Die Digitalisierung bietet ein neues, effizientes Hilfsmittel, den wissenschaftlichen Fortschritt zu unterstutzen. In nahezu allen Bereichen lassen sich mit Hilfe moderner Informationssysteme Forschungsdaten digital archivieren und bei Bedarf leichter wiederverwenden. In der heutigen Zeit, in der das kollektive Wissen ein enormes Ausmas angenommen hat, ist die Systematisierung von Forschungsdaten zwingender denn je erforderlich. Die E-Science-Tage 2019, aus denen dieser Tagungsband hervorgegangen ist, haben neue Wege der Verarbeitung von Forschungsdaten aufgezeigt und durch den regen Austausch von Erfahrungen und Innovationen die digitale Wissenschaft weiter vorangetrieben.

Das Poster stellt die Tatigkeiten und Entwicklung der baden-wurttembergischen Landesprojekte bwFDM-Info I und II vor sowie die Plane fur das kunftige Projekt bw2FDM. Ziel des Projekts bwFDM-Info I war es, freies Material zum Forschungsdatenmanagement fur Forschende auf einem Informationsportal zur Verfugung zu stellen. Sie sollten sich auf forschungsdaten.info uber Forschungsdatenmanagement informieren und an Best Practices orientieren konnen. Das Angebot stutzte sich dabei auf den im Landesprojekt bwFDM-Communities identifizierten Bedarfen. Im Nachfolgeprojekt bwFDM-Info II verschob sich der Fokus hin zum Bekanntmachen der Plattform und Etablieren ihres nachhaltigen Betriebs. Die Universitaten Heidelberg, Hohenheim, Konstanz, Tubingen und das Karlsruher Instituts fur Technologie (KIT) vereinbarten, den langfristigen Betrieb der Plattform sicherzustellen. Im Rahmen eines Beteiligungsmodells konnten weitere institutionelle und individuelle Partner aus Hessen, Nordrhein-Westfalen, Niedersachsen, Thuringen und Sachsen gewonnen werden, die die Plattform inhaltlich und strukturell weiter ausgebaut haben. Daneben haben die Projektpartner eine Instanz des DMP-Tools Research Data Management Organiser (RDMO) eingerichtet und auf der Webseite integriert. Vernetzung und Outreach waren weitere wichtige Themen von bwFDM-Info I und II. Mit dem Arbeitskreis der Forschungsdatenverantwortlichen aller Universitaten im Land (AK FDM) wurde ein Gremium etabliert, in dem sich die Beteiligten regelmasig uber ihre jeweiligen FDM-Aktivitaten austauschen und offene Fragen klaren. Die E-Science-Tage 2017 in Heidelberg haben als erfolgreiche Fachkonferenz masgebliche FDM-Akteure bundesweit zusammengebracht. Mit der Koordination der baden-wurttembergischen E-Science-Projekte aus den Bereichen Forschungsdatenmanagement und Virtuelle Forschungsumgebungen hat das Projekt bwFDM-Info deren Austausch gefordert und Synergien in der Entwicklung geweckt. Dieses erfolgreiche Konzept soll 2019 bis 2023 mit dem neuen Projekt bw2FDM weiterentwickelt werden. Ein Hauptziel ist es, die Aufbauaktivitaten der baden-wurttembergischen Science Data Centers (SDC) koordinierend zu begleiten, deren Vernetzung untereinander zu intensivieren und ihre Sichtbarkeit fur die Forschungscommunities im In- und Ausland zu steigern. Damit soll auch die Nachnutzung der von den SDCs entwickelten Produkten fur die Forschungscommunity ermoglicht werden. Die Weiterentwicklung des Info-Portals sowie die Fortfuhrung der Konferenzreihe „E-Science-Tage“ werden Fachkompetenzen zum Forschungsdatenmanagement auf nationaler Ebene weiter bundeln. Die Projekte zum Forschungsdatenmanagement bwFDM-Info I und II sowie bw2FDM wurden bzw. werden gefordert vom Ministerium fur Wissenschaft, Forschung und Kunst Baden-Wurttemberg.

The increase in compute power and development of sophisticated simulation models with higher resolution output triggers a need for compression algorithms for scientific data. Several compression algorithms are currently under development. Most of these algorithms are using prediction-based compression algorithms, where each value is predicted and the residual between the prediction and true value is saved on disk. Currently there are two established forms of residual calculation: Exclusive-or and numerical difference. In this paper we will summarize both techniques and show their strengths and weaknesses. We will show that shifting the prediction and true value to a binary number with certain properties results in a better compression factor with minimal additional computational costs. This gain in compression factor allows for the usage of less sophisticated prediction algorithms to achieve a higher throughput during compression and decompression. In addition, we will introduce a new encoding scheme to achieve an 9% increase in compression factor on average compared to the current state-of-the-art.

One of the scientiĄc communities that generate the largest amounts of data today are the climate sciences. New climate models enable model integrations at unprecedented resolution, simulating timescales from decades to centuries of climate change. Nowadays, limited storage space and ever increasing model output is a big challenge. For this reason, we look at lossless compression using prediction-based data compression. We show that there is a signiĄcant dependence of the compression rate on the chosen traversal method and the underlying data model. We examine the inĆuence of this structural dependency on prediction-based compression algorithms and explore possibilities to improve compression rates. We introduce the concept of Information Spaces (IS), which help to improve the accuracy of predictions by nearly 10% and decrease the standard deviation of the compression results by 20% on average.

One of the scientific communities that generate the largest amounts of data today are the climate sciences. New climate models enable model integration at unprecedented resolution, simulating decades and centuries of climate change, including many complex interactions in the Earth system, under different scenarios. Previously, the CPU intensive numerical integration’s used to be the bottleneck. Nowadays, limited storage space and ever increasing model output is the bigger challenge. The number of variables stored for post-processing analysis has to be limited to keep the data amounts small. For this reason, we look at lossless compression of climate data to make better use of available storage space. More specifically, we investigate prediction-based data compression. In prediction-based compression, data is processed in a predefined sequence. A prediction is provided for each data point based on prior data in the sequence. We show that there is a significant dependence of the compression ratio on the chosen traversal method and the underlying spatiotemporal data model. We examine the influence of this structural dependency on compression algorithms and explore possibilities to retrieve this information to improve compression ratios. To do this, we introduce the concept of Information Spaces (IS), which helps improve the predictions made by individual predictors by nearly 10% on average. More importantly, the standard deviation of the compression results is decreased by over 20% on average. The use of IS provides better predictions and more consistent compression ratios. Furthermore, it allows options for consolidation and fine-granular tuning of predictions, which are not possible with many common approaches used today.

Im Projekt bwFDM‐Communities wurde Kontakt zu allen wissenschaftlichen Communities aufgebaut, um deren Bedarf an Diensten, Infrastruktur und Unterstutzung beim Umgang mit Forschungsdaten an den Universitaten des Landes Baden‐Wurttemberg zu erfassen. Ziel war es, eine Grundlage fur den nachhaltigen Ausbau von Expertise und Know‐How im Forschungsdatenmanagement an allen universitaren Rechenzentren, Bibliotheken und anderen Wissenschaftseinrichtungen (z.B. Sonderforschungsbereiche, GESIS, ...) Baden‐Wurttembergs zu legen, um den wissenschaftlichen Communities langfristig ein Umfeld bieten zu konnen, in denen sie die neuen Herausforderungen des digitalen Wissenswettbewerbs annehmen konnen.

Background Effective treatment of atrial fibrillation (AF) remains an unmet need. Human K2P3.1 (TASK-1) K+ channels display atrial-specific expression and may serve as novel antiarrhythmic targets. In rodents, inhibition of K2P3.1 causes prolongation of action potentials and QT intervals. We used a porcine model to further elucidate the significance of K2P3.1 in large mammals. Objective The purpose of this study was to study porcine (p)K2P3.1 channel function and cardiac expression and to analyze pK2P3.1 remodeling in AF and heart failure (HF). Methods The porcine K2P3.1 ortholog was amplified and characterized using voltage-clamp electrophysiology. K2P3.1 mRNA expression and remodeling were studied in domestic pigs during AF and HF induced by atrial burst pacing. Results Porcine K2P3.1 cDNA encodes a channel protein with 97% identity to human K2P3.1. K+ currents recorded from Xenopus oocytes expressing pK2P3.1 were functionally and pharmacologically similar to their human counterparts. In the pig, K2P3.1 mRNA was predominantly expressed in atrial tissue. AF and HF were associated with reduction of K2P3.1 mRNA levels by 85.1% (right atrium) and 77.0% (left atrium) at 21-day follow-up. In contrast, ventricular K2P3.1 expression was low and not significantly affected by AF/HF. Conclusion Porcine K2P3.1 channels exhibit atrial expression and functional properties similar to their human orthologs, supporting a general role as antiarrhythmic drug targets. K2P3.1 down-regulation in AF with HF may indicate functional relevance of the channel that remains to be validated in prospective interventional studies.

Research data are valuable goods that are often only reproducible with significant effort or, in the case of unique observations, not at all. Scientists focus on data analysis and its results. By now, data exploration is accepted as a fourth scientific pillar (next to experiments, theory, and simulation). A main prerequisite for easy data exploration is successful data management. A holistic approach includes all phases of a data lifecycle: data generation, data analysis, data ingest, data preservation, data access, reusage and long term preservation. Tackling the challenge of increasing complexity in managing research data, the objective of bwFDM-Communities is to expose problems of research communities. To achieve this goal, the project’s key account managers enter into a dialogue with all relevant research groups at each university in BadenWurttemberg. Next to the identification of best practices, possible developments will be determined together with the scientists. 1 Project Motivation Research data are generally understood as data that are generated during scientific work and they are building the basis for scientific results. Such data can be very heterogeneous. They differ in origin, size and format, but share scientific significance. Research Data Management (RDM) includes all technical and organizational aspects of handling research data. This includes analysis, access, migration, integrity, metadata, visualization, and archiving, as well as cost models and legal aspects. In general, RDM attempts to increase scientific insight. This is usually difficult because research domains are very heterogeneous with respect to gaining knowledge and data demands. While some disciplines need to manage and analyze an enormous flow of data with each measurement they make, other disciplines only produce a few files in the course of their research. Therefore, most researchers do not focus on the management of their data. Nonetheless, the awareness that Especially the long tail of science, see e.g. https://www.ci.uchicago.edu/blog/ unwinding-long-tail-science [last access 30.06.2014] DFG 2010 Call: ”Informationsstrukturen fur Forschungsdaten”, http://www.dfg.de/download/ pdf/foerderung/programme/lis/ausschreibung_forschungsdaten_1001.pdf [last access 30.06.2014]

AimsEffective management of atrial fibrillation (AF) often remains an unmet need. Cardiac two-pore-domain K+ (K2P) channels are implicated in action potential regulation, and their inhibition has been proposed as a novel antiarrhythmic strategy. K2P2.1 (TREK-1) channels are expressed in the human heart. This study was designed to identify and functionally express porcine K2P2.1 channels. In addition, we sought to analyze cardiac expression and AF-associated K2P2.1 remodeling in a clinically relevant porcine AF model.Main methodsThree pK2P2.1 isoforms were identified and amplified. Currents were recorded using voltage clamp electrophysiology in the Xenopus oocyte expression system. K2P2.1 remodeling was studied by quantitative real time PCR and Western blot in domestic pigs during AF induced by atrial burst pacing.Key findingsHuman and porcine K2P2.1 proteins share 99% identity. Residues involved in phosphorylation or glycosylation are conserved. Porcine K2P2.1 channels carried outwardly rectifying K+ currents similar to their human counterparts. In pigs, K2P2.1 was expressed ubiquitously in the heart with predominance in the atrial tissue. AF was associated with time-dependent reduction of K2P2.1 protein in the RA by 70% (7days of AF) and 80% (21days of AF) compared to control animals in sinus rhythm. K2P2.1 expression in the left atrium, AV node, and ventricles was not affected by AF.SignificanceSimilarities between porcine and human K2P2.1 channels indicate that the pig may represent a valid model for mechanistic and preclinical studies. AF-related atrial K2P2.1 remodeling has potential implications for arrhythmia maintenance and antiarrhythmic therapy.

The computational effort of biomolecular simulations can be significantly reduced by means of implicit solvent models in which the energy generally contains a correction depending on the surface area and/or the volume of the molecule. In this article, we present simple derivation of exact, easy‐to‐use analytical formulas for these quantities and their derivatives with respect to atomic coordinates. In addition, we provide an efficient, linear‐scaling algorithm for the construction of the power diagram required for practical implementation of these formulas. Our approach is implemented in a C++ header‐only template library. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011

Cardiac side effects of antidepressant drugs are well recognized. Adverse effects precipitated by the tricyclic drug desipramine include prolonged QT intervals, torsade de pointes tachycardia, heart failure, and sudden cardiac death. QT prolongation has been primarily attributed to acute blockade of hERG/I Kr currents. This study was designed to provide a more complete picture of cellular effects associated with desipramine. hERG channels were expressed in Xenopus laevis oocytes and human embryonic kidney (HEK 293) cells, and potassium currents were recorded using patch clamp and two-electrode voltage clamp electrophysiology. Ventricular action potentials were recorded from guinea pig cardiomyocytes. Protein trafficking and cell viability were evaluated in HEK 293 cells and in HL-1 mouse cardiomyocytes by immunocytochemistry, Western blot analysis, or colorimetric MTT assay, respectively. We found that desipramine reduced hERG currents by binding to a receptor site inside the channel pore. hERG protein surface expression was reduced after short-term treatment, revealing a previously unrecognized mechanism. When long-term effects were studied, forward trafficking was impaired and hERG currents were decreased. Action potential duration was prolonged upon acute and chronic desipramine exposure. Finally, desipramine triggered apoptosis in cells expressing hERG channels. Desipramine exerts at least four different cellular effects: (1) direct hERG channel block, (2) acute reduction of hERG surface expression, (3) chronic disruption of hERG trafficking, and (4) induction of apoptosis. These data highlight the complexity of hERG-associated drug effects.

Motivation: Structure based methods for drug design offer great potential for in-silico discovery of novel drugs but require accurate models of the target protein. Because many proteins, in particular transmembrane proteins, are difficult to characterize experimentally, methods of protein structure prediction are required to close the gap between sequence and structure information. Established methods for protein structure prediction work well only for targets of high homology to known proteins, while biophysics based simulation methods are restricted to small systems and require enormous computational resources. Results: Here we investigate the performance of a world-wide distributed computing network, POEM@HOME, which implements a biophysical model for protein modeling, as a robust computational infrastructure for protein structure prediction. We demonstrate the use of this network for the time-consuming energy relaxations for decoy sets and two targets of the 2010 protein structure prediction assessment (CASP). Conclusion: We demonstrated the use of the POEM@HOME network as a robust computational resource for protein structure prediction based on relaxation in biophysical models. Efforts to implement a web-interface to make this resource available to lifescience researchers are presently under way.

The relevance of receptor conformational change during ligand binding is well documented for many pharmaceutically relevant receptors, but is still not fully accounted for in in silico docking methods. While there has been significant progress in treatment of receptor side chain flexibility sampling of backbone flexibility remains challenging because the conformational space expands dramatically and the scoring function must balance protein–protein and protein–ligand contributions. Here, we investigate an efficient multistage backbone reconstruction algorithm for large loop regions in the receptor and demonstrate that treatment of backbone receptor flexibility significantly improves binding mode prediction starting from apo structures and in cross docking simulations. For three different kinase receptors in which large flexible loops reconstruct upon ligand binding, we demonstrate that treatment of backbone flexibility results in accurate models of the complexes in simulations starting from the apo structure. At the example of the DFG‐motif in the p38 kinase, we also show how loop reconstruction can be used to model allosteric binding. Our approach thus paves the way to treat the complex process of receptor reconstruction upon ligand binding in docking simulations and may help to design new ligands with high specificity by exploitation of allosteric mechanisms. © 2012 Wiley Periodicals, Inc.

The federal state of Baden-Wurttemberg wants to offer scientists the best conditions for research. Against the backdrop of the ever-increasing importance of data and information the bwFDM-Communities project is tasked to develop recommendations that shall enable scientists in our federal state to process and use data without barriers. In order to achieve this objective, we engage an active dialogue with all university research groups in Baden-Wurttemberg (~3000). Next to identifying and advertising best-practice solutions, this project is supposed to gather information on how federal IT support needs to be expanded in order to meet the increasing demands of future research. As this is an ongoing project there may be further results in time, but some early conclusions can be drawn: Scientists want clear-cut requirements and responsibilities for data management and are willing to share their data if there is a proper appreciation model for data publication. Additionally, a lot of scientists complain about too strict law regulations regarding copyright and need better information about available RDM support, partners and opportunities. Final conclusions and recommendations can only be given in the further course of the project, but we are confident that our final recommendations will help the scientists in Baden-Wurttemberg.