shorten DSW preamble section of policy comparisons so it fits a page

assets/policy_comparisons.csv

@@ -29,11 +29,7 @@ coc,24. Manage the collected data carefully and store both the raw and processed
 coc,"25. Contribute, where appropriate, towards making data findable, accessible, interoperable and reusable in accordance with the FAIR principles",The FSS guidelines follow the FAIR principles explicitly.
 coc,"45. As far as possible, make research findings and research data public subsequent to completion of the research. If this is not possible, establish the valid reasons for this.","From the perspective of the VU guidelines, this is redundant with item 11."
 dsw,"1. Preamble 
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-The principles of honesty, scrupulousness, transparency, independence, and responsibility form the basis of research integrity (UNL, 2018). Abiding by these principles enlarges trust and quality of academic research, thereby improving its relevance to society. The current guideline is developed with input from all DSW faculties and offers guidance for the archiving of academic research published by researchers at the Dutch faculties of social and behavioural sciences, drawn from the principles of scrupulousness, transparency, and responsibility. The guideline seeks to improve archiving of social and behavioural research using both quantitative and qualitative methods, in order to safeguard continued availability of qualitative or quantitative research data, detailed descriptions of research materials and approaches, and an overview of the data processing and publication processes after the research has been published. 
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-This guideline is not meant to replace other existing guidelines or regulations related to data management, open science, data processing agreements and privacy aspects in the design stage of a research project. The document can be seen as an initiative that is part of a broader effort to promote research integrity among researchers focusing on both quantitative and qualitative studies at faculties of behavioural and social sciences in the Netherlands. Rather than functioning as a strict straightjacket, it intends to provide a clear guideline, which can be further fleshed out under the motto ‘apply or explain’, taking into account existing regulations at the faculty or university level. 
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 Researchers working in the social and behavioural sciences at a Dutch university will be held to these standards to ensure that research integrity in general and transparency in particular can be ensured. Given the various distinct methodologies of scholarly research carried out under the general “social science” header, there are two main approaches that can be identified and should be implemented to ensure scientific integrity and its future assessment. The first is primarily for quantitative research designs and quantitative data that can most often relatively easily be de-identified (pseudonymized or anonymized) and stored in a repository in full. The second is for scientific research that is structured by qualitative and interpretive research designs and epistemologies that generate data and information that may have a different character and most often cannot be de-identified and stored in an identical manner as quantitative data. Regardless of methodological approach, all researchers have an obligation to follow the standards of integrity and transparency set in this document. All researchers must be aware of the specific regulations that govern their type of research and adhere to these regulations   (except where motivated exceptions are allowed).","FSS guidelines follow the spirit of these guidelines, but  FSS disagrees that qualitative and quantitative data should be treated differently. The reasoning for this can be summarized as follows:
 
 1.	While there is difference in the ease of de-identification of quantitative vs qualitative data, this difference is not such that it should have implications for the way data is handled: it is often still very difficult to fully anonymize quantitative data, and it is possible to pseudonymize qualitative data.