Update 02-rdm.md

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

@@ -22,7 +22,7 @@ Research data are valuable {% cite pauls_2023 %} and therefore need to be manage
 
 ## 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](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 %}. 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 %}.
+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](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 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.