merge with changed in motivation section

paper.tex

@@ -91,10 +91,18 @@ \section{Motivation}
 In this chapter, we motivate dedicated RSE groups in German research organizations.
 Several stakeholder perspectives are discussed and supported by (inter)national examples, including that of RSEs within RSE groups, RSEs embedded in research groups, Researchers in need of RSE resources, organizational management and that of funders.
 
-Research Data Management has proved to benefit data quality through training researchers, the reusability through data repositories and to avoid duplication of effort.
-For over a decade, research funders and organizations made a significant effort to establish RDM and teams around it, for example the Utrecht University Research Data Management Support~\autocite{UtrechtRDM}, University of Stuttgart FoKUS team~\autocite{Boehlke2024} or TUBS.researchdata~\autocite{Grunwald2022} at TU Braunschweig.
-We assume that research software will follow a similar trajectory.
-\footnote{For arguments when research software is unlike data, see \autocite{Lamprecht2020}.}
+We will draw parallels to research data here, because both, data and software, play a fundamental role in all of the research. Over the past decades, Research Data Management (RDM) has evolved into a topic of national interest with NFDI consortia for all disciplines and a research data law. 
+Federal state RDM initiatives\footnote{e.g. HeFDI, SaxFDM, FDM-BBB, FDM-NDS and others} accelerate the topic further providing regional training, networking and other supporting services. 
+Many research organisations have established central RDM groups that support research projects in all aspects from grant proposals to hands-on support and maintaining Data Management Plans (DMP). 
+Funding agencies acknowledge the importance of research data and started to make RDM mandatory in research projects. 
+Due to the similar nature of both, data and software, and their importance in today's digital research, it is reasonable to expect a similar trajectory in the development of research software as a topic.
+%such output has become more important over the last two decades. Since research software can be considered valuable research output as well, we expect a similar trajectory for software.
+%gh training researchers, the reusability through data repositories and to avoid duplication of effort.
+%For over a decade, research funders and organizations made a significant effort to establish RDM and teams around it, for example the Utrecht University Research Data Management Support~\autocite{UtrechtRDM}, University of Stuttgart FoKUS team~\autocite{Boehlke2024} or TUBS.researchdata~\autocite{Grunwald2022} at TU Braunschweig.
+%We assume that research software will follow a similar trajectory.
+%\footnote{For arguments when research software is unlike data, see \autocite{Lamprecht2020}.}
+
+
 While we focus on Germany here, it is beneficial to review how other countries approach research software.
 In the UK, for example, many universities started initiating dedicated RSE departments about a decade ago~\autocite{Crouch2013}.
 
@@ -164,6 +172,7 @@ \subsection{International Comparison and Current Developments}
 the University Utrecht~\autocite{Utrecht2016b},
 and the German Research Council~\autocite{dfg_gsp}.
 %% TODO: Double-check that DLR guidelines are referenced.
+
 Another development taking place worldwide is the encouragement of authors to submit both, data and software, for peer review. As an example, the journal "Nature" initiated such a policy\footnote{\url{https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards}} in 2018~\autocite{Nature2018}.
 RSE groups are able to offer researchers consulting tailored to their specific needs on how to implement and document those policies.