<p>In September and October 2022, the Group on Earth Observations (GEO) Data Working Group implemented a dialogue series to raise awareness about the GEO Data Sharing and Data </p> <p>Management Principles and their benefits with special attention to usability and legal aspects of in-situ and remote sensing Earth Observation data. The intended dialogues’ audience included all Earth observation stakeholders, including data producers, technology providers, scientists, researchers, business developers, decision-makers, and policymakers. Each session covered the theory of the core topic and provided examples of existing implementations.  Leaders and experts presented, via short lightning talks, success stories related to in situ and remote sensing data, the GEO Data Sharing and Data Management Principles, and the FAIR, TRUST, and CARE principles. The diversity of the success stories related to different thematic domains and data types, fostering cross-innovation, and highlighting proven solutions - all serving to introduce discussions. Members of the Earth observation community shared their experiences in implementing the principles, discussed how they tackled challenges and described impacts. The covered topics addressed the data life cycle, the GEO Data Sharing Principles, and the GEO Data Management Principles; elements of Discoverability, Accessibility, Usability (Encoding, Documentation, Provenance, Quality Control), Preservation (Preservation, Verification); Curation (Review and processing, Identifiers); as well as the Data Management Self-Assessment Tool. Standards are instrumental for implementing data management best practices. Broadly sharing a common baseline of understanding and references, the dialogues support community engagement and interoperability. The dialogue series contributes to capacity development on data management in general. All materials, including recordings and presentations, are available on the GEO Knowledge Hub, Youtube, and are open to sharing on other community portals. This communication will present examples of implementations of the GEO DMP extracted from the dialog series as well as new dialogues that will be developed in 2023, with special attention to in-situ data challenges and integration with other data in global solutions.</p>

<p>In the distributed heterogeneous environmental data ecosystems, the number of data sources, volume and variances of derivatives, purposes, formats, and replicas are increasingly growing. In theory, this can enrich the information system as a whole, enabling new data value to be revealed via the combination and fusion of several data sources and data types, searching for further relevant information hidden behind the variety of expressions, formats, replicas, and unknown reliability. It is now visible how complex data alignment is, and even more, it is not always justified due to capacity and business issues. One of the challenging, but also most rewarding approaches is semantic alignment, which promises to fill the information gap of data discovery and joins. To formalise one, an inevitable enabler is an aligned, linked, and machine readable data model enabling the specification of relations between data elements generated information. The Iliad - digital twins of the ocean are cases of this kind, where in-situ data and citizen science observations are mixed with multidimensional environmental data to enable data science and what-if models implementation and to be integrated into even broader ecosystems like the European Digital Twin Ocean (EDITO) and European Data Spaces. An Ocean Information Model (OIM) that will enable traversals and profiles is the semantic backbone of the ecosystem. Defined as the multi-level ontology, it will explain data using well known generic (Darwin Core, WoT), spatio-temporal (SOSA/SSN, OGC Geo, W3C Time, QUDT, W3C RDF Data Cube, WoT) and domain (WORMS, AGROVOC) ontologies. Machine readability and unambiguity allow for both automated validation and some translations.<br>On the other hand, efficient use of this requires yet another skill in data management and development besides GIS, ICT and domain expertise. In addition, as the semantics used in the data and metadata have not yet been stabilised on the implementation level, it introduces a few more flexibilities of data expression. Following the GEO data sharing and data management principles along with FAIR, CARE and TRUST, the environmental data is prepared for harmonisation. Furthermore, to ease the entry and to harmonise conventions, the authors introduce a multi-touchpoint data value chain API suite with an aligned approach to semantically enrich, entail and validate data sets such as observations streams in JSON or JSON-LD based on OIM, through storage and scientific data in NetCDF to exposing this semantically aligned data via the newly endorsed and already successful OGC Environmental Data Retrieval API. The practical approach is supported by a ready-to-use toolbox of components that presents portable tools to build and validate multi-source geospatial data integrations keeping track of the information added during mesh-up and predictions and what-if implementations.</p>

Data Cubes for geospatial information provide the means to integrate observations and other types of geospatial data for use in multiple applications through simplified access and efficient analytics. Acknowledging that diverse implementations already exist, this Community Best Practice, developed by the OGC members and supported by at least 6 independent implementations listed in the document, defines requirements for a core Geospatial Coverage Data Cube infrastructure and guidelines for enhancements and extensions to the basic core. With this paper and other activities, OGC is contributing to the advancement of Data Cubes more generally and as a foundation for federations of Data Cubes for geospatial information.

In order to benefit from the vast amount of Earth observation data, faster transformations of data to information and knowledge is necessary. NextGEOSS data hub and platform enable this through its services. These services are delivered in a 5-step user experience that includes training and capacity building in each step. NextGEOSS is using various methods, such as online training, hackathons, and in person training integrated in a onboarding process applicable to all value chains, resulting in faster up-take of Earth observation data.

In the field of Earth observation, the importance of in situ data was recognized by the Group on Earth Observations (GEO) in the Canberra Declaration in 2019. The GEO community focuses on three global priority engagement areas: the United Nations 2030 Agenda for Sustainable Development, the Paris Agreement, and the Sendai Framework for Disaster Risk Reduction. While efforts have been made by GEO to open and disseminate in situ data, GEO did not have a general way to capture in situ data user requirements and drive the data provider efforts to meet the goals of its three global priorities. We present a requirements data model that first formalizes the collection of user requirements motivated by user-driven needs. Then, the user requirements can be grouped by essential variable and an analysis can derive product requirements and parameters for new or existing products. The work was inspired by thematic initiatives, such as OSCAR, from WMO, OSAAP (formerly COURL and NOSA) from NOAA, and the Copernicus In Situ Component Information System. The presented solution focuses on requirements for all applications of Earth observation in situ data. We present initial developments and testing of the data model and discuss the steps that GEO should take to implement a requirements database that is connected to actual data in the GEOSS platform and propose some recommendations on how to articulate it.