Using science reading to lay the foundation for effective reuse of scientific information: Access to the data alone is not enough to correctly reuse it, we must understand how the data were derived

Research data management is committed to making research data reusable. Although there are many data repositories, a common complaint is that the vast majority of the data sets they contain are at best only partially usable as they lack key information. What has gotten lost in the conversation is that effective research data management is ultimately about the infor-mation needs of people. This means beginning by acknowledging the types of participants involved in data reuse: user and producers of research data. Both require role-specific training. Users must be trained in how to understand and interact with data sets. Producers must un-derstand what data should be preserved and in what form. Further, optimal training for both groups requires flipping roles and seeing data from their counterpart's perspective. For users it means understanding the processes and labor involved in creating data so that it is clear what type of data archiving is and is not possible. For a producer it means understanding the infor-mation needs of users so that the data they need is made available in forms they can use most effectively. As such, the driving force of effective research data management is ensuring not only the preservation, but the usability of scientific information. In this talk, we describe a teaching concept that sets the stage for an effective research data management mindset. We begin by introducing Science Reading [1], [6] as a methodology to help understand the science that was done, as well as possible, given available documenta-tion. Then we introduce Literature Content Management [2] as a tool to organize the key in-formation gathered using Science Reading. We conclude by incorporating the various meth-ods and tools available when searching for scientific information, including papers [3], [4], [5]. Our goal is to show how an understanding of scientific knowledge from a user perspective can more productively shape how research data management is practiced.