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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.

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.

Supporting Helmholtz’s research communities in making their data FAIR is one of the key missions of HMC. A multi-method approach combining quantitative and qualitative methods was developed to understand current data management practices in research field Matter. Quantitative information was obtained from data that was self-reported by Helmholtz’s researchers in the HMC Community Survey 2021. Complementary data on Open and FAIR data practices in research field Matter, gathered in a data mining approach, is visualized in a dashboard. Qualitative understanding of community-specific FAIR data practices was obtained from a manual FAIR assessment based on the FAIR Data Maturity Model. Here we report on a combined interpretation of HMC Hub Matter’s findings from this multi-method approach. Three key areas for future action by HMC Hub Matter are discussed, such as (1) bridging policy and practicability, (2) creating a culture of data reuse, and (3) monitoring and engaging with technical infrastructure.

A central mission of HMC is to support the data producers of the Helmholtz community in making their data FAIR. Developing a sustainable strategy for doing so requires a detailed understanding of community-specific practices, strengths, and limitations related to the application of each FAIR data guideline. We have applied the FAIR Data Maturity Model, developed by the respective RDA working group, to a prototypical data pipeline in the research field Matter. In our poster presentation, we would like to provide an overview of our approach and discuss the lessons learned that helped us identify key activities for meeting community needs.

Publishing data in a FAIR 1 way is already part of good scientific practice. While institutional policy as well as funding and publishing guidelines support this, scientist, technicians, and data stewards struggle to realize it when handling their research data. The reason is that the FAIR principles are high level principles and guidelines rather than concrete implementations. This is one of the key missions of HMC: support the Helmholtz community in making their data FAIR in an easy and comparable way. Developing a sustainable strategy for this requires a detailed understanding of practices, strengths, and deficiencies with respect to applying each of the FAIR principles. Here, tools that assess data FAIRness in comparison to a set of specific implementations in a quantitative fashion can help. When handling a dataset, such measures can aid the understanding of how FAIR a dataset actually is, as well as how to improve its FAIRness. In this Blitzlicht-Talk, HMC Hub Matter and Hub Information will jointly present insights, benefits, and pitfalls from applying and further developing such metrics. For this we used the F-UJI tool 2,3, a python-based development by the FAIRsFAIR project, in two complementary projects. In a first, "repository-based" approach, we evaluate data repositories based on the data contained. The analyzed results are then used towards informing infrastructural development towards improving data FAIRness. In a second, "publication-based" approach, data publications from individual research centers or specific fields are evaluated with F-UJI. The results are gathered and visualized in an interactive pilot dashboard. This helps to identify and quantify the usage of repositories by Helmholtz‘s research communities as well as to better support the development of relevant infrastructure for FAIR data practices. We discuss our experience from these automatic FAIR assessment approaches and compare them to complementary insights from a manual FAIR assessment of a particular data pipeline 4 using the FAIR Data Maturity Model 5. We discuss future plans for metric development and the potential use of such metrics in user-sided tooling. [1] https://doi.org/10.1038/sdata.2016.18 [2] https://doi.org/10.5281/zenodo.4063720 [3] https://doi.org/10.5281/zenodo.6461229 [4] https://doi.org/10.5281/zenodo.6059994 [5] https://doi.org/10.15497/rda00050

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.

The Ni self diffusion in glass forming Pd40Ni40S20, Pd37Ni37S26 and Pd31Ni42S27 melts was probed by incoherent, quasielastic neutron scattering over a temperature range between 773 and 1023 K. The Ni self diffusion coefficients are on a 10 amp; 8722;10 m2 s amp; 8722;1 10 amp; 8722;9 m2 s amp; 8722;1 scale and barely change with composition. Each composition exhibits an Arrhenius type temperature dependence of the Ni self diffusion coefficients, which results in activation energies ranging from EA 348 16 meV for Pd40Ni40S20 to EA 387 6 meV for Pd37Ni37S26. The structural relaxation shows a stretched exponential behavior even far above the liquidus temperatures. In addition, the viscosity of the Pd37Ni37S26 melt was measured under reduced gravity conditions. The diffusion calculated from the viscosity reveals a significant deviation from the measured Ni self diffusion by a factor between 4 and 8. This may indicate a dynamic decoupling between the atoms within the Pd Ni S equilibrium melts

Unter Verwendung von Monte Carlo Simulationen im semi-großkanonischen Ensemble wird die Verformung von mesoporösen Materialien als Folge der Sorption untersucht sowie der Einfluß der Verformung auf das Phasenverhalten des adsorbierten Fluids. Als Modell dient ein einfaches Fluid das von zwei planparallelen Wänden räumlich begrenzt wird. Die Wände bestehen aus einzelnen Wandteilchen, die in Größe und Wechselwirkung den Fluidteilchen entsprechen, d.h. sie wechselwirken wie die Fluidteilchen über (12,6)-Lennard-Jones-Potenziale. Die Wandteilchen sind nicht fest an bestimmten Gitterpunkten platziert, sondern über harmonische Potenziale an diese gebunden. Durch die Kraftkonstante des harmonischen Potenzials wird die Verformbarkeit der Wand von praktisch starr bis zu deformierbar eingestellt. Deformierbar heißt in diesem Zusammenhang, dass sich die Wandteilchen weiter von ihren Gitterpunkten entfernen und damit stärker auf das Fluid reagieren können. Die Porenverformung wird als Ensemble-Mittelwert über die Positionen der Wandteilchen berechnet und kann abhängig von der Wechselwirkung mit den Fluidteilchen eine Kontraktion oder eine Expansion der Pore anzeigen. Die isotherme Verformung während der Sorption steht in halb-quantitativer Übereinstimmung mit Daten von Röntgenbeugung-Experimenten, wenn einige Modell-Parameter optimiert werden. Es werden Verformungsisotherme über einen großen thermodynamischen Bereich berechnet, um die Ursache für die Porenverformung zu untersuchen: Solange sich das Fluid in einem gas-ähnlichen Zustand befindet, wird die Verformung durch die Benetzungseigenschaften des Fluids bestimmt. Am Punkt der Kapillarkondensation zieht sich die Pore aufgrund der attraktiven Wechselwirkung zwischen Fluid und Substrat zusammen. Befindet sich das Fluid in einem flüssigkeits-ähnlichen Zustand, ist die Porenverformung unabhängig von den Eigenschaften des Fluids und offenbart eine nanomechanische Eigenschaft des Substrats. Der Verlauf der Verformungsisotherme in diesem Bereich wird durch eine einfache thermodynamische Betrachtung erklärt. Andererseits hat die Verformbarkeit der Wände einen Einfluß auf das Phasenverhalten des räumlich begrenzten Fluids. Das Phasendiagramm für ein Fluid in einer starren Pore und das für ein Fluid in einer deformierbaren Pore werden berechnet. Unter Verwendung von Finite-Size-Scaling-Konzepten wird der kritische Punkt des Fluids im bulk sowie in räumlich begrenzter Geometrie mit hoher Genauigkeit bestimmt. Die deformierbare Pore hat, verglichen mit der starren Pore, einen Einfluß auf die Phasengrenzen des räumlich begrenzten Fluids und auf dessen kritische Dichte. Monte Carlo simulations in the semi-grand canonical ensemble are employed to investigate the sorption strain of mesoporous materials and their influence on the phase behaviour of the confined fluid. For this purpose, a simple fluid is considered which is confined between two parallel plane walls consisting of single wall particles. The wall and fluid particles are of the same type and interacting via Lennard-Jones(12,6) potentials. The wall particles are not fixed to their lattice sites but bound to them by harmonic potentials. By changing the force constant of this harmonic potential, we are able to control the softness of the wall from an almost rigid structure to more flexible walls. Flexible means that the wall particles can move farther from their equilibrium positions to react to the fluid to a greater extent. The pore strain is calculated as an ensemble average of the positions of the wall particles and may indicate either a contraction or an expansion of the pore, depending on the interaction between the fluid and the wall particles. By tuning the parameters of the model system, a strain isotherm is obtained which is in semi-quantitative agreement with the data of small-angle X-ray diffraction experiments. Strain isotherms over a wide temperature regime and thermodynamic conditions are computed to investigate the origin of sorption strain: if the confined fluid is in the gas-like phase, the strain is dominated by the wetting characteristics of the fluid whereas at capillary condensation the pore shrinks on account of the attractive fluid-wall interaction. Confining a liquid-like phase, the strain behaviour becomes independent of the fluid characteristics and exhibits a nanomechnical property of the confining medium. In this regime, the course of the strain isotherm is explained by a simple thermodynamic analysis. On the other hand, the deformability of mesopores has an impact on the phase behaviour of the confined fluid. The phase diagram for a fluid in a rigid pore and one in a deformable pore are computed. By using finite-size scaling concepts the location of the critical point is determined accurately for the fluid both in confinement and in bulk. Compared with rigid pores, deformable pores affect the phase boundaries of the confined fluid and have an impact on the critical density of the fluid.