After digitizing and processing eleven years worth of registrations lists, I found that of the around 2000 unique names (out of a total of around 5000), the person with the longest history of participation was the librarian Jane Williamson (none of the names in red below are academics).

This makes for a messy looking dataviz and that is precisely my point.

Click to enlarge

Polatnick, also a participant in women’s liberation, wrote specifically to recover the women who had been left out of histories because they “fought” for women’s liberation “in other contexts.”

Using the BYU Corpus Interface for Google Books I scraped the metadata for any references to “Redstockings Manifesto” or “A Historical and Critical Essay for Black Women”  to create the visualization below.

The Redstockings Manifesto, a widely circulated articulation of the principles that would become known as radical feminism,  suggested to me, following other work I’ve done on feminist manifestos, to look at the piece attributed to Robinson,  “A Historical and Critical Essay for Black Women” as a  manifesto would, I argue, function as one.   If in 1970 the two pieces are of equal influence, at least as measured by inclusion in these anthologies, what happens over time, which after all is what historians are interested in?

f the 435 names, the common network reduces to this.

The modern perception of private correspondence was one that simply did not exist in the early modern period. instead, epistolary conventions implicated multiple parties in the composition, transmission, and reception of letters.

Carolee Schneemann’s correspondence, once explored for its content rather than for connections between authors, pointed to female circles around “proto-feminist” art movements in the late 1950s and early 1960s, as well the US feminist artist movement of the 1970s and 1980s.

Analysis of the periodicals indicated that references to manifestos made up a relatively small portion of their overall content and that appearances of the word “manifesto” decreased over time, especially after 1979.46Subsequent analysis was therefore restricted to manifestos included in Deepwell’s anthology that were composed before 1980.

That is, we recognize the institutionalization of public data as it becomes known, read, and put in context, but in comparison, how private data is secured, (almost assuredly) sold to data brokers and possibly eventually leaked is obscured.

View an instance of Ink loaded with Voltaire's correspondence. In combines mapped letters with a timeline stacked bar chart and a relationship viewer.

Extensive excavations carried out by the National Museum of Ireland between 1962 and 1981 revealed a wealth of evidence for the post-917 settlement. The single most important result of these excavations was the information they provided about town layout in the tenth and eleventh centuries. A series of fenced plots or tenements was unearthed and could be traced over a dozen successive building levels. Apart from this, there was also important evidence for house building, and for a succession of waterfronts from between the tenth and thirteenth centuries.

The Vikings settled in Dublin from 841 AD onwards. During their reign Dublin became the most important town in Ireland as well as a hub for the western Viking expansion and trade. It is in fact one of the best known Viking settlements.

Therefore, we must shift the focus from debates over their appropriateness or utility of them to discussion of how our research practices require rethinking in light of them.

One line centres the human relationships and experiences that are mediated by these environments: individuals who are exposed to a wider audience when analogue archives move online, the people who created digitised materials and the infrastructure that facilitates access, and finally, persons who are obfuscated, misrepresented, or entirely absent in digital archival environments.

However, this very abundance may occlude the issue of absences.

Along with these benefits, however, digitisation has wrought concomitant problems. In a consumer-driven academic environment, funding for digitisation may be tied not only to concerns about access and preservation, but also to the need to increase visibility to ensure viability

, I offer three questions researchers should consider before consulting materials in a digital archive. Have the individuals whose work appears in these materials consented to this? Whose labour was used and how is it acknowledged? What absences must be attended to among an abundance of materials? Finally, I suggest that researchers should draw on the existing body of scholarship about these issues by librarians and archivists.

The case of Mario Gonzalez which has tested an individual’s right to have elements of this past removed from easy reach of the public raises ethical questions about the rights of the ‘forgotten’ individuals from the past now being ‘discovered’ within digital archives.

we are looking at documents more generally to see what they might be about. With Big Data, it is sometimes important to let the sources speak to you, rather than looking at them with pre-conceptions of what you might find.

While this is fraught with issues – words change meaning over time, different terms are used to describe similar concepts, and we still face the issues outlined above – we can arguably still learn something from this.

It also represents the inversion of the traditional historical process: rather than looking at documents that we think may be important to our project and pre-existing thesis, we are looking at documents more generally to see what they might be about. With Big Data, it is sometimes important to let the sources speak to you, rather than looking at them with pre-conceptions of what you might find.

Yet we do need to realize that these tools shape our research: they can occasionally occlude context, or mislead u

A key rule to remember is that there is no ‘right’ or ‘wrong’ way to do these forms of analysis: they are tools and for most historians, the real lifting will come once you have the results. Yet we do need to realize that these tools shape our research: they can occasionally occlude context, or mislead us. These questions are at the forefront of this chapter.

The first tool that we sometimes use is called ‘Outwit Hub,’ available at http://www.outwit.com/.

Using APIs is not overly difficult (the Programming Historian explains how to use the Zotero reference and research environment API)

The Programming Historian has a number of lessons explaining how to do this in Python. Here, we describe a way of using three different pieces of software to achieve the same thing. While we would certainly recommend that learning how to do this using Python should be on your to-do list, it certainly is easier, working with students or colleagues, to point to useful browser based tools that can do this.

For historians, however, computational history became associated with demographic, population, and economic histories. For a time in the 1970s, it looked like history might move wholesale into quantitative histories, with the widespread application of math and statistics to the understanding of the past

As historians, we often find ourselves writing historiographies

[11]

OCR

work

The work helps confirm the hypothesis of others, which sees a “civilizing process” as a:

Students and instructors should also keep a keen eye on digitalhumanitiesnow.org

6 Leave a comment on paragraph 6 0 Stack Overflow: http://stackoverflow.com/

open-source program wget,

At least two runic inscriptions carved into the marble walls of the Hagia Sophia may have been engraved by members of the Varangian Guard.

A half-century later, the Vikings would be recruited to defend Constantinople instead of attacking it. When Byzantine Emperor Basil II faced an internal uprising in 987, Vladimir the Great gave him 6,000 Viking mercenaries known as Varangians to differentiate the native Scandinavians from the Rus who by the middle of the 10th century had assimilated with the native Slavs and lost their distinct identity. Impressed by the ferocity with which the Vikings battled the rebels, the emperor established the elite Varangian Guard to protect Constantinople and serve as his personal bodyguards. With no local ties or family connections that could divide their loyalties and an inability to speak the local language, the Varangians proved far less corruptible than Basil’s Greek guards.

It is not known when the Rus first reached Constantinople, but it was before 839 when Rus representatives arrived at the Frankish court as part of a Byzantine diplomatic mission.

The epic voyages of the Vikings to the British Isles, Iceland, North America and points west tend to obscure the fact that the Scandinavian warriors also ventured far to the east across Europe and parts of Asia. While the Danes and Norwegians sailed west, Swedish fighters and traders traveled in the opposite direction, enticed initially by the high-quality silver coins minted by the Abbasid Caliphate that sprawled across the Middle East.

Throughout the 8th and 9th century, Vikings began traveling south from Scandinavia to raid the monasteries and towns of what is today France. By 911, they were so present, and ferocious, that the French king was forced to cede part of northern France to them. Some Vikings settled there permanently, eventually becoming known as the Normans—Norse men—of Normandy. Later, the same Viking spirit saw them traveling throughout the continent, on expeditions to the United Kingdom and southern Italy.

These hulking skeletons are believed to have been the descendants of Vikings who colonized northern France and, later, southern Italy and Sicily.

A visualization is a decision you make based on what you want your audience to learn. That said, there are a great many wrong visualizations.

they represent a unique way of presenting visualizations that can be extremely compelling and effective. They embody the idea that simple visualizations can be more powerful than complex ones, and that multiple individual visualizations can often be more easily understood than one incredibly dense visualization.

Maps are not necessarily always the most appropriate visualizations for the job, but when they are used well, they can be extremely informative.

While few people can label every U.S. state or European country on a map accurately, we know the shape of the world enough that we can take some liberties with geographic visualizations that we cannot take with others. Cartograms are maps that distort the basic spatial reference system of latitude and longitude in order to represent some statistical value.

These visualizations are good for directly comparing absolute values to one another, when geographic region size is not particularly relevant.

Choropleth maps should be used for ratios and rates rather than absolute values, otherwise larger areas may be disproportionately colored darker due merely to the fact that there is more room for people to live.

As content is added to a map, it may gain a layer or layers of information visualization. One of the most common geographic visualizations is the choropleth, where bounded regions are coloured and shaded to represent some statistical variable

Our decisions on how to encode our data and which data to present deeply influence the understanding readers take away from a visualization.

Rectangles are sized proportionally to the amount of money received per category in 2013, and coloured by the percentage that amount had changed since the previous fiscal year.

visualizations still have an important role to play in translating complex data relationships into easily digestible units.

. You may begin your research with a dataset and some preconceptions of what it means and what it implies, but without a well-formed thesis to be argued. The exploratory visualization allows you to notice trends or outliers that you may not have noticed otherwise, and those trends or outliers may be worth explaining or discussing in further detail.

When first obtaining or creating a dataset, visualizations can be a valuable aid in understanding exactly what data are available and how they interconnect. In fact, even before a dataset is complete, visualizations can be used to recognize errors in the data collection process.

Notice how in the chart in figure 5.3, it can easily be noticed that whomever entered the data on book publication dates accidentally typed “1909” rather than “1990” for one of the books.

Visualization is a method of deforming, compressing, or otherwise manipulating data in order to see it in new and enlightening ways.

You may begin your research with a dataset and some preconceptions of what it means and what it implies, but without a well-formed thesis to be argued. The exploratory visualization allows you to notice trends or outliers that you may not have noticed otherwise, and those trends or outliers may be worth explaining or discussing in further detail.

The use of visualizations to show the distribution of words or topics in a document is an effective way of getting a sense for the location and frequency of your query in a corpus, and it represents only one of the many uses of information visualization.

Visualization is a method of deforming, compressing, or otherwise manipulating data in order to see it in new and enlightening ways.

There are two main command-line interfaces, or ‘shells,’ that many digital historians use. On OS X or many Linux installations, the shell is known as bash, or the ‘Bourne-again shell.’

This is a generative approach: big data for the humanities is not only about justifying a story about the past, but generating new stories, new perspectives, given our new vantage points and tools.

an approach to big data for the historian (we argue) needs to be a public approach

This volume represents our view of what some of the most useful of these developing approaches are, how to use them, what to be wary of, and the kinds of questions and new perspectives our macroscope opens up.

A macroscope is a bit like a microscope or a telescope, but instead of allowing you to see things that are small or far away, the macroscope makes it easier to grasp the incredibly large. It does so through a process of compression

AntConc is an invaluable way to carry out some forms of textual analysis on data sets. While it does not scale to the largest datasets terribly well, if you have somewhere in the ballpark of 500 or even 1,000 newspaper-length articles you should be able to crunch data and receive tangible results.

Topic modeling allows us to quantify and visualize this pattern, a pattern not immediately visible to a human reader.

The question remains, how does a reader (computer or human) recognize and conceptualize the recurrent themes that run through nearly 10,000 entries?

hort, content-driven entries that usually touch upon a limited number of topics appear to produce remarkably cohesive and accurate topics. In some cases (especially in the case of the EMOTION topic), MALLET did a better job of grouping words than a human reader.

We cannot rely only on the computer-driven groups to use in analyzing texts.  The next step is to look at the texts that contain repeating word patterns and conduct a close reading to see what we can learn about the topic. Plotting the topic over time enables us to locate trends in how important the topic was to the author, or when we compare them with other authors, we can investigate differences in the ways that two authors valued these topics or the different ways that they expressed themselves.

The ‘model’ in a topic model is the idea of how texts get written: authors compose texts by selecting words from a distribution of words (or ‘bag of words’ or ‘bucket of words’) that describe various thematic topics.

Back to my spreadsheet I went where I discovered that variations in the title of the essay and in the author credits obfuscated the connections, as well as those for the other two essays by black women, Frances M. Beal and Maryanne Weathers.

Almetrics

The most important aspect of choosing an appropriate graphic variable is to know the nature of your data variables.

One popular service is colorbrewer (http://colorbrewer2.org/), which allows you to create a color scheme that fits whatever set of parameters you may need.

Adobe Photoshop and Illustrator, as well as the free Inkscape and Gimp, are all good tools for creating legends.

Formal networks are mathematical instantiations of the idea that entities and connections between them exist in consort. They embody the idea that connectivity is key in understanding how the world works, both at an individual and a global scale.

Historians will want note when their networks are explicit / physically instantiated, and when they are implicit / derived. An explicit network could be created from letters between correspondents, or roads that physically exist between cities. A derived network might be that of the subjectively-defined similarity between museum artefacts or the bibliographic coupling network connecting articles together if they reference similar sources.

A directed edge is one that is part of an asymmetrical relationship, and an easy way of thinking about them is by imagining arrow tips on the edges.

Transitivity is the concept that when A is connected to B and C, B and C will also be connected. Some networks, like those between friends, feature a high degree of transitivity; others do not.

Assortativity, also called homophily, is the measure of how much like attracts like among dyads in a network. On the web, for instance, websites (nodes) tend to link (via edges) to topically similar sites. When dyads connect assortatively, a network is considered assortatively mixed. Networks can also experience disassortative mixing, for example when people from isolated communities with strong family ties seek dissimilarity in sexual partners.

Aggregate all of the data into one giant network representing the entire span of time, whether it is a day, a year, or a century. The network is static. Slowly build the network over time, creating snapshots that include the present moment and all of the past. Each successive snapshot includes more and more data, and represents each moment of time as an aggregate of everything that led up to it. The network continues to grow over time. Create a sliding window of time, e.g. a week or five years, and analyze successive snapshots over that period. Each snapshot then only contains data from that time window. The network changes drastically in form over time.

Edges

Nodes

Entities are called nodes (figure 6.1) and the relationships between them are called edges (figure 6.2), and everything about a network pivots on these two building blocks.

Despite their name, networks are dichotomous, in that they only give you two sorts of things to work with: entities and relationships. Entities are called nodes (figure 6.1) and the relationships between them are called edges (figure 6.2), and everything about a network pivots on these two building blocks.

23. kafliSighvatr Sturluson bjó í Hjarðar-holti nökkura vetr. Síðan keypti hánn Sauðafell, ok fór þangat Nautfellis-vetr, ok bjó þar. Hann görðisk mikill höfðingi ok vinsæll við sína menn. Meðr þeim Kolbeini Tumasyni var in mesta vinátta með tengðum. Kolbeinn réð þá mestu fyrir norðan land, ok hafði öll goðorð fyrir vestan Öxnadals-heiði til mótz við Ávellinga-goðorð; en Þorsteinn Ívarsson gaf Snorra Sturlusyni Ávellinga-goðorð, þat er hann átti. En Mel-menn áttu sínn hluta goðorðz. Fyrir norðan Öxnadalsheiði áttu þeir goðorð, Ögmundr sneis, ok Hallr Kleppjárnsson. Þorvaldr son Guðmundar ins Dýra fékk Sigurði Ormssyni þau goðorð er hann hafði átt. Sigurðr gaf þau goðorð Tuma, syni Sighvatz; ok komzk Sighvatr svá at þeim síðan.

Ultimately, the purpose of this map is to encourage and aid new readings of the sagas. As Franco Moretti writes: “Placing a literary phenomenon in its specific place — mapping it — is not the conclusion of geographical work; it’s the beginning. After which begins in fact the most challenging part of the whole enterprise: one looks at the map, and thinks” (Atlas of the European Novel 1800-1900

Exploring at first hand how the events that the ͍slendingasögur describe are mapped onto and around the landscape and commemorated in place-names was a compelling approach to this remarkable body of literature. No less illuminating was witnessing for myself how modern Icelanders continue to engage with their local and national saga heritage.

We, however, do believe that this all needs to be both contextualized in terms of the ethical implications and the field’s historical development, and nuanced with respects to the knowledge claims that can be made.

This article will demonstrate how the mathematical tools employed by network scientists offer valuable ways of understanding the development of underground religious communities in the sixteenth century, as well as providing different approaches for historians and literary scholars working in archives.

he vast majority of network analysis deals with incomplete networks in the real world, and any statistical treatment of biases has to make assumptions about the distribution of missing links or nodes.

Letters were the method by which people sought patronage, garnered favor, and engineered their social mobility;

A good “sanity check” is to see if the algorithm you’re using puts the people or entities you know should be grouped together in the same community, and leaves the ones that do not belong out. If it works on the parts of the data you know, you can be more certain it works on the parts of the data you do not.

Network analysis aids in finding these unusually connective entities en masse and with great speed, leaving the historian with more time to explore the meaning behind this connection.

If a historical network exhibits a long-tail distribution, with very prominent hub nodes, the structure itself is not particularly noteworthy. What is worthwhile is figuring out which nodes made it to the top, and why. Why do all roads lead to Rome? How did Mersenne and Hartlib develop such widespread correspondence networks in early modern Europe? The answers to these questions can cut to the heart of the circumstances of a historical period, and their formalization in networks can help guide us toward an answer.

Social networks, article citation networks, airline travel networks, and many others feature a significantly more skewed degree distribution. A few hub nodes have huge numbers of edges, a handful more have a decent amount of edges, but significantly fewer, and most nodes are connected by very few edges.

These global metrics are most useful when measured in comparison to other networks; early modern and present day social networks both exhibit scale-free properties, but the useful information is in how those properties differ from one another.

perform math incorrectly if they are included

When two articles reference a common third work in their bibliographies, they get an edge drawn between then; if those two articles share 10 references between them, the edge gets a stronger weight of 10. A bibliographic coupling network, then, connects articles that reference similar material, and provides a snapshot of a community of practice that emerges from the decisions authors make about whom to cite.

The directionality of an edge can have huge repercussions to a network’s structure, and so algorithms made for undirected networks to find local communities or node importance might produce very unlikely results on a directed network. Be careful that you only use algorithms made specifically for directed networks when analyzing them.

Transitivity is the concept that when A is connected to B and C, B and C will also be connected. Some networks, like those between friends, feature a high degree of transitivity; others do not.

vertices, actors, agents, or points

The strength of weak ties has been hypothesized as a driving force behind the flourishing of science in 17th century Europe.[3] Political exile, religious diaspora, and the habit of young scholars to travel extensively, combined with a relatively inexpensive and fast postal system, created an environment where every local community had weak ties extending widely across political, religious, and intellectual boundaries. This put each community, and every individual, at higher risk for encountering just the right serendipitous idea or bit of data they needed to set them on their way. Weak ties are what make the small community part of the global network.

Careful readers will have noted that the definition of a weak tie is curiously similar to that of a bridge. This dichotomy, the weakness of a connection alongside the importance of a bridge, has profound effects on network dynamics.

a hub is a node without which the path between its neighbours would be much larger, and a bridge is an edge which connects two otherwise unconnected communities.

The difference between explicit and implicit networks is not a hard binary; it can be difficult or impossible to determine (or not a reasonable question) whether the network you are analyzing is one that has its roots in some physical, objective system of connections. It is still important to keep these in mind, as metrics that might imply historical agency in some cases (e.g. a community connected by letters they wrote) do not imply agency in others (e.g. a community connected by appearing in the same list of references).

Taking the most recent example, this means you can draw edges from books to authors, but not between authors or between books.

The directionality of an edge can have huge repercussions to a network’s structure, and so algorithms made for undirected networks to find local communities or node importance might produce very unlikely results on a directed network. Be careful that you only use algorithms made specifically for directed networks when analyzing them.

directed networks.

d triadic closure, can

Leave a comment on paragraph 17 0 The smallest unit of meaningful analysis in a network is a dyad, or a pair of nodes connected by an edge between them (figure 6.4). Without any dyads, there would be no network, only a series of disconnected isolates. Dyads are, unsurprisingly, generally discussed in terms of the nature of the relationship between two nodes. The dyad of two medieval manuscripts may be strong or weak depending on the similarity of their content, for example. Study of dyads in large networks most often revolve around one of two concepts: reciprocity, whether a connection in one direction is reciprocated in the other direction, or assortativity, whether similar nodes tend to have edges between them.

edges (figure 6.2),

The further back in history one goes, the less the globe looks like a small world network. This is because travel and distance constraints prevented short connections between disparate areas.

A historian may wish to see the evolution of transitivity across a social network to find the relative importance of introductions in forming social bonds.

These global metrics are most useful when measured in comparison to other networks; early modern and present day social networks both exhibit scale-free properties, but the useful information is in how those properties differ from one another.

As opposed to bibliographic coupling networks, co-citation networks connect articles not by the choices their authors make, but by the choices future authors make about them.

In an evolving network of correspondents, if Alice writes to Bob, and Bob to Carol, we can ask what the likelihood is that Alice will eventually write to Carol (thus again closing the triangle). This tendency, called triadic closure, can help measure the importance of introductions and knowing the right people in a letter-writing community.

The smallest unit of meaningful analysis in a network is a dyad, or a pair of nodes connected by an edge between them (figure 6.4). Without any dyads, there would be no network, only a series of disconnected isolates. Dyads are, unsurprisingly, generally discussed in terms of the nature of the relationship between two nodes. The dyad of two medieval manuscripts may be strong or weak depending on the similarity of their content, for example. Study of dyads in large networks most often revolve around one of two concepts: reciprocity, whether a connection in one direction is reciprocated in the other direction, or assortativity, whether similar nodes tend to have edges between them.

Create a sliding window of time, e.g. a week or five years, and analyze successive snapshots over that period. Each snapshot then only contains data from that time window. The network changes drastically in form over time.

f individual attributes

Edges connect nodes

Entities are called nodes (figure 6.1) and the relationships between them are called edges (figure 6.2), and everything about a network pivots on these two building blocks.

Despite their name, networks are dichotomous, in that they only give you two sorts of things to work with: entities and relationships

Historians can draw a number of inferences from small world networks, including the time it might have taken for materials to circulate within and across communities, and the relative importance of individual actors in shaping the past.

When two articles reference a common third work in their bibliographies, they get an edge drawn between then; if those two articles share 10 references between them, the edge gets a stronger weight of 10. A bibliographic coupling network, then, connects articles that reference similar material, and provides a snapshot of a community of practice that emerges from the decisions authors make about whom to cite.

Because citations go to previous work rather than contemporary similar research, it winds up being difficult for algorithms to find communities of practice in the network.

when it is invoked appropriately, it should be treated with a healthy respect to the many years of research that have gone into building its mathematical and conceptual framework.

Hubs and bridges help connect the local with the global because they are individual metrics that are defined by how they interact with the rest of the network. On its own, a single marriage between two families might seem unremarkable, but if these are royal families marrying into some until-then disconnected foreign power, or two people marrying across faiths in a deeply religious community, one simple bridge takes on new meaning. Network analysis aids in finding these unusually connective entities en masse and with great speed, leaving the historian with more time to explore the meaning behind this connection.

Scale free networks usually have a few very central hubs, which themselves tend to have high betweenness centralities. That is, they sit on the shortest path between many pairs of nodes. These nodes are vulnerable points in the network; without them, information takes longer to spread or travel takes quite a bit longer. A hub in a network of books connected by how similar their content is to one another would have a different meaning entirely: the most central node would likely be an encyclopaedia, because it covers such a wide range of subjects. The meaning of these terms always change based on the dataset at hand.

a hub is a node without which the path between its neighbours would be much larger, and a bridge is an edge which connects two otherwise unconnected communities.

A small world network is one in which the shortest path between any two random nodes grows proportionally to the logarithm of the number of nodes in the network. This means that even if the network has many, many nodes, the average shortest path between them is quite small. These networks also have a high global clustering coefficient, which is the average local clustering coefficient across all nodes.

Graphs have a tendency of making a data set look sophisticated and important, without having solved the problem of enlightening the viewer.[13]

creating structural holes and becoming the link between communities

As much as networks reveal communities, they also obscure more complex connections that exist outside of the immediate data being analyzed.

We need to be extremely careful when analyzing networks not to read power relationships into data that may simply be imbalanced.

There is a tendency when using graphs to become smitten with one’s own data.

The entities being connected can be articles, people, social groups, political parties, archaeological artefacts, stories, and cities; citations, friendships, people, affiliations, locations, keywords, and ship’s routes can connect them. The results of a network study can be used as an illustration, a research aid, evidence, a narrative, a classification scheme, and a tool for navigation or understanding.

Trade networks

The authors connected nearly a hundred 15th century Florentine elite families via nine types of relations, including family ties, economic partnerships, patronage relationships, and friendships.

As much as networks reveal communities, they also obscure more complex connections that exist outside of the immediate data being analyzed.

The following chapter, beyond teaching the basics of what networks are and how to use them, will also cover some of the many situations where networks are completely inappropriate solutions to a problem.

The entities being connected can be articles, people, social groups, political parties, archaeological artefacts, stories, and cities; citations, friendships, people, affiliations, locations, keywords, and ship’s routes can connect them. The results of a network study can be used as an illustration, a research aid, evidence, a narrative, a classification scheme, and a tool for navigation or understanding.

In this case, networks were the subject of study rather than used as evidence, in an effort to see the effects of political change on power structures.

Studies of this sort pave the way for more exploratory network analyses; if the analysis corroborates the consensus, then it is more likely to be trustworthy in situations where there is not yet a consensus.

Their study looks in 280 letters written by Cicero; the network generated was not that of whom Cicero corresponded with, but of information generated from reading the letters themselves.

Network approaches can be particularly useful at disentangling the balance of power, either in a single period or over time. A network, however, is only as useful as its data are relevant or complete. We need to be extremely careful when analyzing networks not to read power relationships into data that may simply be imbalanced.

A citation analysis by White and McCann looking at an eighteenth-century chemistry controversy took into account the hierarchical structure of scientific specialties.[2] The authors began with an assumption that if two authors both contributed to a field, the less prominent author would always get cited alongside the more prominent author, while the more prominent author would frequently be cited alone. One scientist is linked to another if they tend to be subordinate to (only cited alongside of) that other author.

The results of a network study can be used as an illustration, a research aid, evidence, a narrative, a classification scheme, and a tool for navigation or understanding.

We need to be extremely careful when analyzing networks not to read power relationships into data that may simply be imbalanced.

Following the entailogram over time reveals conflict and eventual resolution.

Lineage studies with networks are not limited to those of kinship. Sigrist and Widmer[12] used a thousand eighteenth century botanists, tracing a network of between masters and disciples, to show how botany both grew autonomous from medical training and more territorial in character over a period of 130 years.

dynasty.

For research on organizations, network analysis can provide insight on large-scale community structure that would normally take years of careful study to understand. As much as networks reveal communities, they also obscure more complex connections that exist outside of the immediate data being analyzed.

We need to be extremely careful when analyzing networks not to read power relationships into data that may simply be imbalanced.

What we really want here of course is a visualization that combines all the things, but I’ve resisted creating one for now. The complex historical questions of who gets counted when we count in histories of women’s liberation exists because data reduces people’s lived experiences to columns on a spreadsheet.

I took some important titles, starting in 1975 running up to 1981, digitized the acknowledgements, and pulled the names by hand into a spreadsheet. Of the 435 names, the common network reduces to this.

Turning to a print expression of the movement that is far more grass roots than mass market anthologies, I decided to look for Robinson in the many periodicals that developed out of women’s liberation. Reveal Digital has provided me with uncorrected OCR, machine-readable corpora consisting of over 6000 issues of periodicals from the Left. Here I searched all variations of Robinson’s name. I visualized this metadata and then for comparison repeated the process with the names of the other two black women who overlapped anthologies to visualize the spread of their writing.

Back to my spreadsheet I went where I discovered that variations in the title of the essay and in the author credits obfuscated the connections, as well as those for the other two essays by black women, Frances M. Beal and Maryanne Weathers. I share this not to reveal my own sloppy data, but to highlight the difficulties of doing this kind of visualization.

visualization below.

Using the BYU Corpus Interface for Google Books I scraped the metadata for any references to “Redstockings Manifesto” or “A Historical and Critical Essay for Black Women”  to create the visualization below.

Scraping from online catalogues and then digitizing when I had to, I took the table of contents, separated titles and authors, and put them into a spreadsheet that I then pulled into Palladio where I explored the relationships between both authors and essays as they overlapped.

The complex historical questions of who gets counted when we count in histories of women’s liberation exists because data reduces people’s lived experiences to columns on a spreadsheet.  

This makes for a messy looking dataviz and that is precisely my point. If I had not known my sources so well, I would have drawn erroneous conclusions. In addition to know your data, an important point in working digitally, I want to also ponder what the messiness of my data means for the marginalized. It seems strange to me that there is such variation in their names (although I will note that this happens to white women as well). However the consequences for writing history of marginalized women are more disastrous because the numbers are already so low.

chartjunk

Most visualization software do not automatically create legends, and so they become a neglected afterthought.

If choosing the data to go into a visualization is the first step, picking a general form the second, and selecting appropriate visual encoding the third, the final step for putting together an effective information visualization is in following proper aesthetic design principles.

Keep in mind that most projectors in classrooms still do not have as high a resolution as a piece of printed paper, so creating a printout for students or attendees of a lecture may be more effective than projecting your visualization on a screen.

Your audience should influence your choice of color palette, as readers will always come to a visualization with preconceived notions of what your graphic variables imply.

About a tenth of all men and a hundredth of all women have some form of color blindness. There are many varieties of color blindness; some people see completely in monochrome, others have difficulty distinguishing between red and green, or between blue and green, or other combinations besides. To compensate, visualizations may encode the same data in multiple variables.

These three variables should be used to represent different variable types. Except in one circumstance, discussed below, hue should only ever be used to represent nominal, qualitative data. People are not well-equipped to understand the quantitative difference between e.g. red and green. In a bar chart showing the average salary of faculty from different departments, hue can be used to differentiate the departments. Saturation and value, on the other hand, can be used to represent quantitative data. On a map, saturation might represent population density; in a scatterplot, saturation of the individual data points might represent somebody’s age or wealth. The one time hue may be used to represent quantitative values is when you have binary diverging data. For example, a map may show increasingly saturated blues for states that lean more Democratic, and increasingly saturated reds for states that lean more Republican. Besides this special case of two opposing colors, it is best to avoid using hue to represent quantitative data.

The nature of each of these data types will dictate which graphic variables may be used to visually represent them. The following section discusses several possible graphic variables, and how they relate to the various scales of measure.

The art of visual encoding is in the ability to match data variables and graphic variables appropriately. Graphic variables include the color, shape, or position of objects in the visualization, whereas data variables include what is attempting to be visualized (e.g. temperature, height, age, country name, etc.)

There is no right visualization. A visualization is a decision you make based on what you want your audience to learn. That said, there are a great many wrong visualizations. Using a scatterplot to show average rainfall by country is a wrong decision; using a bar chart is a better one. Ultimately, your choice of which type of visualization to use is determined by how many variables you are using, whether they are qualitative or quantitative, how you are trying to compare them, and how you would like to present them. Creating an effective visualization begins by choosing from one of the many appropriate types for the task at hand, and discarding inappropriate types as necessary.

It is important to remember that stylistic choices can deeply influence the message taken from a visualization. Horizontal and radial trees can represent the same information, but the former emphasizes change over time, whereas the latter emphases the centrality of the highest rung on the hierarchy. Both are equally valid, but they send very different messages to the reader.

Whereas the previous types of visualizations dealt with data that were some combination of categorical, quantitative, and geographic, some data are inherently relational, and do not lend themselves to these sorts of visualizations. Hierarchical and nested data are a variety of network data, but they are a common enough variety that many visualizations have been designed with them in mind specifically. Examples of this type of data include family lineages, organizational hierarchies, computer subdirectories, and the evolutionary branching of species.

Leave a comment on paragraph 47 0 In the humanities, map visualizations will often need to be of historical or imagined spaces. While there are many convenient pipelines to create custom data overlays of maps, creating new maps entirely can be a gruelling process with few easy tools to support it. It is never as simple as taking a picture of an old map and scanning it into the computer; the aspiring cartographer will need to painstakingly match points on an old scanned map to their modern latitude and longitude, or to create new map tiles entirely.

Keep in mind that often, even if you plan on representing geographic information, the best visualizations may not be on a map. In this case, unless you are trying to show that the higher density of populous areas is in the Eastern U.S., you may be better served by a bar chart, with bar heights representative of population size. That is, the latitude and longitude of the cities is not particularly important in conveying the information we are trying to get across.

Even these seemingly straightforward representations are loaded with significant choices, as laying two-dimensional coordinates onto a 3D world means making complicated choices around what map projection to use.

Statistical charts are likely those that will be most familiar to any audience. When visualizing for communication purposes, it is important to keep in mind which types of visualizations your audience will find legible. Sometimes the most appropriate visualization for the job is the one that is most easily understood, rather than the one that most accurately portrays the data at hand. This is particularly true when representing many abstract variables at once: it is possible to create a visualization with color, size, angle, position, and shape all representing different aspects of the data, but it may become so complex as to be illegible.

Our taxonomy is influenced by visualizing.org, a website dedicated to cataloguing interesting visualizations, but we take examples from many other sources as well

Often, because of change blindness, dynamic visualizations may be confusing and less informative than sequential static visualizations. Interactive visualizations have the potential to overload an audience, especially if the controls are varied and unintuitive. The key is striking a balance between clarity and flexibility.

interactive visualizations allow the user to manipulate the graphical variables themselves in real-time

dynamic visualizations are short animations which show change, either over time or across some other variable

Static visualizations are those which do not move and cannot be manipulated

A truly “objective” visualization, where the data speak for themselves, is impossible.

An information visualization differs from a scientific visualization in the data it aims to represent, and in how that representation is instantiated. Scientific visualizations maintain a specific spatial reference system, whereas information visualizations do not.

information visualization is the mapping of abstract data to graphic variables in order to make a visual representation.

Microsoft Excel has a built-in sparkline feature for just such a purpose.

“Cartography is as much an art as it is a science.” While many of these choices are outside the scope of our book, they are significant.

Sometimes the most appropriate visualization for the job is the one that is most easily understood, rather than the one that most accurately portrays the data at hand.

In a public world that values quantification so highly, visualizations may lend an air of legitimacy to a piece of research which it may or may not deserve.

The right visualization can replace pages of text with a single graph and still convey the same amount of information.

Uses of information visualization generally fall into two categories: exploration and communication.

This approach to distant reading– that is, seeing where in a text the object of inquiry is densest– has since become so common as to no longer feel like a visualization. Amazon’s Kindle has a search function called X-Ray (figure 5.2) which allows the reader to search for a series of words, and see the frequency with which those words appear in a text over the course of its pages.

Visualizations can also lie, confuse, or otherwise misrepresent if used poorly.

Visualization is a method of deforming, compressing, or otherwise manipulating data in order to see it in new and enlightening ways

It is also common for visualizations to be used to catch the eye of readers or peer reviewers, to make research more noticeable, memorable, or publishable. In a public world that values quantification so highly, visualizations may lend an air of legitimacy to a piece of research which it may or may not deserve. We will not comment on the ethical implications of such visualizations, but we do note that such visualizations are increasingly common and seem to play a role in successfully analyzing data, proving your case for peer review, or help make your work accessible to a general public. Whether the ends justify the means is a decision we leave to our readers.

When first obtaining or creating a dataset, visualizations can be a valuable aid in understanding exactly what data are available and how they interconnect. In fact, even before a dataset is complete, visualizations can be used to recognize errors in the data collection process.

The downside to this is that there have been many analyses and visualizations that have used the tools and metaphors of network analysis without any real appreciation of the dangers and limitations.