Onward to the future...

One week ago from yesterday I successfully defended my dissertation, "Mesoscale light-matter interactions," which means that I am effectively finished with my graduate school career (woot woot!). I would offer my dissertation for viewing here on the web, but due to publication restraints associated with UCF, I have to wait one year before disseminating the work publicly. This is too bad, but the decision to do this was largely out of my control.

I'm excited to be moving on to a slightly new line of work. Later this year, I'll be moving to EPFL in Lausanne, Switzerland to work on STORM microscopes and problems in single molecule biophysics.

I'm particularly excited about this work because now I'll be able to apply my knowledge of optics to biology problems. Most of my PhD work was focused on designing new optics-based techniques and then looking for a problem to solve; I hope to become immersed enough in the biophysics community that I can identify unknowns in biology first and then design measurements to address these unknowns afterward. I think a problem-driven route is a more appropriate for the design of optical sensing techniques and I just can't wait to begin.

During my last year at graduate school, I've also begun several endeavors that I believe make me a much better researcher and contributor to science. Some of these new practices include:

1. Following a paradigm that makes my research as open as possible. This includes making easily reproducible code and sharing data openly. (I'm exploring the use of Figshare and following @openscience on Twitter. Looking at these resources are great starting points).
2. Related to the first point, I'm writing a Python package for processing dynamic light scattering data and simulating experiments that I intend to release freely and open sourced. I think that my expertise in this area would serve many others extremely well, since DLS is often treated like a "black box" technique by a lot of people who use it for macromolecular studies.
3. Though my writing has waned since I began serious work on my defense, I want to begin writing again as a means of exploring more topics in research and academia.
4. I'm structuring my own research around simple questions. I think simple questions, such as "how do cells respond to light" form great, long term questions in science. More complicated questions often lead to short term research goals.

I'll expound on this last point in a later post. Overall, though, I'm beginning to see how I can contribute to my field beyond just publishing.

I won't get started in my new position until November or December. In the mean time, my wife and I are going on a long climbing and hiking trip out West, so you may have to be patient if you're waiting for posts between September and November.

And if you're still working on your PhD or Masters degree, keep up the hard work. It will pay off :)

Signalling in intrinsically disordered proteins

I take a small interest in biology and biophysics because of its complexity and the large number of unsolved but important problems. Lately I've noticed an increase in the number of popular articles on intrinsically disordered proteins (IDP's). These proteins are shaking up conventional wisdom on how proteins work and the importance of their structures.

This interesting News & Views article in Nature summarizes some recent work on disordered proteins and how they respond to activators and inhibitors. While I don't understand much of the jargon in the article, the overall message is exciting. I found the following excerpts of interest:

The observation of striking differences in the crystal structures of haemoglobin in the presence and absence of oxygen seemed to validate the idea that allostery [the link is my own] can be rationalized, and possibly even quantitatively accounted for, by examining the structural distortions that connect the different oxygen-binding sites... This structural view of allostery has largely guided the field ever since. However, the realization that more than 30% of the proteome — the complete set of proteins found in a cell — consists of intrinsically disordered proteins (IDPs), and that intrinsic disorder is hyper-abundant in allosteric signalling proteins such as transcription factors, raises the possibility that a well-defined structure is neither necessary nor sufficient for signal transmission.

The take-home message of Ferreon and colleagues' work, and the reason that a switch is possible, is that proteins should not be thought of as multiple copies of identical structures that respond uniformly to a signal. Instead, proteins — especially IDPs — exist as ensembles of sometimes radically different structural states. This structural heterogeneity can produce ensembles that are functionally 'pluripotent', a property that endows IDPs with a unique repertoire of regulatory strategies.

I absolutely love that IDP's are currently rewriting the dogmas of much of molecular biology.

Becoming a scientist

I'm not sure if you can read this without a subscription to Nature Jobs, but if you can read it, please do so.

This is an essay written by a post-doctoral fellow in neuroscience named Thomas Schofield. He was tragically killed in a bus accident in 2010.

This brief essay on becoming a scientist is very insightful and may help others to shed some misconceptions about a career in science.

The rules of the scientific game

While listening to NPR recently I encountered the book "The Structure of Scientific Revolutions" by Thomas S. Kuhn. This book, which was published in the seventies, is an explanation of how normal science emerges from times of discord between scientists (the so-called revolutionary periods). It also places the practice of science onto a firm sociological foundation, addressing how scientists think and what drives them to pursue their art.

Kuhn's influential work is credited with introducing the word paradigm into science. A paradigm is a set of beliefs which provides some basis for scientists to do their work. For example, in optics, I always assume the validity of Maxwell's equations to explore more esoteric optical phenomena. A paradigm is not just a scientific theory, though. It is a mode of thought that dictates what theories, experiments, methodologies, and instruments constitute valid science.

A particularly enlightening section of the book has been in Chapter 4, which explains why scientists do their work. Kuhn likens science to puzzle-solving, which is an often-used analogy for attracting students to scientific fields. A scientific puzzle consists in rendering coherent observations and theories from some class of phenomena. A scientific puzzle also consists in extending the theory to a broader range of phenomena, like the particle-physicist's search for a Theory of Everything.

What's necessary for this puzzle-solving to advance, Kuhn argues, is a set of rules for ensuring that a solution to a scientific problem can be found. A paradigm thus becomes a useful tool in filtering out what class of observations can even be addressed with the theory. If observations can not be adequately accounted for, then a theory must either be modified or thrown out altogether.

These ideas are important for any scientist wishing to make his or her own scientific revolution. Moving into uncharted waters is very difficult because one must first upend the existing paradigm, which most-likely exists for good reason. But the most difficult part is addressing those problems which lie outside the scope of the paradigm itself. In some sense, a scientist addressing such observations needs to start from scratch. I credit anyone who is explicitly working in these areas of science because they are likely to be shunned by their peers and encountering problems never before rationalized.

I highly recommend this book to anyone working in science, since its discussions effectively remove us from the constraints of our own tools for understanding scientific problems and lays them bare before us, without the hubris that is sometimes associated with trying to rationalize our own mode of thought.

CDMS and big science reports

This week's issue of Science contains an article entitled "Dark-Matter Mystery Nears Its Moment of Truth." The article details the latest findings by the CDMS experiment that is searching for weakly interacting massive particles (WIMPs). WIMPs are candidates for dark matter because they do not interact strongly with other matter and may fill large areas of "emtpy" space, effectively providing the additional gravitational energy that is missing from observations of the cosmos.

I very much liked the press-release on the CDMS website. It is incredibly clear and honest. Possibilities that would negate their findings are mentioned and the statistics are presented as-is with little attempt to make them sound better than they are.

I wonder if big science is immune to the hubris and bias that is often found in the work of publications numbering less than about ten authors. Is there something that makes science more objective as more people are involved? Of course, there are many difficulties with managing these large scientific endeavors, but I don't think we should discount the fact that involving so many people may make results more objective. This is an argument in favor of big science.

1000 scientists determine genes linked to common cancers

A rather neat and important example of the shift towards big science driven by data has been reported in this Guardian article. It explains how a recent large-scale study in the UK has linked faults in the DNA from thousands of individuals to an increased likelihood of developing prostrate, breast, and ovarian cancers, which are some of the most prevalent and dangerous forms of cancer.

I suspect that the most important problems that mankind faces will be most effectively solved in this manner, combining the efforts of many individuals of differing expertise to mine large banks of data for meaningful correlations.

I also wonder if the Information Age will lead to new advances in how data is collected or generated. Most of what I read about data-centric science assumes that the data we need to solve a problem is already available somewhere in some database connected to the net. But the fundamental hypothesis of science is that our models must match observations, so making a number of observations, in my mind, should come before anything else.

I expect that how we perform our observations and measurements will change as the Information Age matures, not just how we process our data.

A short review of best computing practices for scientists

Best Practices for Scientific Computing is a good read if you, like me, are a scientist who frequently programs but never received proper training in software development. It simply enumerates a list of practices that helps improve the productivity of coders and the reusability of code written in an academic environment. The techniques on this list are well known to software development professionals and have been extensively developed over many years.

Some of the suggestions and points in the article that are of note include:

1. 90% of scientists are self-taught programmers
2. All aspects of software development should be broken into tasks roughly an hour long
3. Provenance of data refers to data that is accompanied by a detailed list of code and operations for recreating the data and code output
4. Programmers should work in small steps with frequent feedback and course corrections
5. Use assertions (executable documentation) to avoid mistakes in code
6. Scientists should reprogram complicated tasks to make them simpler for a human to read instead of including paragraphs of comments explaining how the code works.

While largely approachable, the paper still suffers from a slight overuse of jargon from the software development field. As a result, the importance of some of their recommendations escapes me.

Finding similar ideas in other fields - ecology and thermodynamics of complex systems

I very much like to find connections between ideas in different fields. The Wikipedia page on Ecology contains the following quote:

System behaviors must first be arrayed into levels of organization. Behaviors corresponding to higher levels occur at slow rates. Conversely, lower organizational levels exhibit rapid rates. For example, individual tree leaves respond rapidly to momentary changes in light intensity, CO₂ concentration, and the like. The growth of the tree responds more slowly and integrates these short-term changes.
O'Neill, et al., A Hierarchical Concept of Ecosystems, Princeton University Press, p. 253 (1986).

This is not much unlike the microscopic and macroscopic descriptions of a material system. Small-scale fluctuations in the microscopic arrangement of a material often don't significantly impact the macroscopic qualities of this system [1]. Rather, many small-scale changes must occur in concert to cause a large-scale change. For example, the momenta and positions of various gas molecules in a container at equilibrium with its surroundings may change about some average, but the pressure of the gas on the container's walls will not. If the average molecular speed changes, though, pressure will likely change as well.

In addition, if the interaction between the microscopic constituents is nonlinear, it becomes much more likely that small changes in the microscopic description will result in macroscopic changes as well.

Notes
[1] In fact, most fluctuations are dissipated by the system. If the system is in equilibrium with its environment, then energy conservation, among other things, implies that a fluctuation away from equilibrium of a local volume inside a system will eventually be "smoothed out" by the system. This is an important property of complex systems known as the fluctuation-dissipation theorem.

How does bias impact the physical sciences?

One of my favorite scientists, Ben Goldacre, recently posted a Ted Talk that he gave last year on how biases affect the methodology of food and drug trials and the reporting of results. One solution he proposes is an increase in transparency of reporting on science. Presumably, people are not willing to check all the facts because it is perceived as a tedious and unwelcome job.

It's worth watching and thinking about how similar biases affect the physical sciences. Some important biases to identify in our own work include

1. Publication bias - The increased likelihood of publishing positive results over negative results.
2. Experimenter's bias - The bias to perform an experiment in such a way that one is more likely to achieve an expected result.

Most information I've found on scientific bias comes from medicine and social sciences. Is this because they are more susceptible to bias, because the implications for error are larger, or because the physical sciences have not adequately addressed these issues?

Open-Access Explained by PhD Comics - and more Data-centric science

My girlfriend sent me this video link from PhD comics last week about open-access publishing. The video is narrated by Nick Shockey and Jonathon Eisen, brother to one of the co-creators of the Public Library of Science (commonly known to academics as PLoS).

One of the arguments presented by the two is that research should be free to re-use. I think this means that scientists should be able to use all of the knowledge and material presented in a journal paper to advance their own work. The narrators mention that the full content of papers should be searchable to easily find connections between works and to facilitate locating relevant papers.

This is another hint towards the shifting focus to a data-centric approach to science, only here the arguments for it are coming from the open-access movement.

I also want to add that I support this movement. I can't access a paper that I've written because our campus doesn't have access to the journal. And when I graduate, I will not have access to any of them unless I or my employer has the appropriate subscriptions. Ridiculous.

Data-centric science - Correlation and causation

Yesterday I defined a model as a description that involves a cause-and-effect relationship between phenomena. In contrast, a data-centric approach to science looks only for correlations between data sets to answer scientific problems. This approach relies on very large data sets to come to accurate conclusions.

After thinking about yesterday's post I realized that there is a relationship in my arguments to the common admonishment "correlation does not imply causation." This fallacy is most often made when complex systems made of many interconnected parts are involved, such as in human health. Statements like "taking vitamin C tablets will cause me to not get sick" and "eating vegetables prevents me from getting cancer" are statements about cause-and-effect. As we have been taught again and again, though, taking vitamin C tablets may only decrease the chances that I get sick.

So here is the dichotomy that I was looking for: model-based science is useful in simple systems for which I may make cause-and-effect statements. Data-centric science is more useful for complex, coupled systems for which causality is a poor descriptor.

This is certainly a new way of thinking. Depending on the complexity of what we are observing, we should either employ or abandon causality as a means of interpretation.

Data-centric science - What is a model?

Chris Anderson's The End of Theory: The Data Deluge Makes the Scientific Method Obsolete suggests that models may no longer be necessary to solving scientific problems due to the large amount of data now contained in databases across the globe. Rather, looking for correlations between events may be enough to solve these problems.

I'm going to assume that the article's title is an overstatement; not all scientific problems may be solved with a data-centric approach. Some are very well suited to this method, however. To discern between these types of problems, I think it's necessary to first address the question "what is a model?" After this is answered, I hope to address why models may sometimes be circumvented.

Wikipedia's site on the disambiguation of the word model is quite long. It can mean many things within a scientific context. However, several words continuously appear on this page and its links: description, simulation, representation, framework. More informative (albeit complicated) is the explanation found at the Stanford Encyclopedia of Philosophy. The central question to this post is addressed on this site in Section 2: Ontology. A model may be a physical or fictional object, a description, an equation, or a number of other things.

Based on this information I think it's reasonable to state that a model is an attempt at replicating the behavior of some phenomenon, whether physically or as a result of an application of logical rules. I think further that a model establishes cause-and-effect relationships to do this. For example, Newton's theory of gravity contained the idea that something (gravity) caused the apple to fall. As another example, energy input from the ocean causes (among other things) hurricanes in weather models.

Models satisfy some human desire for causality. I read once (though I don't remember where) that people use reasoning as a coping mechanism for emotionally difficult situations, such as when a loved one dies. Somehow, finding a reason or a cause for things provides us some degree of comfort.

The data-centric approach to scientific problem solving obviates the establishment of a cause-and-effect relationship. Insurance companies don't need to know why married, twenty-something men get in fewer car wrecks than their single companions in order to charge them less. Instead, they only need to know whether this is true.

But other than to make ourselves feel good, why would we need to find a cause-and-effect relationship in the first place? I think that this could be because the ability to make correct predictions is an important part of any model. We make predictions when we're unable to carry out an experiment easily or when we don't have enough data already to answer a question. It is my suspicion that cause-and-effect relationships are central to a model's ability of prediction, though I'm not sure how right now.

So, in summary, a model is a physical or mental construct meant to replicate the behavior of some phenomenon or system. I believe that the main difference between a model-based approach to science and a data-centric approach is that a model-based approach creates a causal chain of events that describe an observation. I don't necessarily see this chain ever ending. Once we determine a cause, we might wonder what caused the cause. And what caused the cause that caused the cause? At some point, data-centric science responds with "Enough! Just give me plenty of data and I will tell you if two events are correlated." That's all we can really hope for, anyway.

Notes: The never-ending chain of causes sounds very familiar to Pirsig's never-ending chain of hypotheses in Zen and the Art of Motorcycle Maintenance. Is there a connection?

Also, I remember E. T. Jaynes arguing in Probability Theory: The Logic of Science that we can't really know an event will occur with 100% probability. This seems to suggest that cause-and-effect relationships do not really exist. Otherwise, we would always know the outcome of some cause. And if they don't really exist but are actually good approximations, then models really are what we've been told since middle-school science: imperfect and intrinsically human attempts at describing the world.

Note, October 25, 2012: I wrote this post late last night after having had a beer with dinner, so my mind wasn't as clear as when I normally write these posts. I realized this morning that the reason for building cause-and-effect relationships is that we can control a phenomenon if we know its proper cause. Many things are correlated, but a fewer number of things is linked by a causal relationship. Therefore, accurate models provide us the ability to control the outcome of an experiment, not just predict it. I don't believe that correlative analytics necessarily allow us to do this.

Data-centric science - My initial thoughts

"The scientific method is built around testable hypotheses. These models, for the most part, are systems visualized in the minds of scientists. The models are then tested, and experiments confirm or falsify theoretical models of how the world works. This is the way science has worked for hundreds of years... But faced with massive data, this approach to science — hypothesize, model, test — is becoming obsolete."

This quote is from a 2008 article in Wired Magazine called "The End of Theory: The Data Deluge Makes the Scientific Method Obsolete" by Chris Anderson. In this article, Anderson addresses our ability to solve scientific problems by looking for correlations in data without the need to form models. This ability has been enabled by the huge amount of searchable data that the internet has generated over the past two decades, which has led us into the so-called Petabyte Age.

This approach, sometimes referred to as analytics, has been successfully employed to translate between written languages, sequence genomes, match advertising outlets to customers, and provide better healthcare to people. Now, Anderson argues, it may be applied to problems across the full range of sciences. This is a welcome evolution, partly because many fields now possess too many theories and lack the experiments to validate or deny their predictions. Take particle physics or molecular biology, for example. There are arguably more theories and models now about these systems  than ever before, and many of them can not be verified. A data-centric approach could solve this problem.

This is a very interesting idea and I've been thinking about it for a few weeks now. I think that, to make any sense of it, I need to address several issues and assumptions. Questions to consider include:

• What is a model? When is it useful and when is it not?
• Are only certain fields of science able to benefit from a data-centric approach?
• What is the human component to research? How would it change if this approach was implemented?
• What has already been done to solve scientific problems with data-driven solutions?
• What are the philosophical implications to changing our idea of science? The scientific method has existed in some form or another for almost 2000 years (I'm referring all the way back to Aristotle, even if his ideas contained flaws). A significant change to the scientific method, especially given its importance to modern society, could have major sociological consequences.

I'll consider these questions in future posts.

Come see my talk at FiO

I'm giving a talk on optically controlled active media today at Frontiers in Optics. The talk number is FTh3D.7 and it's in Highland E.

The talk is about our work concerning the optical forces on colloidal particles in 3D, space and time dependent speckle. I'm pushing it as a model system for testing ideas from nonequilibrium thermodynamics, but I think that our method of treating the field-particle coupling is equally interesting.

Come check it out.

Tommorow at Frontiers in Optics

Right now I'm in Rochester, New York for this year's Frontiers in Optics conference hosted by the OSA. So far I've attended the plenary talks, which dealt with quantum optics and thermodynamics, 2D IR spectroscopy, retinal imaging, and, of course, the Higgs boson. In addition, I visited the Omega laser facility, which was incredibly fascinating. If you're in the area and you have any interest in incredibly powerful lasers or inertial confinement nuclear fusion, then I recommend making a visit.

Tomorrow I plan to visit some talks and work at the CREOL exhibition booth from 1:00 PM to 3:00 PM. Additionally, I'm giving a talk for a group mate who couldn't make it to the conference on Wednesday morning at 11:30 AM and my own talk on optically-controlled active media on Thursday at 3:00 PM. Briefly, the talk deals with solutions pumped by light to expand their free energy so that they may do carry out additional work. I hope the project will eventually be applied to controlling reaction kinetics in cells.

If  you're there, let me know and we can talk over coffee or a beer!

Scientist personality types

Today is Friday, so as you might expect my mind is on the weekend and I don't have the desire to write too in-depth on anything. However, I wanted to make some quick notes about a train of thought I've had recently concerning the types of people who pursue science.

We often stereotype scientists as introverted individuals who tinker away in labs or on computers and who are awkward in social settings. This stereotype has the unintended consequence as portraying all scientists as the same type of person. My experience in graduate school has taught me differently. Scientists engender many different personalities, from brash and confident individuals to shy introverts to downright strange people. As we might expect, each personality type benefits science in a different way.

What are the common personality types in science and what is their impact? My question is motivated partly by the thesis in Five Minds for the Future by Howard Gardner. I have not read it, though I feel I that I have an idea of what his thesis entails.

I also wonder how a personality type might be better matched to a career field before they enter a career path. I suppose this is the work of many guidance counselors and career service offices across the country, but I feel that information provided by these services about some career fields, in particular scientific careers, is simply lacking.

Online career path guidance for science PhD's

I saw a link to myIDP this morning from the Condensed Concepts blog. myIDP (IDP is short for individual development plan) is a free service for assisting individuals who have science PhD's with finding careers that fit their skills and interests. The service presents and discusses a list of possible career routes, including many that lie outside of academia.

I am quickly approaching graduation, so I took a look at the site and filled out a few of the questionnaires. The three primary questionnaires for evaluating my career options considered my interests, skills sets, and values. I was pleased to see that many skills that I consider myself to be strong in (writing, communicating, and presenting, for example) were included in the survey. I was also pleased that "traditional" graduate student and scientific skills (performing experiments, processing data, etc.) filled only a small fraction of the possible skill sets that might be considered important.

myIDP goes further than simply displaying a percentage for how well a career field correlates with my responses to the questionnaires. It listed several questions that were based on my responses that I am to ask myself concerning these fields and my values. The questions relate to my highest rated values and serve as a guide while establishing my goals. For example, I responded that a good work location was very important to me. The questions I need to consider are whether there are geographic clusters for a specific field and whether these  areas would fulfill my lifestyle requirements.

The inclusion of many different career possibilities in myIDP has been very reassuring. I became concerned about two years ago that my original choice of career path, a faculty research and teaching position, did not correlate well with my values. I also realized that my institution and advisor are poorly equipped to place students into non-traditional science jobs. All of this led to some anxiety on my part because I was beginning to suspect that I had taken a wrong career path. Fortunately, I've learned that there are many options for me and that I shouldn't necessarily compromise on my interests and values just to maintain a sort of status quo for scientific careers.

Bottom-up or top-down understanding?

The past few days I've been modeling the dynamic light scattering (DLS) signal from particles embedded in a complex fluid. The following question keeps floating around in my mind, and I don't quite have a good answer to it.

Is it better to first find an empirical model to explain an observed phenomenon and then infer the physical, microscopic reasons for why the model works? Or is it better to first understand the microscopic makeup of a system to arrive at an empirical model that explains the data?

I'm inclined to think that establishing an empirical model first is better. We often have little-to-no idea of the microscopic details of a system. However, empirical models often apply to systems with similar microscopic behaviors, so once the correct model is found, we can establish a stronger intuition for the details.

The scientific method is not universal

I've often heard others quip that physicists worry too little about the details, whereas chemists worry too much. Recently I've come to better understand the attitudes that give rise to statements such as these.

If you've read other posts on this blog, you've probably realized that I most closely associate with experimental physicists. When I approach a problem, I form a model in my mind that makes intuitive sense. Then, I make the model more rigorous through pictures and equations. Throughout this step, I take extra care to ensure that the model parameters can be easily measured, an approach which no doubt adds a certain flavor to my models. My experiments then become realizations of the model to confirm its predictive power. Sometimes, modeling occurs after an experiment, and, almost always, the process jumps back to an earlier step but proceeds with more refinement.

What I've learned to appreciate is that this process flow varies between individuals in different scientific fields. For example, theoretical physicists don't place as much emphasis on how easy it is to measure a model's parameters, but their theories often extend over a wider range of phenomena. As another example, field biologists must take much more care in preparing an experiment than a physicist in the lab due to costly resources, limited time, and small sample sets. All of these factors leave their mark on the steps in the scientific method.

This understanding is valuable because multidisciplinary research is becoming vital to solving many scientific problems. Before we can help each other, scientists must learn to appreciate and understand our differences; otherwise, we'll never take proper account of the details.

Harmful errors in science journalism

Popular science reporting is incredibly important for making current research known to the lay public in a concise and simplified manner. It often acts as the go-between for channeling information from scientists and engineers to the people who could benefit from the research. Unfortunately, translating new concepts from the jargon of specialists to untrained individuals poses challenges, and often the science in these articles is erroneous. This is a rather well-known problem and the reason why I read such articles with skepticism.

Two different friends sent me this article recently from MIT News reporting on the development of a camera for generating "Trillion-frame-per-second video." Armed with my usual dose of skepticism, I glanced quickly through it and the accompanying YouTube video. I think the significance of the work is a bit overstated [1], but the article sufficiently addresses its limitations and applications. Unfortunately, in describing the operation of a streak camera, the author Larry Hardesty notes that

"The aperture of the streak camera is a narrow slit. Particles of light — photons — enter the camera through the slit and pass through an electric field that deflects them in a direction perpendicular to the slit. Because the electric field is changing very rapidly, it deflects late-arriving photons more than it does early-arriving ones."

Electric fields do not deflect photons. Electric fields deflect moving electrons. What is likely happening inside the camera is that photons scattered from the bottle impinge upon a phosphor screen, which in turn ejects electrons. The electrons in turn are deflected by the time-varying field within the camera to different points on the detector. Electric fields can be modulated with RF equipment up to a few hundred GHz, which roughly corresponds to a few tenths of a trillion oscillations of the field per-second. I believe that this is how the noted number was obtained.

So what's the big deal? The point of the article, after all, is to inform a general audience about a newly developed camera—the details of its operation should not matter. Here are a few reasons why errors such as these are harmful:

1. Many people who read these types of articles are students or young people who aspire to be scientists. To provide incorrect information hurts their education by establishing false ideas within their minds.
2. It's dishonest. Whether the author knew he had made a mistake or not, the article should have been reviewed by one of the scientists before its publication [2]. Eventually, people could learn to associate dishonesty with science and this is clearly undesirable.
3. It's distracting and draws attention away from the article's main message. I'll admit that I had made up my mind that the research was flawed after I saw the error. Only a second, careful reading revealed that nothing about the research was in error.

This article is just a minor example of an all-too-common problem with science journalism. Unfortunately, I've seen much worse mistakes. I wonder how many times I've been fooled by a false proposition in a piece of reporting.

[1] It images processes that are stationary over at least one hour in time. For example, it can not image a turtle that walks through its field of view. Dr. Velten's quote in the beginning of the article might be placed in better context by noting that while few things are too fast for the camera, many things are too slow for it.

[2] I was once quoted several times in an article about my research that I had not realized had been published until I chanced upon it while Googling my name. I didn't find it until nine months after it appeared in print. I received no word from the author what-so-ever that it had appeared in the Orlando Sentinel, and was misinterpreted as having stated that our work existed only in theory. If the author had asked me to review the article prior to its publication, this error would not have been made.