In the last post, I went over some of the POV-Ray basics. The toughest part of actually using POV-Ray to render figures of proteins is importing the structure into the rendering package - the complex geometry of the macromolecule has to be translated to the system of simple objects understood by POV-Ray.

As I was looking into some software packages that can output .pov files, I came across another raytracing program called Tachyon which is included (sort of) in the latest version of VMD.  The example images made using the ambient occlusion lighting capability of Tachyon made my jaw hit the floor.  Instead of babbling on about ways to get POV-Ray to play nice, I’ll go over how to get and use VMD/Tachyon.
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I’m a strong believer in the power of good figures.  Everyone knows the tired phrase “a picture says a thousand words”.  In scientific papers, where page charges and editors place restrictions on the amount of text and a color figure might run you $1000, it’s critical to make sure that your figures are absolutely as good as they can be.

One of the best programs for generating images of 3D objects is POV-Ray.  This is a raytracing program - it simulates rays of light and their interactions with a scene.  It’s an incredibly powerful program (just take a look at some of the results in the Hall of Fame), but also fairly complex and not user-friendly.  Some molecular graphics packages have built-in simplified POV-Ray interfaces (pymol and VMD are two that I’m aware of), however the renders produced by these programs just don’t look good enough for me most of the time.

I’m going to try to work out a more “realistic” rendering scene geared mainly towards the visualization of protein structures.  I’ll say from the outset that as of now I’m sort of inspired by the work of David Goodsell, and I’m going to try to incorporate some of that simplicity into the render.

There are a few things that many POV-Ray renders of molecules get “wrong” in my opinion, with the main two being lighting and texture.  This usually leads to sort of plastic-looking things with very harsh lighting and shadows.  Let’s see if we can work on a model system first to try and improve this.
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I have been trying to post once a day, but I’m actually working on something that’s taking me a bit of time.  I’d rather get something useful before posting it here, so that’s why it may be a bit before I get anything online.

I’m planning on doing sort of a three-part post:

1. Generating a more realistic scene in POV-Ray
2. Rendering (protein) structures in this type of scene
3. A python script combining molscript and POV-Ray for setting up the render

Stay tuned.  I’m hoping that by at least posting the agenda, it will give me enough motivation to actually get it done :)

I’m not a psychologist, sociologist, or anthropologist (as a matter of fact, I’m not any kind of -ologist), so today’s post might meander even farther from the realm of accuracy than is typical even on a normal day.

I’ve been thinking a lot lately about the issues of internet anonymity, rational discourse, and the psychology of the village (or small group if you like).  In my mind, there was a time in the not too distant past in which most people lived in relatively small communities.  Those inside the community were neighbors, friends, and family while those outside the community were strangers.  To this day we’re tought as children to mistrust strangers; they might try to hurt us.  As time has gone on, advances in communication technology have enlarged our “community” - that is to say the group of people with whom we can rapidly share news and information - more and more.  The largest community of all is the internet of course.

We are all familiar with the social buffoonery that takes place daily across the web.  We have new words for these actions - spamming, trolling, griefing, etc.  It’s the 21st century version of the stranger who comes into town and steals a sheep.  The problem is that on the web, we are all strangers to one another - there is no real community.  Sure, certain groups may congregate to certain web sites and forums (I think it’s clear that a certain self-selected personality type is in residence at Digg, for instance), but the members of that community don’t really know one another.  The flux of incoming and outgoing members, the ease of creating a new identity, and other similar factors mean that the community never settles down and becomes a wholly familiar environment.  I think that this factors heavily into the reason that negativity gets much more play online than positivity.  The posts in which I am more harshly critical tend to get more views and more commentary than those that are supportive.  Since (as for all bloggers, most likely) I enjoy knowing that someone is paying attention, this is a pressure to write more negatively, which I can’t say that I like.

Now I’ve said all of this while tangentially considering how this applies to the Open/Semantic/Social science movement.  Those who have spent any time around scientists can attest that there are already feuds and animosity amongst competing investigators.  My concern is that if a higher percentage of this interaction moves online, we’re likely to see a deepening of these divides.  The reason is simple: if two people argue during the question and answer session at a conference, they can still go out for a beer afterwards.  If two people argue on the internet, all they do is steam up and get more angry.

You can see this already from time to time around the more popular science blogs on the internet.  There is a certain acidity to the discourse that often seems to be amplified by a misunderstanding, overpersonalization, or overly vehement attack - all hallmarks of web discourse as a whole.  When discussing philosophical ideas or opinions this is fine, but I don’t know that it would better the advancement of science if we debated research findings in a similar way.

Don’t get me wrong, I don’t want to come off as a luddite who thinks that all scientific communiques should be sent via telegram (and I would hope if you’ve read any of my other posts you are aware that it’s quite the opposite).  My point is just that, as we push the practice of science more and more into the impersonal online arena, we should take pains to ensure that the discourse remains civil.

I recently installed the WP-Cache plugin in order to reduce the server load due to PlausibleAccuracy’s massive (double digit!) traffic.  Because of this, it may take some time for the “Latest Post” and “Latest Comment” feeds on the front page to update.

This was more of a proactive move than a strictly necessary one.  I’d rather have things squared away on the back end just in case.

I came across a blog post entitled “15 seconds of fame” on the blog “Spoonful of Medicine” by Juan Carlos Lopez, the editor of Nature Medicine.  Here are his thoughts on Open Access publishing:

In any case, I must confess that the talk got a little boring when people started asking me questions about open-access publishing. It was fascinating to see how difficult it was for some people to understand that scientfic [sic] publishing costs money, and that there are different models to recover your costs — the author-pays model, the subscription model, and everything in between.

First of all, and I really wish someone would explain this to me; once again no mention is made of advertising revenue. To me, this is the prime elephant in the room that no one mentions when discussing recouping costs of publication.

Secondly, he’s right about the extremes of charges - the author pays everything or the journal makes money by selling subscriptions.  But as we all know, it’s disingenuous to implicate that Open Access means author pays through the nose and close access means the author doesn’t pay in anything but sunshine and rainbows.

While Dr. Lopez’s magazine does not levy page charges, they do charge quite a bit for color figures:

There is a charge of $1000 for the first color figure and $250 for each additional color figure. Otherwise, there are no submission fees or page charges.

These limited charges are actually low for “typical” journals, and Nature is to be commended, however I would argue that this still falls into the middle ground between “author pays” and subscription charges only. Consider that the full charge for publishing in PLoS One is the same as a Nature Medicine article with two color figures.  Oh, but you don’t have to pay to then read the PLoS One article, since it’s OA.  I’m not even mentioning other OA journals with lower or nonexistent page charges.

As Dr. Lopez says:

The talk got boring (at least to me) because I have very little patience with this discussion when people stop putting forward compelling arguments in support of their ideas

Indeed.

In my commentary on the Scientific American article on Open Notebook Science, I said that I wished that the author would have talked to some people who were against the idea.  This being the internet, I should have expected that the blogs would take care of that.

PhysioProf thinks that Open Notebooks are just a bad idea:

Reading other people’s lab notebooks will decidely not provide a “leap forward in clarity”. You can barely understand your own lab notebook entries weeks or months after they occur. Lab notebooks contain a huge amount of totally irrelevant obscure information, the vast majority of which relate to failed experiments.

PhysioProf is right on that notebooks do contain reams of stuff that is completely irrelevant.  In my case, these tend to be “Purified protein as before” entries - they don’t provide any new information, but comprise a large percentage of the content.

The thing that the Prof is missing is the opportunities that come from computer indexing of these documents.  As with the scientific literature itself, moving the content of laboratory notebooks into an electronic form means that we can start applying the tools of computer science to extract valuable information from a resource that is currently underutilized.

Don’t get me wrong; I think there are major problems with Open Notebooks.  The first is that, given the recalicitrance of researchers to write coherent entries now, when it’s pen and paper in the lab right next to the experiment, I can’t imagine trying to get them to do so electronically on a machine that may be in another room.  There are the usual issues of people stealing data pre-publication and things like this as well.  I don’t think they are as prevalent as many think, but I don’t think they are nonexistent either.  Although a time stamp on an electronic notebook post might provide some evidence that you came up with the idea first, it doesn’t make a lot of difference once the competitor’s paper is out or their grant is funded based on your work.

My personal goal is the advancement of Open Access to science in all forms.  I think that in the case of published papers, the path is more clear, less controversial, and requires less of a fundamental shift in thinking than for Open Notebooks.  I applaud the work that people are doing in this realm, but I think that it’s premature to push for widespread acceptance of the practice at this time, until some of the more fundamental problems are resolved.  I do support those who are very interested in the idea giving it a shot; only through larger trials can some of the issues be worked out.

But this week’s show (Mistakes Were Made, episode #354) did include an attribution of their use of NIN’s music from Ghosts, an issue I wrote about some time back.

In reading my old post, I realize I probably came off as sort of a jerk.  It’s always weird to go back later, and see how you can write something while of one frame of mind and it ends up sounding like something quite different.  At the time I wasn’t really trying to call out the show for being music thieves, but truly didn’t know the answer to the question I was asking - the proper way to use the CC license that the work was released under.

Regardless of whether they were influenced by PlausibleAccuracy (yeah right), I’m glad that they added the attribution at the end of the show this week.  Also, it was a pretty interesting show this time… go listen to it.  And then (if you like) donate a dime or something to cover your bandwidth (freeloader).

Sagan said it best.

This month’s issue of Scientific American includes an article entitled “Science 2.0 — Is Open Access Science the Future?“.  Before I talk about the content itself I want to indicate my distaste for this “Science 2.0″ terminology.  It’s obviously a play on the “Web 2.0″ idea, in which the idea of community and user-generated content became all the rage in web endeavors.  The fundamental ideals behind Web 2.0 are probably why you are reading so many blogs today.  The term was fine (although overused) when applied to the relatively young internet, but completely falls apart when applied to science.  Are you going to tell me that of the many paradigm shifts in science, a transition to widespread Open Access is really “version 2.0″?  It’s preposterous and uninspired to say the least.  This is especially true in light of the fact that many of the Open Access projects are focused on semantic indexing of scientific knowledge, which ties in more with the semantic web, often thought to be the next iteration of the internet.  So please, don’t call it “Science 2.0″.  Perhaps “Semantic Discovery”?  I don’t know.

On to the article itself.  It is fairly wide-ranging, soliciting comments from Christopher Surridge (from PLoS One), Bill Hooker (who blogs over at Open Reading Frame) and Jean-Claude Bradley (of UsefulChem).  Bradley in particular provides a nice answer to the “we will be scooped” concern that many bring up (and I’m sure he’s tired of repeating this by now):

Ironically, though, the Web provides better protection than the traditional journal system, Bradley maintains. Every change on a wiki gets a time stamp, “so if someone actually did try to scoop you, it would be very easy to prove your priority—and to embarrass them. I think that’s really what is going to drive open science: the fear factor. If you wait for the journals, your work won’t appear for another six to nine months. But with open science, your claim to priority is out there right away.”

On the whole the article does a good job pointing out that one of the main thrusts of Open Science is to encourage collaboration.  Indeed this is one of the factors that makes me so interested; I feel like there is a lot of wasted effort replicating things that don’t work (and therefore never get published).

I do feel like the whole thing peters out a bit at the end.  There is little mention of Open Access publishing and how that fits in with the overall movement towards free access to scientific information, nor is there much discussion of the community’s response to the idea of open notebooks etc.  The author doesn’t mention ways in which this large body of open notebook data can be indexed and shared among the broader community. All of the comments are from supporters of OA, with none from detractors, making the title of the article seem a bit like an attempt to cast this as a balanced piece without the actual opposing viewpoints represented.

Minor points aside, I’m glad to see this issue getting press in the more “popular” scientific publications.