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Exercise 6: Metadata

4 min read

Exercise 6

Playing around with the metadata confirmed a trend we’ve talked about in class: Most of the novels in the corpus were published in London, with a smaller but significant percentage coming out of Dublin and the remainder scattered among other locations. Between 1700 and 1740, 94.9% of the novels in the sample were published in London, only 2.2% in Dublin, and none in other publishing cities such as Edinburgh or Bath. Meanwhile, in the second half of the sampled era, between 1740 and 1779, only 85.7% of the novels were published in London, with 12.3% in Dublin and small but notable percentages in Edinburgh, Glasgow, and a couple of other cities. I think it’s safe to say that this this trend speaks to the increasing popularity of novels outside of centers like London and the growing tradition of reprinting and pirating books. However, this sample of 855 novels is dubiously representative of All 18th Century Novels, and it seems possible that this trend and others speak just as much to idiosyncrasies and oversampling in this particular corpus as to actual patterns. Partly because of this, and because of the unwieldy and idiosyncratic nature of categories like TitleNouns and AuthorDates, I had trouble seeing the utility of the metadata and finding anything really exciting in it when playing around with Google Fusion.

The data, and the tools we have to analyze it, are somewhat limited. I thought it would be interesting to trace the prominence of particular types of paratext in conjunction with each other over time. Specifically, I wanted to see how often pieces of paratext coded as “Preface” and pieces of paratext coded as “To the reader” occurred in the same novel over time. Their co-occurrence might be a rough proxy for the amount of hedging, snark, and/or authorial self-abasement addressed to readers and editors. However, because all the types of paratext (preface, advertisement, errata, etc.) are lumped together in one column (paratextTitleControlled) charting the rise of a couple of individual types of paratext doesn’t seem to be possible. For instance, I would want a filter to pick up on Samuel Richardson’s Clarissa as having both a preface and a “To the reader” section, as the novel is described as having “Preface, Character information, To the reader, Errata.” But from what I can figure out, a Google Fusion bar chart of publication date, filtered by “Preface” and “To the reader” in the category paratextTitleControlled, would only show novels whose paratext has been coded in that order, leaving out novels whose paratext was coded in a different order. The search treats “Preface, Character information, To the reader, Errata” as a different value from “Preface, To the reader” rather than recognizing it as the combined occurrence of a preface AND a “To the reader” along with some other stuff (Errata and Character information) that’s irrelevant in my search.

To illustrate, Image 1 is a chart of paratext over time, filtered by “Preface, To the reader.”

Image 2 is a chart of paratext over time, filtered by “To the reader, Preface.” Even this chart -- the same two types of paratext, listed in a different order -- is totally different.

Notably, neither of the two charts above include Clarissa at all, since the filter can’t pick out the two types of paratext in the orders listed.

Similarly, it might be interesting to look at the volume and frequency of particular title nouns (or adjectives, but I was looking at nouns) over time. The word cloud I made (Image 3) points out which nouns occur most, as raw numbers, out of the whole corpus we have data on, but it doesn’t let you visualize changes over time. A bar chart would be more helpful for that, but again, if you wanted to look at, say, “French” and “amour” in conjunction over time to see what if anything you could learn about how novelists imagined the French, the filter would only be able to pick out titles where the coder had listed “French” and “amour” in the order you entered the terms into the filter.

The human arbitrariness of the way the novels were coded (e.g. some novels’ paratext includes “To the reader, Preface” while some includes “Preface, To the reader”), the way the categories are formatted, and the relative simplicity of Google fusion combine to make looking at how multiple values interact difficult. More sophisticated analysis tools, and a more sophisticated understanding of how to use them on my part, would let me get at more multidimensional ways that different values interact for the different categories of metadata.

Exercise6

Exercise 6: Metadata

3 min read

I thought it would be really interesting to see what places were mentioned in titles to help us think about the obsessions of the time—what places were interesting, what places people wanted to read about. The results of the mapping, though, weren’t all that surprising. The highest concentration was in Europe (mostly the UK), with a sprinkling of places in Africa and Asia and a good number in the USA (what is now the USA). The reliability of this map, however, should be questioned. My favorite example of the flaws of the geocoding is that “The adventures of Abdalia, son of Hanif, sent by the sultan of the Indies, to make a discovery of the island of Borico, where the fountain which restores past youth is supposed to be found. Also an account of the travels of Rouschen, a Persian lady, to the topsy-turvy island, undiscover'd to this. The whole intermix'd with several curious and instructive histories. Translated into French from an Arabick manuscript found at Batavia by Mr. de Sandison : and now done into English by William Hatchett, Gent. Adorn'd with cuts” somehow geocoded to Illinois. There are lots of locations in that title; how it decided it was in Illinois is beyond me. So while the mapping experiment is interesting, it should be taken with a grain or two of salt (much like the mapping Robinson Crusoe project).

As for most of the Fusion charts, I found it difficult to draw meaningful conclusions from them because the data doesn’t necessarily mean all that much. There is, for some reason, a big spike around 1770 in the number of novels, but I’m not aware of a significant reason for that and it could be due simply to the dataset from which we drew. There’s a fairly steady increase in epistolary novels and in the use of non-narrative forms, but since general publications increased and since the data doesn’t take into account percentage of publications, those increases are to be expected and don’t mean much. This is borne out by the fact that the pie chart shows a fairly even distribution among epistolary novels, third-person narrations, and first-person narrations.

I’m a fan of word clouds, so I found the last part interesting. A word cloud of titles revealed (more confirmed than revealed, I suppose) a tendency to give lots of information in the title. Words like “containing,” “price,” “life,” “edition,” “travels,” “volumes,” “history,” “adventures” all show up prominently and all imply a certain piece of information being given in the title beyond the kind of title we would expect from a contemporary novel. Overall, I think these programs are cool and fun to play around with, but drawing definitive (or even speculative) conclusions from them is difficult. I think the further research question I’d be most interested in given the tools (which wouldn’t be that hard) would be the share (percentage) of epistolary vs third person vs first person narratives over time.

Exercise6

Assignment 6

One thing I learned from the publication date bar graph is that most of the novels in the collection were published after 1740. This could be because either more novels were published after that date or because fewer of the novels published before 1740 were collected. The narrative form pie chart was also really interesting. The top three forms seem to be “third person”, “epistolary", and “first person”. This suggests that the epistolary form was popular, as was suggested before in previous exercises.

After that, I used a word cloud to look at the titles of the novels. Frequent words seem to be “adventures”, “history”, “volumes”, “Miss”, “Lady”, “Written”, “Memoirs”, “Vol”, “Edition”, “Life”, “Spy" and “Travels”. The words “Miss” and “Lady” suggest a female protagonist, while the words “history”, “life”, and “memoirs” suggest that the novel focuses on the protagonist’s private life. It would be unsurprising if these latter words were also associated with a female protagonist, as one of the popular novel forms seen so far seems to be about a lady’s inner or private life or thoughts. Back to the list of frequent words, the word “written” is rather interesting. One thing it could mean is that author anonymity is becoming less and less frequent. It could mean instead that the title page insists the novel is written by the protagonist, or that the novel is written by a lady (who may remain unnamed). It would be interesting to go back and look whether the word “written” corresponds to a named author or an anonymous one. One of the words that surprised me was “spy”, as it doesn’t seem suited to an epistolary novel or necessarily to an adventure novel. It could be that novels about spies are also becoming popular, although I don’t think it’s a subject/genre we’ve really looked at before.

The Continued Study of Popular Novels and Their Effect on Literature

4 min read

Before I begin, I’d just like to say that everything written below is based on the assumption that the vast amount of novels that were not picked up by the database were spread evenly throughout the timeline. By this, I mean that the novels not picked up by the database followed the trend showed in the graphs. For example, I’m assuming that half of the novels the database didn’t pick up were all published in 1768, and the other half in 1776. So long as the trends stay the same (ie, there is still a sharp increase from 1740 to 1741 in the publication of novels), then I should still be fine. In the last exercise, I examined whether or not the books we studied in class had a measurable effect on the short-term publication of novels, determining whether or not novels such as Pamela or Evelina changed how novels were written or named. I found that it was difficult to notice this effect over the span of a few years based on the data available. Perhaps there was simply not measurable effect at all in such a short time frame. However, when I began this exercise, I decided that I would continue to look at the effects these popular novels had the European world of literature. Since I was working with a much larger time frame this time around, I was hoping to find some greater evidence that these popular novels impacted the literary sphere in some measurable way. Since I found that trying to see how themes or narrative style changed over time, I decided to use the publication date graph for this exercise, seeing if the publication of these popular novels resulted in a spike in publication. The novels that I was basing this study off of were Robinson Crusoe, Pamela, and Tristram Shandy. Just like the last exercise, I found it was difficult to measure this effect. Robinson Crusoe, which was written in 1719, was one of three novels written in this year according to the data. The only noticeable increase was in the next year, 1720, in which eight novels were published according to the data; after 1720, however, the numbers drop down to the 2-5 range for the next decade or so. Tristram Shandy and Pamela, on the other hand, seemed to have a much larger effect. After Pamela was written, the number of published novels spiked from one to fifteen, and for more than a decade the number of published novels per year did not drop lower than seven. After Tristram Shandy, novel publication jumped from 4 to 33, and then stayed relatively high compared to earlier years into the future. So, even if one of our influential novels didn’t seem influential as we first believed, perhaps these two novels make up for it. However, this may be assuming too much. The data only tells us how many books were published per year; it does not match these increases or decreases to any other numbers. Thus, it’s impossible to claim that these popular novels were the sole, or even major, contributor in the increasing publications of novels. For example, improvement in printing technology could have caused this increase, as well as a multitude of other social factors. So, unfortunately, it’s difficult to make a claim based on this data alone. If I were to return to this point and do some more research, I feel like I would really need to dive into the time period in which we see a rapid increase in novel publication. I need to have a stronger grasp on the social sphere of Europe during these times which, unfortunately, would be difficult, if not outright impossible, to accomplish using something like this. To its credit, however, this software has certainly pointed me in the right direction. And, even if it can’t give me a definite answer to my question or even point me to the wrong place, it is certainly a start.

The French Adventure Travel World History Tale of Mr. Don Lady Character

Visualizing this metadata is super exciting — it seems like there are huge comparative possibilities here, in terms of working with a giant set of information about early novels. Having explored the END website, the existing data visualizations, such as the publisher network, seem like excellent uses of the data, and it also looks like you could combine all of the categories in a staggering variety of permutations to ask and work towards answers of many different research questions. While playing with the data — specifically, visualizing formats with a pie chart — what became fascinating to me was the way in which the metadata draws together a huge variety of literary critical questions about material conditions of publication, reception history, authorial intent, generic categorization and formal analysis. The metadata collected brings up all of these questions for me: what kind of constraints or possibilities were authors working with as they wrote their books for publication, how might material conditions come to bear upon the texts themselves, what do we make of the commonality of certain words in titles, and on and on.

Considering larger things about visualizing and analyzing metadata, I think Ramsay’s article is quite helpful here — I was initially caught up by the fact that this is a small selection of an incomplete set of data (incomplete because we don’t know how many novels/have access to every novel printed, read, etc. in the eighteenth century, for instance) and that drawing conclusions (after properly calculating statistical significance) wouldn’t be possible since this isn’t a random sample or a full set of data. But Ramsay’s point, that our interest as literary critics is not to prove things 100% and rather to open up interesting possibilities and questions, and that digital approaches to texts can assist mightily with this endeavor, clarifies that this is not as big of a problem and doesn’t prevent us from generating criticism, asking/beginning to answer fascinating questions about texts, having new conversations about books, etc.

What specifically excites me is the way in which the metadata provides a way in which to engage with my interest in genres. To pick just one question I thought of while looking at the data — making a word cloud of the TitleNouns category specifically — I’d really like to compare the information from the title pages in order to see if generic assumptions can be supported or challenged with this information (as Ramsay does with the arguments about gender in the criticism surrounding Woolf’s To the Lighthouse). Based on that title information, one might be able to question whether our generic categories are broad or varied or specific enough to actually enhance our understanding of novels, and from there work towards an understanding of one specific dynamic of the relationship between literary and material form.

Exercise 6

2 min read

In conjunction with the other exercises we’ve completed this semester, this week’s assignment highlighted some very familiar trends. Perhaps the easiest trend to recognize from the metadata is that third-person epistolary novels constituted the bulk of novels being written in the late 18th century. While first-person novels certainly weren’t a novelty at the time, they clearly appear to have been in the minority. Also unsurprising from the provided data is that these novels experienced an exponential rise in popularity during the late 1760’s and early 1770’s. Still, as I’ve alluded to, these findings weren’t particularly shocking; they reveal information that previous exercises have already unveiled. While it’s nice to have these expectations confirmed by the metadata provided by exercise six, I went on to create two word clouds in hope that some unfamiliar patterns would emerge.

Using the provided online generator, I began by creating a word cloud using the “TitleAdjectives” column from the metadata. Some of the most common adjectives in the column pertained to sequence and edition number, which was of little help. Once I had omitted these entries using my text editing software, the world cloud proved far more insightful. Some words that immediately struck me were “original”, “unknown”, “new”, and “curious”. Based on this information, it appears that authors and publishers in the 18th century placed a great deal of emphasis on the novel’s ability to tell a story that is new or unusual (hence why they’re called “novels”). The other column I analyzed was “TitleNouns”, which also produced some interesting results, albeit less surprising ones. The usual suspects such as “virtue”, “letter”, “lady”, “history”, “tale”, and “life” all appeared. Also recurrent were “adventure”, “flight”, “voyage”, “travel”, and “world”. These results were hardly unexpected, as they coincide directly with the themes embedded in the novels we’ve read.

Exercise6

Metadata

2 min read

Some general observations from the analysis I performed with Google fusion tables: the number of novels published increased over time between 1700 and 1799 and spikes around 1750. Nearly 30% were epistolary novels, 35% were first person novels and 25% were third person novels. Duodecimo and octavo were the major types of format these novels were published in (65% and 26%, respectively). While we can learn a lot of interesting, quantitative information from this exercise, there are still many aspects about the novels that go undescribed by this particular dataset. If we had data on the number of copies of each novel that were sold, I think it could have been pretty interesting to compare popularity with type of novel (epistolary, dramatic dialogue, etc.).

Next, I made word clouds for titular nouns and adjectives. Some of the most notable nouns that appeared in my cloud were “history”, “adventure”, “letter” and “life”. This cloud wasn’t really that informative and mostly fit my expectations given the other analyses we have done on a large set of novels. My adjective cloud prominently contained the words “entertaining”, “original” and “best”, along with other words that were clearly selected to make the novel more enticing to readers. Again, a pretty unsurprising result.

Overall, I think that while metadata analysis is a useful tool for analyzing a large number of novels, when we break these novels down into pieces of data that fit into uniform categories and make generalizations about them, we lose the some of the uniqueness and individuality that makes an individual novel a true work of art. Exercise6

Metadata Analysis

2 min read

After playing around with the data for a bit, I became curious about the relationship between the dates of publication and other aspects of the data. There seems to be a spike in the production of novels somewhere around 1770 - in contrast the first 20 years of the 1700's seemed to be a slow season for novels (although many people at that time might not have even considered what they were reading to be a different literary genre altogether, as we have discussed in class). I keyworded "translate" to see what the publication date trend among translated novels looks like, and noticed that there is a spike somewhat earlier, sometime around 1750. The two main languages of translation seem to be French and Spanish (although mostly French). When I keyworded "french" and "spanish" I noticed that there seem to be specific moments at which many novels are being published in translation. Perhaps this reflects the political climate at the time, since it is conceivable that there were moments in history when it was less appropriate to publish a work that was originally in French (or Spanish). It would be nice to have data that could account for versions of a text that weren't originally published in English, for there are a number of texts in this database that were published in English much later than when they originally appeared in French or Spanish. This would let us focus in on a specific group of multinational texts and look at how they were modified as they changed from language to language.

Exercise6

Assignment 6: Metadata

The metadata allows us to better understand the details of the literary trend from 1770 to 1779. I wanted a visual of where most publishers congregated and was a little surprised as to the locations. The map of the publishing locations shows that most publishers resided in major cities such as Boston, Dublin, and London. A few publishers were from Glasgow, Edinburgh, Oxford, Cambridge, and Bath. It makes sense that there were many publishers in Dublin, as the city is a port and a centralized location in United Kingdom (making it easier to transport the books to the rest of the country). It also makes sense that London had many publishers, as London was the capital and close to the southern ports where the books could be shipped internationally. The same logic goes for Boston. However, I am surprised that some publishers resided in Bath, Cambridge, Glasgow, Edinburgh, and Oxford. In my opinion, Manchester or Liverpool would have been a better choice than those place, as their locations are excellent for a more efficient distribution system of books. I would attribute their choice of location as to having to do with less business competition, as otherwise it would be an illogical choice in terms of having an efficient business. I do note that the metadata only accounts for a fraction of the novels published, indicating that the sample size is not sufficient for me to make firm conclusions.

On the jasondavies website, I initially made a map cloud of the narrative forms, expecting the top narrative forms to be in third person, first person, and epistolary. I was a little bit surprised that the third most popular narrative form was dramatic dialogue, as I expected that narrative form to be have been more popular in the 17th century. This word cloud did little justice to enhance my understanding of this literary period. So I proceeded to make a word cloud of “Title Nouns”. The most common title nouns were “Adventure”, “Memoir”, “History”, “Volume”, “Manner”, and “Letter”. All these title nouns made sense to me. Many of the male authors in the 18th century wrote about adventures, “embracing” their “masculinity”. Older authors tended to write memoirs, commemorating their lives and trying to justified that they had led fulfilling lives. As for “Volume”, we know that publishers tended to publish books in two or three volumes to increase profit and distribute easier. Lastly, “Manner” and “Letter” were consistent in respect to the epistolary form and the idea of virtue present in 18th century novels. Therefore, this word cloud reinforced my understanding of the 18th century literary period.

Metadata is like... soooo meta

I think exercise 6 was my favorite exercise yet. Despite its limitations, I think Google FusionTables does things it’s supposed to pretty well.

Regarding the PubLocation, I noticed that for the first time, we saw an American publishing location! Maybe. Maybe we’ve seen New York before, or talked about it, but this is the first time I’ve seen/remembered an American publisher making it into our datasets. And I’m not surprised at all that Cambridge is the first one to pop up; isn’t it their thing to pretentiously brag about how they were the first at everything?

In all seriousness, I really enjoyed studying the heat map of the title locations mentioned in this corpus. I started to get a sneaking suspicion that the map of what locations were being mentioned in this particular map were all parts of the British empire at the time, and Google tells me that I was kind of sort of right! Compare the heat map in this post with the map of the 18th century British empire and see for yourself:

http://vignette2.wikia.nocookie.net/pirates/images/3/3b/Britishempirere.png/revision/latest?cb=20110925203420

I guess it could be explained by the fact that early novelists either traveled to or heard a lot about other places in the empire, far more than they heard about places that weren’t British territory. I know as a writer I often subconsciously draw on the things or experiences I hear most often, so it makes sense that British authors would mention British-controlled places the most frequently.

Regarding the bar graphs: I decided to filter PubDate by VolumeStatement, and found that an increase in the number of volumes spiked in parallel with the overall proliferation of novels that peaked in about 1769. After that date, though, it looks like the number of novels published overall decreases faster than the average number of volumes. Maybe publishers realized they could make more money if they printed books in more volumes?

Funny note on the narrative form pie chart: Mine said that the form “4” comprised five percent of the Early Novels Database. Am I completely unaware of a secret narrative form that I’m just being exposed to through this exercise? Probably not. It’s probably an error. But it’s still funny.

Regarding the word cloud exercise: I used Excel’s Find and Replace function and it worked just fine for me, no TextWrangler needed. I also removed the words “first”, “second”, and “third”, as well as “two”, “three”, and “four”, because they were boring. (Sorry, number enthusiasts.) What was left behind was pretty interesting: the new most frequent words included “entertaining”, “original”, “young”, “curious”, “great”, “secret”, “real”, “moral,” and “curious”. I guess novelists were really concerned with making sure readers knew their works were going to be fresh-faced and spunky before they actually sat down to read them.

Overall, I really like FusionTables, and I think it’s a powerful tool for low-level data analysis. If you want to do anything more strenuous, you can always switch over to Stata or RStudio, so I don’t really mind how lightweight it is, and I wouldn’t really want it to have more firepower if the option was available. It’s just accessible enough and user-friendly enough that anyone can create pretty neat observations fairly quickly.

I wonder what would happen if we put the END into Stata?

Exercise 6

2 min read

Google Fusion showed a few trends, none of which were novel in our analysis. For example, the number of novels produced each year seemed to increase until around 1770, and then subsequently decrease through the remaining years of the dataset. Also apparent, we see that epistolary and first person novels are by far the most prevalent narrative forms, and duodecimo is the dominant publishing format. The filter function on Google Fusion allows for a bit more in depth analysis to be done. I found that varying several factors across publication date can show some interesting trends. For one, the proportion of octavo published novels to duodecimo published novels seemed to decrease as we move further into the 18th century. Obviously the gaps in the data make it difficult to draw conclusions, but this may suggest that publishers were responding to the increase in novel output by trying to publish novels on less pages than they would previously use, therefore saving money. Likewise, the appearance of publications in Dublin (which as we discussed in class were often of poor quality or fake publications altogether) coincided with the increase in novel output around 1770, also suggesting a propensity towards cost-cutting during the novel's highest years. The overall gaps as well as the lack of representation of early 18th century in this dataset makes it rather difficult to work with. If we had more constant numbers across years and more overall data, then both of these temporal changes would be interesting to investigate further.

Exercise 6

2 min read

Things that I could observe from the Google Fusion tables were fairly standard and consistent with what we’ve established in class: the third person epistolary form is the dominant form of narrative, novels are becoming more and more popular, and that most of the publishing is done in London. I don’t think that I found anything insightful or surprising in my visualizations. However, I am curious about the trends of the number of novels published in a particular year. From my publication date chart, I could see that the most number of novels published in a year is 58 in the year 1769. The second highest is 40 in 1767. Why does this jump in number occur in 1969? What are the circumstances that allow this to happen? Does the data we have even doesn’t do justice to reality? For example, we have an account of 21% of novels published in the 1760s. But do we have an account of about 21% of novels in the rest of the decades as well? Let’s assume we do. Then the case of 1769 is particularly interesting. We can observe that there was an upwards trend in the number of publications of novels, so a jump may be justified by saying that the industry boomed around that time, but then in just a year there is a trend of steady decline in the number of novels. Is this just a random coincidence, or suggests something greater about the prominence of novels? Another most peculiar case is of the year 1718 in which we have an account of 20 novels, wheres we have an account of 2 and 3 in the previous and following year, respectively. What is the cause of this jump? There is neither an upwards trend before nor a decline after. So what could be the cause of this occurrence? I would love to study something along these lines further.

Exercise6

Exercise 6

2 min read

Google Fusion is pretty darn cool to work with and I was able to visualize some trends, none too surprising or different from what we’ve been going over in class: more novels published as the century progresses, and most novels are written in epistolary form, first person, or third person. We have been looking at an increasingly large amount of data (one book, one bibliography, many bibliographies, as well as one year, then one century) but I found myself wanting even MORE data, because I realized I want a reference for the patterns of the 18th century novels, preferably similar data from 19th century (as an example of after) and maybe works from 17th century (for before, but this might not be so helpful since this info will not be “novels”). This would be a project I’d be interested in pursuing. Maybe. I also think it would be an interesting project to try to weight the data and visualize that weighted data. For example, titles of novels that sold more copies would be “worth” more when the analyzing and visualizing data.That way not every title is created equal when trying to visualize popular words in titles. You would in a way be looking at which words in titles were most influential rather than just frequency. I’m not sure if there are good records of number of copies sold. I would need this information. And I would need some kind of algorithm that weighs the titles according to copies sold.

Keeping on Trying

2 min read

I wrote an entire post and then lost it when I tried to insert some graphs. I'm going to take this opportunity to summarize what I wrote, since my findings were interesting enough, but not particularly different from what I expect everyone else found. I did write a sentence with the word "stymying" in it, which was worth re-typing. I'm linking my fusion tables here: https://www.google.com/fusiontables/DataSource?docid=1yJIR8hVJfi-P03VnghXO-8UxLhZNbAhY4Mw2hf_J.

Basically, after some wrangling, google fusion provided me with some pretty cool tools. I was especially appreciative of the bar graph with filters. The pie chart and word cloud (not fusion, I know) were satisfyingly visual but provided me with little more than the most basic information. A lot of this exercise had that problem, really. It was hard to draw conclusions with the gaps in data (is this because there weren't a lot of data, or is it because not enough data were compiled?). How can you make conclusions about trends in narrative over time if the amount of books published steadily increases over time? I know this would require only a small bit of statistics, but I don't have that at my disposal.

I thought the network graph might be cool. I couldn't figure out how to use it, but I thought it might be able to do things I wanted, like look at the relationships between publishing location and publishing date, or location and narrative form. Visualizing data can be so helpful, or it can be nearly superfluous. It's depends both on the visualization tools and on the type and amount of data collected.

Exercise6