Update 27-data-reuse.md

docs/_RDM-Reuse/27-data-reuse.md

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 Making data reusable benefits researchers who publish their data, researchers who reuse data, and society. 
 
-Researchers who publish their data see an increase in their scientific reputation, citations and collaborations {% cite rehwald_2022 pauls_2023 %}. In addition, researchers who publish their data not only comply with the [FAIR Data Principles](https://nfdi4microbiota.github.io/nfdi4microbiota-knowledge-base/Research-Data-Management/04-fair), but also avoid bias in the body of evidence {% cite IOM_2015 %}, increase transparency and thus trust in research {% cite engelhardt_2022 rehwald_2022 pauls_2023 %}. Finally, by sharing their resources and perspectives, researchers who publish their data enable other researchers to build on their work, accelerating scientific discovery {% cite engelhardt_2022 IOM_2015 rehwald_2022 %}.
+Researchers who publish their data see an increase in their scientific reputation, citations, and collaborations {% cite rehwald_2022 pauls_2023 %}. In addition, researchers who publish their data not only comply with the [FAIR Data Principles](https://nfdi4microbiota.github.io/nfdi4microbiota-knowledge-base/Research-Data-Management/04-fair) but also avoid bias in the body of evidence {% cite IOM_2015 %}, increase transparency and thus trust in research {% cite engelhardt_2022 rehwald_2022 pauls_2023 %}. Finally, by sharing their resources and perspectives, researchers who publish their data enable other researchers to build on their work, accelerating scientific discovery {% cite engelhardt_2022 IOM_2015 rehwald_2022 %}.
 
-Researchers can recycle unique data by performing secondary analyses to answer new research questions and/or with new methods {% cite rehwald_2022 pauls_2023 %}. Reusing data in this way saves resources such as time, energy and money {% cite engelhardt_2022 FSD rehwald_2022 pauls_2023 %}. Data reuse also increases collaboration and, over time, enables the comparison of different samples {% cite rehwald_2022 pauls_2023 %}. Indeed, data reuse is essential for interdisciplinary experiments and cross-cutting research approaches {% cite pavone_2020 %}.
+Researchers can recycle unique data by performing secondary analyses to answer new research questions and/or with new methods {% cite rehwald_2022 pauls_2023 %}. Reusing data in this way saves resources such as time, energy, and money {% cite engelhardt_2022 FSD rehwald_2022 pauls_2023 %}. Data reuse also increases collaboration and, over time, enables the comparison of different samples {% cite rehwald_2022 pauls_2023 %}. Indeed, data reuse is essential for interdisciplinary experiments and cross-cutting research approaches {% cite pavone_2020 %}.
 
 Making data reusable can also benefit society. It reduces unnecessary experimentation {% cite rehwald_2022 %}, avoids duplication of data collection and minimises collection from hard-to-reach, vulnerable or over-researched populations {% cite FSD rehwald_2022 %}. It also enables replication and thus promotes reproducibility. Finally, it benefits teaching and improves the link between academia and industry {% cite rehwald_2022 %}.