DH Project Review- Women’s Worlds in Qatar Iran

The Women’s Worlds in Qajar Iran digital humanities project is an archive containing images of documents and objects pertaining to the lives of women living in Iran during the Qajar period, from 1786 to 1925, but is wholly a digital endeavor (more on this later).

Technical Basics: The digital home of the archives is now the Harvard Library system accessed via its own web address (http://www.qajarwomen.org).  The entire digital archive interface was initially developed by an outside company called Historicus, Inc. which now appears defunct. This interface appears to work and function in a very similar manner as Omeka, but in the project’s FAQ they outline the technology running behind the scenes.

Financial Support: In 2009, the project received a two-year grant from the National Endowment for the Humanities to establish the initial website. After this point in the project’s history as presented on their website, it is unclear exactly how the monetary support system after the initial grant works, and how much control the Harvard Library System exerts over the project.  There is a page dedicated to sponsors which includes Harvard, the NEH, and several foundations working toward the promotion of Persian culture in scholastic fields and general appreciation.

Language Support:  As this is a project/archive concerning a non-English speaking culture, presenting the material in the original language preserves the integrity of the contextual information for the item and respects the original culture. The main page for the Women’s Worlds of Qajar Iran allows the visitor to explore the website in English and in Persian Arabic.  The metadata and search features support both languages and switching back and forth between the languages is fairly simple.  All one needs to do is click on the relevant link in the upper corners ( left corner for Persian to English and right for English to Persian) and the viewer stays on the same page but gets the information in the other language.  This is a nice feature and some sites require the visitor to go out to a main page to change languages.  However, if you do not read either language, you are out of luck with this site as they do not appear to support other language translations.

Archival Items: The items in the collections remain with their respective owners allowing the digital component to transcend geographical constraints, ownership issues, and geo-political barriers. This is both a benefit and limitation of the archive.  On the one hand, it is great to view photographs, letters, documents, or items that belong to private parties around the world, but it means having the ability to handle the objects is extremely difficult if not impossible to orchestrate .  For documents this may not be a big deal, but for objects or artworks this can extremely limit scholarship as texture, tactility, and/or weight is best understood by physically interacting with the object.  Some collections/items do provide holding information in the metadata, but not all does.  Let’s look at one item in the collection:

This item is in an archive that one could theoretically track down and visit, but more concerning is the confusing and missing metadata.  The dimension field lists three different dimensions but does not explain what those mean and much of the contextualizing information is missing (this may be due to the repository not collecting that information when they acquired the item).

Another limitation of the database’s interface is the inability to see thumbnails if more than one image for an item exists.  For example:

Within this single entry, there are 33 images of the various pieces of stationary housed in the wooden box.  In order to view each of these I have go in and out of each link (there is no way to move between them with out coming back out to the item detail page).  It is cumbersome and frustrating as the individual pages are not even labeled.

Overall, this is a great project bringing light to women’s stories in particular period of Iran’s history.  Unfortunately, the lack of metadata displayed and clunky interface limit the usefulness of this archive.  Additionally, I do think the motivation to transcend boundaries and cross archives is beneficial and one that digital humanities and digital archiving is well-situated to achieve, but more thought needs to go into documenting information about the location/archive and details/metadata of the items.

Reflection on my first DH project

main exhibit page from ecotimberline.omeka.net

Holy crap this is a lot of work.

But I love it.

I haven’t even begun to scratch the surface of what I want to do with the information and ideas I’ve gathered and developed for this project.  I’m really proud of what I’ve been able to accomplish in only a few short weeks, but I already see changes I want to make to this project.  I also have some ideas on how I approach and plan future projects.

As an art historian my future project will all be image heavy, just part of the field, but also makes for visually appealing digital projects.  One thing I will differently in my next project before I even touch a computer is to make a preliminary plan of what I think the site might look like.  I know this will change, but as I put together this project  I realized several things about the materials I gathered.  For example, many of my photos were not zoomed in enough, or included too much of the surrounding environment and thus made poor thumbnails.  I also realized that while I had a good understanding of the space and how everything fit together, I was missing contextualizing photos of the broader space.

From a technical perspective, Omeka was an easy and great way to start.  As a platform designed for online exhibits with images and text, a lot of the work was simple uploading and formatting.  There was a bit of a learning curve as the tabs are not super intuitive and the method for images feels a little backwards.  For example, the system presents you with fields to fill out before you come to the upload tab. I also realized I had to decide how I wanted to upload images.  Was each image its own item? No.  Multiple images for the same work of art or craft went into the same item.  But what about works in a series, should I group all of those together under one series or have an item for each component of a series? For this I asked myself, if one could reasonably only look at one component, then that should form the basis for an item.  But what about images from books?  I had a series of images from a unique book, but was unsure if I should upload each page separately and then combine in a collection, or combine them all under one item.  I ended up combine them under one item, but probably should have uploaded each page as its own item. Time consuming, but potentially easier to work with than it is now.

I also found that there was a lot of prep I needed to do before I felt I could even upload my images.  I needed to organize them in to relevant folders and then rename each file because a string of numbers and times are not as easy to keep track of as descriptive file names.  Once I uploaded the images, then I needed to begin to full out the metadata information (Dublin Core).  There was a lot I realized I simply did not have or did not know how I should fill out.  This is something I can plan for in the future by creating a guideline or work stream that includes gathering this information when I take a picture or gather materials in an archive .

As I began working with Omeka, I realized there were some serious limitations.  Storage space and lack of plugins providing versatility in the free version, and the inability to change simple things such as the font and font-size in the web version.  I solved the first hurdle by upgrading to a paid subscription.  I tried editing the html code to fix the second problem, but to no avail.  I finally just made myself stop trying because it was more important to get the project somewhat done rather than visually perfect but lacking content.

Overall, however, I would absolutely recommend Omeka as a beginners DH tool.  It is a tool that assumes the user does not have programming or design skills and because of this someone can simple plug in the relevant information in the various space and create a professional looking website as an end result.

You can check out my Omeka project here: ecotimberline.omeka.net

Adventures in Tableau

Tableau is one of the most popular data visualization tool used by scholars, researchers, and business-people.  But is that a good thing? Does data visualization give the viewer/reader an accurate picture of the information presented? Or does it allow the creator/researcher to hide flaws or omission in their data? The truth is any consolidation of information into easily-digestible chunks distorts the overall picture.  Whether it be an old-fashioned line graph or bar chart, or a dynamic information web, all data visualizations focus on some data while downplaying others.  Additionally, visualizations tend not to convey how data was gather or from where.

The pitfalls of data visualization aside, Tableau provides a fairly easy tool to create fun and vibrant visuals to convey information.  As a traveler, and a fan of cheap trips, I found Sara Loves Data’s visualization of the cheapest cities in Europe to travel to quite interesting and engaging. For the blog post on how she assembled her visualization, go here (https://sarahlovesdata.co.uk/2018/04/03/vizzing-european-cities-for-iron-viz-europe/amp/) For the visualization alone, go here (https://public.tableau.com/profile/sarah.bartlett#!/vizhome/EuropeanCitiesonaBudget/EuropeanCitiesonaBudget)

I particularly liked her simplified use of icons for budget lines, inclusion of climate details for the best times to travel, and a clear disclosure at the top for how she evaluated the costs of a stay in each city.  Her visualization provides interactivity with the user when they scroll over the climate data, as it provides the number of rainy days/month or average temperature when the visitor moves the cursor over those elements.

Starting to Use Tableau

First, you have to download Tableau.  You can use the free version (but if you want to use your creations you must publish them publicly on Tableau Public). If that doesn’t float your boat, you can download and use Tableau Creator for $70/month/person.  Free = Tableau Public. Come on, share your creations with the world.

The first thing you need is a dataset, and you want that to be a cleaned up dataset.  What does that mean?  You want consistent entries, no blanks or nulls, and clear column and row headings.  As you clean up your data, you should think about how you go about doing that clean-up, how those tweaks, additions, or deletions change the data, and what those changes might do to your conclusions.

Tableau Public provides a number of sample datasets to use as you’re learning to use their software.  You can find them here.  You must download those files to your computer to use in the Tableau program.

When you open Tableau, and start a new project you will see this:

At this point you will want to connect your data source.  Select the correct file type under the Connect list on the left side of the screen.  Follow the prompts, which should lead you to this:

If you haven’t cleaned up your data, now is the time to do that.  All set? Now, the fun begins.  Click on the Sheet 1 tab at the bottom of the window.  This will open up the workspace where you can begin to play with the data, and decide which information from your dataset you want to compare and contrast.  This part is mainly trial and error.  The more you move things around the clearer it all becomes.  I chose a dataset of 633 Pokemon and their stats from the Tableau Public site.  After a lot of playing around, cursing, and talking with others who’ve used Tableau in their professional experiences, I began to get a better handle on where to put things, which visualization to use, and how filter the data.  First, you must move the column and row information to where you want it for your visualization.  Like this:

Here I am moving the “Type of Pokemon” to the column space, so my data will organize based upon Type.  Next, I move two of the other attributes that I want to show.  Like this:

These “measures” won’t be displayed on the x or y axis, but rather will be conveyed by size and color, which can be changed and added like this:

Still not a very interesting or helpful graphic.  At this point I also realized that the numbers the program was using were totals of these measures for each type not an average.  You can change this by right clicking (two finger clicking for those fellow Mac users) on the measure, selecting measure and changing the method of calculation.  Like this:

At this point, I went to the right side of the screen and began looking at the different visualizations available with these parameters.  Some work better than others to display the differences between each Pokemon type.  As seen here:

I went back to my initial setup, with the type in the columns and the attack and defense shown in the size and color (I also went back to SUM rather than AVG, why will soon be revealed).  I then added Speed to the row space.  This populated the columns with marks for all of the individual Pokemon in a particular category.  Now, I had something interesting.  I played with how the shapes looked by right clicking on the shape icon and playing with the options. I played with the color the same way as the shape.  When I hovered over a particular mark, it did not tell you the name of the Pokemon for that particular point.  I pulled over the Name “dimension” and bingo, it was all there.  How beautiful is that?   But wait, I learned one more trick.  You can filter the data within the visualization to allow for closer comparisons.  If you add a dimension to the filter box, you unlock a whole other level of comparison power.  Watch here:

Finished product can be found here: Pokemon Fun

I couldn’t get the embedding to work, so you’ll have to go to the source.


While, I’m not sure that this tool will be immediately helpful in my research, I can certainly envision ways to use Tableau to help make sense of large chunks of complicated data.  However, in the advantages lies a weakness, over-simplification.  In the humanities, we live in the minutia, the grey area, the complexity, and exceptions.  Pretty, colorful charts are fun, but only as useful as the conclusions they afford.  It is those conclusions that we as curators of information must remain mindful of their limitations.