Extend RDM with microbiological focus #110

docs/_Research-Data-Management/02-rdm.md

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 ## Definition of Research Data Management (RDM)
 Research Data Management (RDM) is a series of measures that need to be taken during a research project in order to (1) obtain high-quality data (whether produced or reused), (2) make data avaialbe and usable over the long-term and (3) make research findings reproducible beyond the research project {% cite RfII_RD bres_2022 voigt_2022 %}.
 
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+## Research Data Management in microbiology
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+Research Data Management (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, facilitates data sharing and interoperability across different studies, enhancing collaboration and knowledge exchange within the microbiology 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 lifecycle. DMPs serve as roadmaps for RDM, ensuring that data handling procedures adhere to ethical, legal, and funder requirements. Moreover, adopting electronic lab journals (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 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.
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 ## 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 {% cite NTU_LibGuides_RD_life_cycle bobrov_2021 %}. The research data life cycle can be illustrated as follow: