The scientific enterprise, as a form of knowledge that can be applied to better the world, relies on public trust. That trust is earned in part because the scientific method is predicated on being open to scrutiny and primed for correction. With increased reliance on computation in every aspect of science and inquiry, there are more opportunities for close examination of the scientific record. Such examination depends on research practices as well as on the practices of those who are entrusted with keeping, logging, and stewarding the artifacts that comprise the scientific record. Data professionals, including curators, can help ensure the transparency and reproducibility of these artifacts, and shore up the public’s trust in science. In this lightning talk, we will explain why and how traditional curation and stewardship responsibilities must evolve to meet the moment. We will focus on the ways in which a transparency mindset informs curation practices that concretely enhance the quality of scientific artifacts. First, we frame the object of curation as the entirety of the data collection and analysis process, not only its component parts. That is, the activities that verify (to the extent possible) that statistical and analytic claims about given data can be reproduced with that data. Second, we explore how statistical and programming skills can be useful for curators and other data professionals. To illustrate, we will focus on the curation of social science research. The social sciences are concerned with understanding, and improving conditions for, society and individuals, and curation can contribute to public trust in this research by enhancing its transparency and reproducibility. Through a series of examples from social science research, we will highlight the skills and the actions data professionals bring to bear. We will also discuss the benefits reaped as a result of these actions – for science and society, as well as for the researchers and curators