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A key challenge for the European Open Science Call (EOSC) is to develop broadly applicable guidelines, tools, and workflows, and support their cost-effective specialisation for community-specific needs. Our “Fostering the uptake of RDA indicators in Systems Biomedicine as a measure for model quality and FAIRness within the COMBINE community” project, focusing on the application of the RDA indicators for FAIR evaluation of the community-specific COmputational Modeling in BIology NEtwork (COMBINE) model format, is a prime example for this strategy. The project, which is funded by the EOSC Future, aims to develop a guideline for the FAIR model design, to publish the RDA indicators specification and to develop a semi-automatic FAIR evaluation tool. Here, we will briefly report on the major challenges and will present our ongoing strategies towards a community-level standardized FAIR evaluation.

International audience; Standardising the representation of biomedical knowledge among allresearchers is an insurmountable task, hindering the effectiveness of manycomputational methods. To facilitate harmonisation and interoperability despitethis fundamental challenge, we propose to standardise the framework ofknowledge graph creation instead. We implement this standardisation inBioCypher, a FAIR (findable, accessible, interoperable, reusable) framework totransparently build biomedical knowledge graphs while preserving provenances ofthe source data. Mapping the knowledge onto biomedical ontologies helps tobalance the needs for harmonisation, human and machine readability, and ease ofuse and accessibility to non-specialist researchers. We demonstrate the usefulnessof the framework on a variety of use cases, from maintenance of task-specificknowledge stores, to interoperability between biomedical domains, to on-demandbuilding of task-specific knowledge graphs for federated learning. BioCypher(https://biocypher.org) thus facilitates automating knowledge-based biomedicalresearch, and we encourage the community to further develop and use it.

The German Center for Diabetes Research (DZD) has established the DZD CORE DATASET which lists the clinical parameters relevant for diabetes research in related clinical studies. This dataset is found as a MS Excel file available on the DZD website. We conducted a baseline evaluation of how findable, accessible, interoperable and reusable (FAIR) the dataset is by combining the FAIR Cookbook, the GO FAIR website and the SATIFYD. In seeking to improve its FAIRness, we converted the dataset into the recommended format for spreadsheets, annotated the parameters therein with UMLS, licensed the dataset, indexed the metadata in a searchable resource, and generously enriched the dataset with metadata. These resulted in an increase in the FAIRness score by 47%. A key return on investment in the resources employed for this FAIRification journey is the increased certainty of the dataset’s future data readability. Conducting this work led us to observe differences in the interpretation of the FAIR principles between the SATIFYD and the FAIR Cookbook. We have highlighted these differences and we hope that our highlights inform the development of FAIR evaluators in the future. We also hope that this work serves as the basis on which a structured FAIRification workflow for related datasets is established.

The FAIR guiding principles for data stewardship are a set of recommendations for making research objects findable, accessible, interoperable and reusable. FAIR assessment tools implement measures for these principles and thus enable research networks to evaluate how good they comply with current standards in open and reproducible science. Based on questions from two different FAIR assessment tools, we built a tailor-made survey for the FAIR evaluation of projects within the German Network University Medicine (NUM). Established at the start of the Covid-19 pandemic outbreak, NUM addressed the need to collect and integrate Covid-19 data across German University Hospitals. Technical developments aimed to follow, among others, the FAIR principles. Interested in the actual status of FAIRness, we conducted an online survey in 2022 across German Network University Medicine projects. The goal was to identify positive examples of FAIR data in the German Network University Medicine thus to motivate other projects to take similar routes.

Presentation of introducing the EOSCFuture-funded project on FAIR biosimulations at EOSC Symposium, Nov. 16, Prague.

Introduction: The Logical Observation Identifiers Names and Codes (LOINC) is a reference terminology used for sharing laboratory data across laboratory systems internationally [ref:1]. The adoption of LOINC has seen great improvement since its inception and it is now a legal requirement that [for full text, please go to the a.m. URL]

The accomplishment of provenance in the biomedical context is associated with some hurdles. Challenges and potential bottlenecks were investigated during a Scoping Review and presented as preliminary results. 1. Introduction: Scrutinizing provenance information improves the understanding of data [for full text, please go to the a.m. URL]

doch erfüllen Studienprotokolle, eingesetzte Erhebungsinstrumente und erhobene Daten die Anforderungen der FAIR-Prinzipien nicht in ausreichendem Maße. NFDI4Health wird daher eine Struktur schaffen, die eine zentrale Suche nach existierenden, dezentral verwalteten Datenkörpern und zugehörigen Dokumenten sowie einen FAIRen Zugang zu diesen erleichtert. Dazu werden die Auffindbarkeit und der Zugang zu strukturierten Gesundheitsdaten aus Registern, administrativen Gesundheitsdatenbanken, klinischen und epidemiologischen sowie Public Health-Studien verbessert und die Qualität und Harmonisierung der zugrundeliegenden Daten optimiert.Eine weitere Herausforderung entsteht durch die Verwendung personenbezogener Gesundheitsdaten. Diese sind hoch sensibel, so dass ihre Nutzung restriktive Datenschutzbestimmungen und informierte Einwilligungserklärungen der Studienteilnehmenden erfordert, was jedoch ihre Wiederverwendbarkeit einschränkt. NFDI4Health zielt daher darauf ab, den Austausch und die Verknüpfung von personenbezogenen Gesundheitsdaten sowie verteilte Datenanalysen unter Einhaltung datenschutzrechtlicher und ethischer Bestimmungen zu erleichtern. Um dies möglichst effizient zu erreichen, wird NFDI4Health die Entwicklung neuer, maschinenprozessierbarer Zustimmungsmöglichkeiten sowie innovativer Datenzugriffsservices auf Grundlage der FAIR-Prinzipien vorantreiben und die Interoperabilität von IT-Lösungen für Metadatenrepositorien stärken. Komplementiert wird dies durch die Entwicklung entsprechender Angebote für Training und Ausbildung, um der Herausforderung der Umsetzung der Lösungen in den Universitäten und Forschungseinrichtungen zu begegnen. Schließlich wird durch die gemeinsame Arbeit in der NFDI4Health die Kooperation zwischen klinischer und epidemiologischer/Public Health-Forschung gestärkt.

Background: A fundamental component of digital clinical information systems (CIS) are Electronic Health Records [ref:1]. Efforts such as the Medical Informatics Initiative ([link:https://www.medizininformatik-initiative.de*https://www.medizininformatik-initiative.de])[for full text, please go to the a.m. URL], 65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS)

Epidemiologische und klinische Studien sind standardisiert und gut dokumentiert, jedoch erfüllen Studienprotokolle, eingesetzte Erhebungsinstrumente und erhobene Daten die Anforderungen der FAIR-Prinzipien nicht in ausreichendem Maße. NFDI4Health wird daher eine Struktur schaffen, die eine zentrale Suche nach existierenden, dezentral verwalteten Datenkörpern und zugehörigen Dokumenten sowie einen FAIRen Zugang zu diesen erleichtert. Dazu werden die Auffindbarkeit und der Zugang zu strukturierten Gesundheitsdaten aus Registern, administrativen Gesundheitsdatenbanken, klinischen und epidemiologischen sowie Public Health-Studien verbessert und die Qualität und Harmonisierung der zugrundeliegenden Daten optimiert. Eine weitere Herausforderung entsteht durch die Verwendung personenbezogener Gesundheitsdaten. Diese sind hoch sensibel, so dass ihre Nutzung restriktive Datenschutzbestimmungen und informierte Einwilligungserklärungen der StudienteilnehmerInnen erfordert, was jedoch ihre Wiederverwendbarkeit einschränkt. NFDI4Health zielt daher darauf ab, den Austausch und die Verknüpfung von personenbezogenen Gesundheitsdaten sowie verteilte Datenanalysen unter Einhaltung datenschutzrechtlicher und ethischer Bestimmungen zu erleichtern. Um dies möglichst effizient zu erreichen, wird NFDI4Health die Entwicklung neuer, maschinenprozessierbarer Zustimmungsmöglichkeiten sowie innovativer Datenzugriffsservices auf Grundlage der FAIRPrinzipien vorantreiben und die Interoperabilität von IT-Lösungen für Metadatenrepositorien stärken. Komplementiert wird dies durch die Entwicklung entsprechender Angebote für Training und Ausbildung, um der Herausforderung der Umsetzung der Lösungen in den Universitäten und Forschungseinrichtungen zu begegnen. Schließlich wird durch die gemeinsame Arbeit in der NFDI4Health die Kooperation zwischen klinischer und epidemiologischer/Public Health-Forschung gestärkt.

This poster serves as an introduction to the research being done on the FAIR data principles in health data at the Medical Informatcis Laboratory in the Universitätsmedizin Greifswald, Germany.

Background A useful model is one that is being (re)used. The development of a successful model does not finish with its publication. During reuse, models are being modified, i.e. expanded, corrected, and refined. Even small changes in the encoding of a model can, however, significantly affect its interpretation. Our motivation for the present study is to identify changes in models and make them transparent and traceable. Methods We analysed 13734 models from BioModels Database and the Physiome Model Repository. For each model, we studied the frequencies and types of updates between its first and latest release. To demonstrate the impact of changes, we explored the history of a Repressilator model in BioModels Database. Results We observed continuous updates in the majority of models. Surprisingly, even the early models are still being modified. We furthermore detected that many updates target annotations, which improves the information one can gain from models. To support the analysis of changes in model repositories we developed MoSt, an online tool for visualisations of changes in models. The scripts used to generate the data and figures for this study are available from GitHub github.com/binfalse/BiVeS-StatsGenerator and as a Docker image at hub.docker.com/r/binfalse/bives-statsgenerator. The website most.bio.informatik.uni-rostock.de provides interactive access to model versions and their evolutionary statistics. Conclusion The reuse of models is still impeded by a lack of trust and documentation. A detailed and transparent documentation of all aspects of the model, including its provenance, will improve this situation. Knowledge about a model’s provenance can avoid the repetition of mistakes that others already faced. More insights are gained into how the system evolves from initial findings to a profound understanding. We argue that it is the responsibility of the maintainers of model repositories to offer transparent model provenance to their users. © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

This extended abstract was submitted as answer to the call for proposals for the DFG programme NFDI (national research-data infrastructure). The initiative is spear-headed by de-RSE - Gesellschaft für Wissenschaftssoftware, but many other institutes and individuals contributed and are involved as well.

Computational systems biology involves integrating heterogeneous datasets in order to generate models. These models can assist with understanding and prediction of biological phenomena. Generating datasets and integrating them into models involves a wide range of scientific expertise. As a result these datasets are often collected by one set of researchers, and exchanged with others researchers for constructing the models. For this process to run smoothly the data and models must be FAIR—findable, accessible, interoperable, and reusable. In order for data and models to be FAIR they must be structured in consistent and predictable ways, and described sufficiently for other researchers to understand them. Furthermore, these data and models must be shared with other researchers, with appropriately controlled sharing permissions, before and after publication. In this chapter we explore the different data and model standards that assist with structuring, describing, and sharing. We also highlight the popular standards and sharing databases within computational systems biology.

Computational models in biology encode molecular and cell biological processes. These models often can be represented as biochemical reaction networks. Studying such networks, one is mostly interested in systems that share similar reactions and mechanisms. Typical goals of an investigation include understanding of the parts of a model, identification of reoccurring patterns, and recognition of biologically relevant motifs. The large number and size of available models, however, require automated methods to support researchers in achieving their goals. Specifically for the problem of finding patterns in large networks only partial solutions exist. We propose a workflow that identifies frequent structural patterns in biochemical reaction networks encoded in the Systems Biology Markup Language. The workflow utilises a subgraph mining algorithm to detect frequent network patterns. Once patterns are identified, the textual pattern description can automatically be converted into a graphical representation.Furthermore, information about the distribution of patterns among the selected set of models can be retrieved.The workflow was validated with 575 models from the curated branch of BioModels. In this paper, we highlight interesting and frequent structural patterns. Further, we provide exemplary patterns that incorporate terms from the Systems Biology Ontology. Our workflow can be applied to a custom set of models or to models already existing in our graph database MaSyMoS. The occurrences of frequent patterns may give insight into the encoding of central biological processes, evaluate postulated biological motifs, or serve as a similarity measure for models that share common structures. Availability: https://github.com/FabienneL/BioNet-Mining[p] Contact: fabienne.lambusch@uni-rostock.de

Standards are essential to the advancement of Systems and Synthetic Biology. COMBINE provides a formal body and a centralised platform to help develop and disseminate relevant standards and related resources. The regular special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards by providing unified, easily citable access. This paper provides an overview of existing COMBINE standards and presents developments of the last year. published

Summary The number, size and complexity of computational models of biological systems are growing at an ever increasing pace. It is imperative to build on existing studies by reusing and adapting existing models and parts thereof. The description of the structure of models is not sufficient to enable the reproduction of simulation results. One also needs to describe the procedures the models are subjected to, as recommended by the Minimum Information About a Simulation Experiment (MIASE) guidelines.This document presents Level 1 Version 2 of the Simulation Experiment Description Markup Language (SED-ML), a computer-readable format for encoding simulation and analysis experiments to apply to computational models. SED-ML files are encoded in the Extensible Markup Language (XML) and can be used in conjunction with any XML-based model encoding format, such as CellML or SBML. A SED-ML file includes details of which models to use, how to modify them prior to executing a simulation, which simulation and analysis procedures to apply, which results to extract and how to present them. Level 1 Version 2 extends the format by allowing the encoding of repeated and chained procedures.

Explanation for the ExampleArchive.omex. (PDF 190 kb)

Most scientific discoveries rely on previous or other findings.A lack of transparency and openness led to what many consider the "reproducibility crisis" in systems biology and systems medicine. The crisis arose from missing standards and inappropriate support of standards in software tools. As a consequence, numerous results in low- and high-profile publications cannot be reproduced.

STON, SBGN to Neo4j: using graph database technologies for storing disease-relevant biological pathways and networks Vasundra Touré1, Alexander Mazein2, Dagmar Waltemath1, Irina Balaur2, Ron Henkel1, Mansoor Saqi2, Johann Pellet2 and Charles Auffray2 1Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany. 2European Institute for Systems Biology and Medicine (EISBM), Centre National de la Recherche Scientifique (CNRS), Campus Charles Mérieux - Université de Lyon - 50 Avenue Tony Garnier, 69007 Lyon, France; IMI-eTRIKS consortium. Abstract Background: Graph databases can be successfully applied in Systems Biology and in Systems Medicine for managing extensive and complex information. Ultimately, graphs are a natural way of representing biological networks. The use of graph databases enables efficient storing and processing of biological relationships, and it can lead to a better response time when querying the data. Objectives: We would like to use graph databases structure to store and explore biological pathways and networks. Method: Translation rules have been determined to represent biological reaction networks in a graph model, that is to say as nodes, relationships and properties. The reaction networks are provided in the graphical standard Systems Biology Graphical Notation (SBGN). The graph model is stored in a Neo4j database. Results: We present the Java-based framework STON (SBGN TO Neo4j) to import and translate metabolic, signalling and gene regulatory pathways. On the poster, we show examples of networks representing parts of the Asthma Map, the iNOS pathway (a SBGN use case network). Conclusion: STON exploits the power of a graph database for the search in complex biological pathways. Importing biological pathways in a graph database enables: 1) identification of functional sub-modules and comparing different networks in order to discover common patterns. 2) merging multiple diagrams for creating large comprehensive networks for empowering systems medicine approaches. Availability: The STON framework is available here: http://sourceforge.net/projects/ston/.

The slides summarise the first proposal for a SED-ML extension to cover simulations on logical models.The discussion took place at the HARMONY 2016 meeting, Auckland, NZ, 2016. Slides contain a link to the Google doc that summarises the outcome of the discussion.

Quantitative modeling is used in computational biology to study interaction networks (predominantly regulatory and signalling networks). The SBML Qual package is an extension to the SBML Level 3 standard. SBML Qual supports the encoding of qualitative models. It defines the structure and syntax for qualitative models that associate discrete levels of activities with entity pools and the transitions between states that describe the processes involved (Chaouiya et al 2013). SED-ML Level 1 Version 2 already supports different types of simulation experiments on SBML models. However, a few concepts are missing in order to fully support the encoding of models based on the SBML Qual extension. Here is a first proposal for how the missing concepts could be added to the current SED-ML Level 1 Version 2.

Models are regularly updated, even after publication. The BiVeS library [3, 4, 5] offers comparison of versions of SBML [6] and CellML [7] encoded models. The result of such a comparison is a list of changes, based on the differences in both XML files. While it is now easy to see the differences in a network, it is still not possible to provide - or retrieve - information about the causes and effects of these changes. For example, together with a model update it should be possible to provide information on the characteristics of a change, e.g. stating that the changes remove an error in the kinetics.We manually analysed hundreds of model versions and their differences from the BioModels Database [9] and the CellML Model Repository [8] to study the evolution of computationalmodels. We then derived a vocabulary to describe the differences and implemented it in the COMODI ontology. The OWL encoding and a documentation are available from our website [1]. A Java library [2] allows for easy integration in software projects.We envision to use COMODI for automatic annotation of differences generated in BiVeS, and we like to provide it to modellers who wish to documentthe evolution of their models.[1] http://purl.org/net/comodi[2] https://github.com/binfalse/jCOMODI[3] https://sems.uni-rostock.de/bives/[4] Scharm et al.: An algorithm to detect and communicate the differences in computational models describing biological systems Bioinformatics, 2015[5] Waltemath et al.: Improving the reuse of computational models through version control Bioinformatics, 2013[6] Hucka et al.: The Systems Biology Markup Language(SBML): A medium for representation and exchange of biochemical network models Bioinformatics, 2003[7] Lloyd et al.: CellML: its future, presentand past Progress in Biophysicsand Molecular Biology, 2004[8] Lloyd et al.: The CellML ModelRepository Bioinformatics, 2008[9] Liet al.: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models BMC Systems Biology, 2010

Visualization of the iNOS pathway in SBGN PD. This pdf visualizes the iNOS pathway that we designed from the www.sbgnbricks.sourceforge.net using the SBGN-ED tool. The IFNG forms a complex with the interferon gamma receptor. This will activate the phosphorylation of STAT1alpha. After homodimerization, STAT1alpha will bind to the gene IRF1 to activate the transcription of IRF1. This protein regulates the transcription of the iNOS protein, which will links Calmodulin to create a complex that will activate the synthesis of nitric oxide (NO). (PDF 83.4 kb)

STON, SBGN to Neo4j: using graph database technologies for storing disease-relevant biological pathways and networks Vasundra Touré1, Alexander Mazein2, Dagmar Waltemath1, Irina Balaur2, Ron Henkel1, Mansoor Saqi2, Johann Pellet2 and Charles Auffray2 1Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany. 2European Institute for Systems Biology and Medicine (EISBM), Centre National de la Recherche Scientifique (CNRS), Campus Charles Mérieux - Université de Lyon - 50 Avenue Tony Garnier, 69007 Lyon, France; IMI-eTRIKS consortium. Abstract Background: Graph databases can be successfully applied in Systems Biology and in Systems Medicine for managing extensive and complex information. Ultimately, graphs are a natural way of representing biological networks. The use of graph databases enables efficient storing and processing of biological relationships, and it can lead to a better response time when querying the data. Objectives: We would like to use graph databases structure to store and explore biological pathways and networks. Method: Translation rules have been determined to represent biological reaction networks in a graph model, that is to say as nodes, relationships and properties. The reaction networks are provided in the graphical standard Systems Biology Graphical Notation (SBGN). The graph model is stored in a Neo4j database. Results: We present the Java-based framework STON (SBGN TO Neo4j) to import and translate metabolic, signalling and gene regulatory pathways. On the poster, we show examples of networks representing parts of the Asthma Map, the iNOS pathway (a SBGN use case network). Conclusion: STON exploits the power of a graph database for the search in complex biological pathways. Importing biological pathways in a graph database enables: 1) identification of functional sub-modules and comparing different networks in order to discover common patterns. 2) merging multiple diagrams for creating large comprehensive networks for empowering systems medicine approaches. Availability: The STON framework is available here: http://sourceforge.net/projects/ston/.

Supplementary material. This pdf file contains tables with translation rules of STON and a benchmark table on STONâ s performances. (PDF 103 kb)

Background: With the ever increasing use of computational models in the biosciences, the need to share models and reproduce the results of published studies efficiently and easily is becoming more important. To this end, various standards have been proposed that can be used to describe models, simulations, data or other essential information in a consistent fashion. These constitute various separate components required to reproduce a given published scientific result. Results: We describe the Open Modeling EXchange format (OMEX). Together with the use of other standard formats from the Computational Modeling in Biology Network (COMBINE), OMEX is the basis of the COMBINE Archive, a single file that supports the exchange of all the information necessary for a modeling and simulation experiment in biology. An OMEX file is a ZIP container that includes a manifest file, listing the content of the archive, an optional metadata file adding information about the archive and its content, and the files describing the model. The content of a COMBINE Archive consists of files encoded in COMBINE standards whenever possible, but may include additional files defined by an Internet Media Type. Several tools that support the COMBINE Archive are available, either as independent libraries or embedded in modeling software. Conclusions: The COMBINE Archive facilitates the reproduction of modeling and simulation experiments in biology by embedding all the relevant information in one file. Having all the information stored and exchanged at once also helps in building activity logs and audit trails. We anticipate that the COMBINE Archive will become a significant help for modellers, as the domain moves to larger, more complex experiments such as multi-scale models of organs, digital organisms, and bioengineering., 3 figures, 1 table

These are the conference proceedings of COMBINE 2019, the 10th Computational Modeling in Biology Network (COMBINE) meeting that took place in Heidelberg (Germany) from July 15th to July 19th, 2019. The Computational Modeling in Biology Network (COMBINE) is an initiative to coordinate the development of the various community standards and formats for computational models in the life sciences. It was created in 2010 to enable the sharing of resources, tools, and other infrastructure, and to coordinate standardization efforts for modeling in biology. COMBINE brings standard communities together around activities that are mutually beneficial. These activities include making specification documents available from a common location, providing a central point of contact, and organizing regular face-to-face meetings. To this end, the COMBINE network organizes an annual conference style meeting (the COMBINE Forum) and annual hackathon style events called HARMONY (Hackathon on Resources for Modeling in Biology), as well as tutorials and training events. At COMBINE 2019 a combination of keynote lectures, invited talks and interactive breakout discussions, as well as contributed talks, posters and lightning talks, selected from submitted abstracts, provided the basis for the meeting, offering diverse formats to exchange information, to discuss and work on interoperability problems and to demonstrate support for standards implemented in modelling tools, platforms and databases. One special focus of COMBINE 2019 was on the standardization need in systems medicine, which has been recognized as a necessity for computer-assisted personalized medicine. To direct attention to this topic, the European standardization framework for data integration and data-driven in silico models (EUSTANDS4PM) organized a workshop as part of COMBINE 2019. Also, reproducibility in modelling was a main topic, as reflected by several sessions and workshops. Besides these focus themes, also sessions, workshops and breakout discussions around single standards, their further development and their interoperability helped to advance the standardization, standing in the tradition of COMBINE., The proceedings were co-funded by the German Research Foundation DFG (grant agreement MU 3099/4-1), by the the European Union Horizon2020 framework programme of the European Commission Directorate-General for Research and Innovation (grant agreement 825843), by the German Federal Ministry of Education and Research through the German Network for Bioinformatics Infrastructure de.NBIand by the Heidelberg Institute for Theoretical Studies (HITS gGmbH), Germany.

Supplementary material. This pdf file contains tables with translation rules of STON and a benchmark table on STONâ s performances. (PDF 103 kb)

These are the conference proceedings of COMBINE 2019, the10th Computational Modeling in Biology Network (COMBINE) meeting that took place in Heidelberg (Germany)from July 15th to July 19th,2019. The Computational Modeling in Biology Network (COMBINE) is an initiative to coordinate the development of the various community standards and formatsfor computational models in the life sciences.Itwas created in 2010 to enable the sharing of resources, tools, andother infrastructure, and to coordinate standardization efforts for modeling in biology.COMBINEbrings standard communities together around activities that are mutually beneficial.These activities include making specification documents available from a commonlocation, providing a central point of contact, and organizing regular face-to-facemeetings.To this end, the COMBINE network organizes an annual conference style meeting(the COMBINE Forum) and annual hackathon style events called HARMONY(Hackathon on Resources for Modeling in Biology), as well as tutorials and training events. At COMBINE 2019 a combination of keynote lectures, invited talks and interactive breakout discussions,as well as contributed talks, posters and lightning talks, selected from submittedabstracts, provided the basis for the meeting, offering diverse formats to exchange information,to discuss and work oninteroperability problems and to demonstrate support for standardsimplemented in modelling tools, platforms and databases.One special focus of COMBINE 2019 was on the standardization need in systems medicine,which has been recognized as a necessity for computer-assisted personalized medicine.To direct attention to this topic, the European standardizationframework for data integration and data-driven in silico models (EUSTANDS4PM)organized a workshop as part of COMBINE 2019.Also, reproducibility in modelling was a main topic, as reflected by severalsessions and workshops.Besides these focus themes,also sessions, workshops and breakout discussions around single standards, theirfurther developmentand their interoperability helped to advance the standardization,standing in the tradition of COMBINE.

(english version below) Ein bedeutendes neues Handlungsfeld der Forschung, welches im Zuge der Digitalisierung entstanden ist, ist das Management von digitalen Forschungsdaten. Wissenschaftler*innen benötigen für ein nachhaltiges Forschungsdatenmanagement (FDM) neben Kenntnissen und Fähigkeiten im fachlichen Bereich zusätzliche Kompetenzen im Umgang mit digitalen Daten. Die Vermittlung dieser Kenntnisse sollte idealerweise bereits im Studium erfolgen. Zudem besteht ein steigender Bedarf an forschungsunterstützendem Personal, z.B. in Form von Data Stewards, der nur über geeignete Aus- und Weiterbildungsmaßnahmen gedeckt werden kann. Die vorliegende Lernzielmatrix fasst für das FDM relevante Vermittlungsinhalte sowie zugehörige Lernziele auf den Qualifikationsstufen Bachelor, Master, PhD und Data Steward aus einer Reihe von nationalen wie internationalen Projekten und Fortbildungskonzepten zum Themenbereich FDM in einheitlicher Form zusammen und bietet Nachnutzenden eine Orientierungshilfe für die Identifikation von relevanten Inhaltsaspekten sowie eine Arbeitsgrundlage, etwa für eine erweiterte fach- oder veranstaltungsspezifische Ausgestaltung. Die Versionen 1 und 2 wurden hauptverantwortlich durch Mitglieder der UAG Schulungen/Fortbildungen der DINI/nestor-AG Forschungsdaten erstellt. Zwischen Version 1 und 2 gab es keine inhaltlichen, sondern nur redaktionelle Anpassungen. Zusätzlich wurde eine Übersetzung der Inhalte ins Englische vorgenommen und das Layout der Matrix entsprechend angepasst. Mitglieder von NFDI4Health haben bei der englischsprachigen Übersetzung der Version 2 mitgewirkt. Übersetzungsarbeiten würden im Rahmen von NFDI4Health Aktivitäten durchgeführt. NFDI4Health has received funding from the Deutsche Forschungsgemeinschaft (DFG) under Grant Agreement no. 442326535 ______________________________________________________________________________________________________ (Deutsche Version oben) The management of digital research data is an important new field of research that has emerged in the process of digitalization. For sustainable research data management (RDM), scientists need not only knowledge and skills in their research area, but also additional competencies in dealing with digital data. Ideally, this knowledge should already be taught during studies. In addition, there is an increasing demand for research support staff, e.g. in the form of data stewards, which can only be met through appropriate training and continuing education measures. This learning objective matrix summarizes relevant teaching contents and associated learning objectives for the qualification levels Bachelor, Master, PhD and Data Steward from a number of national and international projects and training concepts in the field of RDM in a consistent form and offers subsequent users an orientation for identifying relevant content aspects as well as a working basis, e.g. for an extended subject-specific or event-specific design. Versions 1 and 2 were produced mainly by members of the Sub-Working Group Training/Further Education of the DINI/nestor WG Research Data. Between version 1 and 2 no content-related, only editorial adjustments were implemented. In addition, the content was translated into English and the layout of the matrix was adapted accordingly. Members of NFDI4Health contributed to the English translation of version 2. Translation was conducted in the scope of NFDI4Health. NFDI4Health has received funding from the Deutsche Forschungsgemeinschaft (DFG) under Grant Agreement no. 442326535