Organising your library with Story Turbo

Here is a mini case study on how to organise your physical library of books with the help of Story Turbo (v. 2.2), a virtual corkboard software. (There is also a slightly cheaper version called Story Lite without the image support.) My particular purpose for this exercise was to come up with a project plan for reviewing literature for my dissertation, for which I have only about 9 days available. I have of course analysed my literatures before, but now I need to pull everything together for one last time for writing up my literature review chapter.

Just to be clear, here I’m only talking about organising and analysing an actual library of around 200 physical books which have taken up about five shelves in my bookcase. I need to scan them visually for relevant quotes that I may use in my dissertation. The organisational challenge is to identify exactly which books are the important ones and what literatures are exactly represented on my bookshelves.

(Obviously if I had been using ConnectedText to keep track of my hard copy book readings from the start, I wouldn’t have to be doing any of this. Fortunately my reading notes of electronic articles have been kept in better order in a WhizFolders database. But there is no doubt in my mind that I will be using ConnectedText for all my new reading notes for the foreseeable future.)

STEP 1: Organise your books on the top of a large desk (or on the floor) into thematic groups (distinct areas of literatures). In my case this has resulted in 7 overall groupings, within which there were up to four sub-groups. I organised these piles of books in “columns” and “rows”, imitating the format of a table or spreadsheet in a software.

STEP 2: Open Story Turbo (or Story Lite) on your desktop and represent each pile of books with an index card (I made mine look like yellow post-it notes on a grey background). There was a one-for-one correspondence between a pile of books and an index card, the overall effect being a series of columns and rows of yellow post-it notes on a virtual corkboard in the exact same order as my book piles. I ended up with twenty index cards in Story Turbo, organised into seven columns.

STEP 3: Analyse, organise and annotate your library. You may discover new relationships between the groupings (areas and sub-areas of literatures) and may want to re-organise both the virtual index cards and the corresponding physical piles of books to reflect the new order. You can add as much detail as you like to each index card. If a pile of books is too big and too diverse (e.g. 20 books with 5 sub-themes), then you could create additional index cards and break up the pile into five smaller piles. The goal here is to organise your library and also capture electronically both the overall structure of the library and the descriptions of its content.

STEP 4: One of the main reasons for using Story Turbo is to be able to export the outcome of this analysis in a number of ways. I have chosen an RTF export, as I wanted to manipulate this list further.

STEP 5: I copied the contents of the RTF export from Word and pasted it into Natara Bonsai. It wasn’t the cleanest of exports, in the sense that the order of the list didn’t perfectly reflect the order of index cards in the columns, but it was only one or two items that needed to be repositioned out of 20. Also, the RTF contained some horizontal lines and line breaks which needed to be deleted in Bonsai. However, doing this sort of list editing is a breeze in Bonsai. I reorganised my list into a hierarchical outline where the content was grouped thematically. After a bit more analysis I had realised that the seven larger groups could be consolidated into five groups.

STEP 6: In order to come up with my project plan to review these books, I allocated days according to the importance of each theme to the research question and the number of books available in each pile.

STEP 7: I added the allocated themes (representing piles of books that are still on my desk) as all-day appointments to my Google Calendar, to serve as a reminder, so I know how much time I have available for reviewing each pile. All I need to do know is start working through the piles of books each day, and slowly start clearing my desk as I put the processed books back on the shelf. (By “processing” I mean reviewing my handwritten comments and post-it notes in the books and recording the selected quotes and notes in my ConnectedText database – more on which later).

Modelling process workflow for thesis writing

Recently I’ve been finding that whenever I’m stuck in my odyssey towards writing up my dissertation, modelling my process flow in a concept-mapping software (such as VUE) usually helps. In this (hopefully) final stage of my PhD project there are so many resources scattered around in various software and folders on my computer that I need a formal “concept map” (if that’s the right term) to pull them all together and work out the relationships and interactions between them.

Here is for example my last concept map that I’ve knocked up when I was unsure how to proceed with writing up the first four chapters of my dissertation. There is nothing particularly scientific about this map and it probably doesn’t follow any of the conventions of process workflow modelling. But who cares: it did the trick and allowed me to plan out the next stages of what I need to do.

Actually at least 2 or 3 days of deliberation are captured in this chart. First, I needed to decide whether I was going to use ConnectedText or something else for doing the actual writing. Through trial and error I established that it’s better to use another software because however much I love working in CT, it does have some limitations. One of them is that you can only have one instance of CT running and only one edit/view window open. Since I’ve decided to use CT as my database for my reading notes, I need to use another software, so I can be writing in one software in one monitor, while referring to the CT notes in the other. Also, there isn’t an easy way to track the word count of your document while writing in CT.

I had considered WhizFolders briefly as an alternative, but I find its interface too busy to be able to concentrate on the actual writing. So I settled on Scrivener for Windows, which works well both as a two-pane outliner and as a writing tool with decent word-count tracking.

As the sequence of the process flow is not apparent from the chart, let me describe it briefly. I start with importing my master outline with inline notes from Outline 4D (via Word). The reason I created my outline in Outline 4D is because it is a single-pane outliner that allows you to have inline notes, which you can also view in an index card view on a corkboard. Then I use Scrivener to break up the imported document into a 2-pane outline using Scrivener’s handy “Split with Selection as Title” command. As I start writing the actual text (I’m working on the first 4 chapters of my thesis, which need to be contextualised within their respective literatures, namely the Introduction, the Literature Review, the Conceptual Framework, and the Methodology), I begin to review my existing reading notes.

Over the years I have read all kinds of things that are no longer relevant. Therefore I need to deploy some kind of a filtering process to select the most important notes, as well as any new reading that still needs to be done. To consolidate my final reading list (a list of bibliographic references), I use a Natara Bonsai outline. First I import into Bonsai an existing outline document that contains some of my selected references that I have kept on my iPod/iPad in CarbonFin Outliner. Then I go through my old conference papers and other writings to extract references that are still relevant and which are kept in Word files and an old Scrivener project.

Simultaneously to this process I have also designed a ConnectedText project for keeping my final reading notes and quotes, using a similar model to the one I have developed for my empirical analysis. As my old reading notes and quotes are kept in a WhizFolders database, I will need to review those and transfer them one-by-one to the CT database (I deliberately don’t want to import them en mass, as I need to separate the wheat from the chaff). I will also use the CT project for recording any new reading I still need to do. I am designing this CT database not simply just for this writing project. Very likely it will become my main database for all my future readings for years to come. This is just an opportune moment to get started with it, as I no longer want to use WhizFolders for this.

Getting back to the chart, there are basically two important elements to it: 1) the big blue Scrivener rectangle which represents my writing, and 2) the big green rectangle below it which represents the CT reading notes database. If we look at the arrows pointing to the latter, we see mostly the data that needs to be transferred (by carefully sifting through) from my old files, as well as new reading notes that will be created in iPad.

As for the arrows coming in or out of the Scrivener project, those have to do mostly with referring to external sources. In the end I won’t need Excel for planning the word count because Scrivener has good enough tools for that. I will also use Dragon NaturallySpeaking for dictating, whenever I feel the need. Sometimes it’s easier to write without it, other times it speeds things up. As for EndNote, it is simply the central database for my references, which are linked to the PDFs that may need to be read for the first time or reviewed.

But my main point here is that it was the creating of this concept map that was crucial for getting me started with the whole writing stage. Without it I would have probably sat in front of a blank page with a writer’s block for days. Now I feel fairly confident that I know what I need to do next.