Managing Information on the Web, Wolfram-Style

While Steven Wolfram may not the most, um, orthodox figure in the scientific community (see, for example Steven Levy’s bio, or Cosma Shalizi’s review of the modestly-titled A New Kind of Science), I don’t think anyone doubts the usefulness of Mathematica and the various things associated with it (e.g. MathWorld and the Demonstrations Project). And now apparently his latest production – WolframAlpha, Wolfram’s new Mathematica-based search engine – will be released to the public this Monday. It looks quite interesting.

Finding useful information on the internet can be difficult and incredibly annoying, particularly for scientists or anyone in search of statistics of some sort. Google and Wikipedia, while useful, can often be inefficient or yield inadequate results. Many new search engines tailored to various interests seem to have emerged recently, but I am not aware of any current tools that satisfactorily tackle this particular (non-trivial) problem. One solution for anyone interested in biology is bionumbers, a searchable database of useful biological facts and data taken straight from the literature — but I think it’s quite clear that a more general and comprehensive solution (which WolframAlpha purports to be) would be very cool.

Judging from Wolfram’s promo video and reviews on pcworld, techreview and semantic universe, Alpha seems to be bionumbers made significantly more powerful and comprehensive. You probably won’t want to use it over google to find movie times or track your favorite celebrities’ lovelives; but you will want to use it to find various kinds of quantitative information: various metrics of the weather in Springfield, MA on the day David Ortiz was born, the location and sequence of some gene, the flowfield over a particular airfoil, the current position of the International Space Station, or data on blood cholesterol and potassium levels of middle-aged male smokers, for example. I look forward to pushing the limits of this tool, but it looks very useful.

Not be outmatched, Google recently announced plans to implement a similar kind of service using publicly-available data. I’m not sure when they will be releasing it, though, or how it will compare to WolframAlpha.

Back (no, really)

It’s been a while since I last posted, and I’ve been up to quite a bit since then. For starters, I’m officially done with undergrad – apparently now I’m a bachelor of arts and a master of science.

In other news, I finally (!) decided on a grad school – this fall, I will be moving on from Penn and starting my Ph.D. in physics at Harvard. I actually packed up and moved to Cambridge a week ago, and have been getting settled and starting work in my new lab – I’m doing biophysics work in Dave Weitz’s group, which is really exciting. (More details later…)

Physics bloggers have actually been shuffling around quite a bit: for example, fliptomato has come back to the U.S. from the U.K., while in a happy coincidence Mark Trodden will be leaving Syracuse for… Penn!

A nice little sidenote is that one of my main papers stemming from some of the work I did while at Penn was accepted not too long ago – watch out for it in Nano Letters sometime soon…

And lastly, moving has given me a chance to organize some of the clutter in my life. In that spirit, I’ve decided to give my new personal webpage and this blog a quick makeover.

Graduate School

It’s graduate admissions season, apparently. So far I’ve heard back from six institutions: four acceptances (including a number of my top choices) with fellowships at two of them, one we’ll-fly-you-out ‘interview’, and one phone interview that went rather well… so at the end of the day, figuring out where I want to end up may be nontrivial.

As I noted in a previous comment, my algorithm for deciding where to apply to was pretty simple: I spent a good deal of time soul-searching and deciding (roughly) what I want to do for the next five-ish years, made a list of all the people I thought would make good research advisors to that end, and applied to the n departments that contained the maximal number of the people on my list (where n was determined by how many applications I was willing to fill out; turned out to be thirteen in total – I’m not superstitious).

My algorithm for deciding where to go for grad school will probably be a variant of the algorithm I used to pick my undergrad institution, and again is not too complicated (Sean Carroll has a very nice post on this subject, btw, as does okham as I just discovered):

1. Make a list of all the factors that I care about: for example, number/quality of advisors who I’m interested in (based on various factors like personal interactions, reputation, publication record, how they place people…), how excited I am by current research efforts, intellectual environment, potential for interesting collaborations, other students, location, quality of the department/school/life, bureaucratic requirements, funding, etc.

2. Weight individual factors accordingly: pretty self-explanatory, although this requires a lot of thought.

3. Visit all the places I’m seriously considering/find out as much about them as possible: this is the data collection stage, so that I have a good idea of how various places shape up in terms of the factors I listed. I’m pretty much traveling every weekend from next Friday to the end of March, with a few days in between for the APS March meeting. That’ll be fun.

4. Assign data values corresponding to each factor for each department: i.e. the results of step #3. These data values will obviously have error bars to reflect the subjectivity inherent to the data collection process, but the inverse relationship between error bar size and time spent on step #3 should enable a single-valued result.

5. Plug and chug: go wherever above algorithm says to go. Hey, it worked pretty well for my undergrad.

Speaking of which, I feel compelled to plug Penn. If anyone reading this happens to be a senior who got into Penn (terminology: I refer to UPenn, not Penn State) for something physics / materials science / nanoscience-related, I strongly urge you to think about coming here for grad school. There’s a lot of very exciting work going on here, and a lot of great people to work with – fantastic intellectual environment (I would say Penn does pretty highly on all the factors I mentioned above).

Whew.

I realize that I pretty much dropped off the face of the earth for a while – the result of a crazy semester filled with endless problem sets, writing papers (both research and for classes), working on experiments/having equipment break down on me, the GREs, grad school apps, and random personal issues. Thankfully things are far less stressful nowadays what with the holidays + new semester and all…

One of the highlights was going to Boston to talk about some of my recent work at the Materials Research Society fall meeting. It was great – I had the chance to meet some very interesting people and go to quite a few exciting talks, both of relevance to my research or of personal interest. I might post about some of the ideas/papers I’ve been thinking about since then, some partly stimulated by some of the MRS talks or talks we’ve had here at Penn, some partly stimulated by my recent experiments.

Random thought: a service that I would love to have (I’ve found myself wishing for this kind of thing both when digging through the literature on a particular subject) is an online ’science network visualizer’ application. Basically an online service (maybe provided by ISI, Google scholar or something of that sort?) that would take a given researcher’s name – or perhaps an individual paper citation – as an input, and generate a ‘map’ of what people/groups of people/other papers cite them, and with what frequency. I know facebook has a few things like this (e.g. the many eyes friend network visualizer), and the awesome people at information esthetics created this map of science (of which I have a poster) using a similar algorithm. This kind of service would be invaluable for figuring out who is paying attention to a particular kind of research (and what else they pay attention to), what ‘cliques’ exist in various scientific communities, what the big results are/who the big players are, what connections are just waiting to happen… actually now that I think of it, this kind of thing probably already exists, hopefully in a more general framework that can be used to visualize other kinds of communities as well. I am an avid rss subscriber of visual complexity, a great site by Manuel Lima that documents various kinds of network visualization, but I don’t think I’ve ever come across something that fits this description.

In other news, I’ve started to write my senior/master’s thesis and have had to relearn . At the beginning the learning curve was incredibly frustrating, but things are finally starting to fall into place. After much experimenting, I’ve decided to go with BibDesk for managing citations, as suggested by Andrew Dawes (of The Daily Photon, a valuable new addition to the blogroll). It’s a wonderful program, and now that I’m doing more on the front it makes a lot of sense. Andrew has a post on some mac apps that he uses – similar to my previous post on the subject, and presumably with more to come.

Speaking of which, another new addition to the blogroll is confused at a higher level, an interesting “professional journal” by a physicist in Minnesota. He has a few posts that I thought were particularly interesting, for example documenting various aspects of the peer-review process (like ref comments) or collaborations that he’s working on. Also, he’s a Tufte fan (see my post from ages ago).

PC to Mac

Yup, I’ve caved. I’ve been a PC user all my life, but the one I’ve had for the past several years has been slowly and steadily grinding to a halt, while my digital work load has equally been consistently rising, so it was time for a change. While I get the impression that switching to Mac is the new ‘in’ thing these days – IP has noted that new converts often favor style over functionality (and Macs are pretty, without a doubt) – my main reason was much more simple: it’s true, Macs generally do tend to be more stable, secure, and easy to use, and now with Boot Camp, I’m running Windows as well. There’s nothing to lose and a whole lot to gain, and for people like me who treat life as one giant optimization problem, that’s a big deal. Indeed, making the transition, transferring data, getting comfortable with (mostly) everything, &c. took under a week. Here’s how I do my ‘basic’ tasks…

• Presentations/Word Processing: MS Powerpoint & MS Word 2004. while I’ve heard good things about iWork, I went with MS Office simply because, at the end of the day, most of the files I work with (e.g. in collaborations) are Office files, and I wanted to make sure that I was 100% compatible (even though from what I hear, there aren’t too many major compatibility issues). I’m slightly regretting this now on the Word front (Powerpoint I can live with) – it’s notoriously slow at times. I’m trying to wait it out until Office 2008 for Mac comes out, but I may end up shelling out some more and get Pages (or something free, like OpenOffice).

• E-mail/Calendar: Apple Mail/iCal. Like everything else that came along with this machine, these are wonderful. Plus I can sync the iCal to my iPod, which is great (although I really do hate all these ‘i-’ prefixes).

• RSS reader: Vienna. Open-source, freeware, sleek, built-in browser, and tons of functionality. Works great.

• Plotting data: OriginLab/Plot/Apple Grapher. I’ve always used OriginLab for data analysis, making figures, &c. so I decided to install it on my Windows partition (sadly, no Mac version). However, I recently discovered Plot (a freeware program) and the built-in Grapher app, both of which are wonderful for making high-quality figures, both 2d and 3d. They’re very bare bones, which is great for what I use them for (I fought with Origin for a half hour the other day trying to format a polar plot correctly, whereas Grapher took five minutes to do the same thing).

• General data analysis: OriginLab/Igor Pro. Our lab has a license for Origin, and it’s what I’ve always used, so I installed it – but I wanted something I could run on my Mac as well, without having to switch to the Windows partition. I’ve hear some good things about Igor, and their amazing student personal purchase deal made it economical enough to buy. Haven’t had much chance to play around with it, though.

• AFM/SPM image analysis: Gwyddion, sometimes Image SXM. I do a lot of AFM/SPM image processing and analysis, and have so far tended to use Veeco’s Nanoscope software (again, which isn’t supported on Mac). I’m happy about that though, because I don’t think I would’ve ever bothered to try out Gwyddion, which is a wonderful piece of software. It’s freeware, has just about every kind of functionality imaginable, and best of all: it tells you what it’s doing (with excellent supporting documentation). Installing it is somewhat involved, but not too difficult. (The only major point to note is that Gwyddion for Mac OS X needs X11, which comes on the Mac installer disk). Oh, and before I installed Gwyddion I played around with Image SXM a bit, too – pretty nice as well, but I’m far more impressed with Gwyddion.

• Graphics: GIMP (the GNU image manipulation program). I used to use Adobe Photoshop for all my graphics needs, but it’s ridiculously expensive. This program does, as far as I can tell, pretty much everything Photoshop does – for free. (Again, like Gwyddion, it uses X11, which is a tool OS X uses to run certain open source programs). The only issue is that it’s not that great with certain very simple tasks, like – of all things – drawing a circle.

• Lab Notebook: VoodooPad. Another great free program with a ton of functionality – basically like a personal wiki that I plan to use as a lab notebook, but I haven’t used it enough to pass judgement. Very easy to use, though.

• Organizing Papers: Zotero. I tried the much-raved about Papers for a bit, but soon quit, because they only supported PubMed (which only includes a fraction of the journals I read). Now that they include Web of Science I’d be more inclined to stick with them, but Zotero’s been serving me wonderfully. It’s a simple firefox plug-in that supports pretty much every publisher I’ve encountered. It downloads a paper’s metadata (author list, journal, abstract, etc.) with a click of a button, and lets you store the paper as an attachment, too. Only two issues are (i) integration with Word is poor, but I can easily export my Zotero citations as an endnote file and use endnote to put in citations, as I’ve done before; (ii) data is only stored on this computer, not on some central server somewhere (although they claim to be working on including this, which will be great).

• Windows-in-Mac: None at the moment. A number of programs exist that enable one to use the Windows partition while in conventional Mac mode. I tried CrossOver briefly, but it didn’t work all that great. I plan on giving Parallels Desktop a shot at some point, but frankly, I haven’t really needed Windows all that much. (Which is probably the take-home message from this experience…)

Of course, if anyone has suggestions for good software or simply personal favorites, I’d love to hear about them.

All in all, no complaints so far, except for one, which is a slightly big one that I’m not sure what I’ll do about: it turns out that my laptop doesn’t work with certain kinds of projectors, which sucks when I have to give a talk. Quick googling reveals that this is a problem with many MacBooks, and that not much has been done to acknowledge or fix the problem, which is ridiculous. Particularly since I have to give a talk at a conference in a week or so… I’ll probably have to pdf it and carry it on a USB drive just in case things don’t work out, which stinks (and is definitely not a long-term solution).