Metabolomics is the study of metabolites and metabolic processes. Due to the diversity of structures and polarities of metabolites, no single analytical technique is able to measure the entire metabolome - instead a varied set of experimental designs and instrumental technologies are used to measure specific portions. This has led to the development of many distinct data analysis and processing methods and software. There is hope that metabolomics can be utilized for clinical applications, in toxicology and to measure the exposome. However, for these applications to be realised data must be high quality, sufficiently standardised and annotated, and FAIR (Findable, Accessible, Interoperable and Reproducible). For this purpose, it is also important that standardised, FAIR software workflows are available. There has also recently been much concern over the reproducibility of scientific research, which FAIR and open data, and workflows can help to address. To this end, this thesis aims to assess current practices and standards of sharing data within the field of metabolomics, using metascientific approaches. The types of functions of software for processing and analysing metabolomics data is also assessed. Reporting standards are designed to ensure that the minimum information required to un- derstand and interpret the results of analysis are reported. However, poor reporting standards are ignored and not complied with. Compliance to the biological context Metabolomics Standards Initiative (MSI) guidelines was examined, in order to investigate their timeliness. The state of open data within the metabolomics community was examined by investigating how much publicly available metabolomics data there is and where has it been deposited. To explore whether journal data sharing policies are driving open metabolomics data, which journals publish articles that have their underlying data made open was also examined. However, open data alone is not inherently useful: if data is incomplete, lacking in quality or missing crucial metadata, it is not valuable. Conversely, if data are reused, this can demonstrate the worth of public data archiving. Levels of reuse of public metabolomics data were therefore examined. With greater than 250 software tools specific for metabolomics, practitioners are faced with a daunting task to select the best tools for data collection and analysis. To help educate researchers about what software is available, a taxonomy of metabolomics software tools and a GitHub pages wiki, which provides extensive details about all included software, have been developed.

Die im Rahmen von Forschungsprojekten gewonnenen Forschungsdaten sind eine wesentliche Grundlage der wissenschaftlichen Arbeit. In nahezu allen Fachdisziplinen gewinnen sie immer mehr an Bedeutung. Die Nachvollziehbarkeit und die Qualität wissenschaftlicher Forschung wird durch die Dokumentation, die langfristige Sicherung und Bereitstellung der Forschungsdaten gefördert. Außerdem stellt die Publikation und die langfristige Sicherung von Forschungsdaten bei der DFG, EU und beim BMBF in vielen Fällen eine Voraussetzung für die Förderung von Forschungsvorhaben dar. Was genau sind Forschungsdaten? Was versteht man unter dem FAIR-Prinzip? Offene Forschungsdaten: Welche Vorteile bringen sie für die Forschenden? Wo kann man Forschungsdaten archivieren und veröffentlichen? Welche Services für Forschende der TU Bergakademie Freiberg zum Thema Forschungsdaten bieten die UB Freiberg und die Kontaktstelle Forschungsdaten der SLUB/ZiH Dresden? Diese und weitere Fragen beantworten Manuela Queitsch, Koordinatorin für Forschungsdaten an der SLUB Dresden und Teammitglied an der Kontaktstelle Forschungsdaten in Dresden und Dr. Dana Kuhnert, Fachreferentin für Wirtschafts- und Rechtswissenschaften der UB Bergakademie Freiberg.

Clinical research based on data from patient or study data management systems plays an important role in transferring basic findings into the daily practices of physicians. To support study recruitment, diagnostic processes, and risk factor evaluation, search queries for such management systems can be used. Typically, the query syntax as well as the underlying data structure vary greatly between different data management systems. This makes it difficult for domain experts (e.g., clinicians) to build and execute search queries. In this work, the Core Ontology of Phenotypes is used as a general model for phenotypic knowledge. This knowledge is required to create search queries that determine and classify individuals (e.g., patients or study participants) whose morphology, function, behaviour, or biochemical and physiological properties meet specific phenotype classes. A specific model describing a set of particular phenotype classes is called a Phenotype Specification Ontology. Such an ontology can be automatically converted to search queries on data management systems. The methods described have already been used successfully in several projects. Using ontologies to model phenotypic knowledge on patient or study data management systems is a viable approach. It allows clinicians to model from a domain perspective without knowing the actual data structure or query language.