Research Support Services Staff Publications

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Ethical and Practical Considerations of Open Data Sharing when Conducting Research with Human Participants
(2019-03) Bakker, Caitlin; Hunt, Shanda; Hofelich Mohr, Alicia
The Libraries and LATIS will present on the conflicts that can arise when attempting to balance the protection of participant privacy with expectations for open data. Restrictive language in IRB protocols, consent forms, and participant agreements designed to protect privacy can limit researchers when sharing, storing, or archiving the resulting data. Ethically, what is the appropriate course of action
The Data Management Village: Collaboration among Research Support Providers in the Large Academic Environment
(Databrarianship: The Academic Data Librarian in Theory and Practice (editors Kristi Thompson and Lynda Kellam) Association of College and Research Libraries (ACRL), 2016) Hofelich Mohr, Alicia; Johnston, Lisa R; Lindsay, Thomas A
Data management encompasses the practices and people that acquire, control, protect, deliver and enhance the value of data throughout the research lifecycle. Done well, data management requires that these practices and people be connected throughout the entire research lifecycle. However, much of this work takes place in researchers’ own offices or labs or with the help of specialized support offices on campus, who only directly interact with researchers at single points in their projects. In academic libraries, a data management specialist may only interact with researchers at the beginning and end of a project, assisting with the creation of a data management plan (DMP) and preservation of the data when the research is completed. This poses a challenge when trying to help researchers integrate best practices into their workflows throughout the planning, collection, and analysis stages. Most libraries are focused on providing broad, public access to the content under their stewardship, and given this mission, libraries alone may not be able to offer all of the data services that our researchers need (for example, dark archives for sensitive or private data). Therefore, given the diverse nature of research data and the distributed support researchers may seek throughout their project, universities need a well-connected, distributed way to support data management; it is a service that “takes a village.”
Recruitment, Participation, and Sampling: Researchers’ Results in General Practice
(2011-06-01) Lindsay, Thomas; Sell, Andrew
While large-scale projects have the resources to ensure best practice, most social science researchers face compromises relating to cost, time, and availability of respondents. We worked with researchers to experimentally test specific approaches to sampling and recruitment. We discuss the results of these tests within the framework of theoretical best practices and expectations.
Thinking Inside the Box: Visual Design of the Response Box Affects Creative Divergent Thinking in an Online Survey
(Social Science Computer Review, SAGE, 2015) Hofelich Mohr, Alicia; Sell, Andrew; Lindsay, Thomas
While the visual design of a question has been shown to influence responses in survey research, it is less understood how these effects extend to assessment-based questions that attempt to measure how, rather than just what, a respondent thinks. For example, in a divergent thinking task, the number and elaboration of responses, not just how original they are, contribute to the assessment of creativity. Using the Alternative Uses Task in an online survey, we demonstrated that scores on fluency, elaboration, and originality, core constructs of participants’ assessed creative ability, were systematically influenced by the visual design of the response boxes. The extent to which participants were susceptible to these effects varied with individual differences in trait conscientiousness, as several of these effects were seen in participants with high, but not low, conscientiousness. Overall, our results are consistent with previous survey methodology findings, extend them to the domain of creativity research, and call for increased awareness and transparency of visual design decisions across research fields.
Data Management Needs Assessment - Surveys in CLA, AHC, CSE, and CFANS
(2015) Hofelich Mohr, Alicia; Bishoff, Josh; Johnston, Lisa R; Braun, Steven; Storino, Christine; Bishoff, Carolyn
Researcher's data management needs were assessed at four colleges with in the University of Minnesota: The College of Liberal Arts (CLA), the Academic Health Center (AHC), the College of Science and Engineering (CSE), and the College of Food, Agriculture, and Natural Resource Sciences (CFANS). The initial survey was designed in CLA and featured a branched design that presented researchers one of two versions of the questions based on how respondents described the products of their scholarship - as "data" or "research materials". The survey was then customized for the other colleges, adding or editing questions based on feedback from disciplinary experts, while maintaining comparability across surveys. Surveys were run between September 2013 and and February 2015.
Call me maybe? It's not crazy! Data collection offices are a good partner in data management
(2015) Sell, Andrew; Hofelich Mohr, Alicia
For data management professionals, attention is largely focused on the beginning and ends of the research process, as many researchers are worried about meeting federal requirements for data management plans (DMPs) and are looking for ways to share and archive their data. As a University office specializing in survey and experimental data collection, we have seen how the "middle" steps of data collection and analysis can be influenced by, and be an influence on, these upstream and downstream data management processes. In this Pecha Kucha, we will present relevant data management lessons we have learned from designing, developing, and hosting data collection tools. Challenges of anonymity and paying participants, quirks of statistical files produced by data collection tools, and transparency in the research process are among some of the issues we will discuss. As many of these challenges directly impact later sharing and curation of the data collected, we emphasize that data collection offices can be important partners in data management efforts.
Partnerships in a Data Management Village: Exploring how research and library services can work together
(2015) Hofelich Mohr, Alicia; Lindsay, Thomas; Johnston, Lisa R
Providing data management services is a task that takes a village; a distributed model of support, involving collaboration among diverse institutional offices, is needed to do it well. Researchers especially benefit when specialized institutional support offices are aware of other relevant providers and the impact their services have on the management of data across the research life cycle. However, once a village is assembled, how do we work with members to be committed collaborators, rather than a passive referral network? In this presentation, we will describe a case study of our in-depth collaboration between the University Libraries and the College of Liberal Arts (CLA) at the University of Minnesota. Both groups are developing new suites of data management services to meet evolving researcher needs and rising demands for data management support. Working together has provided many advantages for sharing resources and knowledge, but also has presented challenges, including how to define the respective roles of college-level and university-wide data management services, and how formalized collaborations may work. We will describe these challenges and how the collective and complementary skills of our offices will provide researchers with support across much larger portions of the research life cycle than either office could provide alone.
Understanding Researcher Needs in Data Management: A Comparison of Four Colleges in a Large American University
(2015) Hofelich Mohr, Alicia; Braun, Steven; Bishoff, Carolyn; Bishoff, Josh; Johnston, Lisa R
The diverse nature of research makes identifying needs and providing support for data management a complex task in an academic setting. To better understand this diversity, we compare the findings from three surveys on research data management delivered to faculty across 104 departments in the University of Minnesota - Twin Cities campus. Each survey was separately run in the Medical School, the College of Liberal Arts, the College of Food, Agricultural, and Natural Resource Sciences and the College of Science & Engineering and modified to use language that paralleled the different cultural understandings of research and data across these disciplines. Our findings reveal common points of need, such as a desire for more data management support across the research life cycle, with the strongest needs related to preparing data for sharing, data preservation, and data dissemination. However, the results also reveal striking differences across the disciplines in attitudes and perceptions toward data management, awareness of existing requirements, and community expectations. These survey results can be used by others to demonstrate that a one-size-fits-all approach to supporting data management is not appropriate for a large research university and that the services developed should be sensitive to discipline-specific research practices and perceived needs.