Environmental physics in the undergraduate physics curriculum

Earlier this week, the U.K.'s Physics World featured an excellent op-ed by Peter Hughes about environmental physics education.  He noted the importance of the topic, its practical value, and its incredibly wide disciplinary scope.  His definition, for example, is as follows.

Environmental physics is defined as the response of living organisms to their environment within the framework of the physics principles and processes. It examines the interactions within and between the biosphere, the hydrosphere, the cryosphere, the lithosphere, the geosphere and the atmosphere. Stretching from geophysics, meteorology and climate change to renewable energy and remote sensing, it also covers soils and vegetation, the urban and built environment, and the survival of humans and animals in extreme environments. 

He writes mainly from the perspective of the British university system.  One of his conclusions is "I believe a module on environmental physics should be a component of every undergraduate degree as a minimum, ideally having the same weight as quantum or statistical physics or optics."

While the thought is commendable, let's consider some reasons why it might not fly very far in the United States.

First, at many universities there already exist a robust academic ecosystem in the Earth and environmental sciences, with departments spanning soil physics in the school of agriculture, to atmospheric and oceanic sciences, geosciences, hydrology, civil and environmental engineering, and so on.  I live near a university where most of these disciplines have their own departments.  A physics student interested in this topic would be well advised to pick one of these disciplines as a minor or double major.  I personally find the multidiscplinary aspect of these fields to be quite exciting, but the key is to get out of the physics department and work directly with people who are well trained and active in one or more of these fields.

This leads to my second concern, which is that most physics faculty in the United States are ill equipped to teach or do research in any of these fields, with the possible exception of energy-related technologies.  I claim that within academia, environmental physics is primarily carried out by non-physicists (unless geophysicists are included - however, mostly they are found outside academic physics departments). Let's take a basic subject like fluid mechanics, which is essential for meteorology, climatology, and physical oceanography.  Most physicists have never taken a full class in this subject, and would hardly be qualified to teach one, given the outrageous things they teach about fluids in introductory physics classes.  A crowning example of this is the still often taught "explanation" of aerodynamic lift using Bernoulli's equation.  Granted, some physicists do work with fluid mechanics on a daily basis - plasma physicists, some astrophysicists, some condensed matter physicists, for example - but their focus is not necessarily on the aspects of fluids (like rotating frames of reference) relevant to environmental issues.

Third, it is difficult for me to imagine what, from this incredibly wide field of Earth and environmental physics, could be stuffed into a single undergraduate class.  It would end up being highly dependent on the individual professor teaching it.  I don't know if the Brits have managed to create a standardized curriculum for environmental physics.  

I see there are a few U.S. universities that involve their physics departments in environmental physics, but this is still rare here.  Kudos to them.  For the rest, the fastest way to get a program up and running is to partner with the other departments at the university that have been doing environmental physics from their birth.  In the longer run, physics departments would have to start hiring faculty explicitly in environmental physics.  It could take a decade or so to build a strong program, and not all departments would be well positioned to do so, especially given the hostile funding situation for academia currently prevalent in this country.

While I don't foresee environmental physics being on part with quantum physics, statistical physics, and optics, perhaps eventually it could be on par with solid state physics, astrophysics, or other elective physics courses in the undergraduate program.  However it would take a level of effort and commitment that may not be available in this time of shrinking enrollments and disappearing funding.

 

Thoughts on graduate education in physics

The American Journal of Physics this month features an article by Laurie McNeil, who was given a teaching award named after J. D. Jackson.  The article is based on her acceptance speech for the award, in which she mentions never having taken a full grad course based on Jackson's classic (and notorious) electromagnetism textbook.  But the important point about her article is that it offers a perspective on graduate physics education that focuses not on reproducing physics professors, but on providing success skills for its graduates, most of whom do not make permanent careers in academia.  She does something that few physics departments bother to do - she tried to track down the current positions of all her department's alumni since 1981.  Most physicists only keep track of their graduates who go on to research/academic careers, as she notes, and deem the rest "lost to the profession".

I fully agree with the article.  I should note, though, that the article is a high-level, vision type piece, without a lot of nuts and bolts.  For example, I would advocate that graduate education in physics should include an allowance, if not strong encouragement, for students to take a break to do a summer internship or two in industry, government, or the nonprofit sector.  This is something I've previously discussed on this blog.  If I sat down and thought hard about it, I would add additional recommendations.  The currently common form of physics graduate education is in my view ritualistic and inadequate for purpose.

 

 

Calculating logarithms?!

As I was culling my book collection, I came across three delightful books by Bob Miller, a CCNY math professor:  his Precalc Helper, Calc I Helper, and Calc II Helper, all published in 1991 by McGraw-Hill's Schaum division.  That was around the time I started taking calculus courses, though I do not recall using those books.  I may have acquired them shortly after I completed my year of calculus.

Regardless, as I was leafing through these books on this last day of 2014, nearly a quarter century after they were published, I was particularly struck by Miller's treatment of logarithms.  In the Precalc Helper, Chapter 12, "Modern Logarithms", begins with this paragraph (p. 75):

We will do a modern approach to logs.  Modern to a mathematician means not more than 50 years behind the times.  We will not do calculations with logs (calculations involving characteristics and mantissas).  This is no longer needed because we have calculators.  What is needed is a thorough understanding of the laws of logarithms and certain problems that can only be solved with logs.

If I ever did calculations with characteristics and mantissas, I certainly don't remember them now. It is likely that my high school had already abandoned coverage of that topic by the time I was a student.

Then on page 1 of the Calc II Helper, opening the first chapter, titled "Logarithms", we find the following passage.

Most of you, at this point in your mathematics, have not seen logs for at least a year, many a lot more.  The normal high school course emphasizes the wrong areas.  You spend most of the time doing endless calculations, none of which you need here.  By the year 2000, students will do almost no log calculations due to calculators.  In case you feel tortured, just remember that you only spent weeks on log calculations.  I spent months!!!

I suspect the future arrived a lot sooner than Miller thought it would.  When I was in high school in the late 1980s, we were using a then-new software program, Derive, to graph mathematical functions.  In college, we were using Mathematica.  When I was a teaching assistant in graduate school, graphing calculators were already pervasive, and my students were allowed to use them on exams.  (I have never owned one myself.)  Evidently graphing calculators are still in use, though equivalent apps have been available for smart phones for a few years now.  (I have never owned a smart phone either, but I suspect this will have to change one day.)

At this point I am too far out of touch with mathematics teaching and technology to know what is considered standard of practice.  Nonetheless I am grateful I never had to do endless logarithm calculations by hand.  Make no mistake, logarithms are essential for science, engineering, and medicine.  In fact, I worked with logarithms at work earlier today.  But I let the computer do the calculating.

The Ph.D. Placement Project

Last summer, the Chronicle of Higher Education launched its Ph.D. Placement Project.  Across university graduate programs of all disciplines, it is fairly uncommon for departments to track the "outcomes" of its Ph.D. programs, as measured by the placement of its graduates.  The Chronicle's reporter, Audrey Williams June, presents a case study of one faculty member in one department who actually did create a database of his program's Ph.D. graduates and their subsequent careers (the City University of New York (CUNY)'s Graduate Center's sociology program).

My impression is that in the sciences and engineering, placements are not systematically tracked by graduate departments, and when they are, summary data are not routinely provided to current or prospective graduate students. I welcome data to prove me wrong though.  If I'm right, I believe this is a scandal, and university departments should no longer be able to get away with it.  Departments that are afraid to generate such data or to disclose it, are behaving in a self-serving way, unfitting for their nonprofit status in the economy.  It is difficult for me to conceive of a rational defense of such practices.

Therefore DTLR calls on all graduate degree programs in science and engineering to initiate placement studies of its graduates, whether they stay within the profession or not, and to publicly disclose the results, at least in summary form, once enough data points have been gathered to ensure the privacy of the graduates themselves.  DTLR endorses any efforts by public and private funding agencies and alumni groups to withhold funding from any graduate program that fails to commit to such an initiative.

A review of "Research Methods for Science," by Michael Marder. The compilation.

For convenience, I've compiled here links to the five-part series of posts that make up my review of Research Methods for Science, by Michael P. Marder (Cambridge University Press, 2009).

Part 1. Overview.

Part 2. Randomization.

Part 3. Statistical thinking.

Part 4. More about statistics.

Part 5. Miscellany.

A review of "Research Methods for Science," by Michael Marder. Part 5. Miscellany.

In this post I conclude my review of Michael Marder’s book, Research Methods for Science (Cambridge University Press, 2009).  I collect here a miscellany of other thoughts about the book.

Study designs

In chapter 1, Marder makes a distinction between experimental and observational and exploratory studies.  However, he never quite gets around to outlining the strengths and weaknesses of these three approaches, nor more fundamentally the use of retrospective vs. prospective data.  He also does not compare and contrast specific types of widely-used study designs, for instance, a parallel-group design vs. a cross-over design, or in epidemiology, a cohort study vs. a cross-sectional study.  In my view, such a discussion is not necessarily needed for its own sake alone, but as good training for the critical thinking skills for study design.  (A side effect of teaching this material is that students would become better consumers of medical news.)  Unfortunately, I continue to encounter scientists with doctoral level degrees who seem to be lacking awareness of how some study designs make for weaker conclusions than others.  Marder notes the importance of cause and effect in Sec. 2.1.1, but never really teaches how study design helps secure the attribution of causality once the study is completed. He doesn't even introduce the correlation vs. causation fallacy, one that is frequently committed in the scientific literature. These are colossal lost opportunities to teach critical thinking in the book.

Internal and external validity

Another key but missing concept comes from the social sciences, where often a distinction is made between internal validity and external validity. Internal validity refers to whether the study was designed properly and allows for the attribution of causality. A lack of randomization, blinding, concurrent control group, and so on, would threaten internal validity. External validity, on the other hand, refers to how broadly the study's findings can be generalized. A clinical trial is usually based on a convenience sample, filtered by the inclusion/exclusion criteria. The generalization of its results to a larger population is an inherently non-statistical judgment about how representative the trial subjects are of some larger population of interest. Marder does not discuss these issues at all.

Scientific communication

Entire books have been written on scientific communication, and a concise text like Marder's cannot be expected to cover this important topic in depth. Sadly few science students will ever take a formal course on scientific writing. I won't dwell here on the issue, but one of the best brief guides to writing an abstract (in my view) is Sec. IIB of the AIP Style Manual (1997). The relevant passage is less than a page long, and I would simply provide it as a handout to students. Marder covers abstracts in Sec. 5.3.2, and rightly suggests that the abstract “should probably be the very last thing you write” (p. 154). However, I would have added the first sentence of the AIP Style Manual passage: “The primary purpose of the abstract is to help prospective readers decide whether to read the rest of your paper” (p. 5). This theme is expanded upon in Alley (1996), and it really helps to concentrate the mind when writing an abstract. As a referee and a reader, I've run into too many poorly written abstracts, including some written by journal editors themselves!

Marder's discussion of scientific presentations (Sec. 5.5) begins with a description of the kinds and frequency of talks given by scientists. Unfortunately, his description applies mainly to academic scientists, and does not adequately reflect the experience of industrial or government scientists, for whom the topic is equally important. For those of us in the latter categories, a presentation (as opposed to a written report or publication) may often be the deliverable that most influences decision makers, both within and beyond a research organization. A popular technique, not mentioned by Marder, is to conclude a talk with up to three take-away message(s).

Data graphics

Marder prefers that statistical graphics not show all the raw data, but only display the mean and error bars (Sec. 5.4.2). Plots of the raw data “have the defect of providing a little too much information for rapid understanding” (p. 163). A plot displaying only the mean and standard deviation “does not show all the work you did by performing many different trials” but “The point of a scientific publication is not to explain to everyone how much work the scientist did, but to convey the results. This is the most compact way” (p. 163). I strongly disagree with making such a blanket statement. The choice of what to display is highly context dependent, in my view. Moreover, there are ways to display the raw data (overlaid with boxplots using jittering or beeswarm, or using density plots, etc.) that better tell the story of the data than the straw man example he gives in Fig. 5.2. Statistical graphics is a modern, advanced discipline, informed by an understanding of human visual perception. With 21st century software, there is often no need to hide the actual data behind error bars, as Marder advocates.  Incidentally, users of Marder’s book and anyone else interested in data graphics should consult the paper by Cumming et al. (2007) which provides an excellent discussion of error bars and how they may be misused.  Marder doesn’t fall into any of the traps described by Cumming et al., but he surely fails to warn readers about them.

Literature search

Marder's discussion of literature search (Sec. 5.6) provides good advice to start with ISI's Web of Knowledge. Although I agree with the author here, I work in an organization that does not provide me with direct access to Web of Knowledge. Instead, I am provided with Elsevier's SCOPUS, which doesn't even appear on Marder's otherwise well curated list of literature databases (Table 5.3, p. 171).  (Some in the scientific community oppose Elsevier's journal pricing policies, which have led many to boycott Elsevier products.)

Concluding thoughts

If I wanted to give a budding scientist a nuts-and-bolts guide to both doing research and critically evaluating the research of others, I would not turn to this book.  However, I don’t know if the book I’m really looking for exists at all.  Readers, do you have any suggestions?

References

M. Alley (1996): The Craft of Scientific Writing, 3d ed. Springer-Verlag.

American Institute of Physics (1997): AIP Style Manual, 4^th ed.   

G. Cumming, F. Fidler, and D.L. Vaux (2007): Error bars in experimental biology. J. Cell Biol., 177: 7-11.