Blog 3: Data Collection and Teamwork

Hello again! In my last post, I discussed some of my early challenges with using technology to collect and analyze my data. And I, somewhat prematurely, promised to delve deeper into R and my code in today’s post. But without giving a summary of my methodology for data collection, it will be difficult to outline what I designed my analysis around. So today’s post is all about context and a fairly unique aspect of my project: teamwork. 

Teamwork

Spectral data is a fairly new trend in ecological lab work, and my research is part of a much larger project headed by Dr. Fine and his lab. As a result, much of my time is spent working with other people involved in this project and other spectra-focused projects, to learn how to adopt and properly use this technology for ecology. In my immediate network for this project are two professors (Stephanie and Dr. Fine) and two interns (Kate and Alondra). I have to mention that Stephanie is not officially attached to our research, but her help as an expert on spectra has been invaluable. 

The basic breakdown of our team’s duties is simple: Dr. Fine and I work with researchers from the Harvard ecology department that help us design our data collection/analysis methodology, Stephanie and I work on the analysis of the specimens, and Kate, Alondra, and I work with the FS-4 to collect readings from the specimens and organize our samples. Together, we’ve managed to design an incredibly large and complex project, part of which has become my senior project. 

Data Collection

The method for data collection that Dr. Fine and I came up with and Kate, Alondra, and I practiced is as follows:

We take 12 readings per specimen, spread across 3 leaves; 4 scans per leaf, of which 2 are adaxial (top) and 2 are abaxial (bottom). The scans are ALWAYS taken and recorded in a strict order:

• Leaf 1
  • bottom
  • bottom
  • top
  • top
• Leaf 2
  • bottom
  • bottom
  • top
  • top
• Leaf 3
  • bottom
  • bottom
  • top
  • top

In this way, analysis can be easily automated according to the order of the scans, and when necessary, the data can be adjusted to only look at certain factors. For example, I have on a few occasions looked at only 1 leaf per specimen or at only the adaxial or abaxial measurements.

This system for measurement order is also coupled with a meticulously made datasheet for each specimen AND each reading. This may seem like overkill, but when dealing with so many files, it is almost impossible to keep track of any change without being this careful and precise at every step. Mistakes happen, and the only way to fix them is to have a record of all of them. In the image below, you can see that, along with the file name and specimen code (specimen species for these readings will be added later by Dr. Fine), there is also a record of any slight deviation from the typical order that can be used to account for any mistakes in the analysis.

For the purpose of my analysis, this data sheet is used to relate my file names to their respective specimens, and to then properly sort my files to search for individual, population, and species-level differences. But it also serves as an incredibly useful record of all the conditions that could be causing differences between my readings. How that is used, however, is a discussion for next time. Stay tuned for a deeper dive into my code and the specific protium (Burseraceae) species that I’ve been studying for this project, and I’ll see you all next week!