Abstract: Motivation Results of simulations with climate models form the most important basis for research and statements about possible changes in the future global, regional and local climate. These output volumes are increasing at an exponential rate (Balaji et al. 2018, Stevens et al. 2019). Efficiently handling these amounts of data is a challenge for researchers, mainly because the development of novel data and workflow handling approaches have not proceeded at the same rate as data volume has been increasing. This problem will only become more pronounced with the ever increasing performance of High Performance Computing (HPC) - systems used to perform weather and climate simulations (Lawrence et al. 2018). For example, in the framework of the European Commission's Destination Earth program the Digital Twins (Bauer et al. 2021) are expected to produce hundreds of terabytes of model output data every day at the EuroHPC computing sites. The described data challenge can be dissected into several aspects, two of which we will focus on in this contribution. Available data in the Earth System Sciences (ESS) are increasingly made openly accessible by various institutions, such as universities, research centres and government agencies, in addition to subject-specific repositories. Further, the exploitability of weather and climate simulation output beyond the expert community by humans and automated agents (as described by the FAIR data principles (F-Findable, A-Accessable, I-Interoperable, R-Reusable), Wilkinson et al. 2016) is currently very limited if not impossible due to disorganized metadata or incomplete provenance information. Additionally, developments regarding globally available and FAIR workflows in the spirit of the FAIR Digital Object (FDO) framework (Schultes and Wittenburg 2019, Schwardmann 2020) are just at the beginning. Cultural Change In order to address the challenges with respect to data mentioned above, current efforts at DKRZ (German Climate Computing Center) are aimed at a complete restructuring of the way research is performed in simulation-based climate research (Anders et al. 2022, Mozaffari et al. 2022, Weigel et al. 2020). DKRZ is perfectly suited for this endeavor, because researchers have the resources and services available to conduct the entire suite of their data-intensive workflows - ranging from planning and setting up of model simulations, analyzing the model output, reusing existing large-volume datasets to data publication and long-term archival. At the moment, DKRZ-users do not have the possibility to orchestrate their workflows via a central service, but rather use a plethora of different tools to piece them together. Framework Environment Freva The central element of the new workflow environment at DKRZ shall be represented by the Freva (Free Evaluation System Framework) software infrastructure, which offers standardized data and tool solutions in ESS and is optimized for use on high-performance computer systems (Kadow et al. 2021). Freva is designed to be very well suited to the use of the FDO framework. The crucial aspects here are: the standardisation of data objects as input for analysis and processing, the already implemented remote access to data via a Persisitent Identifier (PID), the currently still system-internal capture of analysis provenance and the possibility of sharing results but also workflows by research groups up to large communities. the standardisation of data objects as input for analysis and processing, the already implemented remote access to data via a Persisitent Identifier (PID), the currently still system-internal capture of analysis provenance and the possibility of sharing results but also workflows by research groups up to large communities. It is planned to extend the functionality of Freva so that the system automatically determines the data required for a specific analysis from a researcher’s research question (provided to the system via some interface), enquires available databases (local disk or tape, cloud or federated resources) for that data and retrieves the data if possible. If data are not available (yet), Freva shall be able to automatically configure, set up and submit model simulations to the HPC-System, so that the required data is created and becomes available (cf. Fig. 1). These data will in turn be ingested into Freva’s data catalog for reuse. Next, Freva shall orchestrate and document the analysis performed. Results will be provided either as numerical fields, images or animations depending on the researcher’s need. As a final step, the applied workflow and/or underlying data are published in accordance with the FAIR data guiding principles. FDOs - towards a global integrated Data Space To make the process sketched out above a reality, application of the FDO concept is essential (Schwardmann 2020, Schultes and Wittenburg 2019). There is a long tradition in the ESS community of global dissemination and reuse of large-volume climate data sets. Community standards like those developed and applied in the framework of internationally coordinated model intercomparison studies (CMIP) allow for low-barrier reuse of data (Balaji et al. 2018). Globally resolvable PIDs are provided on a regular basis. Current community ESS standards and workflows are already close to being compatible with implementing FDOs, however, now we also have to work on open points in the FDO concept, which are: the clear definition of community-specific FDO requirements including PID Kernel Types specifications, the operation of data type registries and the technical implementation requirements for global access to FDOs. the clear definition of community-specific FDO requirements including PID Kernel Types specifications, the operation of data type registries and the technical implementation requirements for global access to FDOs. With these in place and implemented in Freva following standardized implementation recommendations, automated data queries across spatially distributed or different types of local databases become possible. We introduce the concept of implementations in Freva and also use it to highlight the challenges we face. Using an example, we show the vision of the work of a scientist in earth system science .

Abstract: Photosynthetic complex I enables cyclic electron flow around photosystem I, a regulatory mechanism for photosynthetic energy conversion. We report a 3.3-angstrom-resolution cryo–electron microscopy structure of photosynthetic complex I from the cyanobacterium Thermosynechococcus elongatus. The model reveals structural adaptations that facilitate binding and electron transfer from the photosynthetic electron carrier ferredoxin. By mimicking cyclic electron flow with isolated components in vitro, we demonstrate that ferredoxin directly mediates electron transfer between photosystem I and complex I, instead of using intermediates such as NADPH (the reduced form of nicotinamide adenine dinucleotide phosphate). A large rate constant for association of ferredoxin to complex I indicates efficient recognition, with the protein subunit NdhS being the key component in this process.

Abstract: Imperative session types provide an imperative interface to session-typed communication. In such an interface, channel references are first-class objects with operations that change the typestate of the channel. Compared to functional session type APIs, the program structure is simpler at the surface, but typestate is required to model the current state of communication throughout. Following an early work that explored the imperative approach, a significant body of work on session types has neglected the imperative approach and opts for a functional approach that uses linear types to manage channel references soundly. We demonstrate that the functional approach subsumes the early work on imperative session types by exhibiting a typing and semantics preserving translation into a system of linear functional session types. We further show that the untyped backwards translation from the functional to the imperative calculus is semantics preserving. We restrict the type system of the functional calculus such that the backwards translation becomes type preserving. Thus, we precisely capture the difference in expressiveness of the two calculi and conclude that the lack of expressiveness in the imperative calculus is largely due to restrictions imposed by its type system.

Abstract: Institutions driving fundamental research at the cutting edge such as for example from the Max Planck Society (MPS) took steps to optimize data management and stewardship to be able to address new scientific questions. In this paper we selected three institutes from the MPS from the areas of humanities, environmental sciences and natural sciences as examples to indicate the efforts to integrate large amounts of data from collaborators worldwide to create a data space that is ready to be exploited to get new insights based on data intensive science methods. For this integration the typical challenges of fragmentation, bad quality and also social differences had to be overcome. In all three cases, well-managed repositories that are driven by the scientific needs and harmonization principles that have been agreed upon in the community were the core pillars. It is not surprising that these principles are very much aligned with what have now become the FAIR principles. The FAIR principles confirm the correctness of earlier decisions and their clear formulation identified the gaps which the projects need to address.

Abstract: Since 2009 initiatives that were selected for the roadmap of the European Strategy Forum on Research Infrastructures started working to build research infrastructures for a wide range of research disciplines. An important result of the strategic discussions was that distributed infrastructure scenarios were now seen as “complex research facilities” in addition to, for example traditional centralised infrastructures such as CERN. In this paper we look at five typical examples of such distributed infrastructures where many researchers working in different centres are contributing data, tools/services and knowledge and where the major task of the research infrastructure initiative is to create a virtually integrated suite of resources allowing researchers to carry out state-of-the-art research. Careful analysis shows that most of these research infrastructures worked on the Findability, Accessibility, Interoperability and Reusability dimensions before the term “FAIR” was actually coined. The definition of the FAIR principles and their wide acceptance can be seen as a confirmation of what these initiatives were doing and it gives new impulse to close still existing gaps. These initiatives also seem to be ready to take up the next steps which will emerge from the definition of FAIR maturity indicators. Experts from these infrastructures should bring in their 10-years' experience in this definition process.

Abstract: Unsere Zeit der sprunghaft anwachsenden Datenmengen in allen Bereichen erfordert die Ausbildung von Datenkompetenz als Schlüsselkompetenz für das 21. Jahrhundert. Kenntnisse zur Datensammlung, zum Datenmanagement, zur Datenevaluation und zur Datenanwendung bilden die Grundlage für einen kompetenten Umgang mit Daten in Wissenschaft, Wirtschaft und Gesellschaft. Umfangreiche Datenmengen sind heute in allen Lebensbereichen in Wertschöpfungsketten eingebunden, die es zu gestalten und zu bewerten gilt. Insbesondere im Bereich der Wissenschaftsdaten wird dies auch institutionell unterstützt, um aus Daten neues Wissen und neue Einsichten generieren zu können.