Creating Sustainable Data Management Services: the Office of Research and You

The Office of Research supports all aspects of securing funding for research. They: seek out funding opportunities, assist with proposal preparation, represent the institution in accepting the award, manage the funds, and ensure compliance with policies, contracts and laws. In recent years, there have been several case studies of partnerships between the Office of Research and their libraries, both within the United States 1,2,3 and abroad 4,5,6. By pooling resources, both entities get better results. To engage your Office of Research in delivering sustainable data management services, you need to know how to speak their language and identify opportunities for collaboration.No embarg

Yes, but what is it that you do?

Despite carefully crafted job descriptions, e-science or data librarians find that there is little consensus on position responsibilities and required competencies. Consequently, these librarians tend to customize their positions around what their clientele require. Indeed, data “Curation Service Models [are] driven by user requirements”. Therefore, it’s not just about the data – it’s about the people. Specifically, it’s about the different cultures of user groups and librarians and how to communicate effectively across these groups. The “elevator speech” for a scientist might be quite different from the one for your librarian colleague. It’s no wonder that a recent survey of e-science librarian job advertisements found that communication is the most frequently cited personal skill. While each librarian, researcher and student may have different needs, there are usually broad disciplinary cultures that can guide interactions. Understanding some of these broadly-defined disciplinary characteristics can promote successful communication

Getting emotional about data: The soft side of data management services

Original article, published by College and Research Libraries News, can be found at: http://crln.acrl.org/content/76/8/437.fullPublisher does not allow open access until after publicatio

Data in context: Using case studies to generate a common understanding of data in academic libraries

As new expectations emerge in librarianship, librarians find themselves engaging with researchers throughout the entire research process. This includes during early stages, when research outputs are in their infancy. This shift means that any librarian might be faced with a ‘data question’ and be able to assist without necessarily being a ‘data’ expert. As libraries approach professional development in this field, additional difficulties occur as data cannot be easily understood without context. Instead of attempting to comprehensively cover this broad, nuanced, and sometimes vague topic, the authors took a different approach. In order to place ‘data’ in definable contexts, the authors created local, real-world case studies to introduce this topic to the library. This article describes the professional development event, complete with case studies, their development, discussion questions, and observations. As faculty and staff answered guided questions, they self-identified the value of existing librarian capabilities such as the reference interview, information location, and referral systems. This enabled library faculty and staff from across the library to engage positively and proactively, without any extensive background in this field.Publisher allows immediate open acces

Learning Style Dimensions and Professional Characteristics of Academic Librarians

Do librarians with different characteristics, such as type of work responsibilities or age, have different learning styles? The authors analyzed results from over 1,500 responses to a version of the Index of Learning Styles (ILS) questionnaire based on the Felder-Silverman Learning Styles model. This model consists of eight dimensions paired on four scales: Active/Reflective; Sensing/Intuitive; Visual/Verbal; and Sequential/Global. In addition to their scores on the ILS questionnaire, respondents were also asked about demographic and professional characteristics. Statistically significant differences in learning style scores were found to exist between librarians with different types of position responsibilities. In particular, for three out of four scales, catalogers have statistically different learning styles than other librarian groups. Recognition of different learning styles and thoughtful integration of appropriate teaching styles may improve workplace interpersonal communication, enhance professional development and staff training, and strengthen teaching.Publisher allows immediate open acces

The two-for-one workshop: mapping data management services to the research lifecycle

Learn how to partner with offices that are adjacent to the library in the research lifecycle to deliver data management workshops.No embarg

Concerns-Based Adoption Method (CBAM) for Research Data Management (RDM) Education

Not all researchers are thrilled to hear of new research data management expectations. In fact, RDM education is met with varying levels of resistance. Change theory tells us that this resistance is most likely due to underlying concerns. If these underlying concerns can be discovered, then appropriate educational activities can be provided to ameliorate them. Dr. Kim Lightle and I modified an established tool, the Concerns-Based Adoption Method (CBAM), to determine if researchers could be differentiated based on their concerns about research data management. We hypothesize that if we can measure where researchers are at in the change process, we can intervene with timely and appropriate education. Our first step was to deploy the CBAM to our post-doc community. Our results indicate that about two-thirds of our post-docs are at a beginner or intermediate stage of adoption of research data management, while another third are hostile non-users or apathetic. For each post-doc that requested it, we provided a custom report comparing them to their peers and locating them on a progression matrix.No embarg