Reading Wikipedia

I’ve been taught from a young age to be skeptical of Wikipedia – and so I always am. And though I am always skeptical, Wikipedia is still a go-to for quick, basic information – I tend to assume that people know and post accurate information on these subjects.

With that being said, there are many ways to question a Wikipedia article to get more out of it. If you want to really understand an article on Wikipedia, you can use tools on the page to delve into the process of the page’s creation.

First: Scan the page for its organization. See how it’s divided and what the sections are.  If you know anything about this topic, do the subjects given provide a holistic view of the topic? Is anything missing?

Second: Read the page. Note when you have questions, or if you find some piece of information to be wrong. How might you right this article differently if you were an editor?

Third: Note any and all citations. Are there citations in the article? If there are citations, what is being cited? What kinds of references are being used? Are they scholarly or not?

Fourth: Search and scan the edit history. You can do this by clicking the “View History” tab at the top of the webpage. This page will show you all of the edits made to this page and who made them.  How has the page changed over time? Who are the primary contributors? Can you find out any information about them? Are they reliable editors? Would they have any bias in editing this article? You can also search the edits by editor to see all of their edits on the page. Is there a topic they focus on specifically?

Fifth: Find out what sections have been most contested on the page by clicking on the “Talk” heading at the top left of the page. You can view discussions between editors on aspects of the page. On what topics do they conflict the most?

If you’re primarily using Wikipedia for quick and basic information, you may find this to be not helpful. But if you want to better understand the information you’re reading on Wikipedia, there are many tools you can use to get a better understanding of the page. Experiment with these tools – the information you can gain is never ending.

Comparing Tools

In this post, I will compare three tools we learned during this session: Voyant, CartoDB, and Palladio.

Voyant was a text analysis tool, which allows users to analyze word and word usage from documents in a variety of ways. There are some bugs in this program which made it difficult to use. Despite these bugs, it allows for very careful text analysis and a variety of methods with which to analyze documents.

CartoDB is a mapping software that is incredibly easy to use. Not only is it easy to use, but it allows for many different ways to visualize your data. This tool visualizes geographic relationships in data.

Finally, there is Palladio, a tool which allows users to visualize relationships and correlations within data through a network. Unlike CartoDB, Palladio allows for visualizing relationships other than geographic relationships. You can pick specific aspects of your data and Palladio will show you the relationships between them.

All of these tools are useful but for a variety of reasons. While all three are used for visualizing relationships, they are used for different purposes. For those interested in understanding language in their data, Voyant would be the better tool. Those that want to understand geographic or spatial relationships should use CartoDB. And those that want to visualize relationships beyond the spatial should use Palladio.

But no one says you have to pick just one! All three tools are incredibly useful for humanities studies. Put together, you could come to understand your data from many different aspects, creating a holistic view of your data.

Networks with Palladio

Palladio is a tool used to visualize networks. Networks are an important subject matter for the humanities, as humanities scholars ask many questions about relationships and correlations. Using digital tools, such as Palladio, visualizing networks, relationships, and correlations has never been easier.

You start by importing your data. Palladio then allows you to easily link your data to create relationships. We continued to use the data from the ex-slave interviews for this tool.

You then can create a graph. In order to make the graph, you need to pick which two facets of your data you’d like to show the relationship between. For example, we used the aspects “topic” and “state where enslaved”, or “age” and “topic”, or “interviewer” and “topic.” Of course, the options are almost endless!

Here’s an example of one graph, where I chose Male/female aspect and topic of interview to correlate:


You can also manipulate the graph however you want. Each of the orbs can be dragged into a desired position.

This is a useful tool for studying networks. From the many readings on network visualizations we read before working with Palladio, it seems that other network visualization tools are more difficult to use. In order to use Palladio, you do need a guide on how to start if you are new to this sort of program. It is not the most clear site, but with a guide, it would be fairly simple to make your own network graphs.

Mapping with CartoDB

I have had some previous experience working with mapping tools – mainly ArcGIS and QGIS. Those experiences made digital mapping seem exhaustive, difficult, and tedious. CartoDB is quite different – opposite of my experiences with the GIS software.

CartoDB allows anyone with data to be able to map it easily and efficiently. Again, as long as you have a dataset, CartoDB is fairly easy and straight-forward to use. Our dataset for this project was information from a collection of interviews from former slaves. When first uploaded, the map created was simple. It shows the location of each interview. You can also go in and alter the information shown, so that by clicking on the data, a user can see a glimpse of the information about that point.


There are many ways you can utilize CartoDB to alter the information shown through your map.

Intensity – this map feature shows the intensity of the locations of your data through colored dots (as shown below).


You can also show intensity through a heat map, which integrates the location data (as shown below).


Another way to view data is through the Categories option. Through this, I could view the data by slave “type.” The map shows this information by coordinating colors to they types of slaves discussed throughout the interviews. An example of this is shown below.


CartoDB is free to use on the web! Again, I found this program easy to use and user-friendly, especially when compared to other well-known mapping programs. For anyone that wants to use a mapping program, I suggest CartoDB. I am excited to further explore this program with my project for this semester.


Working with Voyant

This was my first experience using a text analysis tool. Overall, I think Voyant seems to be a great tool to use for text analysis for a variety of reasons. First, it is available online and its graphs can be pulled up through URL’s. This tool is also great because of the multiple forms of text analysis it provides.

On the Voyant main screen (once you’ve uploaded your documents) you’ll find five different screens, each with a different purpose. Cirrus shows a word cloud of the most widely used words in the documents. By using the Reader screen, you can read through some of the documents and spot specific terms in each. Trends puts this data into graph form, which can be manipulated to plot the data in multiple ways. There is also a section which shows the most distinctive words from each document, under the heading “Summary.” Finally, you can pick a word to examine and see the words that come immediately before and after the chosen word – this is the screen called Contexts.

This program allows for one to produce fascinating insights on word usage and correlations for humanities research. There are some issues with this program, however. The web format of Volant seems to be very glitchy. I had to start over numerous times because it would not show a particular graph or the program would get stuck. This was fairly frustrating to deal with so hopefully these issues could be fixed as the program gets updated.


Figure 1: Screenshot of main page in Voyant.


Metadata Review

Metadata Review of the Antique Map Resource Database (

The metadata on this site describes the following about the maps:

  • Creator
  • Subject
  • Period
  • Size
  • Condition
  • Source
  • References
  • Pricing Information

The metadata allows for questions on its creation, value, and location. It does not allow for questions on the recent history of the map, where it resided and who used it since its creation.

Database Review

This post is a review of a database in the George Mason Library system.

Antique Map Price Record

Overview: The Antique Map Price Record is a collection of around 200,000 maps with the purpose of serving as an encompassing survey of antique maps. There is even a plan to create yet another guidebook of this database and redesign the website to have more advanced search features and many other new features.

Search: Searching this site is possible through many means. One can search by title words, mapmaker, subject, descriptions, company, regions, dates, and even map dimensions.


  • Date Range: c.1200-1979
  • Publisher: Originally published by David Jolly in 1983. Published on the web by Curt and Marti of Old Maps, LLC.
  • Publisher About Page:
  • Object Type: Maps
  • Full-Type Searchable: No, but searchable by subject matter, title and cartographer

History/Provenance: This database was first created as a guide for antique map collectors in 1983. In 2002 this guide was converted into from the guide to a digital CD-rom format. Today, this database is on the web, searchable in many formats.

  • Original Catalog: Antique Maps, Sea Charts, City Views, Celestial Charts & Battle Plans – Price Guide and Collectors’ Handbook by David C. Jolly (1983)

Access: Check libraries for their subscription to this database. The book format can also be purchased for $75.00.


The process of digitization allows for objects to be viewed from anywhere, online. While many aspects of objects can be viewed through digitization there are many that cannot.

Depending on the quality of the picture, a viewer may be able to see the colors, words, and textures of the object. Based on what else is in the photo, one might be able to analyze the size of the object. However, this could be difficult. Through just a picture of the object, digitization does not allow for sounds and smells of objects.

Other forms of digitization might use videography to show these other aspects of the objects. A viewer might be able to get a better understanding of the object’s size, sounds, etc. through taking a video of the object.

Digitization is incredibly useful for documents. The words on the pages of documents, colors used or not used, images or pictures on the document, can all be transcribed through a photo.

Videos are more useful for objects. A viewer can see the many sides of the object in order to better gauge it. A video provides the opportunity for analysis of objects where digitization through merely a photo might not be as successful.

Digitization is great tool to share objects of historical value to all those who wish to see them. By being online in whatever format, people can view these objects and study them, use them for their own research. In many ways, it is like a digital form of archaeology: instead of studying objects through hands-on interactions we can now study objects through images or videos. This allows for a spread of information and knowledge that has not previously been possible.

National Archives and Records Administration

The National Archives and Records Administration home page.

The NARA is the keeper of some of the most important records from the United States Federal Government. According to their site, most of their images are in the public domain and are therefore free to use. To read more about copyright information, scroll down the page to the “Copyright” section.

“PD_warfiles” , “PD_governmentfiles”