One of the things I’m working on currently is developing a semester long data information literacy course with some of my colleagues for graduate students in the College of Engineering here at the University of Michigan. In constructing this course, I have been thinking about how we could incorporate ACRL’s “Framework for Information Literacy for Higher Education” (hereafter referred to as “the Framework”), particularly the idea of threshold concepts. The framework represents an effort to move beyond a prescriptive skills-based course of instruction as represented by ACRL’s 2000 “Information Literacy Competency Standards for Higher Education” and towards a less directive, student centered model of education that promotes engagement with other fields. Threshold concepts, as defined within the framework, are “those ideas in any discipline that are passageways or portals to enlarged understanding or ways of thinking and practicing within that discipline”.
The Framework is comprised of six threshold concepts that serve as its core, although the framework is not intended to be prescriptive or exhaustive. Librarians are encouraged to apply the Framework in ways that are relevant to their environment and educational objectives. It is with this encouragement in mind that I post some initial thoughts in how the Framework might inform our thinking in developing our DIL course.
- From the Framework: Authority Is Constructed and Contextual
- As applied to data: Your Data Are a Component of Your Professional Identity.
As a graduate student, you are developing your professional identity and becoming an authority in your field. The data you are generating or using in your research serves in part as an indicator of your expertise and credibility as a scholar. As such, it is important that you document and describe your data in ways that enable others to understand, evaluate and trust your work in order to build your authority in your field of research.
- From the Framework: Information Creation as a Process
- As applied to data: Data Creation as Process
The processes of acquiring, preparing, analyzing and summarizing data that are done as a part of the research process affect the utility, accessibility and potential impact of the data. For example, digital data are often migrated from one format into another to enable the data to be interpreted by a particular software package. These processes are components of a larger data lifecycle which include considerations prior to acquiring data (such as planning and discovery) as well as after summarizing the data in reporting research findings (such as dissemination and preservation of the data itself). Defining the lifecycle of your data and seeing how the stages in your lifecycle are connected is critical for understanding how actions taken in one stage may affect another, advancing or restricting what you or others are able to do with the data.
- From the Framework: Information Has Value.
- As applied to data: Data Have Value Outside of the Purpose for Which They Were Generated
Research data are generally created with the intent of addressing a particular question or understanding a particular situation. However, data can also be repurposed and used by others outside of the original researcher or team to ask new questions or support new areas of inquiry. In addition, beyond supporting new research endeavors, data can also be a commodity, a tool for education, a means to persuade opinion or a means of better understanding our world.
- From the Framework: Research as Inquiry
- For Data: Data Have Long-Term Value
Data are often defined as the building blocks of research. Research is an iterative process, where people return to previous findings to question them based on new knowledge or to pose increasing more specific or complex inquiries. As researchers return to past areas of inquiry, the data that underlie these inquiries needs to have been preserved in ways that enable others to revisit and reuse them. In developing your data set, consider the ways in which you can preserve your data to support its continued use in fueling new areas of exploration.
- From the Framework: Scholarship as Conversation
- For Data: Data as a Component of the Scholarly Communications Process
Scholarship can be understood as a discourse amongst communities of scholars, researchers, or professionals engage in which they seek to communicate their insights and perspectives to designated audiences. Data often underlie the findings, arguments or points being made in this discourse. Without access to the data it may be difficult to fully understand or trust the findings or arguments being made. In considering how to share your research, give your research data equal consideration and treatment on par with articles or other more traditional product s of scholarly communication.
- From the Framework: Searching as Strategic Exploration
- For Data: Developing the Human Readable Elements of your Data Set
Data are designed to be consumed by machines (instruments, software, etc.) rather than by humans as articles or most other publications are. However, as the consumption of data becomes increasingly important to researchers (and educators, government agencies, businesses, the general public, etc.), data producers need to consider how their data could be discovered, understood and trusted by others through providing documentation or description written clear and direct language.
This is only a preliminary exploration of how threshold concepts could potentially be used to inform teaching data information literacy. However, I believe that this is a useful area of study and I would love to see more rigorous work done to articulate further the connections between data and ACRL’s Framework for Information Literacy.