Software-driven RDM (sdRDM), a generic and extensible bottom-up research data management concept and its application in biocatalysis and beyond

PN 2-6 (II)

Project description

The goal of this research project is to develop and implement a generic research data  management (RDM) concept which integrates software and data formats based on an object-based data model, and to apply it to the analysis of enzymatic data.

The proposed software-driven research data management (sdRDM) concept

  • is generic and extensible by integrating software and data formats based on an object-based data model,
  • is an application-driven and bottom-up approach, driven by the needs of experimenters and modelers, and based on realistic scenarios,
  • is at the same time independent from, but compatible with existing data formats and contributes to standardization, in tight contact with national consortia such as NFDI as well as international initiatives,
  • enables and promotes F.A.I.R. publication of experiments and their analysis on Dataverse.

The sdRDM concept is based on the EnzymeML toolbox, which has been developed to foster the digitalization of biocatalysis. The toolbox will be refactored according to the proposed sdRDM concept to make it flexible, adaptable, extensible, and generic. Thus, it will be easily transferrable to other scientific fields. The transferability will be demonstrated by collaborations with further partners in PN2 and PN1 and with partners in CRC1333.

Project information

Project title Software-driven RDM (sdRDM), a generic and extensible bottom-up research data management concept and its application in biocatalysis and beyond
Project leader Jürgen Pleiss
Project staff Jan Range, doctoral researcher
Project partners Nicole Radde (PN2-9)

Tobias Siebert, Oliver Röhrle (PN2-8)
Grazia Lamanna, Rico Poser (PN1-3)
Dorothea Iglezakis (FoKUS)
Project duration January 2022 - June 2025
Project number PN 2-6 (II)
  • Preceding project 2-6
    Seamlesss data flow by EnzymeML, an SBML-based exchange format for the integration of enzymatic reaction data.

Publications PN 2-6 and PN 2-6 (II)

  1. 2024

    1. A. Windels, J. Franceus, J. Pleiss, and T. Desmet, “CANDy: Automated analysis of domain architectures in carbohydrate-active enzymes,” PLOS ONE, vol. 19, no. 7, Art. no. 7, Jul. 2024, doi: 10.1371/journal.pone.0306410.
    2. H. F. Carvalho, L. Mestrom, U. Hanefeld, and J. Pleiss, “Beyond the Chemical Step: The Role of Substrate Access in Acyltransferase from Mycobacterium smegmatis,” ACS Catal., vol. 14, pp. 10077--10088, Jun. 2024, doi: 10.1021/acscatal.4c00812.
    3. F. Neubauer, P. Bredl, M. Xu, K. Patel, J. Pleiss, and B. Uekermann, “MetaConfigurator: A User-Friendly Tool for Editing Structured Data Files,” Datenbank-Spektrum, vol. 24, pp. 161–169, 2024, doi: 10.1007/s13222-024-00472-7.
    4. J. Pleiss, “FAIR Data and Software: Improving Efficiency and Quality of Biocatalytic Science,” ACS Catal., vol. 14, no. 4, Art. no. 4, Feb. 2024, doi: 10.1021/acscatal.3c06337.
    5. B. Flemisch et al., “Research Data Management in Simulation Science: Infrastructure, Tools, and Applications,” Datenbank-Spektrum, 2024, doi: https://doi.org/10.1007/s13222-024-00475-4.

  2. 2023

    1. S. Lauterbach et al., “EnzymeML: seamless data flow and modeling of enzymatic data,” Nature Methods, vol. 20, no. 3, Art. no. 3, 2023, doi: 10.1038/s41592-022-01763-1.
    2. T. Giess, S. Itzigehl, J. Range, R. Schömig, J. R. Bruckner, and J. Pleiss, “FAIR and scalable management of small-angle X-ray scattering data,” Journal of Applied Crystallography, vol. 56, no. 2, Art. no. 2, Apr. 2023, doi: 10.1107/S1600576723001577.
    3. S. Höpfl, J. Pleiss, and N. E. Radde, “Bayesian estimation reveals that reproducible models in Systems Biology get more citations,” Scientific reports, vol. 13, p. 2695, 2023, doi: 10.1038/s41598-023-29340-2.

  3. 2022

    1. A. Mack et al., “Preferential Self-interaction of DNA Methyltransferase DNMT3A Subunits Containing the R882H Cancer Mutation Leads to Dominant Changes of Flanking Sequence Preferences,” Journal of Molecular Biology, vol. 434, no. 7, Art. no. 7, 2022, doi: 10.1016/j.jmb.2022.167482.
    2. J. Range et al., “EnzymeML—a data exchange format for biocatalysis and enzymology,” The FEBS Journal, vol. 289, no. 19, Art. no. 19, Oct. 2022, doi: https://doi.org/10.1111/febs.16318.

  4. 2021

    1. J. Pleiss, “Standardized data, scalable documentation, sustainable storage –  EnzymeML as a basis for FAIR data management in biocatalysis,” ChemCatChem, vol. 13, pp. 3909–3913, 2021, doi: https://doi.org/10.1002/cctc.202100822.
To the top of the page