of single-tier storage systems made up of homogeneous hardware  and necessitates multi-tier storage systems consisting of heterogeneous hardware . As a consequence, new issues arise for an efficient, user-friendly data management within each site. Sharing of model outputs that may be produced at different data centers and stored across different multi-tier storage systems poses additional challenges, both in terms of technical aspects (efficient data handling, data formats, reduction of unnecessary transfers) and semantic aspects (data discovery and selection across sites). Furthermore, there is an increasing need for scientifically operational solutions, which requires the development of long-term strategies that can be sustained within the different data centers. To achieve all of this, existing workflows need to be analyzed and largely rewritten. On the upside, this will allow the introduction of new concepts and technologies, for example using the recent zarr file format instead of the more traditional netCDF format.

More specifically, in WarmWorld, the strategy is to create an overarching user interface, to enable the discovery of the federated data, and implement the backend infrastructure for handling the movement of the data, across the storage tiers (SSD, HDD, tape, cloud), within as well as across the HPC centers, as necessitated by the analytical tasks. This approach will also leverage the benefits of community efforts  in redesigning the way km-scale models provide their output, i.e. on hierarchical grids and in relatively small chunks.

We present specific ongoing work to implement this data handling strategy across HPC centers and outline the vision for the handling of high-volume climate model simulation output in the exascale era to enable the efficient analysis of the information content from these simulations. 

How to cite: Modali, K., Peters-von Gehlen, K., Ziemen, F., Saini, R., Grasse, S., and Schultz, M.: A cascaded framework for unified access to and analysis of kilometer scale global simulations across a federation of data centers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19677, https://doi.org/10.5194/egusphere-egu24-19677, 2024.

At the Helmholtz Association, we strive to establish a well-formed harmonized data space, connecting information across distributed data infrastructures. This requires standardizing the description of data sets with suitable metadata to achieve interoperability and machine actionability.

One way to make connections between datasets and to avoid redundancy in metadata is the consistent use of Persistent Identifiers (PIDs). PIDs are an integral element of the FAIR principles (Wilkinson et al. 2016) and recommended to refer to data sets. But also to other meta data such as people, organizations, projects, laboratories, repositories, publications, vocabularies, samples, instruments, licenses, and methods should be commonly referenced by PIDs, but not for all of these agreed identifiers exist. Consistently integrating the existing PIDs into data infrastructures can create a high level of interoperability allowing to build connections between data sets from different repositories according to common meta information. In HMC we start this process by implementing PIDs for people (ORCID) and organizations (ROR) in data infrastructures.

Harmonizing PID metadata, however, is only the first step in setting up a data space. Here we shed some light on which strategies we recommend for the implementation within the Helmholtz Association and make suggestions, which stakeholder groups should be included in order to hold them responsible for maintaining them to shape the Helmholtz Data Space. The conclusions from this process do not only affect the implementation of PID metadata, but may also be used for the harmonization of vocabularies, digital objects, interfaces, licenses, quality flags and others, in order to connect our global data systems, to redefine stakeholder responsibility and to ultimately reach the data space.

How to cite: Soeding, E., Poersch, A., Razeghi, Y., Kottmeier, D., and Malinovschii, S.: Reaching the Data Space – Standard data procedures and defining responsibilities for common data elements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16749, https://doi.org/10.5194/egusphere-egu24-16749, 2024.

An increasing pressure from governing bodies and funding agencies to disseminate research data in an open and FAIR (Findable, Accessible, Interoperable, and Reusable) format has led to an increase in online research portals of varying quality. The task of constructing and maintaining such portals is challenging, especially when left to individuals with limited understanding of modern web architecture. For those starting out on this endeavour, an over-abundance of online advice, coupled with the rapid evolution of “latest technologies”, can be overwhelming. The inevitable uncertainty leads to technologically-isolated portals with limited interoperability that ultimately hinders the exchange of geoscientific information.

To reduce uncertainty for new initiatives, Geoluminate (https://geoluminate.github.io/geoluminate/) – a new micro web framework – offers a simple but robust platform for the rapid creation and deployment of new geoscience research portals. The framework's simplicity ensures that even those with limited expertise in web development can create and maintain effective portals that exhibit consistency in both design and functionality. Geoluminate aims to foster interoperability, reliability and decentralization of geoscience portals by providing a consistent and stable foundation on which they are built.

Leveraging existing features of the Python-based Django Web Framework, Geoluminate offers a comfortable learning curve for those already familiar with Python programming. On top of the feature-rich ecosystem of Django, Geoluminate offers additional features specifically tailored to the needs of geoscientific research portals. Geoluminate is highly-opinionated and comes “batteries included” so that, as a research community, the focus can remain on designing data models that fit specific community needs and less on tedious implementation details.

Currently backed by the international geothermal community as part of the World Heat Flow Database Project (http://heatflow.world/project), Geoluminate is under active development at the GFZ German Research Centre for Geosciences in Potsdam. Under the guidance of the partner repository GFZ Data Services, all data models are intrinsically tied to existing standards of metadata collection (e.g. Datacite, IGSN, ROR, ORCID) such that data publishing is easily facilitated through established pathways.

Geoluminate champions the principles of open science and collaborative knowledge dissemination. This poster presentation aims to showcase the practical implementation and benefits of Geoluminate in creating geoscience research portals that align with FAIR data principles. By fostering a community-centric approach, Geoluminate contributes to the democratization of data management, enabling researchers to actively shape and enhance the landscape of those same portals they likely utilize in their own research.

How to cite: Jennings, S., Elger, K., Fuchs, S., Neumann, F., Norden, B., Frenzel, S., Maes, S., and Ott, N.: Geoluminate: A community-centric framework for the creation, deployment and ongoing development of decentralized geoscience data portals, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19849, https://doi.org/10.5194/egusphere-egu24-19849, 2024.

The National Research Data Infrastructure (NFDI) aims to create a sustainable and networked infrastructure for research data and helps to overcome the challenges associated with the storage, management and processing, security, and provision of research data in Germany [1]. It thus plays an important role in promoting open science and the exchange of FAIR research data. One of the NFDI initiatives is NFDI4Earth, which focuses on Earth System Sciences (ESS) [2]. Within the many ESS sub-disciplines, there is a diverse range of relevant high-quality data, services, tools, software, data repositories, as well as training and learning materials. Thus, it is not easy for researchers to find these various useful resources. Additionally, there is a lack of knowledge on how to use them due to an enormous diversity of standards, platforms, etc.

The NFDI4Earth OneStop4All addresses these issues by serving as the primary user-friendly access point (Web portal) to the relevant ESS resources. It gives a coherent overview of the (distributed) resources for research data management (RDM), and data analysis/data science that are made available by the members of the NFDI4Earth as well as the Earth System Science (ESS) community. In particular, the OneStop4All provides access to data and software repositories, subject-specific RDM articles and a learning management system for open educational resources relevant to ESS researchers. In addition, it guides users through the NFDI4Earth resources according to their specific ESS RDM and data science needs and capabilities. The OneStop4All also promotes seamless access to a distributed user support network.

The design and development of the OneStop4All is centered on the needs of the users. A good user experience requires an understanding of user behaviour, goals, motivations, and expectations and incorporating this knowledge into every stage of the design process. To achieve this, we use methods from user-centered design (UCD), complemented by knowledge and experience in various ESS disciplines from the members of the NFDI4Earth consortium, their extended scientific networks and by directly involving the community. 

We present the process of developing the user interface concept for the OneStop4All concerning usability and user experience and first insights into the platform are given.

References

[1] Agreement between the Federal Government and the Länder concerning the Establishment and Funding of a National Research Data Infrastructure (NDFI) of 26 November 2018: PDF-Datei 

[2] NFDI4Earth Consortium. (2022, Juli 7). NFDI4Earth - National Research Data Infrastructure for Earth System Sciences. Zenodo. https://doi.org/10.5281/zenodo.6806081

How to cite: Anders, I., Braesicke, P., Degbelo, A., Hassler, S. K., Henzen, C., Kleeberg, U., Ryan, M., and Thiemann, H.: Towards a user-friendly NFDI4Earth OneStop4All portal to support researchers in Earth System Sciences in Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17279, https://doi.org/10.5194/egusphere-egu24-17279, 2024.

Identifying, finding and gaining a sufficient overview of the functions and characteristics of data repositories and their catalogues is essential for users of data repositories and catalogues in the environmental and geosciences, as well as in other domains. However, achieving this is not trivial within a reasonable amount of time and effort, especially for less experienced users.  This lack of  transparent, human- and machine-friendly exposure of essential data repository information impacts many possible stakeholders that need up to date and reliable information about data repositories to serve a broad range of users. These include, for example, search engines and registries such as GEOSS, re3data or FAIRsharing.  Researchers need to be able to find FAIR enabling trustworthy repositories to deposit, curate and preserve their own digital objects, as well as  to reliably find FAIR data already gathered by others in order to reuse it. Assessment bodies such as CoreTrustSeal need transparent access to data repositories’ functions and characteristics in order to facilitate their certification process. An  overview of the data and metadata standards, exchange services and interfaces offered by repositories is essential to data scientists in order to effectively integrate these into their workflows. 

In this study we present how seemingly self-evident information about how the identity, purpose ('this is a data repository'), mandate and areas of responsibility of data repositories is exposed to humans and machines via websites and/or catalogues. Our findings are that  such information is difficult to find and in  many cases, machine-readable metadata is not clear, not relevant or missing altogether. We also show that despite all the efforts and successes in developing  discipline specific standards over the last decades, these are insufficiently linked to from more domain agnostic standards. This absence of domain specific information in PID systems and search engines makes it to large extent invisible in the FAIR ecosystem. In particular, relevant metadata representations or links to discipline specific, standardised services, such as the Open Geospatial Consortium (OGC) suite of services, are rarely exposed.

In this paper, we seek to present the simple and effective methods being elaborated within the FAIR-IMPACT project to improve this situation by using existing and emerging methods and standards. To this end, we will show effective ways that repositories can expose services information and standards via typed-link-based sign-posting as currently summarised in the FAIRiCAT approach. We will evaluate the options for implementation across  domain-specific metadata as well as domain-independent formats such as DCAT or schema.org and show how they can be used in combination with FAIRiCAT in practice. We will also present methods for exposing the FAIR status of digital objects and the FAIR-enabling and trustworthiness status of  data repositories to improve cooperation and information exchange between data repositories, registries, assessment providers and certification authorities.

How to cite: Huber, R., Gonzalez Beltran, A., Neidiger, C., Ulrich, R., and L’Hours, H.: Towards Transparent Presentation of FAIR-enabling Data Repository Functions & Characteristics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20387, https://doi.org/10.5194/egusphere-egu24-20387, 2024.

A core element of the National Research Data Infrastructure (NFDI) initiative in Germany is the ambition to harmonize the research data landscape not only on a national level, but to connect to and intertwine with international initiatives in Research Data Management (RDM).  In the context to increase the interoperability between different research data domains, metadata standardization, controlled vocabularies, application programming and the setup of different service interfaces are key areas of interest. As such, the NFDI is the German contributor to the European Open Science Cloud (EOSC), and strives to become a central contact point between German and international stakeholders. To achieve such a harmonized, interoperable and international data landscape, the NFDI Consortium for Earth System Sciences (NFDI4Earth) is open to promote common standards to the national Earth System Science (ESS) community and to support the development of new RDM pathways by connecting and actively participating in international initiatives. NFDI4Earth also strives to foster a cultural change towards increased awareness of FAIR (Findable, Accessible, Interoperable, Reusable) and Open Science principles in Germany. Having a user-friendly technical infrastructure, meaningful services, as well as up-to-date educational resources are all important elements in NFDI4Earth to advance the cultural shift in the ESS research community towards FAIR and open research data management.  Another important part of the cultural change is to acknowledge data and software publications as scientific merit and to recognize those as part of scientific achievements.  

How to cite: Protopopova-Kakar, V., Ott, F., Elger, K., Lorenz, M., and zu Castell, W.: The importance of interlinking Research Data Infrastructures and Research Data Management initiatives, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17495, https://doi.org/10.5194/egusphere-egu24-17495, 2024.

The ongoing digitalisation together with new methods for inter- and transdisciplinary research (e.g., AI, ML) triggered the development of large research infrastructures across the Earth and environmental sciences (e.g. EPOS, EnvriFAIR or the German NFDI), and to increasing demands for seamless data integration and visualisation that requires interoperability of data formats and the used of agreed metadata standards. Especially for data intensive disciplines in geophysics and geodesy, metadata standards are important and already in place and widely adopted (e.g. RineX/SineX formats for GNSS data and GeodesyML metadata for GNSS stations; mSEED format and FDSN metadata recommendations for seismological data). In addition, it becomes increasingly relevant to connect research outputs (papers, data, software, samples) with each other and with the originating researchers and institutions – in unique and machine-readable way. The use of persistent identifier (like DOI, ORCID, ROR, IGSN) and descriptive linked data vocabularies/ontologies in the metadata associated with research outcomes are strongly supporting these tasks.

In this presentation, we will elaborate the role and potential of domain-specific research data repositories for the process described above. Domain repositories are digital archives that manage and preserve curated research data (and/or software, sample descriptions) from specific scientific disciplines. The metadata associated with the DOI-referenced objects is specific for their domain and richer than generic metadata supposed to describe data across many scientific disciplines. They often offer data curation by domain researchers and data specialists and make sure that relevant persistent identifiers are included in the standardised XML or JSON metadata for data discovery that is complementing the disciplinary metadata described above.

Our example is GFZ Data Services, the domain repository for geosciences data, hosted at the GFZ German Research Centre for Geosciences. GFZ Data Services has several partnerships with large research international infrastructures, like EPOS, GEOROC, the World Heat Flow Database Project, and provides data publication services to several geodetic data services of the International Association for Geodesy (ICGEM, IGETS, ISG). Our examples clearly delineate the and the roles of each partner and the benefit of the partnership for the overarching task Open Science.

How to cite: Ott, F., Elger, K., Frenzel, S., Brauser, A., and Lorenz, M.: The role of research data repositories for large integrative research infrastructures, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19436, https://doi.org/10.5194/egusphere-egu24-19436, 2024.

In many scientific disciplines, physical samples represent the origin of research results. They record unique events in history, support new hypotheses, and are often not reproducible. At the same time, samples are essential for reproducing and verifying research results and deriving new results by analysing existing samples with new methodology. Consequently, the inclusion of sample metadata in the digital data curation processes is an important step to provide the full provenance of research results. The largest challenge is the lack of standardisation and the large variety of sample types and individuals involved: Most samples are collected by individual researchers or small groups that may have internal agreements for sample descriptions, but these might only be used for one expedition or within a small community, and rarely reach beyond institutional boundaries.

The International Generic Sample Number (IGSN, www.igsn.org) is a globally unique, resolving, and persistent identifier (PID) for physical samples with a dedicated metadata schema supporting discovery functionality in the internet. IGSNs allow data and publications to be linked directly to the samples from which they originate and provide contextual information about a particular sample on the internet.

The aim of the project FAIR WISH (FAIR Workflows to establish IGSN for Samples in the Helmholtz Association), funded by the Helmholtz Metadata Collaboration (HMC) was to work towards more standardisation of rich sample descriptions. Project outcomes include (i) standardised, rich and discipline-specific IGSN metadata schemes for different physical sample types within the Earth and Environmental sciences (EaE), (ii) workflows to generate machine-readable IGSN metadata from different states of digitisation and (iii) the FAIR Samples Template.

The FAIR SAMPLES Template enables metadata collection and batch upload of samples at various sample hierarchies (parent, children at different hierarchy levels) at once. The ability to fill the FAIR SAMPLES Template by individual researchers or research teams or to create scripts to fill it out directly from databases for a wide range of sample types makes the template flexible with a wide applicability. The structured metadata, captured with the FAIR SAMPLES Template and converted into XML files, already represents an important step for the standardisation of rich sample descriptions and their provision in machine-readable form.

Standardised workflows for metadata documentation and compliance with international metadata standards address the challenges associated with reproducibility of samples and their insufficient documentation. The developments within the FAIR WISH project provide a foundation for a more collaborative and integrated scientific enterprise. Future efforts in this area can build on this framework to further improve the accessibility and interoperability of sample data and advance the collective understanding of Earth's environmental processes.

How to cite: Brauser, A., Elger, K., Baldewein, L., Frenzel, S., Kleeberg, U., Heim, B., Norden, B., and Wieczorek, M.: Towards more standards for sample descriptions: The FAIR WISH project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18037, https://doi.org/10.5194/egusphere-egu24-18037, 2024.

Global compilations of geo- and cosmochemical data are increasingly leveraged to address exciting new research questions through data-analytics and machine-learning approaches. These invaluable datasets are maintained and made accessible as synthesis databases, such as GEOROC and PetDB catering to terrestrial igneous and metamorphic rocks; AstroMat Data Synthesis encompassing diverse astromaterial samples; and GeoReM a comprehensive resource for geochemical, environmental and biological reference materials. The GEOROC and PetDB databases for igneous and metamorphic rocks collectively aggregate data from thousands of publications, combining over 42 million single data values (major and trace elements, stable and radiogenic isotope ratios, radiometric ages) for bulk rock, glass, as well as minerals and their inclusions.

The diverse focus of these data systems include data from different sources and metadata makes data integration and interoperability challenging. The DIGIS and EarthChem projects are working towards designing machine-readable unified vocabularies for their data systems to achieve full interoperability. These vocabularies, associated with primary chemical data as well as geospatial, analytical and sample metadata, encompass many categories describing geographic location, sampling technique, lithology and mineral types, geological and tectonic setting, as well as analytes, analytical methods, reference materials, and more.

Wherever possible, external machine- and/or human-readable external vocabularies from respected authorities are incorporated, such as MinDat’s "Subdivisions of Rock," the International Mineralogical Association’s "List of Minerals" (Warr, 2021), and the International Union of Pure and Applied Chemistry’s chemical terminologies. For remaining categories, a set of local vocabularies are developed by our group (e.g. analytical methods, see Richard et al. 2023). The collaborative effort between DIGIS, EarthChem, and the Astromaterials Data System is leading to an advanced vocabulary ecosystem relating samples, data, and analytical methods in geo- and cosmochemical research that reaches from local- to community-driven and, eventually global connections.

Establishing a globally accepted vocabulary not only contributes to building interoperability between our existing geo-and cosmochemistry synthesis databases, but will also help pave the way toward interoperability with the GeoReM database, linking data with analytical methods and reference materials to provide means for data quality control and assessment of analytical uncertainty.

Finally, the unified vocabularies of EarthChem, GEOROC, and GeoReM will advance the creation of a global network of geochemical data systems as promoted by the OneGeochemistry initiative (Klöcking et al., 2023; Prent et al. 2022), connecting and integrating the broadest range of geoanalytical data generated, for example, in studies of environmental samples, archeological artefacts, or geohealth matters.

We report on these goals, achievements, state of advance, and challenges and seek community engagement and feedback.

References

Klöcking, M. et al. (2023). Community recommendations for geochemical data, services and analytical capabilities in the 21st century. In Geochimica et Cosmochimica Acta (Vol. 351, pp. 192–205).

Prent, A. et al. (2023) Innovating and Networking Global Geochemical Data Resources Through OneGeochemistry. Elements 19, Issue 3, pp. 136–137.

Richard, S. et al. (2023) Analytical Methods for Geochemistry and Cosmochemistry. Concept Scheme for Analysis Methods in Geo- and Cosmochemistry. Research Vocabularies Australia.

Warr, L. N. (2021). IMA–CNMNC approved mineral symbols. Mineralogical Magazine, 85(3), 291-320.

How to cite: Kallas, L., Klöcking, M., Profeta, L., Richard, S., Johansson, A., Lehnert, K., Luzi-Helbing, M., Sarbas, B., Sweets, H., Garbe-Schönberg, D., Willbold, M., and Wörner, G.: Unified Vocabularies for Geo- and Cosmochemical Data Systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18193, https://doi.org/10.5194/egusphere-egu24-18193, 2024.

The exponential growth of data due to technological developments along with an increased recognition of research data as relevant research output during the last decades substantiates fundamental challenges in terms of interoperability, reproducibility and reuse of scientific information. Being cross-disciplinary at its core, research in Earth System Science comprises divergent domains such as Paleontology, Marine Science, Atmospheric Sciences and Molecular Biology in addition to different types of data such as observation and simulation data. Within the various disciplines, distinct methods and terms for indexing, cataloguing, describing and finding scientific data have been developed, resulting in several controlled Vocabularies, Taxonomies and Thesauri. However, given the semantic heterogeneity across scientific domains, effective utilisation and (re)use of data is impeded while the importance of enhanced and improved interoperability across research areas will increase even further, considering the global impact of Climate Change to literally all aspects of everyday life. There is thus a clear need to harmonise practices around the development and usage of semantics in representing and describing information and knowledge.

Using Ontologies (as a formal mechanism for defining terms and their relations) can help to address this issue, especially with regard to discovery, comprehension and metadata enrichment. If used and maintained, Ontologies also encourage metadata standardisation, idealiter across Disciplines. Examples for enhanced search options include (but are not limited to): term relations for variables as well as for topics and locations; Synonyms and Homonyms; autocomplete function for search terms; support of multiple languages. Indexing of research data can be improved using Ontologies e.g. by proposing terms for variable names or measurement units. Depending on their richness, ontologies ease e.g. finding, comprehension, processing, and reuse, both for human users as well as for automatic reasoning and processing.

Ontologies can represent different levels of granularity, connecting domain specific Vocabularies as e.g. Climate Forecast conventions with generic Taxonomies for e.g. Scientific Disciplines or Funding Policies, thus extending the reach of scientific data to other user groups such as Journalists, Politicians or Citizens.

For a beneficial usage of semantic artefacts, sustainability is the key: any kind of terminology service must be maintained to guarantee that terms and relations are offered in a persistent way. But if they are, Vocabularies, Taxonomies, Thesauri and Ontologies can serve as a driving force for improved visibility and findability of research output within and across different research areas. Why Ontologies matter, what they are, and how they can be used will be depicted on our Poster in an easy-to-understand way.

How to cite: Lammert, A., Martens, C., and Loehden, A.: Benefits of Ontologies in Earth System Science, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18751, https://doi.org/10.5194/egusphere-egu24-18751, 2024.

Research data in the Earth System Sciences (ESS) are managed in diverse repositories with varying aims, publishing, and curation approaches, as well as technical solutions. The resulting heterogeneity often hampers implementing interoperability and harvesting concepts. From the researchers' perspective on integrative data-driven research questions across repository borders, this leads to ineffective search and reuse of the data. We consider it is vital to train researchers to provide high-quality FAIR data and metadata. However, it is even more important to enable repository providers to act as multipliers, as this enables them to provide researchers with suitable repository solutions. This can be done, for example, by implementing fit-for-purpose metadata schemas and interfaces.

In Germany, several initiatives serve as umbrellas for joint activities with ESS repository providers. In collaboration of the German national data infrastructure for Earth System Sciences (NFDI4Earth) and the Helmholtz Metadata Collaboration (HMC), we have developed a roadmap that enables repository providers to meet the needs of researchers and technical requirements. 

As an initial step, we developed recommendations in a community-driven process across NFDI4Earth and HMC. These recommendations provide common steps to foster interoperability, particularly with regard to search and harvesting. Moreover, we have identified a first set of use cases for specific types of ESS data that complement the developed recommendations, e.g. underway measurements of seawater temperature. Through regular updates in the form of community consultations and workshops, we will identify further community needs, as well as support updates and developments of metadata standards, e.g. the implementation of underway measurements in GeoDCAT. In this contribution, we will describe our recommendations, use cases, and lessons learned from the activities for a community-driven process to enable repository providers.

How to cite: Henzen, C., Degbelo, A., Grieb, J., Heß, R., Klammer, R., Koppe, R., Lorenz, C., and Müller, C.: When Metadata crosses Borders - Enabling Repository Providers with Joint Forces in Earth System Sciences, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20238, https://doi.org/10.5194/egusphere-egu24-20238, 2024.