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In this document we present our proposal of basic properties that should be part of every PID Kernel Information Profile and PID Record created in the framework of the Helmholtz Metadata Collaboration (HMC). By following these suggestions, we aim to establish a top-level commonality across all research fields in the Helmholtz Association allowing to base cross- community services on top. However, the results presented herein are not limited to the Helmholtz Association, but can also be adopted outside the Helmholtz Association in order to connect contents of data infrastructures. Before reading this document, we recommend to familiarize with basic terms and concepts like Persistent Identifiers, PID Kernel Information Profiles and FAIR Digital Objects as we will touch them only briefly.

Annotation of research data with rich metadata is important to make that data findable, accessible, interoperable, and reusable (Wilkinson et al. [2016]). This ensures the conducted research data is durable. Within the Helmholtz Association, the Helmholtz Metadata Collaboration (HMC) coordinates the mission to enrich Helmholtz-based research data with metadata by providing (information about) technical solutions, advice and ensuring uniform scientific standards for the use of metadata. In 2021, HMC conducted its first community survey to align its services with the needs of Helmholtz researchers. A question catalogue with 49 (sub-)questions was designed and disseminated among researchers in all six Helmholtz research fields. The conditional succession of the questions was aligned with predetermined expertise levels ("no prior knowledge", "intermediate prior knowledge", "high level of prior knowledge"). 631 completed survey replies were obtained for analysis. The HMC Community Survey 2021 provides insight into the management of research data as well as the data publication practices of researchers in the Helmholtz Association. The characterization of research-field-dependent communities will enable HMC to further develop targeted, community-directed support for the documentation of research data with metadata.

Five Helmholtz Centers are participating in the Research Field Energy, three of them are directly contributing to Hub Energy. To be well prepared for their supporting tasks in establishing a FAIR data ecosystem within the energy research community at Helmholtz, the team members of Hub Energy study relevant use cases and develop software tools in close cooperation with FAIR Data Commons. This poster presents four examples for this work: A photovoltaic system requires ontology development and data models based on standards like IEC 61850 or SensorML as well as on FAIR Digital Objects (FDO). In another use case, RO-Crates are automatically generated for data of the KIT Campus North energy and water consumption. The aim is to study methods for a detailed metadata desciption in data publication processes. In the field of software development, an FDO browser offers cascading search for metadata and application data entities and a metadata editor supports users in creating and editing schemas and instances as well. The presented activities foster close contact between Hub Energy and Helmholtz energy researchers and, thus, essentially support the formation of a FAIR energy data management. Use cases feed technical details into the Hub's energy knowledge pool and they are also a nearly perfect training programme for the Hub personnel. In doing the presented software development work, deep insights into energy data landscapes and an improved sense for user requirements are induced, even if in the end more elaborated and harmonized solutions from FAIR Data Commons may be adopted.

Annotation of research data with rich metadata is important to make that data findable, accessible, interoperable and reusable (Wilkinson et al., 2016), thereby rendering the carried out research more sustainable. Within the Helmholtz Association, the Helmholtz Metadata Collaboration (HMC) coordinates the mission to enrich Helmholtz-based research data with metadata by providing (information about) technical solutions, advise and ensuring uniform scientific standards for the use of metadata. In 2021, HMC conducted its first community survey to align its services with the needs of Helmholtz researchers. A question catalogue with 49 (sub-)questions was designed and disseminated among researchers in all six Helmholtz research fields. The conditional succession of the questions was aligned with predetermined expertise levels ("no prior knowledge", "intermediate prior knowledge", "high level of prior knowledge"). 631 completed survey replies were obtained for analysis. The HMC Community Survey 2021 provides insight into the management of research data as well as the data publication practices of researchers in the Helmholtz Association. The characterization of research-field-dependent communities will enable HMC to further develop targeted, community-directed support for the documentation of research data with metadata.

We present three use cases which showcase methods of providing a detailed metadata description with the goal of increasing the reusability of data. First, Hub Energy presents a photovoltaic system which required ontology development and the implementation of data models based on standards like IEC 61850 or SensorML as well as on FAIR Digital Objects (FDO). The backend was realized using the Metastore software from the Fair Data Commons while a FDO browser was implemented for visualization which offers a cascading search for metadata and application data. In a second use case of Hub Energy, time series data of the energy consumption of the buildings on KIT's Campus North are described by automatically generated RO-Crates. This allows energy researchers to use these time series data without any knowledge about the structure of the database and provides a case study on working with RO-Crate technology. The third use case is provided by Hub Matter, in the research field of high energy physics, and shows the optimization of a typical data set for data publication. To increase FAIRness of the distributed file set, (meta)data is (i) enriched by metadata, (ii) converted to a machine- as well as human-readable format and (iii) linked to a central file to create scientific context. By abstracting from community-specific details these measures can serve as a general approach to make data publishable. The variety of use cases presented provides a menu of technologies and approaches implemented in diverse contexts to enhance the reusability of data, along with general advice for anyone looking to do the same.

Ein Teil des Helmholtz-Inkubators Information und Data Science ist die Helmholtz Metadata Collaboration (HMC). HMC soll die Beschreibung von Forschungsdaten durch Metadaten zu deren besseren Auffindbarkeit vorantreiben sowie organisatorisch und technisch umsetzen. Metadaten sind essentielle Infor¬mationen über Forschungsdaten, die für deren Auffinden und Verstehen sowie für deren Vernetzung und Nachnut¬zung im Sinne der FAIR-Prinzipien erforderlich sind. Zur Umsetzung wird die wissenschaftliche Expertise zum Thema Metadaten aus einzelnen Fachdomainen in sogenannten Metadata Hubs der einzelnen Forschungsbereiche zusammengefasst, auf übergeordneter Ebene harmonisiert und, mit Hilfe zentral entwickelter Methoden und Werkzeugen, Metadatenplattformen bereitgestellt. Für den Forschungsbereich Energie ist der HMC Hub Energie verantwortlich. Aufgabe ist hierbei die vorhandenen Standards zur Energiedaten- und Metadatenbeschreibung, etablierte Beschreibungs- und Erfassungsprozesse sowie zugehörige Softwarewerkzeuge zu erfassen, Lücken zu identifizieren und Szenarien zur Ergänzung und Weiterentwicklung in der Domäne Energie zu entwerfen. Einheitliche Ziele von HMC sind die einfache und FAIRe Erschließung und Nutzung vorhandener und zukünftiger Datensammlungen der Forschungsbereiche sowie die Befähigung der Forschenden FAIRe Daten (semi-) automatisch zu erstellen. Das Poster beschreibt die Struktur von HMC allgemein und dem Hub Energie im speziellen, die entwickelten Methoden und Werkzeuge und gibt anhand von Anwendungsbeispielen Impulse für die Umsetzung der Methoden und Werkzeuge hin zu FAIRen Metadaten. Weiterhin werden Verknüpfungen zu Trainings- und Schulungsunterlagen von HMC hergestellt. Das Poster soll dazu einladen mit dem HMC Hub Energie Kontakt aufzunehmen um von den Arbeiten von HMC profitieren zu können.

To reach the declared goal of the Helmholtz Metadata Collaboration Platform, making the depth and breadth of research data produced by Helmholtz Centres findable, accessible, interoperable, and reusable (FAIR) for the whole science community, the concept of FAIR Digital Objects (FAIR DOs) has been chosen as top-level commonality across all research fields and their existing and future infrastructures. Over the last years, not only by the Helmholtz Metadata Collaboration Platform, but on an international level, the roads towards realizing FAIR DOs has been paved more and more by concretizing concepts and implementing base services required for realizing FAIR DOs, e.g., different instances of Data Type Registries for accessing, creating, and managing Data Types required by FAIR DOs and technical components to support the creation and management of FAIR DOs: The Typed PID Maker providing machine actionable interfaces for creating, validating, and managing PIDs with machine-actionable metadata stored in their PID record, or the FAIR DO testbed, currently evolving into the FAIR DO Lab, serving as reference implementation for setting up a FAIR DO ecosystem. However, introducing FAIR DOs is not only about providing technical services, but also requires the definition and agreement on interfaces, policies, and processes. A first step in this direction was made in the context of HMC by agreeing on a Helmholtz Kernel Information Profile. In the concept of FAIR DOs, PID Kernel Information is key to machine actionability of digital content. Strongly relying on Data Types and stored in the PID record directly at the PID resolution service, PID Kernel Information is allowed to be used by machines for fast decision making. In this session, we will shortly present the Helmholtz Kernel Information Profile and a first demonstrator allowing the semi-automatic creation of FAIR DOs for arbitrary DOIs accessible via the well-known Zenodo repository.

In the concept of FAIR Digital Objects, PID Kernel Information is key to machine actionability of digital content. Strongly relying on Data Types and stored in a PID record directly at the PID resolution service, allows PID Kernel Information to be used by machines for fast decision making. To make a first step into the direction of standardizing PID Kernel Information, the RDA Working Group on PID Kernel Information has defined a first proposal of a core Kernel Information Profile (KIP) together with a list of seven guiding principles helping to decide on which information could be part of a KIP and which information should be stored elsewhere. The Helmholtz Metadata Collaboration (HMC) Platform is a joint endeavor across all research areas of the Helmholtz Association, the largest association of large-scale research centers in Germany. The goal of HMC is to make the depth and breadth of research data produced by Helmholtz Centres findable, accessible, interoperable, and reusable (FAIR) for the whole science community. To reach this goal, the concept of FAIR Digital Objects has been chosen as top-level commonality across all research fields and their existing and future infrastructures. In order to fulfill this role, a common Helmholtz KIP has been agreed on serving as basis for all FAIR Digital Objects created in the context of HMC. This poster describes the Helmholtz KIP and elaborates on decisions leading to differences compared to the core KIP recommended by the RDA. While remaining mostly compatible to the RDA core KIP, the Helmholtz KIP adds some additional properties that satisfy the multidisciplinary environment it is made for. Thus, it serves as a good starting point for rolling out the FAIR Digital Object concept over all Research Data Management Infrastructures of the Helmholtz Association and beyond. In addition, the poster provides a first impression of a demonstrator, which is currently under development and should serve as showcase. In the first step, we will allow to transform arbitrary datasets from Zenodo into FAIR Digital Objects using our Helmholtz KIP. In a next step, we plan to also include datasets from infrastructures hosted at Helmholtz Centres to create a huge and unprecedented network of FAIR Digital Objects, which provides scientists with an incredible pool of linked and searchable research data. This work has been supported by the research program ‘Engineering Digital Futures’ of the Helmholtz Association of German Research Centers and the Helmholtz Metadata Collaboration Platform.