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Establishing semantic data and knowledge graphs in scientific working groups is no easy feat. In most cases there is neither a user friendly tool chain nor experience with ontologies for the respective research field. But without a start, said experience can never be gained. The same is true for individuals that want to start into the field. We thus see knowledge graph development not as a task of expert individuals that already know everything, but as a collaborative (learning) process of working groups and organisations. At the start of this process the right ontologies are not known and the individuals do not yet have experience with expressing information in knowledge graphs. Thus, a tool chain must provide basic knowledge to help newcomers to get started. It must also support the learning process and the selection of terms and ontologies, while users are already working with their own data and metadata. Additionally, the tool chain must support cooperation and lateral transfer of knowledge within organisations and working groups as well as between working groups world wide. We therefore propose to establish a data infrastructure in every research organisation consisting of the following elements: An organisational knowledge graph, integration of (global) ID services, links to FAIR ontologies, policies, and a graph editing tool. This editing tool must support simultaneously the input of graph data, the extension of ontologies, the development of data structures, and finding and reusing existing ontologies and data structures not only from other persons inside the organisation but also from globally emerging metadata standards. While searching for a fitting term from a predefined set of ontologies, the tool would also allow for the creation of an internal term, when no fitting one is found. While trying to create a new term, fitting ones are automatically searched and proposed. The here proposed graph editing tool would provide the possibility to refactor existin

Data are an essential part of every scientific endeavour. An efficient and future oriented research data management is therefore essential in order to ensure long-term availability of the generated data. This in turn ensures the reproducibility of scientific results. In order to facilitate FAIR data management within the Helmholtz community the incubator platform “Helmholtz Metadata Collaboration (HMC)” was established. HMC develops and provides services, tools and trainings to support and improve FAIR (meta)data management in the Helmholtz Association and aligns these approaches with national and international approaches and initiatives (e.g. RDA, EOSC, NFDI) to ensure compatibility with international research communities. To achieve this goal, HMC builds its work along three strategic areas: (1) Assessing and monitoring the state of FAIR data across Helmholtz, (2) Facilitating the connectivity of Helmholtz research data, and (3) Transforming (meta)data recommendations into implementations. At the centres, HMC supports research communities and data professionals with six research-field specific hubs: At HZDR HMC is represented locally by a unit dedicated to research field Energy and remotely by a unit for research field Matter. In our poster we will illustrate how research and data professional communities at HZDR can benefit from HMC's services, tools and trainings.

A dataset is considered complete if, in addition to the pure data (e.g. measured values), further information, such as origin and license for use, is also available. This requires not only the complete collection of data and metadata, but also assistance or instructions for collection and use, as well as an infrastructure for storage, publication and unique identification. Such a "value chain" is made up of various processes that should be handled by people in defined roles. In this context, it is also necessary to provide a technical infrastructure which, on the one hand, generates a precise input mask for the user's specific case, but which, on the other hand, must direct general search queries in a targeted manner to the correct data records. And of course, this entire construct cannot be controlled without rules and documentation, and must be equipped with an adapted training offering and an intuitive user interface. The training offer is also aimed at people with roles or assigned tasks in the data management system to ensure that the roles are performed with the necessary quality by defining the necessary qualifications. A plausibility check, which takes into account the expected flexibility of the system, guarantees the consistency of the data records. In one of the highest levels of sophistication, the plausibility check is followed by a kind of self-healing mechanism that suggests a data set to the user according to the specifications or makes the user's re-intervention obsolete altogether. In this contribution, the focus will be on the roles and on the interaction with the data management system. All necessary qualifications and tasks are assigned to the roles. The advantage of the subdivision into roles is that it remains open whether the additional tasks are covered by existing or new personnel or whether one person holds several roles. This means that the specific personnel approach can be adapted to the institution's own needs.

Training and Outreach are important tasks of HMC. The CCT2 Training and Outreach takes care of that.

In this lesson we will look at the intricate relationship between (digital) research data, metadata and knowledge, discuss why metadata is critical in today’s research, as well as explain some of the technologies and concepts related to structured machine-readable metadata. Have you ever struggled to make sense of scientific data provided by a collaborator - or even understanding your own data 5 months after publication? Do you see difficulties in meeting the data description requirements of your funding agency? Do you want your data to have lasting value, but don’t know how to ensure that? Precise and structured description of research data is key for scientific exchange and progress - and also for the recognition of your effort in data collection. The solution: make your data findable, accessible, interoperable and reusable by describing them with metadata. This course is targeted at scientific staff and researchers from all fields who are interested in annotating their research data with well-structured and useful metadata.

The Energy Hub of the Helmholtz Metadata Collaboration (HMC) conducted interviews with various stakeholders from the Helmholtz Research Field Energy on the topic of research data management (RDM) in 2022. The intentions were to build and serve a metadata community in the energy research field and to extend the Helmholtz-wide survey conducted by HMC in 2021 Arndt et al., 2022). Besides the deeper insight into the current state of RDM and metadata handling at the Helmholtz sites relevant to the Energy Hub the interviews focused on the related needs and difficulties of researchers and their satisfaction with the current state. Furthermore, we tried to discover already existing workflows and software solutions, to establish contacts and to make HMC better known.

Making research reproducible and FAIR (Findable, Accessible, Interoperable, and Reusable) often requires more information than what is commonly published within scientific articles. There is a growing number of repositories for publishing additional material like data or code. However, articles are still at the center of most scientific work and thus efforts on gathering information which is important for reproducibility but not for the article itself are often only started at a later stage. This usually makes the collection more tedious, error-prone, and less comprehensive. In order to lower the barrier for recording all relevant information directly when it is generated, we propose a design for a data and metadata entry and editing tool. It should allow researchers to create metadata for the files and assets which they already have and offer a possibility for structured entry of new data. To support consistent (meta)data entry over time, the user will be able to create forms which can enforce comprehensiveness and correctness. Furthermore, data FAIRness is supported through the automated usage of established ontologies for (meta)data annotation. This will be done by a background process so the user isn´t involved in these technologies. Nevertheless the tool will grant further possibilities to those who are aware of ontologies used in their domain. Resources can be referenced consistently across (meta)data sets of many stakeholders through identifiers provided by central sources. In conjunction, the usage of these common identifiers and ontologies forms large knowledge graphs of the data recorded with our tool. This contribution will be a discussion of the various components of such a tool, potentially used metadata standards, possible variations, important features, and most relevant: How it can be useful for your work!

The necessity of exhaustive documentation of research data arises from an increasing depth of scientific understanding and investigations of unknown phenomena with research teams of different areas and fields. Different methods and definitions and insufficient documentation of field work, experimental and numerical examinations lead to information loss, especially over time. To counteract this problem the scientific community aims to make research data Findable, Accessible, Interoperable and Reusable (FAIR). Unfortunately infrastructure, tools, personnel and acceptability for these additional steps are often missing and result in the mentioned paucity of information and data. Within the Helmholtz Association the Helmholtz Metadata Collaboration (HMC) has taken on the task of building this infrastructure to support high quality data documentation and publication throughout the entire lifecycle of research data and to raise the awareness for necessary structural changes in the wider scientific community. One goal of HMC is the mapping of existing data management structures and demands in the different research fields of the Helmholtz Community. These fields are especially addressed with Hubs, being the connection between HMC and the specific needs of the research fields. Based on the collected information HMC will implement tools to assist scientists, data managers and IT administrators in making their research data FAIR. Furthermore members of HMC will connect with other (meta-)data initiatives to work towards necessary structural changes in the world of scientific research by e.g. defining standards. In this poster we will discuss the FAIR principles and introduce the Helmholtz Metadata Collaboration and their tasks. Also, we will show concrete examples from the geoscientific part of Hub Energy. The Hub in which we are active.

The necessity of exhaustive documentation of research data arises from an increasing depth of scientific understanding and investigations of unknown phenomena with research teams of different areas and fields. Different methods and definitions and insufficient documentation of field work, experimental and numerical examinations lead to information loss, especially over time. To counteract this problem the scientific community aims to make research data Findable, Accessible, Interoperable and Reusable (FAIR). Unfortunately infrastructure, tools, personnel and acceptability for these additional steps are often missing and result in the mentioned paucity of information and data. Within the Helmholtz Association the Helmholtz Metadata Collaboration (HMC) has taken on the task of building this infrastructure to support high quality data documentation and publication throughout the entire lifecycle of research data and to raise the awareness for necessary structural changes in the wider scientific community. One goal of HMC is the mapping of existing data management structures and demands in the different research fields of the Helmholtz Community. These fields are especially addressed with Hubs, being the connection between HMC and the specific needs of the research fields. Based on the collected information HMC will implement tools to assist scientists, data managers and IT administrators in making their research data FAIR. Furthermore members of HMC will connect with other (meta-)data initiatives to work towards necessary structural changes in the world of scientific research by e.g. defining standards. In this poster we will discuss the FAIR principles and introduce the Helmholtz Metadata Collaboration and their tasks. Also, we will show concrete examples from the geoscientific part of Hub Energy. The Hub in which we are active.

Die Patienteninformationssysteme (PIS) in der Arztpraxis entwickeln sich immer stärker zu einem multifunktionalen Werkzeug. Der rasche Zugriff auf relevante medizinische Information ist dabei von besonderer Bedeutung. Moderne Software und Internet-basierte Services ermöglichen heute den Einsatz dieser Informationswerkzeuge während der Konsultation. Die Entwicklung und Implementierung ist allerdings auf die Mitarbeit der Benutzer, das heisst der Ärzte angewiesen.

Plinian eruptions belong to the most devastating types of volcanic activity, thus posing a dramatic hazard for people and property far beyond the immediate surroundings of a volcano. Assessing volcanic hazards consists of a number of aspects. First, detailed isopach and isopleth mapping of past eruptions' deposits facilitates to constrain typical eruption behaviour in terms of eruption magnitudes, intensities, and affected areas. The second component integrates a variety of surveillance techniques, including seismic recording, compositional and thermal monitoring of quiescent gas emissions, ground deformation, etc. In many cases such monitoring efforts can signal that a volcano is building up towards an eruption, and may allow for a timely alert and mitigation actions. In addition to the knowledge on expected consequences an eruption might entail, and to the recording of physical processes, the hazard and risk evaluation strongly depends on the likelihood that an eruption may happen. It is therefore of pivotal interest to understand the chronological character of a volcano or a volcanic area. Statistical time series analyses of eruption records provide a powerful tool for meeting the growing demand for probabilistic eruption forecasting. The Central American Volcanic Arc (CAVA) has been site of numerous exceptionally explosive and voluminous plinian eruptions over the past 200 000. We here process the long-term plinian eruption record of the subduction system by statistical means, considering felsic and mafic eruption sequences separately. After ensuring that the necessary conditions for the analysis are fulfilled, the Kaplan-Meier method is applied to estimate survival functions. Subsequently, the exponential, the Weibull, and the log-logistic distribution are fit to the data. All functions are confirmed by goodness-of-fit tests to describe the data sets adequately well. Future eruption probabilities within a given time interval are then estimated from the survivor