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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, whether produced or reused and (3) make research results reproducible beyond the research project {% cite biernacka:2020 bres_2022 RfII_RD voigt_2022 pauls_2023 bres_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 {% cite pauls_2023 %} and therefore need to be managed systematically and responsibly {% cite biernacka:202 0%}. 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 {% cite pauls_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.
+Research data are valuable {% cite pauls_2023 %} and therefore need to be managed systematically and responsibly {% cite biernacka:2020 %}. 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 {% cite pauls_2023 %}. Such practices also ensure integrity and help maximise the impact, reproducibility, transparency and rigour 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
+## Benefits 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), data ownership (i.e. "the possession of and responsibility for information" [NCATS Toolkit](https://toolkit.ncats.nih.gov/)) {% cite bres_2022 jacob_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 {% cite assmann_2022 bres_2022 bres_2023 %}. For the research group, good RDM enables knowledge management, transfer and preservation, while improving teamwork and saving time, money and resources {% cite assmann_2022 bobrov_2021 bres_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 {% cite assmann_2022 bobrov_2021 bres_2022 jacob_2022 voigt_2022 assmann:2022-08 %}. 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) {% cite assmann_2022 bobrov_2021 engelhardt_2022 jacob_2022 lindstädt_2019 voigt_2022 bres_2023 %}.
 
 There are also consequences of poor RDM practices, such as retractions of papers. For example, Dan Ariely, a professor of psychology and behavioural economics at Duke University, had one of his papers on dishonesty retracted. He could not remember in what year and in what form he had received the data from the company he was working with. Nor did he check the data for irregularities. The company could not find the data either {% cite bartlett:2021 %}. 
-
-## Problems in RDM
----
-Current issues and challenges in RDM can be classified by stakeholder, as individual researchers, research funders, research organisations, librarians and reviewers have different needs {% cite science_europe:2024 %}. 
-
-For individual researchers, the different organisational requirements can be confusing, especially if they work with different organisations, change their home institution or collaborate with researchers from other organisations where different rules apply  {% cite science_europe:2024 sheikh:2023 %}. The lack of connectivity between tools used at different stages of the research data lifecycle can also be a barrier to the proper management of their data.
-
-For research funders, the development of technological infrastructure can be difficult {% cite sheikh:2023 %}. 
-
-For research organisations, the institutional commitment and academic engagement required can be overwhelming. The lack of policy, funding and storage also hinders progress in RDM {% cite sheikh:2023 %}.
-
-For librarians and RDM staff, raising awareness among researchers of the benefits of data sharing remains a challenge. On another note, librarians need (discipline-specific) skills and competencies to provide RDM-based services {% cite sheikh:2023 %}.
-
+Another consequence might be that a paper, or in this case a book, has to be corrected and submitted again for review: Eliran Bar-El, a sociologist at the University of York had to correct his book “How Slavoj Became Žižek - The Digital Making of a Public Intellectual” because of “[several insufficient, missing, or erroneous citations of source material upon which the author builds his argument](https://web.archive.org/web/20231109095858/https:/press.uchicago.edu/books/Book-Pages/9780226823508.html)” {% cite joelving:2023 %}. 
 
 ## Research data life cycle
 ---
@@ -65,13 +45,44 @@ If the steps of the research data life cycle are not completed, data and results
 
 ![Lost Data Map]({{ '/assets/img/lost_data_map_rfii_Mau_CC-BY.png' | relative_url }}){:width="70%"}
 
-## Current developments and initiatives
+Below are measures of good RDM, grouped according to the steps in the research data life cycle. These measures are largely based on {% cite biernacka:2020 pauls_2023 steen:2022 %}, and some are explained in more detail on other pages of this Knowledge Base.
+
+### Plan
+Planning a research project includes creating a research design, planning for data management (i.e. creating an initial DMP that outlines how data will be collected, processed and shared), exploring existing data sources and planning for consent to share data.
+
+### Collect
+Even before the collection of research data, adopting an Electronic Lab Notebook (ELN) may be a good measure to take. By digitising research notes, protocols and experimental results, an ELN can streamline data organisation and collaboration between team members. In addition, ELNs provide version control and real-time data capture, enabling seamless integration with RDM workflows. For example, researchers studying microbial communities could use an ELN to record observations, generate graphs and annotate results, all collaboratively, ensuring transparency and reproducibility.
+Collecting primary research data requires the creation of clear protocols for data collection, whereas collecting secondary data, i.e. the acquisition of existing third-party data, may require obtaining permission to reuse the data. 
+Collecting research data also involves capturing data with metadata. For example, researchers studying bacterial evolution should carefully document their sampling procedures, including information on sampling sites, environmental conditions and sampling techniques to ensure reproducibility. 
+Finally, collecting research data includes data validation (i.e. data cleaning and quality control), the use of acceptable file formats, and data check.
+
+### Process & Analyse
+Processing research data begins with the proper documentation/description of the data. In terms of documenting scripts, code and software, software tools (from small analysis scripts to machine learning models) are integral to the processing, analysis and interpretation of complex microbiology data sets. Therefore, documenting the software environment, version numbers and dependencies used in data analysis workflows is critical to ensure reproducibility and transparency. For example, a study investigating the taxonomic composition of the gut microbiota may rely on custom Python scripts for data pre-processing and statistical analysis. By documenting these scripts, along with the parameters and input data used, researchers can enable others to replicate their analyses and validate their findings. In addition, the use of version control systems (VCS) such as Git, and the hosting of Git repositories on platforms such as GitHub or GitLab, ensures the traceability and accessibility of software artefacts. By incorporating such software management practices into their RDM strategy, microbiology researchers can improve the reproducibility, transparency and rigour of their computational analyses, thereby advancing scientific knowledge in the field. When it comes to documenting models, with the increasing use of machine learning in microbiology (e.g. to predict antibiotic resistance or classify microbial species), it is imperative that the underlying models are managed transparently. Researchers should document model architectures, training data and performance metrics to facilitate model validation and comparison across studies.
+Before research data can be analysed, it needs to be digitised, transcribed, translated and possibly anonymised. Clear protocols for data analysis must then be established. Finally, the data can be interpreted and research findings produced.
+
+### Preserve
+### Share
+### Reuse
+
+## Issues and challenges in RDM
+---
+Current issues and challenges in RDM can be classified by stakeholder, as individual researchers, research funders, research organisations, librarians and reviewers have different needs {% cite science_europe:2024 %}. 
+
+For individual researchers, the different organisational requirements can be confusing, especially if they work with different organisations, change their home institution or collaborate with researchers from other organisations where different rules apply  {% cite science_europe:2024 sheikh:2023 %}. The lack of connectivity between tools used at different stages of the research data lifecycle can also be a barrier to the proper management of their data.
+
+For research funders, the development of technological infrastructure can be difficult {% cite sheikh:2023 %}. 
+
+For research organisations, the institutional commitment and academic engagement required can be overwhelming. The lack of policy, funding and storage also hinders progress in RDM {% cite sheikh:2023 %}.
+
+For librarians and RDM staff, raising awareness among researchers of the benefits of data sharing remains a challenge. On another note, librarians need (discipline-specific) skills and competencies to provide RDM-based services {% cite sheikh:2023 %}.
+
+## Developments and initiatives in RDM
 ---
 Internationally, the increasingly frequent requirement to produce a DMP has stimulated interest in RDM {% cite yamaji:2024 %} and encouraged libraries to take an active role in RDM through advocacy, policy development, and advisory and consultancy services {% cite cox:2017 %}. Some institutions, such as KU Leuven, have also developed a dashboard to review datasets to meet funder requirements {% cite yamaji:2024 %}.
 
 In Germany, the National Research Data Infrastructure (NFDI) funds nearly 30 discipline-specific consortia to help researchers make their data reusable in the long term.
 
-## Further resources
+## Resources
 ---
 * General resources:
     * Brief Guide - Research Data Management: [Training Expert Group 2020](https://doi.org/10.5281/zenodo.4000989)
@@ -81,4 +92,3 @@ In Germany, the National Research Data Infrastructure (NFDI) funds nearly 30 dis
     * [Coscine](https://coscine.de/) by [RWTH Aachen](https://www.rwth-aachen.de)
     * [BEXIS2](https://demo.bexis2.uni-jena.de) by [NFDI4Biodiversity](https://www.nfdi4biodiversity.org/en/) at [FSU Jena](https://www.uni-jena.de)
     * [GfBio](https://www.gfbio.org) consortium services
-