I wrote the following for an SPIE paper I presented on lessons learned from the Sloan Digital Sky Survey, but I still think there is material to discuss here. I also plan to expand upon these thoughts in my next post and talk about the Scientist Dilemma as it pertains to management.
Astronomy projects often need very specifically-skilled people to play largely support roles. Scientists are not always needed in all skilled positions, but when they are, they present an additional complication: they usually want to do science. Rewards and professional development need to be included in their work plan. The conflicting needs of the project (support) and the desires of the scientists (science) form what may be referred to as the scientist dilemma. The scientist dilemma occurs whenever highly-skilled, scientifically motivated people are needed for support work. This work could be, as in the case discussed here, operations and observations, but the dilemma applies equally well to programmers, data analysts, archivists, etc.
The SDSS collaboration realized early on that Ph.D.-level people were going to be required for nightly operations. It wasn’t so much the degree itself that was necessary, but several factors that come with it: observing experience, data handling and analysis, scientific context, problem solving, and an exposure to scientific computing environments. It is certainly possible to find these skills and experiences in someone without a Ph.D., but they are more common in those with it. The telescope, instrument, software, and data systems were complex enough that a high level of skill was demanded to successfully use and develop them. In addition, the nightly observing plan was flexible enough to the current conditions that scientific tradeoffs between different courses of action would need to be evaluated in real time to optimize each night’s observations. We also realized that a stable group of skilled observers could not only hone the operating systems and procedures to improve both efficiency and data uniformity, but could also take over some of the software and hardware development work as well, more finely tuning the initial efforts to fit real observing conditions. This work resulted in continual operational efficiency improvements and left the system in such a state that by the end of the project, Ph.D.-level scientists were no longer required to make operations successful.
The problem with this approach is that whereas the project wanted Ph.D.-level astronomers to learn and understand the complex operational systems, spend non-observing time improving the systems and performing required auxiliary tasks (instrument calibration, data integrity checks, etc.), decide coherent efficient nightly observing strategies, and operate the telescopes and instruments nightly during observations, most Ph.D-level astronomers want to do (at least some) astronomy — hence the dilemma.
The only way to really address this dilemma is to simply staff accordingly, allowing your professional staff enough time to do their three main tasks: in this case, observing, system verification and development, and scientific research. Without the latter, not only do you not have happy workers willing to devote themselves to the project for its duration and give you the benefits of their scientific activities, but you also leave them with no career path beyond future, non-scientific support work.
I went on to discuss other aspects of this issue, its solution and how to keep people through the end of a project, but I think these few paragraphs get the main point across and will provide the background for my expansion of this dilemma into scientific management.
Scot did his Ph.D. thesis largely on G29-38, a very interesting pulsating white dwarf star with an infrared excess caused by a circumstellar debris disk as pictured above. His thesis, however, had nothing to do with debris disks and instead used G29-38 as a prototype to understanding the pulsational, and hence, compositional, properties of this subclass of white dwarf pulsators. These days, he is using SDSS data to produce new catalog of white dwarf stars to better understand their global and peculiar properties.