Language leads to scientific understanding

I am currently working on my Ph.D. candidacy report. The topic is on optical sensing and manipulation of cells. While brainstorming for the abstract, I wrote the following expression: "Organisms are organized hierarchically..."

Of course I immediately realized that "Organisms are organized" sounds redundant. But it did make me notice the connection between the two words and their common root. Life is built from inter-dependent structures that form higher levels of  organized complexity; from a reductionist standpoint, it is a system of organization built on lower level systems of organization. Hence, we have the word organism.

This little personal epiphany probably impresses only me, but I think it serves as a reminder to everyone that language can very often lead to a deeper understanding of a topic. We need only look to the literal meaning behind an object or phenomenon's name to connect it with a more familiar idea.

What's also interesting about this particular example is that the meaning of the word "organism" suggests that when the term was coined people already understood that lifeforms were made of some sort of hierarchical structure. According to Dictionary.com, the word's origin comes from around 1650. Not surprisingly, this is roughly the same time that Robert Hooke began looking at cells through microscopes. So not only can the literal meaning of a phenomenon's name lead to a more intuitive understanding of the phenomena, but so to can the name's etymology place the idea within a historical context.

One more note about numerics

A friend of mine pointed out that numerics are very useful in fabrication since they allow one to predict the behavior of a material or device without having to perform costly trials in the lab. I completely agree that this is another strength of simulations.

A demonstration of this idea can be found in this Nature paper, where the authors first performed calculations of the surface free energy of a crystal before fabricating them. These calculations enabled them to develop titanium dioxide crystals with high surface reactivity for use in solar cells and photocatalysis. Without the numerics, it is likely that countless experimental trials would have been required to grow the crystals.

(I came across this paper via Ross McKenzie's condensed matter blog)

The purpose of numerics

Optics is a field to which numerical simulations contribute greatly. For example, Maxwell's equations can only be solved for a very small range of geometries. Different routines such as finite-difference time-domain (FDTD), the coupled dipole approximation (CDA), and the T-matrix method have been developed to solve specific problems dealing with the propagation and scattering of electromagnetic waves.

I find that numerics such as these are used in research for a large number of purposes. Some groups publish entire journal papers about the design of a certain algorithm. Other papers deal with numerical simulations of a phenomenon, often times without carrying out a physical experiment because of limits on money, time, and practicality. Still more use numerics to check experimental data against.

Are all of these good reasons to use numerics in research, or are some better reasons than others? I ask this question because I tend to give more credit to papers that perform a physical experiment that is backed up by numerics than to a paper that contains only a study involving a simulation. In the second example in the paragraph above, I mentioned that some papers perform numerical studies because a physical experiment is not practical. If, however, the goal of science is to discern the workings of the natural world, then I think that these types of studies provide little contribution to our body of scientific knowledge, since no knowledge about the real world is gained. A simulation is, after all, subject to the bounds and constraints of a model, and models are often far from accurate representations of the world.

If, however, we (the scientific community) only assign merit to studies that use numerics to verify experimental data, then it seems to me that the role of simulations becomes greatly diminished to the point that they are unnecessary. After all, data is presumably collected by honest and accurate means. Any other reproduction of the data, numerical or otherwise, could be seen as mere redundancy. Under what context then, should numerics be used in research?

My personal opinion is that they are fantastic aids in helping one understand a problem and predict outcomes from experiments. This, I think, is a subtle but important point. A good scientific theory predicts the results of an experiment; a good simulation will do the same. Under this context I think simulations can be put to good and valid use within research.

Guidelines for writing, pt. 1

I've spent some time thinking about it, so I suppose I had better get started. These are the first few points I've thought of for my personal list of writing guidelines for a scientific paper. I've tried to focus on the issues that I find most problematic when reading other papers. Some are technical while others are cosmetic. Of course, I can't claim that the following suggestions are without their own flaws, so judge them critically and apply them in any manner you see fit. In no particular order:

1. Figure captions should be able to stand alone. Do not include abbreviations or references to the text.
2. Make all your points in the introduction, figures and their captions, and conclusion. Use the body of the text to add detail and repeat your main points.
3. Eliminate redundant and unnecessary words. My favorite example from papers on light scattering is the term "material system" for describing matter that interacts with light. Either "material" or "system" can work, but using both just takes up space.
4. Do not list the paper's section titles and their descriptions as is commonly done in the last paragraph of the introduction. Almost every experimental paper (at least in optics and physics) follows the same format of introduction/theory/experiment/results/conclusion, so an outline of the paper is rarely needed by the reader.
5. Use descriptive titles for sections. Compare "Theory" to "Model for Partially Developed Speckle."
6. Use one tense throughout. I prefer past tense since you are reporting what procedure you followed and the results you obtained to the scientific community, not experiments that you are currently doing.
7. Do not refer to previous publications to describe experimental setups.

Feel free to add your own. I will be adding more in time.

But that makes it sound obvious

There is a brief article in Nature Physics addressing the issue of poor writing skills in scientific journal papers and other communications. The article mentions that writing in technical fields is a skill that is assumed to be had by students at the beginning of their research careers. I think that it's fairly obvious that this is very often a false assumption. The article also mentions the common misunderstanding among scientists that verbose and complicated sentences communicate a deeper understanding possessed by the authors. In reality, however, this practice only serves to befuddle readers and acts as a barrier for the proliferation of one's research.

I find it interesting that several experts suggest a linear style of writing journal papers in which ideas are presented in series. This reminds me of how a manual is written. I suppose journal papers should ultimately communicate the steps that were taken to obtain a given set of data and their corresponding analyses.

The article can be found here.