Students as Wikipedia Teachers Creating an Authentic Peer Learning Experience with a Wikipedia Edit-a-thon

Wikipedia has become a core part of the information landscape, and many librarians have added Wikipedia-related discussions and activities to their teaching. At the same time, gaps in Wikipedia coverage due to its largely White and male editorship has spawned a proliferation of edit-a-thons designed to add representative content to Wikipedia, notably the Art + Feminism editing community and events.1 This chapter explores a first-year seminar course centered on Wikipedia where students not only authored an article, but also organized an edit-a-thon they publicized, managed, themed, and created training materials for.</p

Big Data Research at the University of Colorado Boulder

Background: Big data, defined as having high volume, complexity or velocity, have the potential to greatly accelerate research discovery. Such data can be challenging to work with and require research support and training to address technical and ethical challenges surrounding big data collection, analysis, and publication.&nbsp; &nbsp; Methods: The present study was conducted via a series of semi-structured interviews to assess big data methodologies employed by CU Boulder researchers across a broad sample of disciplines, with the goal of illuminating how they conduct their research; identifying challenges and needs; and providing recommendations for addressing them.&nbsp; &nbsp; Findings: Key results and conclusions from the study indicate: gaps in awareness of existing big data services provided by CU Boulder; open questions surrounding big data ethics, security and privacy issues; a need for clarity on how to attribute credit for big data research; and a preference for a variety of training options to support big data research.&nbsp; &nbsp; Recommendations: Evaluation of current access to existing research infrastructure at CU Boulder across departments and disciplines, including recommendations for how inequities could be addressed. Development of big data training curriculum, particularly for big data ethics, privacy and security, through a variety of channels (e.g., documentation and context-specific consultations for specific big data services, more general course-based curriculum).&nbsp; Consider how to address the complexity and dynamic nature of big data in the IRB process in a manner that fully and reasonably considers the ethical, security and privacy implications of a given big data research project. Creation of CU Boulder guidelines for attributing credit to the myriad contributors in big data research projects, and considering the sometimes unconventional contributions, with the goal of helping departments develop clear policies and incentives for researchers performing big data research. Development of marketing and outreach strategies to increase awareness of existing and forthcoming big data research support services at CU Boulder, in a manner that promotes equitable access to services across disciplines.&nbsp; Assessment of staffing gaps and staff-training needs in order to support big data curriculum and services. Periodic evaluation of emerging trends and needs in big data research, in order to adjust strategies and services appropriately to ensure CU Boulder is providing state-of-the-science support and infrastructure. An optimal way of addressing the complex questions above may be to establish a steering committee composed of a broad range of CU Boulder (and possibly external) stakeholders and decision makers. </p

Understanding Research Data Practices of Civil and Environmental Engineering Graduate Students

Research data management is essential for high-quality reproducible research, yet relatively little is known about how research data management is practiced by graduate students in Civil and Environmental Engineering (CEE). Prior research suggests that faculty in CEE delegate research data management to graduate students, prompting this investigation into how graduate students practice data management. This study uses semi-structured interviews and qualitative content analysis to explore how CEE graduate students work with data and practice data management in their research, as well as what resources and support would meet their needs. Many respondents touched on data collection, data management, disseminating research outputs, and collaboration and learning in their interviews. Several themes emerged from the interviews: data quality as a concern, as many CEE graduate students rely on secondary data for research; a gap between values and enacted practices; a connection between disseminating data and reproducibility; and a reliance on peer and self-directed learning for data management education. Based on these themes, the study recommends strategies for librarians and others on campus to better support CEE graduate student research data practices. &nbsp;</p

Facilitating Research Consultations Using Cloud Services: Experiences, Preferences, and Best Practices

The increasing complexity of the information ecosystem means that research consultations are increasingly important to meeting library users' needs. Yet librarians struggle to balance escalating demands on their time. How can we embrace this expanded role and maintain accessibility to users while balancing competing demands on our time? One tool that allows us to better navigate this shifting landscape is Google Appointment Calendar, part of Google Apps for Education. It makes it easier than ever for students to book a consultation with a librarian, while at the same time allowing the librarian to better control their schedule. Our research suggests that both students and librarians felt it was a useful, efficient system