DOI: 10.26165/juelich-data/vlbzia
title: Supplementary material: Model coupling through reproducible adapter workflows based on shared transformation functions

description: This supplementary material provides resources supporting the implementation of the refined DataDesc metadata schema and the ioProc workflow manager, described in the article, “Model coupling through reproducible adapter workflows based on shared transformation functions.” It aims to facilitate data and software integration, as well as data processing, by formalizing interface and data model descriptions and automating the identification of data transformation requirements. Using the ioProc and Snakemake workflow tools, these resources support reproducible, transparent adapter workflows with reusable transformation functions. The DataDesc schema is applied to create machine-interpretable descriptions of transformations, ensuring transparency in data handling across complex, coupled workflows. Organization The project is organized into sections for DataDesc Annotations, Data Models, Workflows, and Shared Data. DataDesc Annotations Data: Contains JSON files with metadata annotations for energy technologies and renewable resources: PEM electrolysis (86 PEMEC 100 MW.json) Solar PV (ninja_pv_country_DE_merra-2_corrected.json) Wind onshore (ninja_wind_country_DE_current-merra-2_corrected.json) Technology data for electricity and district heating (technology_data_for_el_and_dh.json) Technology data for energy storage (technology_datasheet_for_energy_storage.json) Specific technologies, including gas turbines, heat pumps, and lithium-ion batteries. Data Models: JSON files representing data models for different workflows: FINE framework models (fine_dd.json, fine_merra_comparison.json) REMix framework models (REMix_dd.json, remix_readRemixCsv.json) Workflows FINE Workflow and REMix Workflow: Workflow configuration files for each framework, implementing the transformations and data processing steps. Shared Data Contains .xlsx and .csv datasets shared across workflows: Renewable fuels data (data_sheets_for_renewable_fuels.xlsx) Technology data for electricity, heat, and storage (technology_data_for_el_and_dh.xlsx, technology_datasheet_for_energy_storage.xlsx) High-resolution generation data for Germany from renewables.ninja, including solar PV and wind (ninja_pv_country_DE_merra-2_corrected.csv, ninja_wind_country_DE_current-merra-2_corrected.csv). README.md: General project instructions and usage guidelines. License All source code is licensed under the BSD 3-Clause License, including the jupyter notebooks. All input data from the Danish Energy Agency and Renewables Ninja have their respective licenses specified in accompanying license files. All other data is licensed under CC-BY 4.0 Attribution 4.0 International. Copyright (C) 2023-2024 FZJ-ICE-2 and Deutsches Zentrum für Luft- und Raumfahrt Acknowledgement The authors would like to thank the Federal Ministry for Economic Affairs and Climate Action of Germany (BMWK) for supporting this work with a grant for the project LOD-GEOSS (03EI1005A-G). Furthermore, the authors are grateful to the German federal government, the German state governments, and the Joint Science Conference (GWK) for their funding and support as part of the NFDI4Ing consortium, managed by the German Research Foundation (DFG) – 442146713. This work was also supported by the Helmholtz Association as part of the program "Energy System Design".