Data management throughout the research lifecycle
This model of the research lifecycle includes data managment actions at each stage.
Spectral variability of the particulate backscattering ratio
The spectral dependency of the particulate backscattering ratio is relevant in the fields of ocean color inversion, light field modeling, and inferring particle properties from optical measurements. Aside from theoretical predictions for spherical, homogeneous particles, we have very limited knowledge of the actual in situ spectral variability of the particulate backscattering ratio. This work presents results from five research cruises that were conducted over a three-year period. Water column profiles of physical and optical properties were conducted across diverse aquatic environments that offered a wide range of particle populations. The main objective of this research was to examine the behavior of the spectral particulate backscattering ratio in situ, both in terms of its absolute magnitude and its variability across visible wavelengths, using over nine thousand 1-meter binned data points for each of five wavelengths of the spectral particulate backscattering ratio. Our analysis reveals no spectral dependence of the particulate backscattering ratio within our measurement certainty, and a geometric mean value of 0.013 for this dataset. This is lower than the commonly used value of 0.0183 from Petzold’s integrated volume scattering data. Within the first optical depth of the water column, the mean particulate backscattering ratio was 0.010.
Autonomous observations of in vivo fluorescence and backscattering in an oceanic oxygen minimum zone
Effects of bulk particle characteristics on backscattering and optical closure
Light scattering by random shaped particles and consequences on measuring suspended sediments by laser diffraction
Spectral backscattering properties of marine phytoplankton cultures
The New Age of Hyperspectral Oceanography
Thoughts on “eResearch”: a Scientist’s Perspective
In response to the burgeoning practice of collaborative, networked, data-intensive research (known as eScience), university and research libraries are devoting significant consideration, effort and resources toward expanding their responsibilities to include research data services. The jargon that the librarianship community uses to discuss data -driven research is inconsistent and confusing, especially to non-librarians. This is problematic because when we attempt to engage research scientists in an effort to provide services, we risk alienating our potential stakeholders by using language that they don’t understand. As a recent transplant to the library community, the difference between librarian and research scientist perceptions of data-driven research, and the vocabulary surrounding it, have been surprising. This paper summarizes the problem of “eResearch,” spoken from the perspective of a recent scientist-turned-data librarian. The main conclusions reached are that “eResearch” is a meaningless term that should be avoided, and that data support services needn’t be couched as an eScience issue.