An Exploratory Sequential Mixed Methods Approach to Understanding Researchers’ Data Management Practices at UVM: Findings from the Quantitative Phase

This article reports on the second quantitative phase of an exploratory sequential mixed methods research design focused on researcher data management practices and related institutional support and services. The study aims to understand data management activities and challenges of faculty at the University of Vermont (UVM), a higher research activity Research University, in order to develop appropriate research data services (RDS). Data was collected via a survey, built on themes from the initial qualitative data analysis from the first phase of this study. The survey was distributed to a nonrandom census sample of full-time UVM faculty and researchers (P=1,190); from this population, a total of 319 participants completed the survey for a 26.8% response rate. The survey collected information on five dimensions of data management: data management activities; data management plans; data management challenges; data management support; and attitudes and behaviors towards data management planning. Frequencies, cross tabulations, and chi-square tests of independence were calculated using demographic variables including gender, rank, college, and discipline. Results from the analysis provide a snapshot of research data management activities at UVM, including types of data collected, use of metadata, short- and long-term storage of data, and data sharing practices. The survey identified key challenges to data management, including data description (metadata) and sharing data with others; this latter challenge is particular impacted by confidentiality issues and lack of time, personnel, and infrastructure to make data available. Faculty also provided insight to RDS that they think UVM should support, as well as RDS they were personally interested in. Data from this study will be integrated with data from the first qualitative phase of the research project and analyzed for meta-inferences to help determine future research data services at UVM

Models of everywhere revisited: a technological perspective

The concept ‘models of everywhere’ was first introduced in the mid 2000s as a means of reasoning about the environmental science of a place, changing the nature of the underlying modelling process, from one in which general model structures are used to one in which modelling becomes a learning process about specific places, in particular capturing the idiosyncrasies of that place. At one level, this is a straightforward concept, but at another it is a rich multi-dimensional conceptual framework involving the following key dimensions: models of everywhere, models of everything and models at all times, being constantly re-evaluated against the most current evidence. This is a compelling approach with the potential to deal with epistemic uncertainties and nonlinearities. However, the approach has, as yet, not been fully utilised or explored. This paper examines the concept of models of everywhere in the light of recent advances in technology. The paper argues that, when first proposed, technology was a limiting factor but now, with advances in areas such as Internet of Things, cloud computing and data analytics, many of the barriers have been alleviated. Consequently, it is timely to look again at the concept of models of everywhere in practical conditions as part of a trans-disciplinary effort to tackle the remaining research questions. The paper concludes by identifying the key elements of a research agenda that should underpin such experimentation and deployment

The evolution of bits and bottlenecks in a scientific workflow trying to keep up with technology: Accelerating 4D image segmentation applied to nasa data

In 2016, a team of earth scientists directly engaged a team of computer scientists to identify cyberinfrastructure (CI) approaches that would speed up an earth science workflow. This paper describes the evolution of that workflow as the two teams bridged CI and an image segmentation algorithm to do large scale earth science research. The Pacific Research Platform (PRP) and The Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI) resources were used to significantly decreased the earth science workflow's wall-clock time from 19.5 days to 53 minutes. The improvement in wall-clock time comes from the use of network appliances, improved image segmentation, deployment of a containerized workflow, and the increase in CI experience and training for the earth scientists. This paper presents a description of the evolving innovations used to improve the workflow, bottlenecks identified within each workflow version, and improvements made within each version of the workflow, over a three-year time period

e-Social Science and Evidence-Based Policy Assessment : Challenges and Solutions

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