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Research Data Management (RDM)


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Definition of Research Data Management (RDM)

Research Data Management (RDM) is the care and maintenance required to (1) obtain high-quality data (whether produced or reused), (2) make the data available and usable in the long term, independent of the data producer and (3) make research results reproducible beyond the research project (Biernacka et al., 2020; Bres et al., 2022; Research Data, n.d.; Voigt et al., 2022; Pauls et al., 2023; Bres et al., 2023). It complements research from planning to data reuse and deletion.

RDM in microbiology

RDM is crucial in microbiology to ensure the integrity and accessibility of data throughout the research process. One essential aspect of RDM is establishing clear protocols for data collection, storage, and analysis. For instance, researchers studying bacterial evolution should document their sampling procedures meticulously, including information on sampling sites, environmental conditions, and sampling techniques, to ensure reproducibility. Additionally, adopting standardized data formats, such as FASTA or GenBank Flat File Format, facilitates data sharing and interoperability across different studies, enhancing collaboration and knowledge exchange within the microbiology research community. Proper metadata annotation is also paramount, as it provides essential context for interpreting the data. Researchers in microbiology should develop comprehensive data management plans (DMPs) outlining how data will be collected, processed, and shared throughout the research data lifecycle. DMPs serve as roadmaps for RDM, ensuring that data handling procedures adhere to ethical, legal, and funder requirements. Moreover, adopting electronic lab notebooks (ELNs) can streamline data organization and collaboration by digitizing research notes, protocols, and experimental results. ELNs enable real-time data capture, version control, and collaboration among team members, facilitating seamless integration with RDM workflows. For example, researchers investigating microbial communities could use ELNs to record observations, generate graphs, and annotate findings collaboratively, ensuring transparency and reproducibility. Researchers working on sensitive information, such as patient data in clinical microbiology studies must take care of data protection and security measures to safeguard this information. Embracing open science practices by depositing data in public repositories like NCBI’s GenBank or the European Nucleotide Archive fosters transparency and long-term preservation of microbiological data, ensuring its availability for future research endeavors. Therefore, microbiology researchers should integrate robust RDM practices into their workflows from the outset to maximize the impact and reproducibility of their findings while contributing to the advancement of the field.

Addtionnaly, researchers should address the management of software tools, including small analysis scripts and machine learning models, within their RDM framework. These tools are integral for processing, analyzing, and interpreting complex microbiological data sets. Therefore, documenting the software environment, version numbers, and dependencies used in data analysis workflows is crucial for ensuring reproducibility and transparency. For instance, a study investigating the taxonomic composition of gut microbiota may rely on custom Python scripts for data preprocessing and statistical analysis. By documenting these scripts along with their parameters and input data, researchers enable others to replicate their analyses and validate their findings. Moreover, utilizing version control systems like Git and hosting repositories on platforms like GitHub or GitLab ensures the traceability and accessibility of software artifacts. By incorporating software management practices into their RDM strategies, microbiology researchers can enhance the reproducibility, transparency, and rigor of their computational analyses, thereby advancing scientific knowledge in the field.

With the growing application of machine learning in microbiology, such as predicting antibiotic resistance or classifying microbial species, it becomes imperative to manage the underlying models transparently. Researchers should document model architectures, training data, and performance metrics to facilitate model validation and comparison across studies.

Relevance of RDM

Research data are valuable (Pauls et al., 2023) and therefore need to be managed systematically and responsibly (missing reference). Incorporating robust RDM practices from the outset of a research project helps make research data accessible, reusable, and verifiable throughout the research process and in the long term, regardless of the data producer (Pauls et al., 2023). Such practices also ensure integrity and help maximize the impact, reproducibility, transparency, and rigor of researchers’ analyses and findings. Finally, robust RDM practices enhance collaboration and knowledge sharing and help preserve the scientific record, and advance scientific knowledge.

Advantages and drawbacks of RDM

As noted above, there are many benefits to incorporating robust RDM practices from the outset of a research project. For researchers, good RDM enhances visibility, reputation (by ensuring the quality of research), and data ownership (i.e. “the possession of and responsibility for information” NCATS Toolkit) (Bres et al., 2022; Jacob et al., 2022) and helps them to meet formal requirements from third parties (e.g. research funders, institutions, and publishers). For the project, good RDM brings clarity and findability, supports coordination, data security, and good storage practices, helps to keep track of the project and deal with legal aspects, and increases eligibility for funding (Assmann et al., 2022; Bres et al., 2022; Bres et al., 2023). For the research group, good RDM enables knowledge management, transfer, and preservation, while improving teamwork and saving time, money, and resources (Assmann et al., 2022; Bobrov et al., 2021; Bres et al., 2022). For third parties, good RDM practices increase transparency, make data FAIR (i.e. findable, accessible, interoperable, and reusable (no need for unnecessary duplication)), and increase collaboration (Assmann et al., 2022; Bobrov et al., 2021; Bres et al., 2022; Jacob et al., 2022; Voigt et al., 2022). Last but not least, good RDM practices help to address societal challenges by ensuring reproducibility, availability and verifiability, preventing data loss and preserving the scientific record, ensuring good research practice (GRP) and supporting open science (i.e. open transfer of research knowledge, open access to research data) (Assmann et al., 2022; Bobrov et al., 2021; Engelhardt et al., 2022; Jacob et al., 2022; Lindstädt et al., 2019; Voigt et al., 2022; Bres et al., 2023).

There are also consequences of poor RDM practices, such as the retraction of papers. For example, Amorós and Puit 2015 had their paper retracted due to inconsistent and non-reproducible values and loss of raw data.

Research data life cycle

The research data life cycle is a model that illustrates the steps of RDM and describes how data should ideally flow through a research project to ensure successful data curation and preservation (Research Data Lifecycle, n.d.; Research Lifecycle Guide, 2024) [NTU Library, Princeton Research Data Service]. The research data life cycle can be illustrated as follow (RDMkit, 2021):

Research data life cycle

Further resources

References

  1. Biernacka, K., Bierwirth, M., Buchholz, P., Dolzycka, D., Helbig, K., Neumann, J., Odebrecht, C., Wiljes, C., & Wuttke, U. (2020). Train-the-Trainer Concept on Research Data Management. Zenodo. https://doi.org/10.5281/ZENODO.4071471
  2. Bres, E., Rudolf, D., Lindstädt, B., & Shutsko, A. (2022). Research Data Management in Medical and Biomedical Sciences.
  3. Research Data. Retrieved June 6, 2023, from https://rfii.de/en/topics/#forschungsdaten
  4. Voigt, P., Frericks, S., Lindstädt, B., Shutsko, A., & Vandendorpe, J. (2022). Workshop on Research Data.
  5. Pauls, C., Feeken, C., Steen, E.-E., Lindstädt, B., Vandendorpe, J., & Markus, K. (2023). Workshop on Research Data Management.
  6. Bres, E., Rudolf, D., Lindstädt, B., Markus, K., Vandendorpe, J., & Riedel, R. (2023). Workshop on Research Data Management.
  7. Jacob, B., Kroehling, M. A., Mertzen, D., Straka, J., Lindstädt, B., Shutsko, A., & Vandendorpe, J. (2022). Workshop on Research Data.
  8. Assmann, C., Gadelha, L., Markus, K., & Vandendorpe, J. (2022). Workshop on Research Data Management.
  9. Bobrov, E., Adam, L.-S., Söring, S., Jäckel, D., Herwig, A., Lindstädt, B., Vandendorpe, J., & Shutsko, A. (2021). Workshop on Research Data.
  10. Engelhardt, C., Biernacka, K., Coffey, A., Cornet, R., Danciu, A., Demchenko, Y., Downes, S., Erdmann, C., Garbuglia, F., Germer, K., Helbig, K., Hellström, M., Hettne, K., Hibbert, D., Jetten, M., Karimova, Y., Kryger Hansen, K., Kuusniemi, M. E., Letizia, V., … Zhou, B. (2022). D7.4 How to be FAIR with your data. A teaching and training handbook for higher education institutions. https://doi.org/10.5281/ZENODO.6674301
  11. Lindstädt, B., Vandendorpe, J., & von der Ropp, S. (2019). Research Data Management.
  12. Research data lifecycle. Retrieved June 6, 2023, from https://libguides.ntu.edu.sg/rdm/researchdatalifecycle
  13. Research Lifecycle Guide. (2024). Princeton Research Data Service. https://researchdata.princeton.edu/research-lifecycle-guide/research-lifecycle-guide
  14. RDMkit. (2021). Data life cycle. ELIXIR. https://rdmkit.elixir-europe.org/data_life_cycle