TY - JOUR A1 - Ghiringhelli, Luca M. A1 - Baldauf, Carsten A1 - Bereau, Tristan A1 - Brockhauser, Sandor A1 - Carbogno, Christian A1 - Chamanara, Javad A1 - Cozzini, Stefano A1 - Curtarolo, Stefano A1 - Draxl, Claudia A1 - Dwaraknath, Shyam A1 - Fekete, Ádám A1 - Kermode, James A1 - Koch, Christoph T. A1 - Kühbach, Markus A1 - Ladines, Alvin Noe A1 - Lambrix, Patrick A1 - Himmer, Maja-Olivia A1 - Levchenko, Sergey V. A1 - Oliveira, Micael A1 - Michalchuk, Adam A1 - Miller, Ronald E. A1 - Onat, Berk A1 - Pavone, Pasquale A1 - Pizzi, Giovanni A1 - Regler, Benjamin A1 - Rignanese, Gian-Marco A1 - Schaarschmidt, Jörg A1 - Scheidgen, Markus A1 - Schneidewind, Astrid A1 - Sheveleva, Tatyana A1 - Su, Chuanxun A1 - Usvyat, Denis A1 - Valsson, Omar A1 - Wöll, Christof A1 - Scheffler, Matthias T1 - Shared metadata for data-centric materials science N2 - The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on “Shared Metadata and Data Formats for Big-Data Driven Materials Science”. We start from an operative definition of metadata, and the features that a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them. KW - Library and Information Sciences KW - Statistics, Probability and Uncertainty KW - Computer Science Applications KW - Education KW - Information Systems KW - Statistics and Probability PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-584846 DO - https://doi.org/10.1038/s41597-023-02501-8 VL - 10 IS - 1 SP - 1 EP - 18 PB - Springer Science and Business Media LLC AN - OPUS4-58484 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Janssen, Jan A1 - George, Janine A1 - Geiger, Julian A1 - Bercx, Marnik A1 - Wang, Xing A1 - Ertural, Christina A1 - Schaarschmidt, Joerg A1 - Ganose, Alex M. A1 - Pizzi, Giovanni A1 - Hickel, Tilmann A1 - Neugebauer, Joerg T1 - A Python workflow definition for computational materials design N2 - Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats, hindering interoperability and reproducibility of workflows in the field. To address this challenge, we introduce here the Python Workflow Definition (PWD) as a workflow exchange format to share workflows between Python-based WfMS, currently AiiDA, jobflow, and pyiron. This development is motivated by the similarity of these three Python-based WfMS, that represent the different workflow steps and data transferred between them as nodes and edges in a graph. With the PWD, we aim at fostering the interoperability and reproducibility between the different WfMS in the context of Findable, Accessible, Interoperable, Reusable (FAIR) workflows. To separate the scientific from the technical complexity, the PWD consists of three components: (1) a conda environment that specifies the software dependencies, (2) a Python module that contains the Python functions represented as nodes in the workflow graph, and (3) a workflow graph stored in the JavaScript Object Notation (JSON). The first version of the PWD supports directed acyclic graph (DAG)-based workflows. Thus, any DAG-based workflow defined in one of the three WfMS can be exported to the PWD and afterwards imported from the PWD to one of the other WfMS. After the import, the input parameters of the workflow can be adjusted and computing resources can be assigned to the workflow, before it is executed with the selected WfMS. This import from and export to the PWD is enabled by the PWD Python library that implements the PWD in AiiDA, jobflow, and pyiron. KW - Automation KW - Workflows KW - Materials Design KW - Multi-scale simulation KW - Digitalization PY - 2025 DO - https://doi.org/10.5281/zenodo.15516179 PB - Zenodo CY - Geneva AN - OPUS4-63233 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Janssen, Jan A1 - George, Janine A1 - Geiger, Julian A1 - Bercx, Marnik A1 - Wang, Xing A1 - Ertural, Christina A1 - Schaarschmidt, Joerg A1 - Ganose, Alex M. A1 - Pizzi, Giovanni A1 - Hickel, Tilmann A1 - Neugebauer, Joerg T1 - A Python workflow definition for computational materials design N2 - Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats, hindering interoperability and reproducibility of workflows in the field. To address this challenge, we introduce here the Python Workflow Definition (PWD) as a workflow exchange format to share workflows between Python-based WfMS, currently AiiDA, jobflow, and pyiron. This development is motivated by the similarity of these three Python-based WfMS, that represent the different workflow steps and data transferred between them as nodes and edges in a graph. With the PWD, we aim at fostering the interoperability and reproducibility between the different WfMS in the context of Findable, Accessible, Interoperable, Reusable (FAIR) workflows. To separate the scientific from the technical complexity, the PWD consists of three components: (1) a conda environment that specifies the software dependencies, (2) a Python module that contains the Python functions represented as nodes in the workflow graph, and (3) a workflow graph stored in the JavaScript Object Notation (JSON). The first version of the PWD supports directed acyclic graph (DAG)-based workflows. Thus, any DAG-based workflow defined in one of the three WfMS can be exported to the PWD and afterwards imported from the PWD to one of the other WfMS. After the import, the input parameters of the workflow can be adjusted and computing resources can be assigned to the workflow, before it is executed with the selected WfMS. This import from and export to the PWD is enabled by the PWD Python library that implements the PWD in AiiDA, jobflow, and pyiron. KW - Automation KW - Workflow KW - Materials Design KW - Multi-scale simulation KW - Digitalization PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-632328 DO - https://doi.org/10.48550/arXiv.2505.20366 SP - 1 EP - 12 AN - OPUS4-63232 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Janssen, Jan A1 - George, Janine A1 - Geiger, Julian A1 - Bercx, Marnik A1 - Wang, Xing A1 - Ertural, Christina A1 - Schaarschmidt, Jörg A1 - Ganose, Alexander Miguel A1 - Pizzi, Giovanni A1 - Hickel, Tilmann A1 - Neugebauer, Jörg T1 - A Python workflow definition for computational materials design N2 - Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats, hindering interoperability and reproducibility of workflows in the field. To address this challenge, we introduce here the Python Workflow Definition (PWD) as a workflow exchange format to share workflows between Python-based WfMS, currently AiiDA, jobflow, and pyiron. This development is motivated by the similarity of these three Python-based WfMS, that represent the different workflow steps and data transferred between them as nodes and edges in a graph. With the PWD, we aim at fostering the interoperability and reproducibility between the different WfMS in the context of Findable, Accessible, Interoperable, Reusable (FAIR) workflows. To separate the scientific from the technical complexity, the PWD consists of three components: (1) a conda environment that specifies the software dependencies, (2) a Python module that contains the Python functions represented as nodes in the workflow graph, and (3) a workflow graph stored in the JavaScript Object Notation (JSON). The first version of the PWD supports directed acyclic graph (DAG)-based workflows. Thus, any DAG-based workflow defined in one of the three WfMS can be exported to the PWD and afterwards imported from the PWD to one of the other WfMS. After the import, the input parameters of the workflow can be adjusted and computing resources can be assigned to the workflow, before it is executed with the selected WfMS. This import from and export to the PWD is enabled by the PWD Python library that implements the PWD in AiiDA, jobflow, and pyiron. KW - Worklows KW - FAIR Workflows KW - Automation KW - Materials Acceleration Platforms PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-643325 DO - https://doi.org/10.1039/D5DD00231A SN - 2635-098X SP - 1 EP - 14 PB - Royal Society of Chemistry (RSC) AN - OPUS4-64332 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -