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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Topic modeling can offer us some new groupings of documents that we might have overlooked, and it will give us the capacity to analyze Sanger’s rhetoric over time, looking for key changes. An example might be the belief among women’s historians that Sanger abandoned her feminist rationales for birth control in the late 1910s and early 1920s as she sought support from experts in the fields of medicine, social work and eugenics.

The most descriptive label I could assign this topic would be EMOTION – a tricky and elusive concept for humans to analyze, much less computers. Yet MALLET did a largely impressive job in identifying when Ballard was discussing her emotional state. How does this topic appear over the course of the diary?

What topic modeling can offer a historian is an objective snapshot of the content of the collection.  Rather than relying on our own readings of documents to combine them together into subject categories, we look instead to the words that appear together most frequently and then label those words in ways that make sense to us. 

EMOTION words

As you read that essay, consider for yourself the choices we have made in how we perform the topic model, and in how we visualize the results. How justified are we in those choices?

What is a topic, anyway?

Yet we believe that for all the importance of Big Data, it does not offer any change to the fundamental questions of historical knowing facing historians.

visible data work and what remains as invisible labor.

The history and development of how we became data subjects in cultures of data are just now beginning to be told by historians and sociologists.

"era of data" with sensor networks and 5thgeneration mobile networks, as personal computing devices and internet saturation become tighter and tighter in our homes, institutions, and public spaces.

. But as the following contributions show, data is often characterized as a natural resource out in the wild to be discovered and sourced, with all the requisite taming of nature, including the frontierism metaphors of cowboys, lumberjacks, and gold rush miners.

it provides a quite quick and relatively painless way to get a broad sense of what is going on within your documents.

it provides a quite quick and relatively painless way to get a broad sense of what is going on within your documents.

Thus, you could use a spreadsheet program to create bar charts of the count of documents with an ‘architecture’ tag or a ‘union history’ tag, or ‘children’, ‘women’, ‘agriculture,’ etc. We might wonder how plaques concerned with ‘children’, ‘women’, ‘agriculture’, ‘industry’, etc might be grouped, so we could use Overview’s search function to identify these plaques by search for a word or phrase, and applying that word or phrase as a tag to everything that is found. One could then visually explore the way various tags correspond with particular folders of similar documents.[5]

Why does this division exist? That would be an interesting question to explore.

Why does this division exist? That would be an interesting question to explore.

. This default version is hosted on the McGill University servers, which limits the ability to process very large datasets.

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.