D2.6 First reference implementation of the data repositories features

<p>Interoperability has long been an objective for research infrastructures dealing with research data to foster open access and open science. More recently, FAIR principles (Findability, Accessibility, Interoperability and Reusability) have been proposed. The FAIR principles are now reference criteria for promoting and evaluating openness of scientific data. FAIRness is considered a necessary target for research infrastructures in different scientific domains at European and global level.</p><p>Solid Earth RIs have long been committed to engage scientific communities involved in data collection, standardization and quality management as well as providing metadata and services for qualification, storage and accessibility. They are working to adopt FAIR principles, thus addressing the onerous task of turning these principles into practices. To make FAIR principles a reality in terms of service provision for data stewardship, some RI implementers in EPOS have proposed a FAIR-adoption process leveraging a four stage roadmap that reorganizes FAIR principles to better fit to scientists and RI implementers mindset. The roadmap considers FAIR principles as requirements in the software development life cycle, and reorganizes them into data, metadata, access services and use services. Both the implementation and the assessment of “FAIRness” level by means of questionnaire and metrics is made simple and closer to day-to-day scientists works.</p><p>FAIR data and service management is demanding, requiring resources and skills and more importantly it needs sustainable IT resources. For this reason, FAIR data management is challenging for many Research Infrastructures and data providers turning FAIR principles into reality through viable and sustainable practices. FAIR data management also includes implementing services to access data as well as to visualize, process, analyse and model them for generating new scientific products and discoveries.</p><p>FAIR data management is challenging to Earth scientists because it depends on their perception of finding, accessing and using data and scientific products: in other words, the perception of data sharing. The sustainability of FAIR data and service management is not limited to financial sustainability and funding; rather, it also includes legal, governance and technical issues that concern the scientific communities.</p><p>In this contribution, we present and discuss some of the main challenges that need to be urgently tackled in order to run and operate FAIR data services in the long-term, as also envisaged by the European Open Science Cloud initiative: a) sustainability of the IT solutions and resources to support practices for FAIR data management (i.e., PID usage and preservation, including costs for operating the associated IT services); b) re-usability, which on one hand requires clear and tested methods to manage heterogeneous metadata and provenance, while on the other hand can be considered a frontier research field; c) FAIR services provision, which presents many open questions related to the application of FAIR principles to services for data stewardship, and to services for the creation of data products taking in input FAIR raw data, for which is not clear how FAIRness compliancy of data products can be still guaranteed.</p>

It is crucial for both the research community and the public that AV materials are archived in a FAIR manner. This paper is about AV materials contained in DANS’s EASY repository, and their depositing and archiving according to the FAIR principles. Using an Oral History collection in EASY as use case,, it is explored to what extent the FAIR principles are followed and where their implementation is challenging. Moreover, tools currently developed to help assess the FAIRness of datasets before or after deposition are introduced.

Addressing global environmental and societal challenges requires robust, interdisciplinary data ecosystems that support collaboration across geographic, cultural, and disciplinary boundaries. AuScope, Australia’s National Research Infrastructure (NRI) provider for the geoscience community, collaboratively tackles interdisciplinary grand challenges such as climate change, natural resource security, and natural hazards. AuScope is funded by the Australian Government’s National Collaborative Research Infrastructure Strategy (NCRIS) and integrates tools, data, analytics, and services across Australian research and government agencies, in particular, partnering with organisations at the forefront of research data systems and infrastructure.Through collaborations with CSIRO, Geoscience Australia, state and territory geological surveys, universities, and other NCRIS facilities, including the National Computational Infrastructure (NCI), the Terrestrial Ecosystem Research Network (TERN), and the Australian Research Data Commons (ARDC), AuScope is addressing the complexities of modern FAIR data management at scales ranging from small scale local installations to co-located High Performance Compute and Data (HPCD) Platforms. Key AuScope initiatives such as Geophysics 2030 Collections (https://ardc.edu.au/project/2030-geophysics-collections/), AusGeochem (https://ausgeochem.auscope.org.au/), the Modelling Atlas of The Earth (M@TE; https://mate.science), and the AuScope Data Repository (https://repository.data.auscope.org.au/) exemplify how the FAIR principles can be operationalised to support impactful research both within and beyond the geosciences and at multiple scales.Nationally, AuScope collaborates with other Earth and Environmental Research Infrastructure providers, working to transform Australia’s research capabilities through, for example, Coastal Research Infrastructure (CoastRI) and implementing the National Digital Research Infrastructure Strategy (NDRI). Globally, AuScope contributes to initiatives like OneGeochemistry, the CODATA-led WorldFAIR Plus project, EarthScope (US), EPOS, Geo-INQUIRE, and ChEESE (Europe), ensuring compatibility with international research infrastructures, data standards, and best practices while at the same time, aligning with Australia’s geoscience priorities. This presentation will highlight how AuScope is progressively operationalising the FAIR and TRUST principles across its investments by focusing on place-based research to foster interoperability, strategic collaboration, and Open Science practices. By aligning with the CARE principles as well as advancing collaborative data infrastructure, AuScope creates trusted, interoperable data ecosystems that empower researchers to effectively and efficiently address pressing interdisciplinary societal challenges at both a national and international scale.

FAIR principles have become reference criteria for promoting and evaluating openness of scientific data and for improving datasets Findability, Accessibility, Interoperability, and Reusability. This also applies to Research Infrastructures (RIs) in the solid Earth domain committed to provide access to seismological data, ground deformations inferred from terrestrial, and satellite observations, geological maps, and laboratory experiments. Such RIs have been indeed committed for a long time, well before the appearance of FAIR principles, to engage scientific communities involved in data collection, standardization, and quality control as well as in implementing metadata and services for qualification, storage and accessibility. By addressing open science and managing scientific data, they are working to adopt FAIR principles, thus having the onerous task of turning these principles into practices. In this work we argue that although FAIR principles have the merit of creating a common background of knowledge to engage communities in providing data in a standard way thus easing interoperability and data sharing, in order to make the adoption of FAIR principles less onerous there is an urgent need of clear models, reference architectures and technical guidelines which can support RI implementers in the realization of FAIR data provision systems. We therefore discuss the state of the art of FAIR principles ecosystem and open new perspectives by discussing a four-stages roadmap that reorganizes FAIR principles in a way that better fits to the approach of RI implementers, and a FAIR adoption process that relates FAIR principles to technologies for their implementation.

In 2023, a team from a local grant-funded medical data repository requested guidance from Penn Libraries on evaluating the extent to which their repository was FAIR-enabling. After a consultation with the repository team, our research data experts discovered that many of the current self-assessments of the FAIR guidelines were for data creators rather than data repository managers. In addition, we wanted a self-assessment tool similar to the process and guidance created by CoreTrustSeal but focusing explicitly on the FAIR Principles. In answer to their request, the Penn Libraries Research Data Engineer conducted a literature review and coalesced current guidance and assessment tools on the principles. After this review of the existing documentation, a small team developed a FAIR Principles self-assessment tool for repository teams. In addition to several iterations of the tool, we also met with the repository team for feedback on making the tool more understandable. Our conversation provided insights into the challenges of explaining the FAIR Principles to those without information or data science backgrounds. The discussion and creation of this self-assessment tool helped develop a more transparent and trustworthy repository. This paper will discuss our process for developing the assessment, the goals for utilizing the tool, and the lessons learned. Reporting our findings as they currently stand will prompt the research data management field to ruminate on the adoption of FAIR Principles for data repositories. We also intend to encourage conversation on the usability of the FAIR Principles for professionals without an information or data science background.

Previously, collecting, storing, owning and, if necessary, digitizing data was vital for any data-driven application. Nowadays, we are swimming in data, whereby one could postulate that we are drowning. However, downloading vast data to local storage and subsequent in-house processing on dedicated hardware is inefficient and not in line with the big data processing philosophy. While the FAIR principles are fulfilled as the data is findable, accessible, and interoperable, the actual reuse of the data to gain new insights depends on the data user’s local capabilities. Scientists aware of the potentially available data and processing capabilities are still not able to easily leverage these resources as required to perform their work; while the analysis gap entailed by the information explosion is being increasingly highlighted, remediation lags.The core objective of the FAIRiCUBE project is to enable players from beyond classic Earth Observation (EO) domains to provide, access, process, and share gridded data and algorithms in a FAIR and TRUSTable manner. To reach this objective, we are creating the FAIRiCUBE HUB, a crosscutting platform and framework for data ingestion, provision, analysis, processing, and dissemination, to unleash the potential of environmental, biodiversity and climate data through dedicated European data spaces.In order to gain a better understanding of the various obstacles to leveraging available assets in regard to both data as well as analysis and processing modalities, several use cases have been defined addressing diverse aspects of European Green Deal (EGD) priority actions. Each of the use cases has a defined objective, approach, research question and data requirements.The use cases selected to guide the creation of the FAIRiCUBE HUB are as follows:Urban adaptation to climate change Biodiversity and agriculture nexus Biodiversity occurrence cubes Drosophila landscape genomics Spatial and temporal assessment of neighborhood building stock Many of the issues encountered within the FAIRiCUBE project are formally considered solved. Catalogues are available detailing the available datasets, standards define how the datasets are to be structured and annotated with the relevant metainformation. A vast array of processing functionality has emerged that can be applied to such resources. However, while all this is considered state-of-the-art in the EO community, there is a subtle delta blocking access to wider communities that could make good use of the available resources pertaining to their own domains of work. These include, but are not limited to:Identifying available data sources Determining fitness for use Interoperability of data with divergent spatiotemporal basis Understanding access modalities Scoping required resources Providing non-gridded data holdings in a gridded manner There is great potential in integrating the diverse gridded resources available from EO sources within wider research domains. However, at present, there are subtle barriers blocking this potential. Within FAIRiCUBE, these issues are being collected and evaluated, mitigation measures are being explored together with researchers not from traditional EO domains, with the goal of breaking down these barriers, and enabling powerful research and data analysis potential to a wide range of scientists.

The GEOROC synthesis database, a pioneering open-access resource for geochemical and isotopic data, marks 25 years of service to the geoscience community. Over its history, GEOROC has compiled data from more than 22,750 publications in the field of geochemistry, and provides free access to over 39 million individual data values, primarily on igneous and metamorphic rocks, minerals and their inclusions. As a cornerstone for interdisciplinary research, GEOROC is complementary to other geochemical synthesis databases like PetDB, AstroMat and GeoReM, in facilitating reuse of data for innovative studies that leverage data analytics and machine-learning approaches across geoscientific disciplines and beyond.The Digital Geochemical Data Infrastructure (DIGIS) project for GEOROC 2.0 is providing an up-to-date IT infrastructure that aligns GEOROC with the FAIR principles. Data findability and accessibility are ensured through the newly developed API and the improved GEOROC web interface that allows users to retrieve a variety of distinct data products and services, including a fully customizable search functionality. Interoperability is achieved via implementation of a feature-based data model compatible with the OGC Observations and Measurements standard and controlled, machine-readable vocabularies that harmonize geospatial, analytical and sample-related metadata, and enabling seamless integration in multiple databases and portals (e.g., EarthChem). Reusability is further supported by archiving time-stamped GEOROC data products in the DIGIS Data Repository, hosted by GFZ Data Services, where datasets with digital object identifiers (DOIs) are archived for the long-term. Additionally, researchers are encouraged to directly submit new or already “published” datasets to this domain repository—through standardized (meta-)data templates, ensuring high-quality data submissions that facilitate data quality assessment and reuse.In collaboration with national and global initiatives, such as OneGeochemistry and NFDI4Earth, the DIGIS project further promotes practical approaches to the FAIR principles for geochemistry by developing unified controlled vocabularies for geochemical data and their metadata (e.g., analytical methods, sample description, location). These vocabularies also integrate external standards, such as the International Mineralogical Association’s "List of Minerals" and MinDat’s "Subdivisions of Rocks," alongside newly developed (and published) frameworks for categories such as geological setting and analytical methods (collaboration with EarthChem). By harmonizing metadata across geospatial, analytical and sample-related categories, these efforts ensure consistency, improve data quality assessment and control and enhance interoperability across data systems, including but not limited to GEOROC, PetDB, and AusGeochem. Such advancements expand the potential applications of geochemical data, fostering innovation in fields such as environmental science, remote sensing, archaeology and geohealth.With 25 years of experience and ongoing innovation through the DIGIS project, the GEOROC database exemplifies how operationalizing the FAIR principles enhances its value as a critical resource for the geoscience community. By providing both FAIR and open data, GEOROC empowers researchers to conduct reproducible, impactful studies and fosters interdisciplinary collaboration, driving innovation and advancing progress across the geosciences.

The FAIR principles (Findability, Accessibility, Interoperability, and Reusability) constitute a guide whose aim is to improve the management of digital scholarly resources. Nevertheless, the literature regarding data services other than data repositories is still scarce.OpenEdition is a digital infrastructure for open scholarly communication in the Social Sciences and Humanities (SSH) that carried out an internal full review to assess the degree of FAIRness of its activities. The objective of this paper is to present the methodology employed by OpenEdition’s team and the recommendations for the FAIRification of a publishing system, and hence, the elements for the FAIR Publishing Toolkit. The FAIR review was conducted in three main phases: preparation, assessment, and result phase, which listed the recommendations for the FAIR principles implementation. The preparation phase gathered the available information to define the perimeter of the FAIR review. It comprised two steps: the landscape study and the exam of actual use cases. The assessment phase contextualized the FAIR principles according to the scholarly publishing context, defined the datasets to be analyzed, carried outa FAIR maturity review per dataset, and analyzed the state of the art of some important FAIR-related elements. The result phase produced the recommendations, organized as priorities and extended objectives. The priority recommendations regard persistent identifiers and licensing policies. The extended objectives focus on authors' information management, controlled vocabularies, machine-actionability, and Digital Management Plans.

The topic of data storage, traceability, and data use and reuse in the years following experiments is becoming an important topic in Europe and across the world. Many scientific communities are striving to create open data by the FAIR principles. This is a requirement from the European Commission for EU-funded projects and experiments at EU-funded research infrastructures (RIs) and from many national funding agencies. This is challenging for users of large-scale RIs such as neutron, photon, synchrotron and free-electron laser facilities. Users of photon and neutron (PaN) RIs employ a wide range of scattering, imaging and spectroscopic methods investigating the behaviour of matter with a broad scientific base across physics, chemistry and biology, including engineering, environmental, cultural heritage and medical applications. They produce large data volumes of up to 1 PByte per day in some cases. To ensure all these data are FAIR requires an enormous effort from PaN RIs. It requires not only the expansion of data storage capacity, but also the development and deployment of software for effective data storage, metadata schemes and implementation of effective data pipelines at each individual experiment across RIs. FAIR data also affect the carbon footprint related to large amounts of data and raise questions related to user authentication, rights of access and cyber security. The RIs alone cannot achieve such a transformational process. For successful open science, cooperation of the user communities is essential as they need to create and utilize existing tools to deliver FAIR data. In this white paper, the European PaN community outline and discuss the role and responsibilities of the users and RIs and their common accountability to achieve FAIR data. This paper shall serve as a starting point for a common user and RI approach on the European scale to achieve FAIR data.

Making fair data a reality for photons and neutrons Andy Götz, the coordinator of PaNOSC for ESRF, looks at FAIR data – an initiative to deliver research data to the scientific community to use. The scientific world is undergoing a major revolution with the widespread adoption of the so-called FAIR principles for research data. FAIR stands for Findable, Accessible, Interoperable and Reusable and was first published in a paper in Nature in 2016(1). The catchy mnemonic was originally proposed at a Lorentz Workshop in Leiden, The Netherlands, in January 2014, named: Jointly designing a Data FAIRport. In a nutshell, the FAIR principles were proposed to ensure research data are made available to the scientific community in such a way that they can be found, downloaded, understood and reused.

BackgroundMany organizations face challenges in managing and analyzing data, especially when relevant datasets arise from multiple sources and methods. Analyzing heterogeneous datasets and additional derived data requires rigorous tracking of their interrelationships and provenance. This task has long been a Grand Challenge of data science and has more recently been formalized in the FAIR principles: that all data objects be Findable, Accessible, Interoperable, and Reusable, both for machines and for people. Adherence to these principles is necessary for proper stewardship of information, for testing regulatory compliance, for measuring the efficiency of processes, and for facilitating reuse of data-analytical frameworks.FindingsWe present the Contextual Ontology-based Repository Analysis Library (CORAL), a platform that greatly facilitates adherence to all 4 of the FAIR principles, including the especially difficult challenge of making heterogeneous datasets Interoperable and Reusable across all parts of a large, long-lasting organization. To achieve this, CORAL's data model requires that data generators extensively document the context for all data, and our tools maintain that context throughout the entire analysis pipeline. CORAL also features a web interface for data generators to upload and explore data, as well as a Jupyter notebook interface for data analysts, both backed by a common API.ConclusionsCORAL enables organizations to build FAIR data types on the fly as they are needed, avoiding the expense of bespoke data modeling. CORAL provides a uniquely powerful platform to enable integrative cross-dataset analyses, generating deeper insights than are possible using traditional analysis tools.

Towards FAIR Data Access

The FAIR Data Principles propose that all scholarly output should be Findable, Accessible, Interoperable, and Reusable. As a set of guiding principles, expressing only the kinds of behaviours that researchers should expect from contemporary data resources, how the FAIR principles should manifest in reality was largely open to interpretation. As support for the Principles has spread, so has the breadth of these interpretations. In observing this creeping spread of interpretation, several of the original authors felt it was now appropriate to revisit the Principles, to clarify both what FAIRness is, and is not.

As communities increasingly aim to understand and demonstrate how their data repositories support the FAIR Principles, decision-makers need tools that provide a clear, actionable snapshot. This paper introduces FIP Check, a rubric-based tool for assessing FAIR Implementation Profiles (FIPs) through a structured assessment of FAIR Enabling Resources (FERs). Built on the FIP ontology, the FIP Check brings three key innovations to FAIR assessment. First, it enables granular and practical evaluation by using a FAIR Principle-aligned rubric to assess individual FERs. Second, it measures partial FAIRness through a progressive scoring scale that captures varying levels of FAIR alignment, offering constructive, context-aware feedback rather than purely binary results. Additionally, this tool complements existing FAIR assessment tools by focusing on FERs as the units of assessment rather than on entire repositories or datasets. Third, it ensures transparent and inspectable balance by embedding expert-informed assessments within a standardized rubric and making all scoring decisions directly visible. FIP Check was piloted across seven biomedical data repositories, testing its utility in identifying strengths and actionable gaps, and supporting more informed FAIR improvement efforts. It enables communities to assess their current practices and plan targeted enhancements. By providing detailed insights into how individual FERs contribute to FIP’s FAIR alignment, the FIP Check transforms the FAIR Principles from abstract ideals into practical guidance that support strategic alignment, foster shared understanding, and encourage repository owners to see their resources not only as providers of data, but as infrastructure components.

The adaptive immune receptor repertoire community as a model for FAIR stewardship of big immunology data