Viewing database query results in the terminal is fine, but sometimes I need to get the results into Excel quickly so I can prod and analyze the data to see if I need to modify the query. Ideally I'd just query directly from Excel using and ODBC driver but our options for connecting to most databases via ODBC with OS X are either complex or expensive. I came across a handy tip on macosxhints.com that I made some modifications to and it's been working great. Read the rest of this article to see my version of the script and how you can adapt it for your situation.
The technical specifications for the recently announced Quad 2.5 Ghz G5 Power Mac gives the MacResearcher plenty to salivate over. The first thing you'll notice is that the Quad CPU model actually sports two dual-core CPU's. All this means is that the wizards at IBM have managed to squeeze two logical CPU's in to the same physical packaging. Therefore if you popped the cover off this baby you would only see what appeared to be two physical CPU units when in fact there are four. Since the G5 CPU is 64-bit you are now able to put up to 16GB of RAM into the machine. Why is this? As a very simple rule the more bits a CPU has, the greater the portion of memory it can address. Throw four 64-bit CPUs into the same machine and you get access to a whopping 16 GB of RAM. Another highlight of the machine is the three open PCI-X expansion slots. I wont get into the technical details of PCI-X, but I can say that you could put a high end video card in each of these open slots and have the ability to connect up to eight displays up to a single workstation. The visualization capabilities of such a configuration would be mind blowing. There is even an option to add a stereo 3D graphics port for use with third-party 3D goggles, which is great for visualizing molecules or terrain models.
The educational institution price for the new Quad G5 machine is $2,999.00. Definitely not pocket change, but if you consider how much computing power that is per dollar then it's a pretty competitive price. My only gripe is the skimpy 512MB of RAM that ships standard on the machine. I can understand 512 MB of RAM for a consumer desktop, but I think it's unreasonable to ship a Quad CPU machine with less than 1GB or RAM.
If anyone has found independent benchmarks for this machine let me know. I'd like to see how it performs out in the wild.
I hate being forced to register for a site as much as the next MacResearcher. That's why I decided to enable anonymous comment and forum posting as of today. Now you can comment to your heart's content without revealing how much time you spend on this amazing website :-).
Also note that anonymous users can also submit stories, news, tips and tricks. Just click on the "contribute" link under the header graphic, or a link with the same name in the navigation menu. While comments and forum messages submitted by anonymous users will be posted to the site immediately, stories or news items sent by an anonymous user will be placed in a moderation queue for approval. Now, I implore you, inundate the site with your brilliant comments anonymous MacResearchers.
I love finding scientific applications that are 100% pure OS X goodness, not some ported application from a lesser operating system. EnzymeX is one of those applications, built on 100% OS X technologies, that shows the beauty and functionality of OS X. EnzymeX is another great application from the dynamic duo Mek&Tosj, who developed the similarly stunning 4Peaks DNA sequence visualization application. EnzymeX is worthy of highlighting because it does what all scientific applications on OS X should do, save the user time, make them more productive, and give them access to advanced technologies through a simple, intuitive interface. Planning experiments that involve restriction enzymes is never particularly fun, but EnzymeX makes it point-click-done easy, and that's the OS X way.
It looks like the next version of EnzymeX, soon to be released, will allow a user to import sequence data from file or directly from NCBI's online database and experience "a revolutionary new way of browsing through all your enzymes and shows you where they cut. You can even drag up to four of them into a virtual reaction mix and the program shows you immediately what fragments will be produced.". Cool stuff!
Statistical analysis can be a painful and laborious facet of the MacResearcher's daily life. Commercial and all-in-one statistical software can be great, but quite often the need arises to build our own unique statistical tools. This often entails the development of a PERL script or a crazy Excel macro in which the statistical wheel is reinvented many times over. Enter the R-Project. Think of "R" (yes that's the name, this project needs a marketing department) as the swiss army knife of statistical software tools. It's a robust statistical environment with an intuitive programming language and an expansive collection of computational and graphical modules that allow rapid development of custom statistical tools. R is easily extended through a package system and it can even be linked to C, C++, and Fortran code at runtime.
R enjoys widespread development and use in the scientific community and several important software projects use R at their foundation. Here are some notables:
- BioConductor - an open source software project for the analysis and comprehension of genomic data.
- The Omega Project - distributed statistical computing.
- gR - Graphical Models in R.
R enables the most non-technical of researchers to develop custom statistical tools rapidly and relatively easily. For the hard-core techie, R provides enough power and extensibility to be considered for the most complex statistical analysis scenarios.
Information on installing and using R on Mac OS X can be found on the R for Mac OS X download page.
I just stumbled across a killer keyboard shortcut reference for OS X at Creative Bits. Apple put a lot of thought into the OS X keyboard shortcuts and once you learn a few you'll be less reliant on the mouse and much more efficient. Here are some of the shortcuts I use on a regular basis:
- "Press T during startup" = Start up in FireWire Target Disk mode
- "Command-W" = Close Window
- "Command-Tab" = Switch application
- "Command-T" = Show Font palette in application
- "Command-Shift-3" = Take a picture of the screen
- "Command-Shift-4" = Take a picture of the selection
- "Command-`" = Cycle through windows in application or Finder (if more than one window is open)
- "Option-Command-esc" = Force Quit
In a recent post on the ever enthralling Brain Waves blog, neuroscience guru Zack Lynch discusses Apple's increase in "popularity and in adoptions in neuroimaging labs". The bulk of the discussion revolves around a gorgeous new neuroscience application for OS X called Neurolens, under development in the A. A. Martinos Center for Biomedical Imaging at Harvard. I have to say that Neurolens is a shining example of OS X's potential in the sciences as it was developed exclusively with Apple technologies like XCode and the Cocoa API. The results are a gorgeous application that plays well with other applications and stands out among all the other applications in the field. I hope applications like Neurolens give other open-source scientific application developers a glimpse of the elegance and capabilities of OS X.
Apple made its own highlight of its neuroscience success in a recent Ad/Profile of Dr. Nouchine Hadjikhani.
SciPy is an open source library of scientific tools for Python. For the uninitiated, Python is a relatively young but robust scripting language that currently enjoys widespread use across several operating systems. SciPy supplements the popular Numeric Python module, gathering a variety of high level science and engineering modules together as a single package.
SciPy includes modules for graphics and plotting, optimization, integration, special functions, signal and image processing, genetic algorithms, ODE solvers, and others.
A convenient OS X installer package for SciPy can be found here.
Biologists might also want to make note of the BioPython Project which is a somewhat similar collection of Python tools for molecular biology.
With the recent release of EndNote 9 from Thomson ResearchSoft users of OS X 1.4 (Tiger) get to take advantage of the advanced metadata indexing engine, called Spotlight, to search for references in thier EndNote libraries. Why is this important? The EndNote library format is proprietary which caused the contents to be hidden from previous generations of desktop search tools. Additionally, Spotlight can provide an aggregate view of your primary source materials due to its ability to index a multitude of file types. For example, a search for "Drosophila Embryo" might return a collection of related EndNote references, PDF journal articles, web bookmarks, and images in a single-window view.
As many OS X users know, the UNIX underpinning of OS X and its POSIX compatibility allows users of OS X to tap the big and beautiful world of open-source UNIX software. MacResearchers from all disciplines struggle with the need to render complex graphs and plots that are often above the capabilities of Excel. Luckily things have never been better for the MacResearcher. The freely available AquaTerm plotting front end can connect with your favorite open-source plotting back-end to render the high-resolution, aqualicious plots you'd expect from OS X. Read the rest of the article to learn more about AquaTerm and which backend plotting tool is best for your research.