Scientists and the cultural politics of academic disciplines in late 19th-century Germany: Emil Du Bois-Reymond and the controversy over the role of the cultural sciences

This article is concerned with interactions between the natural and the human sciences. It examines a specific late 19th-century episode in their relationship and argues that the schism between the two branches of knowledge was due to cognitive factors, but consolidated through the social dynamics of institutionalized disciplines. It contends that the assignment of a social function to the human sciences to compensate for the self-destructive tendencies inherent in the technological society was expressed even by those, at the end of the 19th century, who were fervent advocates of a science- and technology-driven modernization.

What about us? We have careers too! The career experiences of Australian sport scientists

 he professionalisation of sport has provided career opportunities for athletes, coaches and sport scientists alike. The career development literature for athletes is well established and the empirical career literature for coaches is growing, but little is known about the careers of sport scientists. The purpose of this investigation was to explore and examine the career experiences of Australian sport scientists. In-depth interviews were conducted with six practicing Australian sport scientists at different career stages. Several themes emerged from the data on careers of sport scientists that are unique to sport. Sport scientists identify strongly with their role in sport success and yet they receive little recognition for what they do. All participants experienced career dissonance as they transitioned from practitioner to another career such as academia or sport management. Feelings of loss were identified by participants as their applied work diminished when they moved away from their early career service roles. All six participants believed that in order to advance their career in sport their options were moving overseas, working in academia, or retraining for a career in sports management. It is recommended that sport scientists be provided with better career education and more structured professional development.

Supporting scientists in re-engineering sequential programs to parallel using model-driven engineering

Developing complex computational-intensiveand data-intensive scientific applications requires effectiveutilization of the computational power of the availablecomputing platforms including grids, clouds, clusters, multicoreand many-core processors, and graphical processingunits (GPUs). However, scientists who need to leverage suchplatforms are usually not parallel or distributed programmingexperts. Thus, they face numerous challenges whenimplementing and porting their software-based experimentaltools to such platforms. In this paper, we introduce asequential-to-parallel engineering approach to help scientistsin engineering their scientific applications. Our approach isbased on capturing sequential program details, plannedparallelization aspects, and program deployment details usinga set of domain-specific visual languages (DSVLs). Then, usingcode generation, we generate the corresponding parallelprogram using necessary parallel and distributedprogramming models (MPI, OpenCL, or OpenMP). Wesummarize three case studies (matrix multiplication, N-Bodysimulation, and signal processing) to evaluate our approach.