Abstract
Process metadata constitute a relevant part of the documentation of research processes and the creation and use of research data. As an addition to publications on the research question, they capture details needed for reusability and comparability and are thus important for a sustainable handling of research data. In the DH project SDC4Lit we want to capture process metadata when researchers work with literary material, conducting manual and automatic processing steps, which need to be treated with equal emphasis. We present a content-related mapping between two process metadata schemas from the area of Digital Humanities (GRAIN, RePlay-DH) and one from Computational Engineering (EngMeta) and find that there are no basic obstacles preventing the use of any of them for our purposes. Actually a basic difference rather exists between GRAIN on the one hand and RePlay-DH and EngMeta on the other, regarding the treatment of tools and actors in a workflow step.
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References
Axtmann, A.: Data center 4 science: Disziplinspezifische und disziplinübergreifende Forschungsdateninfrastrukturen und abgestimmte Bearbeitung von Querschnittsthemen in Baden-Württemberg. In: Poster at E-Science-Tage 2021: Share Your Research Data, Heidelberg (2021). https://doi.org/10.11588/heidok.00029607
Blessing, A., et al.: SDC4Lit - a science data center for literature. In: Poster at DH 2020, Ottawa (2020). http://dx.doi.org/10.17613/cyg5-3948
DataCite Metadata Working Group. DataCite Metadata Schema Documentation for the Publication and Citation of Research Data. Version 4.0. (2016). DataCite e.V. http://doi.org/10.5438/0012
Gärtner, M.: RePlay-DH Process Metadata Schema (2019). https://doi.org/10.18419/darus-474
Gärtner, M., Hahn, U., Hermann, S.: Preserving workflow reproducibility: the RePlay-DH client as a tool for process documentation. In: Calzolari, N., et al. (eds.) Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Paris (2018)
Jung, K., Gärtner, M., Kuhn, J.: Fine-GRAINed process metadata. In: Garoufallou, E., Fallucchi, F., William De Luca, E. (eds.) MTSR 2019. CCIS, vol. 1057, pp. 373–378. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36599-8_33
King, G.: An introduction to the Dataverse network as an infrastructure for data sharing. Sociol. Methods Res. 36(2), 173–199 (2007). https://doi.org/10.1177/0049124107306660
Lebo, T., et al.: PROV-O: the PROV ontology. In: W3C Recommendation, World Wide Web Consortium, United States (2013)
Schembera, B.: Like a rainbow in the dark: metadata annotation for HPC applications in the age of dark data. J. Supercomput. 77(8), 8946–8966 (2021). https://doi.org/10.1007/s11227-020-03602-6
Schembera, B., Iglezakis, D.: The genesis of EngMeta - a metadata model for research data in computational engineering. In: Garoufallou, E., Sartori, F., Siatri, R., Zervas, M. (eds.) MTSR 2018. CCIS, vol. 846, pp. 127–132. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14401-2_12
Schembera, B., Iglezakis, D.: EngMeta: metadata for computational engineering. Int. J. Metadata Semant. Ontol. 14(1), 26–38 (2020)
Wilkinson, M.D., et al.: The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3(1), 1–9 (2016). https://doi.org/10.1038/sdata.2016.18
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Jung, K., Schembera, B., Gärtner, M. (2022). Best of Both Worlds? Mapping Process Metadata in Digital Humanities and Computational Engineering. In: Garoufallou, E., Ovalle-Perandones, MA., Vlachidis, A. (eds) Metadata and Semantic Research. MTSR 2021. Communications in Computer and Information Science, vol 1537. Springer, Cham. https://doi.org/10.1007/978-3-030-98876-0_17
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DOI: https://doi.org/10.1007/978-3-030-98876-0_17
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