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    <title language="eng">Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing</title>
    <abstract language="eng">AbstractHigh-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management.</abstract>
    <parentTitle language="eng">Integrating Materials and Manufacturing Innovation</parentTitle>
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Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. 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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Ghezal Ahmad Jan Zia</author>
    <author>Thomas Hanke</author>
    <author>Birgit Skrotzki</author>
    <author>Christoph Völker</author>
    <author>Bernd Bayerlein</author>
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    </subject>
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      <type>uncontrolled</type>
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    </subject>
    <subject>
      <language>eng</language>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>FAIR research data management</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reproducibility</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>microstructural changes</value>
    </subject>
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    <title language="eng">A Perspective on Digital Knowledge Representation in Materials Science and Engineering</title>
    <abstract language="eng">The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples.&#13;
&#13;
In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge.</abstract>
    <parentTitle language="eng">Advanced Engineering Materials</parentTitle>
    <identifier type="doi">10.1002/adem.202101176</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-546729</identifier>
    <identifier type="issn">1438-1656</identifier>
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    <enrichment key="date_peer_review">02.05.2022</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Bernd Bayerlein</author>
    <author>T. Hanke</author>
    <author>Thilo Muth</author>
    <author>Jens Riedel</author>
    <author>Markus Schilling</author>
    <author>C. Schweizer</author>
    <author>Birgit Skrotzki</author>
    <author>A. Todor</author>
    <author>Benjami Moreno Torres</author>
    <author>Jörg F. Unger</author>
    <author>Christoph Völker</author>
    <author>Jürgen Olbricht</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data infrastructures</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital representations</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital workflows</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Knowledge graphs</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Materials informatics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ontologies</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Vocabulary providers</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">1 Analytische Chemie; Referenzmaterialien</collection>
    <collection role="institutes" number="">1.3 Instrumentelle Analytik</collection>
    <collection role="institutes" number="">5 Werkstofftechnik</collection>
    <collection role="institutes" number="">5.1 Mikrostruktur Design und Degradation</collection>
    <collection role="institutes" number="">5.2 Metallische Hochtemperaturwerkstoffe</collection>
    <collection role="institutes" number="">7 Bauwerkssicherheit</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="themenfelder" number="">Material</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="institutes" number="">8.0 Abteilungsleitung und andere</collection>
    <collection role="institutes" number="">7.7 Modellierung und Simulation</collection>
    <collection role="themenfelder" number="">Materialdesign</collection>
    <collection role="institutes" number="">VP Vizepräsident</collection>
    <collection role="institutes" number="">VP.1 eScience</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/54672/Adv Eng Mater - B_Bayerlein - A Perspective on Digital Knowledge Representation in MSE.pdf</file>
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</export-example>
