Adoption of good research data management practices is increasingly important for research teams. Despite the work the research community has done to define best data management practices, these practices are still difficult to adopt for many research teams. Universities all around the world have been offering Research Data Services to help their research groups, and libraries are usually an important part of these services. A better understanding of the pressures and factors that affect research teams may help librarians serve these groups more effectively. The social interactions between the members of a research team are a key element that influences the likelihood of a research group successfully adopting best practices in data management. In this article we adapt the Unified Theory of the Acceptance and Use of Technology (UTAUT) model (Venkatesh, Morris, Davis, & Davis, 2003) to explain the variables that can influence whether new and better, data management practices will be adopted by a research group. We describe six moderating variables: size of the team, disciplinary culture, group culture and leadership, team heterogeneity, funder, and dataset decisions. We also develop three research group personas as a way of navigating the UTAUT model, and as a tool Research Data Services practitioners can use to target interactions between librarians and research groups to make them more effective.

Stratplan for a suite of DH services. Uncompromising about the necessary resource allocations; also very clear about where the money's going.

The axiom to “do more with less” in university research libraries is increasingly untenable, as budgets continue to shrink and demand for novel services continues to rise. The impacts of such existential uncertainties are self-evident and widely discussed in the current literature—staff burnout, lowered morale and increased toxicity, weakened local collections, and limited capacity for ambitious and genuinely innovative work. Like many institutions, the University of Maryland (UMD) Libraries has found itself reinventing many of its services and initiatives to meet the increasingly technical needs of students and researchers in our campus community. New services and positions for research data services, GIS librarianship, discovery services, and digital scholarship are all popular and increasingly central to the information service mission of the Libraries. Like many more institutions, demand for such services often outstrips the Libraries’ operations and collections budgets, as well as labor capacity for project-driven initiatives and highly technical service requests.

Article: Data Discovery Paradigms: User Requirements and Recommendations for Data Repositories

Curating Reuse An Institutional Approach to Statistical and Computational Reproducibility Don’t forget to introduce yourself! Katie Mika | @kmika11 | University of Colorado Boulder | CNI Fall Meeting 2018

Applying Digital Preservation Management to Digital Scholarship Service Models Good morning everyone and thank you for being here today! Our talk is entitled “Applying Digital Preservation Management to Digital Service Models” and we’re part of a cluster of talks about digital preservation, but t...

Automated "quality checks" by databases of incoming records fsck a whole bunch of data up. Heckuva job, programmers. I bet y'all didn't think you needed human review.

Vicky Steeves is the Librarian for Research Data Management and Reproducibility at New York University - a dual appointment between NYU's Division of Libraries and Center for Data Science.

There is a large variety of repositories that are responsible for providing long term access to data that is used for research. As data volumes and the demands for more open access to this data increase, these repositories are coming under increasing financial pressures that can undermine their long-term sustainability. This report explores the income streams, costs, value propositions, and business models for 48 research data repositories. It includes a set of recommendations designed to provide a framework for developing sustainable business models and to assist policy makers and funders in supporting repositories with a balance of policy regulation and incentives.

Over the past few years, research reproducibility has been increasingly highlighted as a multifaceted challenge across many disciplines. There are socio-cultural obstacles as well as a constantly changing technical landscape that make replicating and reproducing research extremely difficult. Researchers face challenges in reproducing research across different operating systems and different versions of software, to name just a few of the many technical barriers. The prioritization of citation counts and journal prestige has undermined incentives to make research reproducible. While libraries have been building support around research data management and digital scholarship, reproducibility is an emerging area that has yet to be systematically addressed. To respond to this, New York University (NYU) created the position of Librarian for Research Data Management and Reproducibility (RDM & R), a dual appointment between the Center for Data Science (CDS) and the Division of Libraries. This report will outline the role of the RDM & R librarian, paying close attention to the collaboration between the CDS and Libraries to bring reproducible research practices into the norm.

Article: Organizational Resilience in Data Archives: Three Case Studies in Social Science Data Archives