Just today I began reading "The German Genius", the industrial-sized history of German Kultur recently published by Peter Watson. At 856 pages it's the second largest book I own behind only Victor Hugo's burning but only half-engulfed Les Miserablés.

My interest in the book is motivated by a simple question: What is Germany? 

Since my initial exposure to German and after having visited Germany on more than one occasion, I am left with the impression of a deep fracture between the Germany of my American imagination and the embodied Germany of today. Indeed, my repeated visits to book stores and libraries in search of a history of Germany are always frustrated by the narrow but over-sold window which opens up (muddily, I might add) upon the years between 1933 and 1945. The awkward amalgam of American fetishism for the technologies of war-making ("Fighter Planes of World War II") as well as an undying obsession with the gratuitous violence enacted upon minorities in other countries ("Auschwitz: a New History") produces a cartoonish understanding of Germany. (In fact, one of the most popular graphic novels today is "Maus", a history of Nazi Germany told through a comic book mice.) 

I often try to imagine if historians reduced U.S. history to its corresponding exterminations and institutional racisms for the kind of popular public consumption that drives World War II literature: "Tools and Technologies of Slavery", and "Native Americans: The Extermination of Those People". This is no apologetic for what should rightly be a thoroughly condemning examination of the atrocities of the past. But I suspect, first, that such a narrow view of Germany misses important historical events (if you'll excuse a momentary lapse into historical fundamentalism), and second that a focus on World War II in Germany creates a moral landscape which valorizes warfare and justifies American aggression in its purported incomparability to the Holocaust.

Watson's claim is to deal directly with both the history of Nazism and German history without compressing one under the teleology of the other. It's a book I expect to take the better part of this year to complete. But having launched effortlessly into the first 100 pages of rewarding and well-composed text this evening, I expect the time to be worth it.

With my thesis completed and the defense pending, I can get on with a much-deserved relaxing summer. This means, of course, that I have already begun to tug at the several inviting strands poking out of the tangled ball of string that occupies the cavity behind my eyes where my brain ought to be. 

One of those strands is what I guess I'll call "research management". Even when I started the MA program I knew this: the goal of a research-oriented MA degree is to demonstrate a rough ability to come up with a project and execute it. It's a bit like making it through nursing school or any professional program. You don't get out of a nursing program ready to work with patients. Having a nursing degree just tells employers you're slightly less likely to kill someone under your care than someone off the street. Similarly, for as much as I – and my hard-working peers – do our very best to come up with well-theorized research projects grounded in original and convincing data, the real goal of our theses is to show that we can do social science research without breaking any major laws, causing and personal or public harm, and, if it's not too much bother, even say something novel about the world. Assuming that I make it through the defense (not an assumption I take lightly), I will have the chance to do more research. Isn't that enough?

No, probably not. One of the things that bugged me throughout the MA was the difficult time I had corralling pieces of the project together. Trying to keep track of all the relevant (or possibly relevant) theoretical sources, scraps of qualitative and quantitative data, and optimizing time spent on field research was a bit like herding the proverbial family of very ornery cats. This is natural in any field, not just academic research – just look at the office of any attorney, private practice doctor, or school teacher. However, as I've always been a bit obsessed with organization, I consider it a personal goal to get better at managing the research process and the data generated from research.

How to proceed? A few years ago I read an article by David Allen, creator of the Get Things Done system of task management. (Okay, it's kind-of cheezy pop business lit, but give me a break.) He said one thing that stuck with me: the human mind is terrific at analyzing one thing, but terrible at keeping track of lots of different things. For instance, when I focused on particular problems within my field research, I could generate new ideas, make connections to related issues, and so on. But it was difficult to keep track of all the pieces of information that make up the whole research project, or even just remember the final conclusions from my last intense work session. In the first example, I could focus and think through a specific problem. In the second example, my brain had to keep track of dozens of files and ideas spread across documents, scraps of paper, my field notebook, maps in ArcGIS, and PDF's on both my Mac and Windows partitions. My brain wasn't so good at the second part. 

One thing that helped greatly was Scrivener. Scrivener is a piece of software that lets you keep all your different chapters, sections, and ideas together in one project while you work on smaller pieces at a time. For example, in one project titled "MA Thesis", I had all my conference papers, grant proposals, term papers, and concrete ideas together in one place. This made some parts of writing the thesis much easier. Word is the dominant writing software, though, so I regularly had to export it to Word for comments and edits, and finally to format it for the University. Word is not my friend, and while I did get a final draft out, it was too much hassle for my taste.

Scrivener, however, is writing oriented, not analysis oriented. (Not that I'm making a too narrow distinction between the two.) So when I needed to look at my original data I had it spread across tons of Excel spreadsheets, several versions of ArcGIS projects, exported maps, interview transcripts, and documents I obtained in the field. I should say that I am pretty well-organized, and I could usually get to the data pretty easily. But no matter how clearly and logically folders on my three hard drives were organized, I could never look at all my data in one place. Furthermore, I didn't have metadata that tied all this data together. You can only append Excel files with versions ("Statistical Analysis Spreadsheet v.7") so many times before it becomes completely meaningless. 

So here's the end of that matter. I'm surveying a number of software solutions to these problems. Here's the list of packages I'm exploring to help for the PhD.

Filemaker Pro 11 – I should have used this from the beginning to code documents instead of putting it into an Excel spreadsheet. Excel is great at statistics, but it's lousy at organizing and searching data. I think four times out of five, a database should be used instead of a spreadsheet. It's possible to create a database accessible by other researchers which can serve as a repository of raw data, field contacts, and research activity. There's also a mobile app call Filemaker Go which has some fieldwork possibilities, as well.

Nvivo – There's debate about the value of qualitative analysis software. Clearly the most important thing from a research perspective is to have a clear framework for your research. However, Nvivo appears to be a terrific solution for organizing research. In fact, I much prefer that Nvivo (like Atlas ti and Maxqda) be called "qualitative research organization software" than "qualitative analysis software". It doesn't actually analyze anything for you; you have to do that yourself.

Adobe InDesign – For producing a final draft of a long document, Adobe InDesign is superior. It give you much more control – and more predictable control – over the the layout, text, and graphics. I suppose if you don't have any graphics whatsoever InDesign is a waste. But then again, if you don't have pictures...that's just, well, boring. Apple Pages is actually my favorite because it handles graphics and text so well, but it lacks in some essential areas such as making a list of figures, charts, and chapters (it can only do one at a time, not all three). 

ArcGIS – I already used ArcGIS for some data analysis, but now that I've learned a bit more about databases in general through Filemaker Pro, I'm beginning to see some interesting and unconventional uses of ArcGIS to store and represent data. I need more time with this software.

All three of these (and others that I'm looking into) have a steeper learning curve than typical software. However, I think the payoff will be huge in terms of organizing and using data instead of spending time hunting for it and re-creating it every time I want to put it into a document. (I can't tell you how many times I re-made maps in ArcGIS because the PDF's I exported were just off slightly. I lost a year of my life from stress, I'm sure.) So that's it. I'm having fun exploring these software packages and I'll hopefully have a more professional workflow for my PhD research.