When researchers share their research as a published journal article, it includes an abstract. We understand these well: we don't always follow best practices, but there is substantial literature on what should be in an abstract, guides to writing effective abstracts, and journal-level standards for highly structured abstracts. The same is not true for descriptions of published data sets, and the advice for journal articles is not universally applicable. Researchers are increasingly incentivized to make research data available in a public data repository, documented using controlled vocabularies and well-defined metadata fields. Most data documentation standards include a title and at least one field for summarizing the data set which are open-ended, plain text, unconstrained fields. These "unmanaged” fields are particularly important for most search engines, as they are the metadata fields most consistent with natural language, for which search has been highly optimized. They play an important role in some search and filtering tasks, particularly among emerging scholars and novice users. While expert data managers and others have developed the ability to write thorough and useful dataset descriptions, we've observed that data repositories and catalogues, and most data documentation standards, have inconsistent and vague instructions on the content of the dataset description field. Broadly, our objective is to establish evidence-based guidance for effective documentation of datasets using unstructured text fields. We have reviewed existing literature and best practices to establish core guiding principles to support the authors of dataset descriptions. These principles have been refined through consultations with data librarians, data repository managers, and other experts. This poster describes the refined proposed guidelines, explains the reasoning behind each, and solicits input and feedback on what is required for a set of guiding principles to help users write better, more useable data set descriptions.