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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.

The necessary transformation of energy systems towards net zero greenhouse gas emissions provides a plethora of new research challenges. New interconnections between different energy sectors, such as power, heat and mobility, increase the system's complexity. In this context, the digitalisation towards cyber-physical energy systems (CPES) alleviates change, and equally affects technical, social, and societal topics, as well as the mode of research in the CPES research community. Research efforts towards CPES heavily rely on modelling and (co-)simulation-based approaches. Tracking of models together with all data creates a complex software and data management challenge, which needs to be addressed in each research project. To this end, nfdi4energy covers the whole research and transfer cycle of projects in energy system research ranging from (1) identifying relevant competences for a project; (2) defining relevant scenarios and experimental setup; (3) integrating models and data; coupling tools and laboratories; (4) extracting results, facilitating public consultation; to (5) identifying research challenges for follow-up activities. We define the following key objectives: (1) Establish common research community services for FAIR data, models, and processes in energy system research and motivate its use in the community. (2) Allow traceability, reproducibility, and transparency of results for the scientific community as well as for the society, improving the overall FAIRness. (3) Enable and motivate the involvement of society for the identification and solution of relevant research questions. (4) Promote better collaboration and knowledge transfer between scientific research institutes and business partners via FAIR research data management. (5)Simplify identification, integration, and coordination of simulation-based models. (6) Integrate the provided services for energy system research within the wider NFDI ecosystem to improve cross-domain collaboration. To fulfil these objectives, nfdi4energy concentrates on five key services to provide a non-discriminatory access to digital research objects: (1) Competence to help to navigate the interdisciplinary research field, (2) Best Practices to get information about successful conduct of research including research data management, (3) Registry to find suitable data and software, (4) Simulation to couple existing simulations and, therefore, reuse software artefacts, and (5) Transparency to involve more stakeholders in all research stages. With these services, nfdi4energy aims to develop and provide an open and FAIR research ecosystem in the energy system domain containing a large share of common workflows from data gathering to the inclusion into research software, together with data publications for researchers spanning from single component development (like battery storage systems or other new smart grid equipment) up to system-of-systems research (based on mathematical or analytical models).

This paper is motivated by, but not limited to, the task of scheduling jobs organized in workflows to a computational grid. Due to the dynamic nature of grid computing, more or less permanent replanning is required so that only very limited time is available to come up with a revised plan. To meet the requirements of both users and resource owners, a multi-objective optimization comprising execution time and costs is needed. This paper summarizes our work over the last six years in this field, and reports new results obtained by the combination of heuristics and evolutionary search in an adaptive Memetic Algorithm. We will show how different heuristics contribute to solving varying replanning scenarios and investigate the question of the maximum manageable work load for a grid of growing size starting with a load of 200 jobs and 20 resources up to 7000 jobs and 700 resources. Furthermore, the effect of four different local searchers incorporated into the evolutionary search is studied. We will also report briefly on approaches that failed within the short time frame given for planning.

Power networks will change from a rigid hierarchic architecture to dynamic interconnected smart grids. In traditional power grids, the frequency is the controlled quantity to maintain supply and load power balance. Thereby, high rotating mass inertia ensures for stability. In the future, system stability will have to rely more on real-time measurements and sophisticated control, especially when integrating fluctuating renewable power sources or high-load consumers like electrical vehicles to the low-voltage distribution grid. In the present contribution, we describe a data processing network for the in-house developed low-voltage, high-rate measurement devices called electrical data recorder (EDR). These capture units are capable of sending the full high-rate acquisition data for permanent storage in a large-scale database. The EDR network is specifically designed to serve for reliable and secured transport of large data, live performance monitoring, and deep data mining. We integrate dedicated different interfaces for statistical evaluation, big data queries, comparative analysis, and data integrity tests in order to provide a wide range of useful post-processing methods for smart grid analysis. We implemented the developed EDR network architecture for high-rate measurement data processing and management at different locations in the power grid of our Institute. The system runs stable and successfully collects data since several years. The results of the implemented evaluation functionalities show the feasibility of the implemented methods for signal processing, in view of enhanced smart grid operation. © 2015, Maaß et al.; licensee Springer.