The EGU General Assembly 2024 brought together geoscientists from all over the world to one meeting covering all disciplines of the Earth, planetary, and space sciences. The EGU provides a forum where scientists, especially early career researchers, can present their work and discuss their ideas with experts in all fields of geoscience.
The WorldFAIR project was present with representatives from the coordination team and WP5 (Geochemistry), with participation in the following sessions:
Cross-Domain Standards, Tools, and Technical Approaches: EOSC “Climate Neutral and Smart Cities” and the WorldFAIR CDIF Framework
Arofan Gregory
The use of data across disiplinary boundaries is a challenge at many levels, but in order for researchers to make sense of often-unfamiliar data, they must be provided with a wealth of information regarding the provenance, methodology, structure, and semantics of data. Historically, such information has been modelled and implemented in different ways within different scientific domains. Approaches to geo-spatial data are especially problematic when we consider disiplines such as Environmental Science and Social Science. Recent work on cross-domain exchange of such metadata suggests that there are ways to improve this situation, making it far easier to support collaborative research.
The EOSC “Climate Neutral and Smart Cities” project has demonstrated how improved tools for describing provenance and data processing could be developed for researchers, based on existing metadata standards such as DDI Lifecycle and DDI Cross-Domain Integration (DDI-CDI). Some of the same standards – notably DDI-CDI – are also at the core of an emerging framework designed to address the needs of cross-domain FAIR data exchange. This framework, the Cross-Domain Inteoperability Framework (CDIF) , is being developed through the WorldFAIR project, which looks at eleven different domain use cases. It exemplifies the kind of interoperability framework recommended by the EC’s “Turning FAIR into Reality” report (doi: 10.2777/1524).
Collaborative research involving environmental, climate, and social data is increasingly relevant as we try to understand how our world is changing, and what policies will best help us to address these changes. Aligning our data management and documentation systems on emerging best practice will make this collaborative research easier and more effective, helping us to understand the issues we face.
FAIR Convergence using FAIR Implementation Profiles and the FAIR evolution pathways concept: lessons learned from the WorldFAIR Geochemistry Work Package
Alexander Prent and Rebecca Farrington
Interdisciplinary science missions rely on the ability to combine data from across many research domains. Convergence of data can be achieved through adoption of the FAIR principles for data assets, making them Findable, Accessible, Interoperable and Reusable. In order to make data FAIR beyond a limited number of researchers, a broader research community has to declare which schemas, data standards, protocols and other resources are used for metadata and data. These resources, when published, are FAIR Enabling Resources (FERs). Listing which FERs are used to make a dataset FAIR helps the community towards interoperability between datasets. FAIR Implementation Profiles (FIPs) list FERs for each FAIR principle through a systematic question and answer based form and can be the basis for comparing FERs used in different data assets.
Through comparison of different communities’ FIPs, mappings and crosswalks can be developed between datasets, resulting in interoperability between datasets. Employing a FIP comparison strategy enables a group to grow the FAIR data asset size. Comparing FIPs with regards to a specific community can help grow it in both size and complexity, adding additional community members and their related interoperable datasets. FAIRness here evolves both on data asset size as on the community complexity level.
Elaborating on this; intercommunity agreement on FER usage, or the development of mappings and crosswalks between FERs, increases the communities FAIRness, growing its complexity and size. Growth of FAIR data assets can be achieved when multiple datasets use the same FERs and become a FAIR data collection. Additionally, complexity of the FAIR community goes hand in hand with growth of the FAIR data asset as multiple groups are generally involved in the collation of multiple datasets. FAIRness also increases if FERs are aligned for data types from different instruments, resulting in their various methodologies also becoming interoperable. With FAIRness increasing between methodologies the community complexity generally increases as for the combining of datasets.
Here we will present key outcomes from the WorldFAIR Geochemistry Work Package on how FAIRness of a community and its constituent data assets can evolve along three pathways. FAIRness can be increased for the community (complexity), for data assets (size) and between methodologies or (sub)disciplines with FIPs as a means to document FERs used for community, data or methodologies in a structured manner, the comparative FIPs approach can form the basis for convergence and FAIR evolution on either of the three pathways.
FAIR to Enable Cross-Domain Research
Simon Hodson
The major global scientific and human challenges of the 21st century (including climate mitigation and adaptation, environmental sustainability, biodiversity and ecosystem management, disaster risk reduction, the interplay of society, the economy and energy policy) can only be addressed through cross-domain research that seeks to understand complex systems through machine-assisted analysis at scale. Our capacity for such analysis is currently constrained by the limitations in our ability to access and combine heterogenous data within and across domains. The FAIR principles and the frameworks set by Open Science provide a significant part of the solution. Attention needs to be paid to the interfaces where data is used between disciplines: the geosciences have a vital role to play in this work.
To help address these issues, CODATA has been entrusted by the International Science Council (ISC) to develop a programme of activity: ‘Making Data Work for Cross-Domain Grand Challenges’. After some exploratory work, the flagship activity is the WorldFAIR project which focuses on the implementation of the FAIR principles both within and across 11 different domain and cross-domain case studies, with a central effort to understand and guide cross-domain FAIR. It is the first broad-based effort to understand the issues around cross-domain and cross-infrastructure FAIR implementation through a case study driven methodology. Ultimately, WorldFAIR will provide guidance for FAIR implementation both within specific domains and infrastructures and across them. The necessity, affordances and opportunities for cross-domain research are often overlooked, partly due to entrenched academic disciplines. This presentation will outline a number of concrete examples of work to advance cross-domain interoperability of relevance to the geosciences community.
The I and the R of FAIR pose considerable challenges but are fundamental to addressing complex issues where datasets need to be combined and in enhancing scientific rigour and reproducibility. Consequently, increasing attention is being paid to semantics, the maintenance of referenceable vocabularies and ontologies and to metadata profiles—and to tools that facilitate the tracking of provenance and process, or that use variable level metadata and semantics to facilitate data integration. The semantics of space are particularly important in data linking and combination. WorldFAIR is also developing the Cross-Domain Interoperability Framework (CDIF) which identifies a set of functional requirements for interoperability, particularly for steps in data combination, and recommends good practices for each of these requirements, in relation to the use of existing or emerging standards and specifications. The CDIF is categorically not a new standard, but is intended to act as a lingua franca across domain data practices and encourage the incorporation of a number of standards that perform important and specific functions across domains. We are keen to test this approach with colleagues from as many disciplines and application areas as possible.
This talk will explore these developments in detail, make a case for the importance of further work on the I and the R of FAIR, and invite the geosciences research community to participate in the wider WorldFAIR initiative.
Cross-Domain Interoperability and Deeptime Digital Earth Data
Simon Hodson
This presentation, as part of a DDE-convened Union Symposium at EGU, will discuss avenues to pursue to enable greater Interoperability and Reusability of Deeptime Digital Earth Data, particularly in cross-domain research scenarios. CODATA is the Committee on Data of the International Science Council (ISC). Consequently, an important part of its mission is to engage with International Scientific Unions and related initiatives, on data issues. The ISC has entrusted CODATA to develop a programme of activity: ‘Making Data Work for Cross-Domain Grand Challenges’. After some exploratory work, the flagship activity is the WorldFAIR project which focuses on the implementation of the FAIR principles both within and across 11 different domain and cross-domain case studies. Other related work includes the recommendations on FAIR vocabularies, with the International Union for the Scientific Study of Populations (report https://doi.org/10.5281/zenodo.7818157), and in relation to the ISC-UNDRR Hazard Implementation Profiles. Similarly, CODATA is working with a number of International Scientific Unions, notably IUPAC, around the Task Group on Digital Representation of Units of Measure. The common threads of this work are both to encourage the adoption and implementation of the FAIR principles, and to explore the requirements for better enabling cross-domain research. Such work is of paramount importance: the major global scientific and human challenges of the 21st century (including climate mitigation and adaptation, disaster risk reduction, the interplay of society, the economy and energy policy) can only be addressed through cross-domain research that seeks to understand complex systems through machine-assisted analysis at scale. Our capacity for such analysis is currently constrained by the limitations in our ability to access and combine heterogenous data within and across domains. CODATA has recently concluded a Memorandum of Understanding with the Deeptime Digital Earth initiative. This agreement indicates a number of shared interests. Particularly important is collaboration among DDE, IUGS CGI (Commission for Geosciences Information), and the wider CODATA and FAIR communities on the further development and representation of key terminologies. Additionally, through a case study approach, DDE, IUGS CGI, CODATA and other partners plan to explore the applicability of the WorldFAIR methodology and the use of FAIR Implementation Profiles to understand FAIR requirements, progress and alignment. Finally, the applicability of the emergent Cross-Domain Interoperability Framework (CDIF) will be explored, and further refinements and recommendations made. This presentation will describe the context for this collaboration and outline the specific activities. It will be an important opportunity to socialise the community to this initiative, to get feedback and advice on the approach and to invite collaboration and expert input from the wider EGU community.
Related talk:
Semantic Interoperability Profiles as knowledge base for semantic solutions
Barbara Magagna, Marek Suchánek, and Tobias Kuhn
Central for research is the capability to build on existing research outcomes and to aggregate data from different sources to create new research findings. This is particularly true for environmental research, which tries to face global challenges like climate change and biodiversity loss by integrating diverse long-term monitoring and experimental data.
Interoperability is the ability of computer systems to exchange information but to get a shared understanding of the meaning of that information semantic interoperability is required. Shared understanding between all parties involved can be achieved using common standards like vocabularies, metadata and semantic models.
But how can researchers find out which standards are used and by whom? FAIR Implementation Profiles (FIPs), co-developed by GO FAIR Foundation and ENVRI-FAIR in 2020 (https://doi.org/10.1007/978-3-030-65847-2_13) and used by more than 120 communities so far like ENVRIs and WorldFAIR (see also https://fairdo.org/wg/fdo-fipp/), might be a good source of knowledge. This socio-technical approach drives explicit and systematic community agreements on the use of FAIR implementations including domain-relevant community standards, called FAIR-Enabling Resources. The FIP Wizard (https://fip-wizard.ds-wizard.org/) is implemented through the DSW open-source tool as a user interface by which the researcher is asked to answer questions related to each of the Principles by selecting FERs expressed as nanopublications. A nanopublication (https://nanopub.net/) is represented as a machine-interpretable knowledge graph and includes three elements: assertions, provenance, and publication info where in the context of FIPs the assertion contains essential metadata about a FER.
Using the same approach and technology but focusing on semantic interoperability aspects the Semantic Interoperability Profile (SIP) was developed in the context of the EOSC Semantic Interoperability Task Force to interview semantic or data management experts involved in research projects or infrastructures to collectively contribute to a knowledge base of interoperability solutions (https://doi.org/10.5281/zenodo.8102786). The SIP focuses on standards used to implement the Principle F2 (metadata) and the Interoperability Principles (I1, I2, I3 related to semantic artefacts) but queries also about the services used to generate, edit, publish, and transform them, altogether called FAIR Supporting Resources (FSRs). The survey is an ongoing effort and everybody can contribute to it via the SIP Wizard (https://sip-wizard.ds-wizard.org/). In summary, a SIP is a machine-interpretable collection of resources chosen by a community whereby the collection can be made specific for a data type and a semantic interoperability case study.
FAIR Connect (https://fairconnect.pro/) is being developed to provide a user-friendly, graphics rich dashboard and search engine on nanopublications of type FSR. It will enable users to find FSRs based on its type or label and will inform at the same time by which communities it is used. In a future iteration it will also enable filters on data types and case studies.
