Avoid Computer Injury - Repetitive Strain Injury (RSI) Software

I've recently succumbed to the unforgiving punishment of continued computer use. I purchased my first computer in 1995. It had an Intel Pentium 133 MHz processor. I don't recall the memory size. It had a 4GB hard disk drive (called Quantum BigFoot). And so began my computing journey. I was inseparable from this machine. I vaguely remember a day passing without me using a computer. Of course, I had no hint that muscle injuries could occur due to extended and imporper use of computers. I don't think that everybody agrees the proper computing practices, but no one argues that regular breaks and stertches must be carried out during computer usage sessions.

Looking back at things, I averaged  about 10 hours a day using a computer. So, for the past 15 years, that's: 5,479 days or 54,790 hours!!! To be a bit conservative, I will take an average of 7 hours a day (to account for occasional breaks). That would only take it down to 38,353 hours... that's a lot of hours.

After looking at these numbers, and the stiffness in my shoulders, I decided that it is time to look for options. Of course, forcing myself to take breaks never worked, so the ideal candidate was a software that lurks in the background and tracks my computing levels to suggest micro and macro breaks.

Micro breaks seem to be the most important. These are very short breaks (10 to 30 seconds) that are to be taken between intervals of intense keyboard usage activity (~50 words per minute). In general, taking a micro break every 2 to 4 minutes is a good option. Ive looked at a few software and here's what I liked so far:

MacBreakz - I use that on my Mac. It tracks your activity and suggests breaks with stertching exercises. I like it a lot. It is very cheap ($25 for a single license) and it is worth every penny.

Wellnomics Workpace - Windows only. I used it for a while. The user interface is a bit ugly, but the software has great features. It can perform statistics and does real time keyboard tracking. It is expensive: $69 for a single license.

Workrave - Windows/Linux, and most importantly open source! It may not have all the advanced features of Workpace, but for the price, I'm taking it.

So here you go. My advice, don't underestimate this problem. Take the proper measures to reduce the impact of computer usage on your muscles and ultimately your career.

Cite as:
Saad, T. "Avoid Computer Injury - Repetitive Strain Injury (RSI) Software". Weblog entry from Please Make A Note. https://pleasemakeanote.blogspot.com/2010/07/avoid-computer-injury-repetitive-strain.html

Fallacies in Scientific Research: Appeal to Popularity

This is my second post on logical fallacies in scientific research. Today's subject discusses how the "Appeal to Popularity" fallacy can hinder the research environment. This one in particular is a bit tricky because, at the face of it, an individual may use it as evidence.

Definition: Appeal to popularity is a logically fallacious argument in which an individual is lead to believe that something is true (valid, moral...) because it is widely accepted or used. The person arrives at this belief without any reference to evidence supporting the validity of the claim.

Examples:

• The majority of people use brand X car. Then it must be the safest car.
• Laptop Y is very popular among university students. Therefore, it must be the best laptop.
• The majority has opposed this law. It means that the law is bad.

This fallacy is a very delicate one as I mentioned previously. There are two points in every one of the above statements: the "factual" part and the illogical inference. It may be true that the majority favors brand X or Laptop Y, but inferring that it is a good product is wrong. There is no immediate link between these two points. 

It may also be true that car X is one of the safest cars, but it is not because everybody owns one. Such a statement should be validated by data, experimental tests between a variety of cars and so on. Interesting, for the most part, one can revert the above statements and obtain a valid argument. For instance, because car X is one of the safest cars, it has a wide customer base.

At the face of it, it seems that by appealing to popularity, one is using statistical data. This becomes a problem in Scientific research. As usual, examples from personal experience:

• Fluent is the most popular CFD code used. Then it is the best CFD software out there.
• The Finite Volume Method is the most popular discretization technique. Then it must be the best.
• Everybody is getting funding from the industry. Then, this is the best source of funding. 

Again, these are all invalid arguments for making decisions especially in scientific research. To stretch things a bit, these arguments may be massaged a bit to lend them some credibility by isolating the statistical component of each argument and using it as data input for making decisions. Here's how I think these should be amended:

• Fluent is the most popular CFD code used. We should list it as one of the software to consider for purchase. But first, we must compare its performance to the other software we are considering for this particular problem and then make an informed decision.
• Fluent is the most popular CFD software. We should consider it in our modeling efforts to reach a wider audience. (I'm not too fond of this particular way of putting it as this borders on the marketing side).
• The Finite volume method is a very popular discretization method. Based on the literature we reviewed, the method was successfully used to simulate a wide range of physical phenomena. There's also a large amount of evidence that the method is particularly suited for transport phenomena. We should consider it as a viable method for solving our hypersonic design problem.
• I don't have any comments on the last one.

When it comes to science, our conclusions should be entirely based on the data. But when it comes to decision making, data is only a part of the process. There are existing and expected experiences that come into play and those may not be entirely rational. The problem is not also in the statistics. If the statistics point to the fact that 80% of the simulation science is done using the finite volume method, then, in the context of science, this should only mean that we should consider the finite volume method as option and test its performance for our problem. Appeal to the number by itself is meaningless. What percentage have reported positive results in this case? If the argument was: 75% of the scientists have reported positive results for using the finite volume method for compressible flow problems, then things are quite different. This is no longer appeal to popularity, it is an appeal to evidence.

There are many other details about this logical fallacy. For an excellent discussion, please visit the wikipedia entry for this fallacy.

References:

http://en.wikipedia.org/wiki/Argumentum_ad_populum
http://www.nizkor.org/features/fallacies/appeal-to-popularity.html

Cite as:
Saad, T. "Fallacies in Scientific Research: Appeal to Popularity". Weblog entry from Please Make A Note. https://pleasemakeanote.blogspot.com/2010/07/fallacies-in-scientific-research-appeal_24.html

Fallacies in Scientific Research: Appeal to Common Practice

In this series of articles, I will discuss some of the most annoying logical fallacies that research scientists fall a victim to. I will start with a very common fallacy known as Appeal to Common Practice. As the name designates, this logical fallacy stands in its own right as an insult to logic and rationality. Here's a formal definition (based on wikipedia entry):

Appeal to common practice is a logical fallacy in which a thesis is deemed correct [moral, rational, sound, justified...] on the basis that it correlates with some past or present tradition.

In other words, if some action "A" is common among the masses, then it must be true, justfied, or it is morally correct to do it.

Appeal to authority falls under several categories. Here are a few examples.

• Bribery is illegal... but hey! everybody does it! So it is okay to do it.
• Cheating on tests is unethical, but everybody cheats, so it is justifiable to do it.
• It is illegal to pass a stop sign without stopping, but everybody does it! So it is okay to do it.

Therefore, appeal to common practice requires that the established action to be unlawful, or irrational (...) of some sort. So in a country where bribery is legal (??), the example on bribery is no longer a fallacy. Thus, based on the established norm, one should draw the proper conclusions. In issues dealing purely with quantifiable items, one can draw more absolute conclusions. For example, if person X works more hours than person Y, and if they both get paid by the hour, then person X is expected to get paid more.

In light of the previous examples, I would like to now focus my attention to the use of this fallacy in the context of scientific endeavors. Here are two examples drawn form personal experience:

• The majority of journals do not allow the publication of an already published paper in a different journal, but these days, almost everybody is doing that! So it is okay to do it!!!
• It is unethical and illegal to use grant money for personal benefit, but most PIs do it! So it's alright to do it! (An example of using grant money for personal benefit is hitting two birds with one stone: say you've always wanted to visit Italy, then, you'd claim that it is important to collaborate with some university in Italy and go ahead for the trip. Of course, you may do great science over there, but you've also managed to implicitly gain personal benefits...)

(please also feel free to add if you have examples on this).

It is a shame for a scientist or researcher to think in this manner. It is an insult to the mind and the scientific community when these things happen. Based on what i've seen in graduate school, there is a lack of proper education regarding ethics in science. On one hand, it is the duty of the seasoned academic advisor to properly educate his pupils on scientific ethics. On the other hand, it is also the responsibility of the aspiring graduate student to educated themselves on these subjects. There are many books out there on scientific ethics that ALL graduate students in science should read. Here's a good resource to start with:
http://www.files.chem.vt.edu/chem-ed/ethics/vinny/ethxbibl.html

References:

http://en.wikipedia.org/wiki/Appeal_to_tradition
http://www.sjsu.edu/depts/itl/graphics/adhom/practic.html#1c
http://www.nizkor.org/features/fallacies/appeal-to-common-practice.html

Cite as:
Saad, T. "Fallacies in Scientific Research: Appeal to Common Practice". Weblog entry from Please Make A Note. https://pleasemakeanote.blogspot.com/2010/07/fallacies-in-scientific-research-appeal.html

LaTeX Thesis and Dissertation Template

Here's the LaTeX class template that I developed for my dissertation. It is hosted at the University of Tennessee. You can download it from here:

http://web.utk.edu/~thesis/files/ut-thesis-template.zip

The class has some really neat features. It can automatically generate the approval pages for up to 6 committee members. All you have to do is type in their names.

Enjoy your thesis/dissertation writing!

Cite as:
Saad, T. "LaTeX Thesis and Dissertation Template". Weblog entry from Please Make A Note. https://pleasemakeanote.blogspot.com/2010/07/latex-thesis-dissertation-template.html

LaTeX Letter Template

Here's a LaTeX letter template for two print formats. Use the first to print on paper WITHOUT a letter head and the second on paper WITH a letter head.

• LaTeX letter template for paper without letter head [tex][pdf]
• LaTeX letter template for paper with letter head [tex][pdf]

Voila!

Cite as:
Saad, T. "LaTeX Letter Template". Weblog entry from Please Make A Note. https://pleasemakeanote.blogspot.com/2010/07/latex-letter-template.html

Some Words to Avoid in Scientific Papers and Manuscripts

Avoid using emotional and vague words. Avoid unnecessary fillers. Be precise, specific, and objective. Define all your words and use quantitative numbers as much as possible. Here's a list:

plenty, very much, a lot, short (define what short is!), long (define that as well!), really, heavy, light, somehow, sort of, kind of, in a sense, for sure, simply, obvious, unfortunately, hopefully, remarkable, impossible, lovely, interesting, miraculous, nice, fun, happy...

Some expressions that should never be used in an article (and that really "grind my gears" in every day conversations):

bottom line, brute force approach, the best, cutting edge, nonsense, tip of the iceberg, scratch the surface, state of the art, loaded to the teeth...

I will keep this list updated as I find new words. Feel free to contribute.

(This image is copyrighted, © xkcd.com)

Cite as:
Saad, T. "Some Words to Avoid in Scientific Papers and Manuscripts". Weblog entry from Please Make A Note. https://pleasemakeanote.blogspot.com/2010/07/some-words-to-avoid-in-scientific.html

My Journey into Open Source and Cross Platform Independence

When you advance in your career as a scientist, the choices that you make to accomplish certain research tasks become of crucial importance. If you spend a year collecting data and analyzing in Excel for instance, creating all sorts of plots and customizations, it will be very hard to make the switch to another software, say OpenOffice. Data analysis is not the only crucial component of a successful research endeavour. Your entire digital world is at stake here. The way you manage your emails, code, graphics, presentations etc... will have an impact to the way you handle things. From sharing data with collaborators, to publishing in journals, to accessing your files from anywhere on the planet; the way you do things can make all this truly enjoyable and lasting.

Part of the problem lies in the fact that there are no unified standards to doing things, especially when the tasks become "high level", such as a graphic presentation. Let me give you an example. If you are writing a piece of code in C, then you can rest assured that your code can be made to run on any type of computer. I call this type of approach "low level". In contrast, say that you are preparing a high quality presentation in PowerPoint or KeyNote. Your presentation is full of graphics and animations. Then it can be safe to say that yours will only work using the software that you used to create it. Of course, keynote can read pptx, but you'll spend more time fixing the presentation that you may as well just do it from scratch. This type of task is high level because of the advanced nature of the software and proprietary nature of some of its features.

For these reasons, and many others as well, I have decided to align my choices with three premises: (1) Low Level Approach, (2) Open Source, (3) Platform Independence. Here are the choices that I made

Operating System(s)

I use all of the following: Windows, MacOSX, and Linux. Because I am heading towards open standards, I have had very little problems handling files across these platforms.

The Cloud: Email, Calendar...

1. Google goodness: I use almost all google services. In particular, I use gmail to handle all my mail accounts and Google calendar for events. I only use the browser to check & send mail. I have not used an email client since 2009.
2. Live Mesh, Sync: I keep a copy of all my research files online AND across all my computers. For that purpose, i've been quite happy with Windows Live Mesh and Windows Live Sync. When go beyond the storage limitations, I may just move to drop box or some similar service.

Manuscript Preparation

1. Word Processing: I ONLY use LaTeX to prepare my documents (even short letters). The last document that I have in Word is my CV which I am now converting to LaTeX. However, many scholars (especially in engineering) prefer to use Word, therefore I keep a copy of OpenOffice on all my computers in case I need to use it or I use Google docs.
   My policy on this is simple: If I am leading a project, I will exclusively use LaTeX to document our findings. If someone else wants me to help with their project, I will use whatever they have prepared their report in.
2. Reference Management: I use Mendeley. It keeps a bibtex library constantly updated so that I only reference that in all my LaTeX documents.
3. Graphics: I use InkScape! That was one of my most valued discoveries this year. It is cross platform and uses an open source graphics format called SVG. SVG stands for Scalable Vector Graphics. So you would expect really hight quality graphics in your PDFs!
4. Plotting: Here's one problem I have not resolved yet. I now use OriginLab and I find it to be a very good piece of software given all its programming capabilities. Unfortunately, it is not platform independent. So far, I have not found a decent replacement for OriginLab and I may have to stick with for a while. I have looked at plotting with PSTricks, PGF/TikZ, and GnuPlot, but was not satisfied with the process.

Presentations

1. So far, I am quite stuck with Powerpoint. I am very impressed by its capabilities and will find it quite hard to move to an open source presentation software. Because this type of work is high level, it may be hard to adopt a simple open standard approach.

Scientific Software

1. C/C++/Java etc... Just need an editor! I use different editors on different platforms.
2. Mathematica: This is one piece of great software that I would not get rid of. I have looked at open source alternatives such as Sage, but found that it lacks several features related to symbolic analysis. Mathematica's symbolic capabilities are the main reason for me using it.

I may have missed a few points, but will add them later. If you have any suggestions or know of any software that would handle plotting and symbolics, please let me know.

Finally, here's a message from good ol' uncle Sam

Cite as:
Saad, T. "My Journey into Open Source and Cross Platform Independence". Weblog entry from Please Make A Note. https://pleasemakeanote.blogspot.com/2010/07/my-path-into-open-source-and-cross.html

Deleting duplicate BibTeX entries from Mendeley

I've recently moved to using Mendeley as my main reference management tool. It has some pretty neat features except for a few that keep bugging me. One of those is that Mendeley seems to maintain record of deleted entries in its database and those end up in its BibTeX export. Worst of all is that I am always getting duplicate entries in the BibTeX file.

Today I found a neat solution based on the post by Simon Greenhill. Here's how it works for the citation key:

1. Locate your Mendeley SQLite Library (~/Library/Application Support/Mendeley Desktop/youremail@www.mendeley.com.sqlite)
2. from your terminal type:
   sqlite3 your-email-address\@www.mendeley.com.sqlite
3. Type:
   SELECT COUNT(*) as entries, citationkey FROM Documents GROUP BY citationkey HAVING entries > 1;
   This basically queries the table "Documents" for all duplicate citation keys.
4. Then,
   DELETE FROM Documents WHERE id NOT IN (SELECT MAX(id) FROM Documents GROUP BY citationkey);
   

Voila!

Cite as:
Saad, T. "Deleting duplicate BibTeX entries from Mendeley". Weblog entry from Please Make A Note. https://pleasemakeanote.blogspot.com/2010/06/deleting-duplicate-bibtex-entries-from.html

EndNote to JabRef to EndNote

1. In Endnote, select the citations you want to export and go to “File / Export”
2. Select “RefMan (RIS) Export” in the Output Styles dropdown box
   (in case it is not there, choose “Select Another Style…” and look for RefMan RIS Export)
3. After exporting the file, go to JabRef and import as RIS

 

Voila!

There are many ways of doing this. Here’s one on fellow blog Fundamental Thinking. Josh’s method is based on the JabRef Endnote Export Filter.

To go from JabRef to Endnote

1. In JabRef, select the citations you want to export and go to “File / Export”
2. Select “Endnote” from the dropdown box
3. In Endnote go to “File / Import”
4. Choose the data file that you just exported from JabRef
5. Under import option, choose “EndNote Import”

Voila!

Cite as:
Saad, T. "EndNote to JabRef to EndNote". Weblog entry from Please Make A Note. https://pleasemakeanote.blogspot.com/2009/04/endnote-to-jabref-to-endnote.html

How to Resolve or Link to a DOI

Given a DOI number (for a journal manuscript for instance) one can link to this DOI in a word document for example or in an HTML page by invoking the following

http://dx.doi.org/YOUR-DOI-NMBER

Try this for example

http://dx.doi.org/10.1103/PhysRev.47.777

Voila!

The DOI, or Digital Object Identifier system, is a systematic way of identifying digital resources in a rather permanent way. It is a neat way for tracking and managing digital resources in an absolute reference manner. So, for example, if you want to link to a journal paper, you can either use the permalink for that specific paper or simply link to the DOI. In theory, no matter what happens, the DOI number will always locate that paper.

Cite as:
Saad, T. "How to Resolve or Link to a DOI". Weblog entry from Please Make A Note. https://pleasemakeanote.blogspot.com/2009/04/how-to-resolve-or-link-to-doi.html

The Story with Euler…

Recently, I have been deeply immersed in studying some of Euler’s original works (based on translations where applicable, of course). Here is my conclusion:

Whatever you think you have discovered… Euler may have beaten you to it!

and yes… please quote me for that one!

I made it a necessary condition in my research, and before publishing any manuscript, to check with Euler first. Many of his results and especially the intricate steps of his derivations are lost within the edifices of his collected works (or simply because later generations of mathematicians have superseded his proofs with modern versions). Yet, you will be surprised to find in one of those steps, the exact same thing that you may be currently discovering!

Cite as:
Saad, T. "The Story with Euler…". Weblog entry from Please Make A Note. https://pleasemakeanote.blogspot.com/2009/03/story-with-euler.html