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We discuss two techniques for processing and analysing data that at the same time generate the required metadata to create FAIR digital objects. These objects can then be published as supporting information for the results obtained. This approach requires minimum intervention from the researcher performing the processing and analysis tasks. Consequently, these methods are ideal for improving the practices of publishing data, facilitate reproducibility of results, and support greater reuse of published data. Both techniques produce the metadata required for reproducing the results, including data used, parameters set at each stage, sequence of operations and mapping between inputs and outputs. The use of these type of tools reduces the work for documenting supporting data, enables comparison of results when varying parameters, and supports exploratory testing of different parameter combinations.
Poster presented during the Annual Digital Catalysis & Catalysis-Related Sciences Conference 2023 (ADCR 2023) 2 - 3 November 2023 DECHEMA House, Frankfurt, Germany
Publishing Research Data, Scientific Workflows, FAIR Data Objects, Metadata generator, Galaxy Workflows, Jupyter Notebooks
Publishing Research Data, Scientific Workflows, FAIR Data Objects, Metadata generator, Galaxy Workflows, Jupyter Notebooks
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