Abstract:
As material modeling and simulation has become vital for modern materials science, research data with distinctive physical principles and extensive volume are generally required for full elucidation of the material behavior across all relevant scales. Effective workflow and data management, with corresponding metadata descriptions, helps leverage the full potential of data-driven analyses for computer-aided material design. In this work, we propose a research workflow and data management (RWDM) framework to manage complex workflows and resulting research (meta)data, while following FAIR principles. Multiphysics-multiscale simulations for additive manufacturing investigations are treated as showcase and implemented on Kadi4Mat – an open source research data infrastructure. The input and output data of the simulations, together with the associated setups and scripts realizing the simulation workflow, are curated in corresponding standardized Kadi4Mat records with extendibility for further research and data-driven analyses. These records are interlinked to indicate information flow and form an ontology-based knowledge graph. Automation scheme for performing high-throughput simulations and post-processing integrated with the proposed RWDM framework is also presented.
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Abstract:
In this paper, we introduce our approach in using the web-based application Kadi4Mat (KadiWeb) as an electronic laboratory notebook (ELN) combined with an integrated instrument database to facilitate Findable - Accessible - Interoperable - Reusabe (FAIR) research data. Facing transmission electron microscopy (TEM), focused ion beam (FIB), atom probe tomography (APT), or scanning electron microscopy (SEM) tasks, including sample preparation challenges, we developed a strategy to document the complex processes in our user facility KNMFi. To create appropriate records in Kadi4Mat we are comprising one central record for the material/sample to be investigated, a record for the sample preparation, a record for the investigation/experiment, and a record for the data evaluation. Therefore, a set of appropriate templates for the categories ‘sample preparation general,’ ‘sample preparation for TEM,’ ‘Focused Ion Beam and Scanning Electron Microscopy,’ ‘Transmission Electron Microscopy,’ ‘Atom Probe Tomography,’ and ‘Data Evaluation’ was developed in ‘atomistic units.’ The templates can be combined easily and have been designed to be user-friendly, but at the same time requesting the relevant metadata in a structured and standardized way. The documentation process, including MaTeLiS-instrument database, is demonstrated in a use-case with several sample preparation steps and different investigation methods. The developed templates can be exported in JSON-format and might be used as models for other tasks.
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Abstract:
Scientific processes produce huge amounts of data that are usually acquired, transformed and analyzed on a regular basis. Translating these processes into automatable and reproducible workflows is considered to be an efficient way to support scientists in performing repeated processes that would otherwise be time-consuming and error-prone tasks. Consequently, the quality of scientific research can be accelerated and enhanced. In this article, we present for the first time a use-case of KadiStudio as a tool to automate analysis procedures of scientific data that are repeatedly acquired from in situ scanning electron microscope (SEM) micromechanical testing. KadiStudio provides a desktop-based workflow editor as part of the ecosystem of Kadi4Mat: Karlsruhe Data Infrastructure for material Science. The presented workflow includes nodes for processing and analysis of different types of data, namely mechanical response in text format and a series of SEM images in video file format acquired during in situ SEM deformation tests. In addition, the raw and analyzed data are automatically uploaded to the KadiWeb repository via nodes based on the kadi-apy library.
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Abstract:
Interdisciplinary approaches and diverse research data management tools are required to design and fabricate new materials with macroscopically observable properties based on changes at the molecular level. The Science Data Center MoMaF is developing strategies to enable research data management across scales using the Chemotion and Kadi4Mat RDM tools. The study presents a use-case concept showing how both tools can be used conjointly to record molecular descriptions and manage simulations of microstructures across scales. The analysis of completed projects yields a concept for future processes, emphasizing the importance of efficient and consistent research and documentation across disciplines. The conjoint use of different RDM tools bridges the gaps between research fields, such as chemistry and materials science, and pushes the frontiers of interdisciplinary research.
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Abstract:
FAIR handling of scientific data plays a significant role in current efforts towards a more sustainable research culture and serves as a prerequisite for the fourth scientific paradigm, that is, data-driven research. To enforce the FAIR principles by ensuring the reproducibility of scientific data and tracking their provenance comprehensibly, the FAIR modelling of research processes in form of automatable workflows is necessary. By providing reusable procedures containing expert knowledge, such workflows contribute decisively to the quality and the acceleration of scientific research. In this work, the requirements for a system to be capable of modelling FAIR workflows are defined and a generic concept for modelling research processes as workflows is developed. For this, research processes are iteratively divided into impartible subprocesses at different detail levels using the input-process-output model. The concrete software implementation of the identified, universally applicable concept is finally presented in form of the workflow editor KadiStudio of the Karlsruhe Data Infrastructure for Materials Science (Kadi4Mat).
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Abstract:
Solutions for the generation of FAIR (Findable, Accessible, Interoperable, and Reusable) data and metadata in experimental tribology are currently lacking. Nonetheless, FAIR data production is a promising path for implementing scalable data science techniques in tribology, which can lead to a deeper understanding of the phenomena that govern friction and wear. Missing community-wide data standards, and the reliance on custom workflows and equipment are some of the main challenges when it comes to adopting FAIR data practices. This paper, first, outlines a sample framework for scalable generation of FAIR data, and second, delivers a showcase FAIR data package for a pin-on-disk tribological experiment. The resulting curated data, consisting of 2,008 key-value pairs and 1,696 logical axioms, is the result of (1) the close collaboration with developers of a virtual research environment, (2) crowd-sourced controlled vocabulary, (3) ontology building, and (4) numerous – seemingly – small-scale digital tools. Thereby, this paper demonstrates a collection of scalable non-intrusive techniques that extend the life, reliability, and reusability of experimental tribological data beyond typical publication practices.
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Abstract:
Bioprinting is a method to fabricate 3D models that mimic tissue. Future fields of application might be in pharmaceutical or medical context. As the number of applicants might vary between only one patient to manufacturing tissue for high-throughput drug screening, designing a process will necessitate a high degree of flexibility, robustness, as well as comprehensive monitoring. To enable quality by design process optimisation for future application, establishing systematic data storage routines suitable for automated analytical tools is highly desirable as a first step. This manuscript introduces a workflow for process design, documentation within an electronic lab notebook and monitoring to supervise the product quality over time or at different locations. Lab notes, analytical data and corresponding metadata are stored in a systematic hierarchy within the research data infrastructure Kadi4Mat, which allows for continuous, flexible data structuring and access management. To support the experimental and analytical workflow, additional features were implemented to enhance and build upon the functionality provided by Kadi4Mat, including browser-based file previews and a Python tool for the combined filtering and extraction of data. The structured research data management with Kadi4Mat enables retrospective data grouping and usage by process analytical technology tools connecting individual analysis software to machine-readable data exchange formats.
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Abstract:
The ever-increasing amount of data generated from experiments and simulations in engineering sciences is relying more and more on data science applications to generate new knowledge. Comprehensive metadata descriptions and a suitable research data infrastructure are essential prerequisites for these tasks. Experimental tribology, in particular, presents some unique challenges in this regard due to the interdisciplinary nature of the field and the lack of existing standards. In this work, we demonstrate the versatility of the open source research data infrastructure Kadi4Mat by managing and producing FAIR tribological data. As a showcase example, a tribological experiment is conducted by an experimental group with a focus on comprehensiveness. The result is a FAIR data package containing all produced data as well as machine- and user-readable metadata. The close collaboration between tribologists and software developers shows a practical bottom-up approach and how such infrastructures are an essential part of our FAIR digital future.
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Abstract:
The concepts and current developments of a research data infrastructure for materials science are presented, extending and combining the features of an electronic lab notebook and a repository. The objective of this infrastructure is to incorporate the possibility of structured data storage and data exchange with documented and reproducible data analysis and visualization, which finally leads to the publication of the data. This way, researchers can be supported throughout the entire research process. The software is being developed as a web-based and desktop-based system, offering both a graphical user interface and a programmatic interface. The focus of the development is on the integration of technologies and systems based on both established as well as new concepts. Due to the heterogeneous nature of materials science data, the current features are kept mostly generic, and the structuring of the data is largely left to the users. As a result, an extension of the research data infrastructure to other disciplines is possible in the future. The source code of the project is publicly available under a permissive Apache 2.0 license.