This version replaces version v2.0 (December 2025). Changes included in this version are listed at the end of this text. Creep testing of metallic materials for high temperature applications, e.g., in turbines and power plants, yields valuable datasets. High experimental efforts are necessary to ensure stable high temperature and constant loading conditions in these long-running tests. Proper, comprehensive documentation of these experiments is a key element to enable the assessment of the quality of respective creep datasets and their targeted use (and future re-use) for specific applications. The attached data schema for creep tests was developed within the German NFDI-MatWerk initiative (https://nfdi-matwerk.de/) and follows the reference data methodology outlined in 1. Please consider citing this work 1 if you use the data schema for a scientific publication. With the data schema, it is intended to define a structured approach for collecting all required information on a creep experiment using the established terminology of the respective test standard ISO 204:2022. The development goals encompass the following primary aspects: To ensure a comprehensive description with a hierarchical data structure that can be implemented to data management platforms, To define a scope of documentation that allows the assessment of a dataset’s quality by different end users who retrieve datasets from multiple data providers, To foster the exchange of high-quality creep datasets according to the FAIR principles 2, by providing easy interoperability and full reusability.· The data schema was originally developed for datasets of Ni-based high-temperature alloys. It is not intended to be exhaustive. However, the data schema is largely agnostic to the type of material and may be adapted or extended to accommodate additional parameters or test variants, or for creep testing of different metallic (and other) materials. This version of the data schema covers creep tests with the following features: It considers single- and polycrystalline material Terminology is aligned with ISO 204:2022 Creep test under tension and constant force Temperature measurement with thermocouples Contacting extensometer system A very detailed approach was chosen to ensure the collection of all related pieces of information, e.g., including a complete description of the material’s manufacturing history and a comprehensive description of the laboratory equipment. In this way, the developed data schema serves to describe or identify high-quality datasets, which can be considered as reference data of materials for verification purposes. It is acknowledged that certain datasets, depending on their origin and intended use, may not require this full depth of documentation. Still, the suggested data schema can help to decide which parts of information are relevant for the respective purpose. The authors encourage the materials science and engineering, and especially the creep community to provide any feedback that can lead to optimizing the data schema, including its structure. Future versions could include, for instance, modules for non-contacting extensometer methods or temperature measurement with thermal imaging cameras. The data schema is provided in the following formats: XLSX, *.lis, and JSON, and it is also available in a git repository (https://git.rwth-aachen.de/nfdi-matwerk/iuc02). The XLSX/*.lis version of the data schema contains 12 columns. The columns labelled as “Category I” to “Category IV” define the overarching structure of the schema (see Figure 1). This structure mimics the way how a domain expert would structure the data. The subsequent columns “Entry”, “Entry – Additional Information”, “Symbol”, “Unit”, “Data Type”, and “Exemplary Answer or Options Separated by Slash in Case of Data Type Drop-Down List” specify the fields to be completed by the user. Where applicable, standard-compliant symbols and community-common units are provided.. The columns “Data Type” and “Exemplary Answer or Options Separated by Slash in Case of Data Type Drop-Down List” offer initial guidance on how each field should be populated; most entries include exemplary answers. For fields defined as “drop-down list”, the selectable options are indicated in the format “Option A / Option B / …”. The column labeled “Requirement” presents the requirement profile, while the final column, “Comments”, provides additional explanations related to requirements, dependencies, and the applicability of specific entries. The JSON schema follows the same categorization as the XLSX/’*.lis files (see Figure 1) and includes concepts defined in the data schema. For each entry, the requirement profile and data type are essential attributes that must be provided. The JSON schema is structured with ”Category I” as the first level of categorization, upon which the mapping is further built. For the implementation of technical validation workflows of the requirement profile, it should be noted that dependencies specified in the “Comments” column of the accompanying XLSX file have not yet been implemented in the JSON schema. The requirement profile refers to the highest quality class of reference creep data, taken from calibrated instruments, and which shall enable the following usages: Checking one’s own creep test results on nominally similar material Verification of own testing set-up (e.g., by testing the same or similar material) Using the data as input data for simulations of creep behavior for design and alloy development Other quality classes or typical research datasets require less documentation. The current definition for reference data of materials is available here 3. Although the data schema and provided requirement profile were originally designed for reference data, extending the concept to research data represents the next logical step. This extension, however, will require considerable community and technical effort, the latter for example through the use of data mining technologies to incorporate literature data. In the authors’ view, research data should be documented as comprehensively as possible voluntarily, as this is essential for correct data interpretation and reuse. The intention is not to mandate the use of this schema by researchers. Rather, it is intended as a tool to support standardized data reporting throughout the entire publication process, from manuscript preparation to article and data publication, thereby improving data findability, accessibility, and reusability. Changes v1.1 The amendments were mainly editorial and formal changes and are as follows: Column “Comment”: Text in row 193 was revised, and cross-references to rows were revised and corrected for all entries. Row 142, column “Requirement”: “Deviation detected during calibration” (data acquisition temperature-measuring system) changed to optional. Rows 164 and 165, column “Entry”: “…primary data and processed data” was changed to “…primary and processed data series”. Column “Exemplary answer/options (in case of drop-down list)”: Capitalization was revised and corrected. The above-mentioned changes refer to the XLSX and CSV files. The JSON schema was adapted accordingly. The data schema structure, Figure 1, remained unchanged. v2.0 An agreement on content between national experts was reached. As a result of this community interaction, several amendments were implemented in both the data structure and the individual fields. The data schema (XLSX/CSV/JSON) and related data schema structure, Figure 1, were adapted accordingly. A new column labeled “Entry – Additional Information” has been added to enhance clarity regarding the requested information. An alignment of the vocabulary with several domain- and application-level ontologies, such as the Reference Dataset Ontology (RDO) with its application-level extension for reference data on creep testing (RDOC), and the PMD Core Ontology (PMDco), was actively pursued. Finally, this version also includes minor editorial changes, such as deleting the previous column “Row”. v2.1 A CSV file is not provided. Instead, a *.lis file is provided. The overarching structure of the JSON schema was revised to ensure correct nesting. This change concerns mainly categories I and II. In the JSON schema, the fields corresponding to chemical composition were revised. Since this version, it is possible to report the chemical composition either by entering element by element or by adding a link to an external resource. For the measured chemical composition, the measurement method can be added for each element. Revision of entries “k-Value” and “Ratio reference length to diameter” regarding the units and to cover Lr = Lo and Lr = Le. Entry “Description of the loading system” was revised. Optional entry Leverage ratio added in case of lever arm test machine type. Category III “Elongation values and cross-sectional dimensions” was split into two: “Cross-sectional dimensions” and “Elongation values”. References 1 L.A. Ávila Calderón, Y. Shakeel, A. Gedsun, M. Forti, S. Hunke, Y. Han, T. Hammerschmidt, R. Aversa, J. Olbricht, M. Chmielowski, R. Stotzka, E. Bitzek, T. Hickel, B. Skrotzki, Management of reference data in materials science and engineering exemplified for creep data of a single-crystalline Ni-based superalloy, Acta Materialia 286 (2025) 120735, DOI: 10.1016/j.actamat.2025.120735 2 Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). DOI: 10.1038/sdata.2016.18 3 Hickel, T., Richter, S., Bitzek, E., Ávila Calderón, L. A., Gedsun, A., Forti, M., Hammerschmidt, T., Olbricht, J., & Skrotzki, B. (2024). NFDI-MatWerk/IUC02 Definition for Reference Data of Materials (1.0). Zenodo. DOI: 10.5281/zenodo.11667673 Acknowledgment This work was carried out in the framework of NFDI-MatWerk and funded by
Calderón et al. (2026) studied this question.