Dissertations Content Analysis Data Set, Diving Deep into Dissertations article, College & Research Libraries

We present findings from an exploratory quantitative content analysis case study of 156 doctoral dissertations from Georgia State University that investigates doctoral student researchers’ methodology practic es (used quantitative, qualitative, or mixed methods) and data practices (used primary data, secondary data, or both). We discuss the implications of our findings for provision of data support services provided by the Georgia State University Library’s Res earch Data Services (RDS) Team and subject liaison librarians in the areas of instructional services, data software support and licensing advocacy, collection development, marketing/outreach, and professional development/expansion

Turbulent Energy Dissipation Rate and Turbulence Scales in the Blade Region of a Self-Aspirating Disk Impeller

Instantaneous radial and axial velocitieques of water in the tank with a self-aspirating disk impeller operating without gas dispersion were measured by the PIV method. A comparison of mean square velocity pulsations confirmed previous observations that the area in which turbulence is non-isotropic is small and extends about 3 mm above and under the impeller and radially 12,5 mm from the impeller blade tip. Based on velocity measurements, the distributions of energy dissipation rates were determined using the dimensional equation = C•u’3/D and Smagorinsky model. Adoption of the results of the dimensional equation as a reference value allowed us to determine the Smagorinsky constant value. This value appeared to be smaller than the values given in the literature. It has been shown that eddies in a small space near the impeller had sufficient energy to break up gas bubbles flowing out of the impeller. Based on the obtained energy dissipation rate distributions, appropriate turbulence scales were determined

The SunPy Project: Open Source Development and Status of the Version 1.0 Core Package

The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the sunpy core package and supporting an ecosystem of affiliated packages. This paper describes the first official stable release (version 1.0) of the core package, as well as the project organization and infrastructure. This paper concludes with a discussion of the future of the SunPy project