Author: Michelle Fikrig (Page 1 of 2)

Crowdsourcing: What is the role of the museum in today’s world?

November 14, 2019 / Michelle Fikrig

The role of crowdsourced material in museums has always been a difficult theme for me to navigate. As someone pursuing an advanced degree in art history in the hopes of working in museums, it often puts me in an uncomfortable position. On the one hand, I totally acknowledge that museums are inherently elitist institutions that are not accessible to large groups of the population, which is a huge issue. On the other, I have put a lot of time and resources into getting a degree that I hope will help me land a job in one of these institutions. I think museums and cultural institutions do need to fundamentally change the way they engage with their audiences and the public at large, but I’m not always sure that crowdsourcing information is the best way to achieve that goal. Obviously it is harder to change the overarching issues: how museums are funded, who has access to art history courses and certain disciplines from an early age, who has access to higher education, the entire unpaid internship system that so much of the art world relies on, entrance cost barriers, the list can go on. In many ways it’s easier to have visitors submit a selfie of them with a work in the collection and print those or highlight them on the museum’s Instagram story than to address those larger systemic issues, but those posts still neglect the fundamental issue we as a society have with these institutions. It often seems to me to be more of a way for museums to stay relevant in an increasingly digital world than a way to really alter their relationship with their audiences.

There are definitely instances where digitally crowdsourcing information is beneficial. I find that it tends to work better in smaller institutions that are already more tightly interwoven into their communities than huge ones such as the Metropolitan. Crowdsourcing is a way for these smaller spaces to connect with and and learn from their publics in a symbiotic way. In class we talked about how local historical societies or preservation groups can rely heavily on crowdsourced information because some people do have inherently have more knowledge (perhaps local elders, or folks who have been in an area for generations have old letters, simply remember buildings that have been torn down, or have kept old family photographs). In bringing up this example I hope I can highlight that I am not against crowdsourcing knowledge, but that I am wary of incorporating it into museum programs without regard for the expertise that certain individuals do already have. Museum education and public programming departments exist for a reason in order to facilitate this type of engagement already. I also think it is necessary to distinguish when it is used with the true aim of achieving greater accessibility versus when it is a catchy marketing tool to appeal to the “digital masses.” If the goal is to increase accessibility then I think we as a field need to have the much harder and more complicated discussion of how the entire structure of the discipline needs to change rather than just how works of art are chosen for an exhibition or what the label says (although I agree these do show inherent power). This has been a ramble, and for that I apologize. Let’s turn to an example of where I think crowdsourcing of information does have a lot of pros, Wikipedia.

Wikipedia as an example…

Wikipedia is an online source that everyone has probably used at some point before. It’s great for quickly learning about a topic or finding other “more reputable” sources. This week we looked at it as our “digital tool” and discussed the pros and cons of working with the platform. I haven’t edited anything on the site before or participated in an edit-a-thon, but it is something that I’ve been interested in. I’ve heard a lot about Art+Feminism edit-a-thons which strive to improve Wikipedia’s content and coverage of gender, feminism, and art related topics. They host events and dialogues to train and facilitate editing and the creation of new pages.

An interesting thing I learned about Wikipedia this week was the way you have to build your presence as a contributor on the site. Although it is tempting to dive right in and create a new page for someone/something, a classmate recommended that to begin you should simply add citations to or edit the writing of existing pages. In this way, the editors of Wikipedia (volunteers who have a bit more authority than us lowly contributors) will start to know your contributions and will be less likely to delete your work. One way you can add in information is using Citation Hunt to address gaps in what information needs to be supported by citations.

I thought I’d make a few attempts at editing this week. I had initially wanted to create a whole page for a wonderful artist I recently spoke with, Mikael Owunna, but after my peer’s comments I thought I’d start with a few minor edits. I ended up adding a bit of information to South African artist Zanele Muholi’s page. I added one recent exhibition and one more collection their work is in.

You can see here that I added that they have work in the Tate Modern museum in London. This is a major collection to be in so I wanted to make sure it was present. I linked their artist page on the online collection as a citation. I took a screenshot to show how easy the process was: basically you paste in a link and it generates the citation for you.

Overall, the experience was remarkably easy. The new “visual editor” tool makes editing on Wikipedia very similar to the editing process on WordPress. Wikipedia also already has a lot of the tools you need embedded into the page so that you can just cut and paste a lot of information. I’m sure starting a page from the ground up would be much harder, but simply adding in citations was surprisingly easy and quick! I’m excited to keep working with the platform.

3D Modeling: Google SketchUp and Replicas in Museums

November 7, 2019 / Michelle Fikrig

This week has been all about 3D modeling. We looked at a lot of examples from scholars recreating ancient or medieval architecture and objects. There are so many benefits to 3D modeling in those realms, but I want to focus in my post on how I have used the tools and how I can envision using them in my own work as I continue to get better at them.

I want to begin this post with the only experience I’ve had prior to this class with 3D-modeling, which is working with Google SketchUp. I’ve worked with it during a variety of internships at multiple museums as part of exhibition planning. In those internship contexts I didn’t appreciate how much goes into using the program. It is easy to “hang” works in the galleries on SketchUp and to populate the architecture with works of art (you can adjust proportions and manipulate placement very easily). Because the museums already had exact models of their gallery spaces, what I didn’t realize was how much background works goes into building the physical space that I was then putting art into. That part is the real work. Since I don’t have access to most of the SketchUp files I created in those contexts, I’ll show another example that I’ve made using SketchUp for a class.

For a project in a seminar in undergrad, I was tasked to reimagine a way in which to engage with Confederate monuments. I looked at New Orleans as a case study because of how many news stories were coming out of the city regarding the topic at the time. I looked at previous examples of museum exhibitions that looked at colonial and military histories that I felt were relevant in looking at strategies to incorporate in this example.

Screenshot of my “exhibition” of New Orlean’s Confederate monuments

After trying in vain to build a museum space myself, I ended up borrowing the architectural rendering from one of the museums I had worked at. In the gallery space shown you can see the empty pedestals of the Beauregard Equestrian Statue and the statue to Confederate President Jefferson Davis are on view. Behind both are photographs of either the vandalized original statues, or edited photographs of what the monument could be. For example, behind the Jefferson Davis pedestal is an artist reimagining of the statue as a monument to Angela Davis. The literal absence of the physical statues emphasizes the possibility of reimagining them, as well as decentralizes the figures from the narratives and instead underscores the response from the community.

Back to the point of this example though, you can see that my use of SketchUp is pretty limited. It is easy to incorporate flat images (see the images on the walls), but I had difficultly demonstrating that objects were three-dimensional. I wanted to show that I was including the actual pedestals (not the sculptures, just the pedestals with graffiti), but since I couldn’t include an actual 3D model I simply added a box with the same image on all four sides. I consider this a low-tech solution. Remember, that when you’re in the actual program, you can drag yourself through the space so when you’re “walking” around the center pedestal for example, you do get some sense of what you’re seeing even with just the pictures.

Let’s turn to objects…

I’d like to pivot now to a discussion of 3D scanning and the modeling of objects rather than spaces. In their article, “3D Scanning and Replication for Museum and Cultural Heritage Applications,” Melvin J. Wachowiak and Basiliki Vicky Karas write that “3D scanning neither replaces nor is fully comparable to photography, structural imaging such as radiography, computed tomography (CT scan), colorimetry, and other measurement techniques.” There are already so many tools at museum’s disposals that are used to catalog and record information regarding their collections. It is a simple next step to begin to incorporate 3D modeling into this data collection.

Beyond keeping thorough records, I think there are a number of ways in which models and replicas that are scanned and 3D-printed can be used and incorporated into museum collections. Just one example of how replicas have been used in museums to improve visitor experience is their use in allowing visually-impaired visitors to interact with the art by actually touching the recreation. The Smithsonian Magazine has a great article on this. In that article, David Hewitt writes, “The solution, the curators concluded, was not simply offering audio or braille guides, but to create elaborate 3-D replicas of key works, which visitors could touch.” 3D modeling allows curators to go beyond what can be conceived of as traditional solutions to allow for greater accessibility to collections by visitors who would otherwise be left out of traditional art museum contexts.

In addition, 3D modeling and scanning can be used in object repatriation cases and the study of indigenous art and artifacts. Again, the Smithsonian Magazine has a great article on how the tool can be used in this way. The article discusses a collaboration between the museum and the Tlingit tribe of southeastern Alaska. As a fun shout out to my university, University of North Carolina-Chapel Hill student and photogrammetry specialist Abigail Gancz was a part of this project. During a conference on the topic of 3D modeling, multiple clan artifacts were digitally scanned and replicated as “insurance” for the clan against future loss. They cited instances where important objects were lost or damaged and had to be recreated by memory. Now, with the help of this new technology, there will be thorough records that can be used to recreate these important objects.

I’d be interested to look more into how many museums have used 3D modeling as a solution for repatriation issues. By making replicas from the original object, museums that have acquired items in less than admirable ways could keep the information in their collections while still sending the originals back to their country/peoples of origin. I’d be interested to see how 3D scanning and modeling would work on classical African artifacts. Many masks or sculptures are made of multiple materials and have had multiple substances applied to them over the years so I wonder if scans could adequately capture those specificities.

Networks and how they can connect to art history

October 31, 2019 / Michelle Fikrig

I want to start with a basic example of a network that may be a helpful jumping off point. I presume most of you have seen a subway map. If you have, you’ve seen and worked with a network before! A network seems like it should be confusing, but really it is just a way to show relationships between different “nodes” or pieces of information. One professor I had in undergrad used this analogy to introduce network theory and, interestingly enough, my professor at UNC used the subway analogy as well to demonstrate her point. Obviously, it works!

Figure 1. Part of the New York City Subway Map
Figure 2. Part of New York City Subway Map With Adjusted Nodes

Let’s take a look at this image of the NYC subway map. Each station represents a node on the network, and each line in between the stations is an edge. Depending on your discipline, the terms node and edge may vary, but for clarity’s sake I’ll stick with these. An important concept that is related to nodes is their centrality. Centrality very simply measures the importance of a node. One way of thinking about this is how many connections are being made to a certain node, those with more connections are more “central.” Let’s look back at the subway map as an example of this concept. In Figure 2 I’ve adjusted some nodes; I’ve made two station’s nodes larger to represent that more lines converge there. Times Square in Manhattan and Atlantic Terminal in Brooklyn both are meeting points of multiple lines so I’ve enlarged their nodes to note their centrality. This makes the network graphic more useful. Without the adjustment, it seems as though the east 23rd street station on the 6 line and the Times Square station are the same size, which if you’ve ever been on the NYC subway you know is definitely not the case. Another way that changing nodes would be helpful in this case would be to show at which stops the local and express trains overlap. Let’s look at the two green lines, the 4/5 express trains and the local. They are depicted side by side and in places where all three stop there are simply 2 nodes. It could be easier, or at least a more simple graphic, if you had one green line representing all three train lines and smaller nodes would represent local stops by larger nodes would represent where both the local and express trains stop (since there are no stops that are only express and not local this would work). But enough about subways…

If you’re still not comfortable with the basics of networks, see Scott Weingart’s post on “Demystifying Networks.” Weingart does a great job of explaining the basic terminology, different types of networks (bimodal or multimodal for example), and how to read them. Something that Weingart mentions that I want to highlight is that a lot of context is stripped away in order to achieve readable network. I highlight this because the subway example doesn’t necessarily demonstrate the risks of reducing information well.

Let’s take a look at more examples

I want to preface this section with my usual cynicism. While I definitely think networks are useful, I think they are best used as presentational tools rather than research tools because of how much information is stripped from them. While they are helpful to include in presentations or to inspire research questions, applying research questions to them risks oversimplifying the issue. With this caveat in mind, let’s look at some of the examples that were brought up in class.

Let’s look briefly at how Scott Weingart introduced networks and an example he provided. In the example he provides a bimodal network. If we remember from earlier, this means there are 2 types of nodes, they can be either books (in blue) or authors (in red). In this example, he also has two types of edges. The black lines represent the relationship between author and book, i.e. who wrote what. The pink edge shows authors who collaborated with one and other. This is a relatively clear example of a network. There is not much overlap between concepts and relationships are obvious. It is almost clear to the point that you may ask: why bother with a network graphic? That’s exactly what I asked when I first read his post at least. The configuration of the different lines as swooping around and the apparently hierarchy of the nodes in the image makes it appear as if I should be getting more out of the image than I really need to. It seems just as easy that Weingart could have written “Edith Hamilton –> Mythology” on a horizontal plane rather than swooping the line dramatically to connect two floating bubbles. Again for me this represents an instance where it is definitely possible to make basic facts visual, but why. Obviously he merely intended this as a simplified example, but I think my point can be applied more broadly.

ORBIS: The Stanford Geospatial Network Model of the Roman World

Let’s talk about a finished example for a second. Take ORBIS: The Stanford Geospatial Network Model of the Roman World. This project is a massive undertaking. Poke around on it for a few minutes and you’ll realize not only how much labor went into making it, but how much data and research went into making it accurate. When I first interacted with the site I skipped the introduction and tutorial, and let me tell you that was a mistake. This is a fabulous tool, but there are so many moving parts it is difficult to use intuitively. At first glance it definitely doesn’t look like a network in the sense of the subway map or author/book examples I’ve discussed up until now. However, we can see that it is demonstrating a system of relationships in a visual way. Different places act as nodes and travel routes as edges that show the complex interaction of trade goods and distribution in the Roman World.

Screen shot of using ORBIS

I looked at the fastest route you could take with a porter (on roads) from Constantinopolis (in modern Turkey) to Corduba (in modern Spain). The fact that the menu on the left has so many ways to specify the travel is already amazing– you can specify you want to take an ox cart or a donkey rather than walking! We talked a lot in class about how all of us felt like we were using GoogleMaps when we were choosing the routes on the website. It was truly fun to play around with. But back to the point, this is still a network analysis! You can see the nodes on the map as the little circles that represent locations and the edges as the travel route. Thinking through networks in this way as not only a conceptually simple map, but also as something that is so malleable and manipulatable is really important. You can have the same base map (here of the Roman World) and demonstrate multiple networks on it using different nodes and edges in it. A cheaper route to Corduba from Constantinopolis may take me a completely different way via different roads (or edges) and stopping at different cities to restock (so at different nodes).

My own example

Network showing where some artists in the Tate collection were born, created using Palladio

Using that public Tate collection data I’ve used before, I created a network showing where artists were born. I used Palladio, which is an online tool. I found it quite easy to use. Honestly, once you upload or copy and paste your data into it it does pretty much all the work for you; really all you need to do is organize an Excel sheet with your data. Although the screenshot is small, you can see that there are nodes that denote artist names, and nodes that are locations of where they were born. I’ve sized the location nodes so that nodes with more centrality, which if we remember means more edges connect to it, will appear bigger. In this screenshot we see that London is a much larger node than Beijing because many more artists were born in London. I have a similar critique to Weingart’s example where the random placement of the nodes seems to imply something even though it really doesn’t. It doesn’t bother me as much in this example, but I still can’t figure out a rhyme or reason to the placement of the nodes on the graphic. It would be interesting to overlay this example onto a map perhaps, since it seems silly to have London, Venezuela, and Beijing right next to each other here when the network doesn’t seek to show any relationship between the places.

This network is simplistic and I’m not sure exactly how I’d use it in my art historical research, but I can see a multiplicity of uses for museum administration and acquisition meetings (sensing a theme anyone?). It is very easy to visually see which locations are represented more strongly in the collection. For a general public this could also be helpful as an introduction to the museum collections. I can envision this type of network being implemented into a larger interactive digital collection initiative like the Artlens Wall at the Cleveland Museum of Art which we’ve discussed in class.

I want to quickly mention we also played around with Gephi, but I wasn’t crazy about it. It did give you a few more options, but I didn’t think it was as intuitive to use as Palladio so at my level I’d likely stick to Palladio.

Let’s talk about timelines

October 23, 2019 / Michelle Fikrig

I’m a big timeline fan. I’ve always thought they were fun to make for school projects, and as a visual learner they have always helped me make sense of complicated histories. As an obsessive organizer, I am particularly fond of color coordinated or otherwise embellished timelines. I think it not only adds a little pizazz, but also helps the reader navigate the information.

I’ve worked with Timeline JS before and think it is a really easy tool to incorporate into your own practice. My first experience with it was a collaborative timeline of German history for a Bauhaus seminar this year. As a class we each added events from a 20 year period to a communal timeline that spanned German history from something like 1700-1940. We covered a lot of information. The platform was easy to use collaboratively because it operates based on a Google sheet, so it constantly is updating to reflect what your peers are adding. We only had one issue of items not coming up on the final product because apparently you can’t have any blank rows in the sheet between events– overall a minor and easily fixable issue once we all understood what was wrong. Based on that experience, I knew I’d have an easy time making my own timeline using the platform, but wanted to see if I could tweak it at all to suit my specific needs. Interestingly, TimelineJS encourages you not to have more than 20 events for your reader to click through. I know our Bauhaus timeline had at least 75 events, probably more, and it was a little long but the length didn’t change anything about the tool itself, just the class’s attention span. Which reminds me, as a user or reader Timeline JS is also very user friendly. It has easy arrow buttons on either side to toggle through events, or you can swipe across the overall timeline at the bottom to move more quickly across a longer span of time to find a specific year. This is much more user friendly in my opinion than one of the other examples we looked at in class by BBC that was a History of England. Moving through that one is much more cumbersome and the individual events are less visually engaging to the reader. However, the BBC timeline is definitely not limited to just 20 events as Timeline JS apparently suggests, so they do a good job of presenting a truly massive amount of data.

For this week’s digital assignment I made a very basic and incomplete timeline of South African history. Sure enough, when I went to create my own timeline I thought it was easy to create events and to add text and images to them. I’ve been having trouble embedding links into my posts, so click here for a link to my timeline.

Screenshot of my timeline on South African history. Click here to view the whole thing.

Something I realized this time around that I didn’t last time was that I wish I could overlay multiple timelines into one. What I mean is that I wish I could have had a “colonial” timeline and a “local” timeline that existed on the same spreadsheet/timeline, but were somehow distinct (maybe different colors? maybe one at the top of the screen one at the bottom?). As a side note, I know that “colonial” and “local” timelines are problematic in that those terms are very loaded, but the point is that I think it’s perhaps not the best way to have the European presence and local histories (whether Xhosa, Zulu, etc) charted on the same timeline. Unfortunately, I couldn’t find a way to manipulate the data in a way that differentiated the two groups. Maybe that isn’t necessary as all the history has becomes so intertwined, but I still think it would be interesting to see just the events related to the Zulu kingdom, or just the Dutch colonial involvement for example. In an ideal world, I could color code the various events in a way that would correspond to overarching groups or themes (Zulu history could be blue, Dutch orange, British red for example) and then I would have a menu on the side that when one of those colors was selected, a timeline of just those events would pop up on top of the page so you could toggle through just those events more easily. A set up like this would show that there were a lot of events happening around the country at the same time. While the British and Dutch were landing on and arguing over the Cape Coast, the Zulu Empire was very much at its height and expanding as just one example. This could also help to remedy any length issues, as you could “shorten” the timeline by just viewing one group at a time.

Interestingly, one of the examples that Timeline JS provides is a timeline of Nelson Mandela’s life. Although much more specific in scope than my own attempt, it is interesting to compare the two. The formatting of the two are quite similar. In fact, there are a lot of similarities, including the image and structure of the event for Mandela being freed from jail (I promise not copied, it’s just the standard image of the event!). The similarities hint at the fact that there is very little customizing you can do using the tool, which really is my biggest critique. One thing I wasn’t crazy about that the example timeline did was that it includes the “time” of the event, however almost all of them apparently occurred at 12 am. I assume this was made in an attempt to fill in all the data fields of the spreadsheet, but it seems like superfluous information to then translate to the finished product unless the exact time of the event was truly relevant (I know not all of them occurred at midnight).

Screenshot of an example of TimeMapper which incorporates maps into a basic timeline format.

The other program we looked at this week was TimeMapper. I hadn’t used the program before, but it seems equally as intuitive to use. It also uses Google spreadsheets to organize and add data, which makes it really easy to use. One aspect that is cool is that it incorporates a map. Although, in the example they provided on the website (see the screenshot I added), the map wasn’t terribly helpful except for as a way to toggle through events. Still, the concept is interesting. Using the screenshot as an example, I’m not sure I really needed the pin in Italy to understand that Thomas Aquinas was Italian when the text clearly states that he was an “Italian Dominican priest.” I can conjure up a basic map of Europe in my head enough that this visualization didn’t elucidate anything new. Nevertheless, I can still definitely imagine using this map feature in a timeline on South African history as I think this would provide important visual context for where different events were taking place since there were colonial and tribal divisions that did constantly shift. This type of map-based timeline reminds me of the New York Times project “Riding the New Silk Road” that we looked at for class. While I think the map adds something to the NYT project, like in TimeMapper I’m not sure how much it really adds aside from a way to toggle through events. In both cases, I wish that the map provided more interaction to the user rather than just a way to get to the next static timeline event.

Screenshot from the NYT “Riding the New Silk Road” project

It isn’t totally clear from just a screenshot of the NYT project, but you can kind of see that the “map” is really just an interesting way to put events on a squiggly timeline. As you click through the timeline, you “travel” along the route from one static event or location to another, really just scrolling down the webpage. Each point on the map/timeline corresponds to one image and text. Sure, you get a geographic idea of where the events are happening in relation to each other, but I’m not sure I would really say you are experiencing riding on the new silk road through this format. Based on the NYT example, I’m less inclined to use TimeMapper over Timeline JS, because I’m not convinced that the inclusion of maps into a timeline adds enough to warrant the extra work. Maybe that’s pessimistic –I definitely tend to be critical to a lot of these tools– but I also know I have limited time and resources and want to be efficient in what I choose to incorporate into my own research.

In the spirit of collaboration and crowdsourcing that is so important for digital humanities, if anyone knows why I can’t embed anything into my posts, please help a scholar out! For the time being, here is the link to my timeline project. I’ve included it in hyperlinks throughout, but I know my theme makes it hard to see those sometimes and I want to make sure my readers can learn a little something about a few events in South African history and see how Timeline JS works!

So here’s the link: https://cdn.knightlab.com/libs/timeline3/latest/embed/index.html?source=1D5sFTliTBMk2c8dxBek4ggXEVfEr7zVK_vv8zkjW8F8&font=Default&lang=en&initial_zoom=2&height=650

Data visualization: can art historians use Excel?

October 10, 2019 / Michelle Fikrig

This week has been all about data visualization and its ability to clarify abstract data and aid in our ability to read and absorb large amount of it. I’ll admit I was skeptical when we began our workshop in this section of the course with Excel, but I am now convinced that these tools do actually have something to offer art history. It’s important to note that although I associate Excel with middle school science projects and finance spreadsheets, both the information (the data) that art historians are displaying and generally the types of charts or visuals we are creating are quite different.

When I think about data visualization in the context of art, I think immediately of Guerrilla Girls. I wasn’t going to focus on this connection since I have tended to focus on artists using digital tools rather than art historians in my blog posts (see my last post here), but Taylor’s comment on my last post made me realize that this is actually important as artists’ use of these tools will serve as an important impetus for art historians to get on board the digital history train. Anyways, back to the Guerrilla Girls’ use of infographics and data visualization. Take for example their “Bus Companies Are More Enlightened Than NYC Art Galleries” graphic that shows the percentage of women in various jobs. The percentages themselves are easy to understand, but I think it is an instance where a graph may help to really show the discrepancies. Many of their charts and “report cards” have the potential to be visualized in this way as well. For now, I’ve taken the liberty of making a very rudimentary graph for this one graphic.

Courtesy guerrillagirls.com
My bar graph showing the Guerrilla Girls’ data

I’m definitely not providing new information or really asking any new questions with the graph of the “data” from the image, but I think it is perhaps easier to read. Having both images is redundant, but perhaps incorporating data visualizations into their infographics would be a good strategy for the Guerrilla Girls.

Let’s take a (small) step back into some theory

I think the question of “am I asking or answering any new questions” is important. In my Guerrilla Girl example, I was not, and honestly I’m struggling to think of a way that a lot of these data visualizations would ask new research questions in and of themselves. A good way to think about this conundrum would be the questions posed by Shazna Nessa in “Visual Literacy in an Age of Data,” :

Who am I creating this for?
What journalistic impact should the visualization have?
If I opt for novel graphical/interaction styles, what guidance will I provide the audience?
Should I blend exploratory aspects with explanatory aspects?
How will I expose the story?
Can I add a narrative, causation information, or a news peg?
Although I’ve edited the data already, is there superfluous data that I can still edit out?

Although these questions aren’t necessarily specific to art history, I think they are interesting and vital to interrogating the role of visualizations in the field. I’d propose the addition of a few other questions: Is this visualization asking a new research question or answering an established one in a new way? Is the information that it is sharing already explained clearly enough in my writing and therefore is it redundant? There are so many visualization tools — charts, word maps, image charts, the list goes on– that it is tempting to include at least one in your project. You can easily make one of the visualization types work for your project, but should you? I’m not convinced that just because these tools can work for our discipline that they belong there. They seem to live squarely in the history side of the field rather than the art. To me, if we are to include graphics in our research, it seems best to include images of the objects we are exploring rather than graphics that visualize what we are saying about them.

So I tried to make a few visualizations…

And honestly, they didn’t turn out too well. In class we played around with the Tate’s data on the artists and artworks in their collection. This is a lot of data to handle, so usually we tended to break up the data into more manageable groupings. For example, I tended to not only to just focus on the “A’s” (meaning artists whose last name started with A), but even just a small set of those artists. First I poked around with Excel and couldn’t really make any visual aids that I thought were useful enough to include here. We did make a pie chart of male vs. female artists, which could be helpful. However, we had to switch the data input to be able to chart this. We had to switch the word “male” or “female” in the column to a numerical datapoint that the computer could add up, which was hard necessarily, but definitely took up time. Next we worked with Tableau. In some ways, I found Tableau to be a bit more intuitive, but I still struggled with this assignment. A lot of these struggles may be because I didn’t really have control over the data collection and data set. It may have been easier had I gotten my own data and chosen the fields more carefully to be able to structure my visualizations around a certain argument. In the end I only made a few visual aids that I thought could be useful. I managed to make the following graph that looked at how many pieces in a certain media various artists had in the Tate collection. Including ALL the artists in the data set was unwieldy, as was even just focusing on the A’s, so here is an instance that I included only 30-something of the artists whose last name started with A.

My attempt at a graph showing how many pieces in a certain medium are in the Tate collection by artist.

My main issue with the graph is aesthetic. The way the artists’ names appear on the top is unclear and hard to read. I could have used fewer artists to alleviate this, but then I don’t get to compare as many artists which limits the scope of my research. It is interesting to see the distribution of media in the collection, and this graph definitely does show that pretty clearly in the length of the bars, but I’m not sure it was worth the data manipulation. A simple chart or a paragraph of text could probably achieve the same result.

I want to return back to those questions posed by Nessa to evaluate my graphic. Who am I creating this for? I could be creating this graphic for an acquisition committee. It could be useful for the board to see what holes there are in the collection and to determine if another oil painting or print by a certain artist is really a necessary purchase. This visualization may be useful in that boardroom setting when making decisions if the members don’t have a firm grasp of all the items in the collection (which is nearly impossible with a collection the size of the Tate’s). What journalistic impact should the visualization have? Going forward with that acquisition committee example, this graphic should demonstrate the breadth of the collection and act as a simple representation of the distribution of media and artists’ works. If I opt for novel graphical/interaction styles, what guidance will I provide the audience? I think this is an important question for this particular graph. I would need to perhaps supplement with text outlining where the pieces came from (if groups of prints were bequeathed together for example) and when they were acquired by the museum. This historical acquisition data would be necessary to understand the graph. How will I expose the story? I would include that contextualization first and then turn to this graph to reiterate a point rather than begin with it. This would incorporate the narrative quality in another one of Nessa’s questions. Although I’ve edited the data already, is there superfluous data that I can still edit out? Here I think I’ve edited out the superfluous data. But even if I didn’t think I had, Tableau requires a certain number of fields to create certain graphic types, so I needed to include what I did.

How can we best use huge amounts of data?

October 3, 2019 / Michelle Fikrig

This week in class we are discussing data, data “tidying” and visualization, and data mining. We looked at theory and a variety of examples of how various scholars have used amalgamations of huge data sets to reach conclusions and visualize trends. We noted that some of these examples were more successful than others, and as a whole the class seemed to reach a rather pessimistic conclusion: so what? What do these data sets really tell us that furthers our understanding? We looked at the example of organizing paintings by color. I wholeheartedly agreed with a classmates questioning of how useful a data set of 40,000 blue images could be. Sure, she argued, we could look at the spread of pigment geographically, iconography associated with the color, or a host of other topics, but does a massive collection of images really help a scholar on that quest? I also wasn’t convinced. To further dissuade me from thinking it would be helpful, I hadn’t even thought about the way these large data sets could be skewed. Professor Bauer brought up “color pollution” or the idea that the background color of an object would also be mined for these color sets. This means that many coins are placed in black sets because of the black velvet drapes they are photographed against for collections, or that sculptures generally were not accurately tagged because of the wall color they were photographed against. So, if we were to run with the hypothetical collection of 40,000 images of works that are mainly blue, not only is this huge collection perhaps not useful to me as an individual scholar trying to make a claim, but it may not even be accurate.

Data mining is also used to identify trends in textual sources. Dan Cohen’s “Searching for the Victorians” is a great example of this, but it also raises the “so what” question from skeptics. Cohen and his fellow researchers were able to code over a million books (!!) thanks to widespread digitization of Victorian era literature by projects like Google Books and Hathitrust. Below is a graph of the number of books that reference “Revolution” in their titles (for now, only titles are analyzed, but analyzation of full text is in the pipeline for the project):

Graph showing the frequency of the word “Revolution” in the title of Victorian books from Dan Cohen’s “Searching for the Victorians”

The graph is interesting in that it lets us see how much revolution (and therefore perhaps political in/stability and social unrest) was present in the consciousness of society. The spike in the middle of the graph seems interesting and draws the viewer’s attention, but any historian would immediately know that this spike coincides with the French revolution about which there was a lot published and discussed. So again, you may be left with the question, “so what? what does this actually tell us?” In fact, some commenters asked just that in regard to Cohen’s post.

I don’t mean to be pessimistic about the use of data in the humanities, I think there is huge potential to incorporate it into research in art history and beyond. Returning to Cohen’s revolution example, I actually think there is value in simply visualizing trends. Being able to look at not only a small sample, but virtually all examples of Victorian literature and plotting trends in words used shows the general attitude of the population and what is important. Sometimes just showing data and trends is as valuable to scholarship as distinct arguments.

Forensic Architecture at the Whitney Biennial as Another Case Study

Film still from “Triple Chaser” by Forensic Architecture on view at the 2019 Whitney Biennial

I want to shift back towards collecting and mining images for a brief discussion on the piece made by Forensic Architecture included in this year’s Whitney Biennial. Forensic Architecture is an agency which comprises about 20 full-time researchers, filmmakers, and technologists, along with a team of fellows that looks into global violence, corruption, and conflict. They provide an interesting example of the ways in which image recognition and data amalgamation can be useful: as a journalistic pursuit (they try to showcase the role of a Whitney board member in profiting from violence), as a tool to recognize very different images and sort through huge sets of them, but also simply to create art (they are exhibiting in the Whitney Biennial after all!).

Forensic Architecture has enlisted artists, filmmakers, writers, data analysts, technologists, and academics in an intensively collaborative process. Maps and digital animations often play a critical role in the group’s work, allowing for painstaking recreations of shootings and disasters, and images are often culled from social media and scrutinized for information. Forensic Architecture’s work suggests a union of institutional critique and post-internet aesthetics, and it exists in many forms. On the group’s website, it lives as design-heavy interactive presentations. In museums, their work takes the form of installations dense with videos, diagrams, and elements of sound.

Alex Greenberger, “For Whitney Biennial, One Participant Targets Controversial Whitney Patron“

I encourage you to look more into how Forensic Architecture made the video that was on display at the Whitney that resulted from the larger project because my lack of understanding of the machine learning processes that made it possible also hinders my ability to talk insightfully about the piece. However, very simply, Forensic Architecture trained AI to identify images of Safariland tear gas canisters. In order to train image recognition software you need A LOT of images, it’s one of the major barriers to use. To get around this, they crowdsourced for images of the canisters (and received a disturbing amount from activists around the world). They then put these canisters against various backgrounds and repositioned them from various angles to help train the software further. Again, this is hugely simplifying the process, and the video that they produced and which was displayed at the museum goes through the process in much better detail.

I bring up this example both because I think it’s an amazing work of art and incredibly thought provoking, but also because I think this sort of image recognition training is how I can envision using large amounts of data most effectively. I can see how useful it would be to identify objects (like a teargas canister) or symbols and then train machines to find them in huge collections of images. On a grand scale this could show cross-cultural connections if we see objects or symbols in use across large geographic or temporal divides, but also in a logistical sense help viewers make sense of blurry or degrading images that the human eye may have trouble discerning.

I know in my own work when I look at colonial photographs, many photographers used the same props in multiple photos in order to create “authentic” portraits that satisfied what the colonists envisioned of the “primitives” they controlled. Using image recognition, I could potentially find all the instances in which a certain prop (or type of prop) was used and use this to highlight the fictitious nature of these photographs. Perhaps with the current state of machine learning this wouldn’t be possible, after all I would need a huge data set to train the machine, but as opposed to some of the examples we looked at in class, this type of image recognition data project may help us answer that nagging “so what” question. I’m not sure I’ll ever be able to code this type of software, although I could definitely find wonderful scholars to collaborate with. Perhaps text data would be most useful and realistic for me. I could easily chart biographical data of subjects or photographers using the basic Excel skills I already know, or use existing text mining software to go through records to pull out relevant information for my research. I’ve been the intern that has to “tidy” this type of data before for projects, so I am used to the type of work that goes into amassing data in a way that is useful for these tools. Although I have not used text-mining services in the past, I would love to work with these tools in the future as it would greatly improve my ability to get through vast archives of information. Perhaps these text based approaches are a better place for me to start as an amateur digital art historian.

Digitizing Spacial Histories

September 25, 2019 / Michelle Fikrig

Historians by definition focus on time. Chronology will always remain at the heart of a discipline that seeks to explain change over time, but this has left historians open to the charge from geographers that they write history as if it took place on the head of a pin. The charge is not true, but sometimes it is uncomfortably close to being true.

Richard White, “Spatial History Project“

Incorporating mapping technologies into digital humanities projects is one way to address the reduction of history into a chronology that can appear to be disembodied from real experiences. This “attempt to do history a different way” as White words it, could be useful in a variety of art historical contexts. White references Henri Lefebvre’s The Production of Space and the three components of spatial history Lefebvre lays out: spatial practice, representations of space, and representational space. Spatial practice refers to the experience of moving through a space (our movement through our houses, our commute to work along sidewalks and on subways). Representations of space are, “the documents of architects, city planners, politicians, some artists, surveyors and bureaucrats.” These are not completely divorced from the physical space, as they try to put in words and on paper the tangible or measurable physical aspects of the space. Finally, representational space is an overlay on a space that imbues it with significance based on symbolic use of objects. White writes, “It is what marks a church or mosque or synagogue; it is what religious people feel in a sacred space; it is a room in a library or a university building; it is an art gallery.”

Digital Harlem

I’d like to use the Digital Harlem mapping project as an example of how the three aspects of spatial history White references can be combined effectively in a map. To begin, a user can toggle through the map, tracing roads and locating specific spaces they could inhabit in the real world. The ability to put the map on “satellite” mode facilitates this spatial practice effectively as a user can truly project themself into the space and relate to streets or areas that they have walked on themselves. In terms of the representation of space used to produce the project, I would expand upon the list sources that White provides as the Digital Harlem mapping project shows the potential for other sources to elucidate spacial histories. The project incorporates data from District Attorney case files and Probation case files both stored in the city’s Municipal Archives, newspaper clippings from various publications, and from the WPA Writer’s Program Collection. I would argue that the project could have included census data. For example, someone brought up in class that overlaying data regarding socioeconomic status or race could be an interesting addition to understanding how the specific locations pinpointed on the map related to the neighborhood at large. Finally, by breaking up the map into categories that the user can toggle between such as “Churches” and “Nightlife,” the project associates spaces with symbols that create significance. When locations are clicked on, more information pops up in a window. This includes ownership information as well as information on the use of the space and what events may have occurred there. The addition of this helps to understand how the space was conceived of by its inhabitants. It can be said that this project focuses more on the spatial practice and representation of space, however, I do think it attempts to conquer representational space. In fact, this unequal focus is common. As White writes, “Human beings, who create all three, can, but do not always, move seamlessly between them. Lefebvre’s triad does not always, or even usually, add up to a seamless or congruent whole. His space, as he admits, is full cracks and fissures.” What is important, White would argue, is that there is a focus on spatial experience over simple language about space.

While I could critique the interface of the resource (the fact that you can’t enlarge the map window because of how large the thumbnails are on the right bothers me), I think overall it is an effective use of mapping. It draws upon a variety of sources that help to provide a comprehensive view of Harlem. The ability to toggle between the timeline and the map is also a key feature that I think adds to the project.

My own mapping project:

Click here to view my map of Cape Town!

Above you can see my experiment with mapping. Using Google Maps, I made an interactive map of Cape Town and the surrounding areas that I can envision sharing with friends and colleagues who go to the area for research. I’ve tagged my favorite places to walk, museums to explore, and restaurants to eat at so that friends can enjoy the city as much as I did.

While my Cape Town map was mostly for fun and isn’t scholarly (although still useful!), I can see myself using mapping technologies in my own academic research. In my project looking at the intersection of colonialism and photography, it would be interesting to map where images were taken and where they ended up. For example, tracking African “postcard” photographs that were taken in African colonies to be viewed by “cultured” Europeans back in the metropoles would be an interesting way to visualize the creation and consumption of these images. Tracing their movement would help me to establish how colonial images influenced local artists working both in Europe and Africa. Another layer I could add to this hypothetical map would be mapping where the photographers were coming from and where they were working. Tracing the journey of European photographers working on the African continent would help elucidate stylistic trends and influences as well. For example, creating a map of Irishman Alfred Duggan-Cronin’s photographic work through South Africa and Namibia would be interesting to see how he moved from Europe and make sense of what might have brought him to various areas of southern Africa.

References:

Lefebvre, Henri. The Production of Space. Chicago, Illinois : Blackwell Publishing Limited, 1991. pp.37-41.

White, Richard. “Spatial History Project.” Stanford University. https://web.stanford.edu/group/spatialhistory/cgi-bin/site/pub.php?id=29

Testing out Thinglink

September 19, 2019 / Michelle Fikrig

This week we also played around with Thinglink. I’m really excited about the annotation potential that Thinglink has, and I can actually see myself using this tool in my own research to keep track of individuals in portraits or other images and add in relevant information. Below I’ll embed some images that I’ve annotated. I took all the images using a GoPro on a trip to Bonaire I took back in 2016. I’m a big diver, so some are from dives I took around the island, and others are from little day trips we took on land. As a note, I tried to upload a video I took of a cute little sea horse I ran into on a dive, but I had trouble working with the video file and sharing it publicly. That was particularly frustrating since I had no trouble at all working with image files.

For context, Bonaire is a small island in the Caribbean right near Aruba. The country is a “special municipality” of the Netherlands.

I’ve had trouble embedding the Thinglink images in an aesthetically pleasing way (re: I can’t embed the images at all), which is also frustrating. But, I’ve included links below! Click on the image numbers and it will direct you to the various annotated images.

The Potential of Photo Matching for Archives

September 19, 2019 / Michelle Fikrig

When I think of what I’d like this blog to be or how I’d like it to be used, John Resig’s post “Using Computer Vision to Increase the Research Potential of Photo Archives” comes to mind as a model. In a marathon post Resig lays out an entire experiment on the efficacy of image recognition tools. His post provides not only the results of this inquiry, but a thorough yet concise summary of what tools are out there, why they are important, and how to most effectively use them. He opens the post with a brief summary and an explanation of the blog format:

I’m taking the approach of publishing my results openly on my site so that they can reach a wider audience. Please feel free to share this with whomever you think will find it useful. If you have any questions, comments, or concerns I welcome an email with your feedback

John Resig

Not only is his project of digital image recognition and matching a digital humanities one, but the way in which Resig is conceiving of it and sharing it further the accessibility aims of digital art historical projects.

Now that I’ve made my side comment on the accessibility of digital humanities projects and how fabulous it is that digital humanists are so willing to openly put their work out there, I want to turn to the specifics of the project. Much of the nitty gritty technological details of the tools Resig references still allude me, but he does do a great job of writing in plain enough language that I was able to grasp the essential details. Essentially, by tweaking existing open source image matching systems Resig was able to quickly go through incredibly vast (think tens to hundreds of thousands) numbers of images with much higher accuracy than human archivists could ever accomplish due to input and cataloging errors. He claims that utilizing these types of image matching systems would help in analysis and error correction, expediting the digitization process, and facilitate the merging of vast archives.

I’d like to first turn to the assertion that these services could help in analysis and error correction. While I assume that the majority of metadata input by archivists is accurate (and that Resig perhaps overemphasizes human error), I will concede that older archives especially could benefit from the implementation of these systems. For one, old duplicate images could be eliminated or at least grouped. Alternate views of the same works (one perhaps including the frame in the image for example) can also be grouped using this tool. Aside from this perhaps more obvious benefit, I think the point Resig brings up regarding the matching of images of works before and after restoration is interesting. This could help us to track changes to works over time and is something that I can agree may be harder for the human eye to detect. Similarly, the detection of detail shots from larger works is important as the more closely cropped images may be so disarticulated from the original that an archivist may misinterpret them as their own works. Resig’s discussion of the the various images of portions of the work he labels as “Florentine, 13th century, Uffizi Museum in Florence” demonstrate this capability really well (see screen shot below). These portions have no overlapping segments, but the image matcher was able to accurately group them so that users can better understand the whole work and its context.

Second, I’d like to comment on the use of these tools in the merging of archives. I hadn’t thought of this issue prior to the reading, as I’d always assume archivists would somehow know where the overlap was in their collections and avoid double-digitizing. This, I realize now, is naive. I didn’t fully comprehend the scale of some of these archives. That the Frick Photoarchive alone has 1.2 million photographs of works of art is mind boggling to me. With this quantity no archivist could truly have a handle on the contents of the entire archive, making a seamless merge between two huge collections nearly impossible. I also hadn’t taken into account language barriers that would impede merging metadata in an efficient way between archives from various countries.

GALA Archive: A Case Study

September 4, 2019 / Michelle Fikrig

This week I’d like to take a look at a specific resource that I’ve used in the past for my own research. While doing my research I realized the limitations of the archive, but was unable to really articulate them. Now that I’m looking into digital humanities more, I’ve realized the concrete ways in which this particular resource could expand and be of more scholarly use if it implemented more digital tools.

Gala is a South African archive and platform formed in 1997 to address the lack of representation of LGBTIQ (lesbian, gay, bisexual, transgender, intersex, and queer) folks in (South) Africa. Originally called the Gay and Lesbian Archives, Gala works to collect and preserve local African LGBTIQ narratives from both the public and private realms. The collection is primarily made up of objects on paper: namely letters, diary entries, legal documents, and photographs.

The majority of the objects in the archive are not digitized. Thus, a meeting with the archivist and a visit to the reading room is necessary to utilize the resource to its full capacity. This is obviously a major drawback to scholars of African queer history who do not find themselves in Johannesburg, myself included. While Gala does have an Archival Guide that lists and explains the holdings in the collection, a lack of searching capabilities makes it cumbersome to use. Although you can use “control F” search functions on the pdf, this unofficial search tool relies on the user to make use of exact keywords that they are looking for and requires a lot of sifting through unnecessary additional information. While we have seen that digitizing holdings represents a significant cost, since Gala does already employ an archivist, it seems as though digitizing at least some of their collections could be a vital step in their growth. Alternatively, a searchable collections tool or inventory could help users understand the holdings even if the objects themselves are not digitized.

As a digital resource, Gala is perhaps most useful because of its resource list. This list is made up of thumbnails of a handful of books. When clicked on, a new tab opens with information on the book including publication information and a summary. While, again, this page is not searchable, the comparably small scale and the use of thumbnails makes it more easily used.

In addition to this list of resources, there is a physical library associated with the project, the Cooper-Sparks Queer Community Library and Resource Center. The library is in the process of being inventoried and cataloged which will greatly increase the ease of use and the functionality of the library itself. It is important to note that in the publication tab there are a few books that are listed that are no longer in print, however, it is stated that they are available to read through the library. This encourages physical interaction with the organization itself which is a great resource for those in Johannesburg. These resource lists and collections of reading materials are complemented by Gala’s publishing branch, MaThoko’s Books, which seeks to provide publishing support to those who would like to further queer narratives and marginalized voices. Although this particular facet of Gala does not fall under the digital humanities umbrella, I think it is vital that Gala have both tangible and digital resources available, particularly because much of its audience in South Africa may have limited or unreliable internet access.

Taking a step back, part of what I appreciate so much about Gala is how it has transformed over the 22 years it has been operating. On the basic level of its name, the archive has continually adapted to better serve the community it represents. It consciously moved away from the title “Gay and Lesbian Archives” in a move to be more inclusive to a range of identities and orientations. Since this original change they are still open to changing their name if the LGBTIQ community’s needs and desires change and are not met by the archive. This sort of flexibility and community-centered approach is what makes Gala so unique as a research tool. While the objects in their collection remain the same, they are constantly reframing their approach and bringing new insights onto the objects to reflect societal changes and attitudes toward the project.

One may compare this archive to the Lesbian Herstory Archive located in New York City which is considered to be the world’s largest collection of materials about lesbians and their communities. Like Gala, their collection is not fully digitized, although they have begun doing so and are farther along in the process with audio files digitized and many photographs. They do say that if you are unable to visit the location in NYC, that, “In order to use the Archives from a distance it is best if you have a specific request, such as a certain article in a specific journal, along with the author, date and title, rather than a broad request such as “Do you have any material on Lesbian mothers?” in which case the answer would be “yes, a tremendous amount.” We can point you towards published periodical listings as well.” Put simply and without delving into why this might be the case, the Lesbian Herstory Archive has a lot of the same limitations to use that Gala does half a world away.