Introduction to Digital Humanities

For my digital humanities class we used Omeka to create a website to explore how the Grimm Brother’s stories have influenced modern society. Our project is called A Grimm Exploration.

Hey there!

This will be our final project. We hope you enjoy it. If you want to learn more, visit the main blog post or take a look at the website itself!

Fantastic Disney Fanatics

Hello all,
This is the link to our finished final project. Our Omeka site encompasses popular Disneyland Park rides from “It’s a Small World” to “Autopia” and explores the rich histories of the rides as well as how they have changed over the years. Some rides such as “It’s a Small World” have additional pages that explore what happens to the ride during the holiday season or any special events that happen on that ride. “Matterhorn Bobsleds” has a page that is dedicated to the climbers that climb the mountain and also to Tinkerbell who flies off of the mountain.

Click on ‘browse exhibits’ to explore!
Link is below, hope you enjoy!
-Jillian

Disneyland Rides Exhibit

Hello! The semester is coming to an end, and I’d like to finally reveal the final group project that I’ve been collaborating on.
I present to you (the Internet), A Grimm Exploration!

My efforts in this project are a cumulation of what I have learned through the Fall course Intro. to Digital Humanities at UOP.

Thanks for reading!

So our final for my Digital Humanities class is due today and we finally finished up the last few things needed for our final project! I had Ray, Manni, and Leslie as my group members they were the absolute best! Although we all have such hectic schedules, it was sooo very appreciated that everyone made such an effort to meet and be on time. I really enjoyed working with these particular people we had a great time and I feel we all learned so much through the process. We all kept in touch through phones/ texting and met up to work on the website together.
Our final project was to build a website using online applications that we have worked with throughout the course. My group decided to use wordpress, google fusions, and google spread sheets for our website project. We initially were going to build the website using html and coding but soon realized it was too difficult for us to do; so we ended up using wordpress which we had worked with during the beginning of the course. Our topic was to evaluate the changes in tuition costs, attendance, freshmen enrolled, room and board, and books and supplies. I learned how to make a spread sheet of datasets and how to center and format them correctly!! I also learned how to make a chart! This group project has to be one of my favorites mainly because I enjoyed creating and learning more about the topic of our rising tuition and mainly because I had a great time working with my group members.

Here is the link to our final project!!

https://tigersdemographics.wordpress.com/credits-2/

This table was done through google fusion and

Another exciting journey in my adventure with the Digital Humanities. In the link above you can see a group of data that might seem like a bunch of names and numbers, but as per usual , there is a meaning behind it all.

Previously, I made a blog post about Palladio and creating “special” maps. This week in class, we went a little further and used Palladio to make a map of networks. In the article “Demystifying Networks”, Scott Weingart explains network in the simple terms of “stuff” and their relations.

We were given a data set to use by Marten Durer h people in the time of World War II that showed such things as t heir gender, who gave/received help, and who may have given help or received it. It was a lot of information to take in since it was all on a spreadsheet, but once I put it into Palladio and generated a network map, there were different options that I could choose so I could see the information in different ways.

The first attempt of the networking maps that I made is of the one above. I screenshotted this from the Palladio website and whenever I do that it does come out blurry so the names are probably hard to read on this. For this map, I selected it to show the network between the gives and receivers.  Essentially, this map shows who gave help to who and who received it and the string of networks that these people were in. It’s hard to see, but there are darker circles in this map that represent people who both gave help and received it as well.

The second map I attempted to do was “race”. This map essentially made separate networks of people based on their race and it was actually kind of interesting. Part of the map cut off in the screenshot, but there were more networks down below that were very minimal. The biggest network in the middle was all German names and had multiple repeats. The people with the same last name were networked together. Below the large network were two smaller network of people also grouped together by their race. What I found interesting was that “AushweisNazi” was on the edge of the large network alone connected down to a smaller network. I am not quite sure what that means and couldn’t really figure it out.

I think I had a harder time deciphering what these maps. With the Spacial Maps, everything is clearly labeled and obvious to explain. With networks, there are so many names that are connected that for me, it is hard to tell what they represent. Overall, it is interesting to see how many data points connect though.

So, how ’bout them networks, huh? They’re a pretty intriguing way to look at data sets, if I do say so myself. Unlike with a regular old chart or graph, networks allow you to explore different connections between “types” of data, such as the example of books and authors Scott Weingart gives in his article, Demystifying Networks. He breaks down the complexities of networks into very simple terms, explaining their benefits through, essentially, relations of stuff. In the case of the books example, books and authors are both “stuff,” and a network would show the relations between them through a fancy display of nodes and edges. And while Weingart warns that networks shouldn’t be used for everything, they can definitely come in handy for a lot of data, particularly sets that only deal with one or two types of “stuff.”

Of course, perhaps my favorite thing about networks is that they’re mobile. You can move around the different nodes, make them bigger or smaller, change their colors, and each new thing you do to them gives you a new perspective on the data you’re networking. Let’s take a look at some, shall we?

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Now, the first network I’ve embedded here is from Google Fusion Tables. It was made using a sample data set taken from Palladio, a free site that allows you to create maps and networks at your leisure. The data set displays a list of influential people who went to Monaco at some point in their lives, along with several attributes about them – their dates of birth, death, birthplaces, etc. In my little network here (which you, humble reader, can drag around if you so desire), I’ve chosen to display connections between people’s places of birth and places of death. The colors of the nodes differentiate between the two different types of data – yellow is for birthplace, blue is for death place. And Google Fusion Tables has kindly sized the nodes for me, so it’s easier to see which birth and death places were common among these people (the biggest nodes being the places that correlated the most, like, unsurprisingly, Monaco). I also chose to make the links between nodes directional, giving them cute little arrows so you can get a better sense of how these connections flow. The wider arrows, like the bigger nodes, show a greater amount of connections between nodes than the smaller arrows do.

Moving away from Google Fusion Tables, I have a few networks I created in Palladio as well. These came from a different data set – a spreadsheet that includes the names and relationships of people who helped one another in a historical documentation of the Holocaust. (The data was collected by Marten Durer and can be found here.)

This network up above (though a little blurry, I apologize) shows the web of connections between people in Durer’s list who both gave and received help from each other. I chose to highlight the recipients of help in a darker gray, and to size the nodes so that it was clear just how much help each one received from other people. Rita and Ralph Neumann were obviously the biggest recipients out of everyone, but they were not the only ones; they also gave help to several different people, as the network demonstrates with, say, their middle-ground connection to an Ausweis Nazi. It’s interesting to see the relations of aid that occurred between all these people, because even though some may have never met, or didn’t help each other out, they’re still connected through their helping of someone like Rita or Ralph.

Now, this last network is a little different. Instead of focusing on givers and receivers, I decided to look strictly at receivers, and the types of help they received. Durer organized his data on types of help into a numeric system, hence all the numbers in this network instead of word labels. Though I can’t actually tell you what each number means, it is interesting to see how this kind of network differs from one dealing with two different lists of people. For one thing, the amount of names on this list is a lot smaller. With all the givers gone, it’s easier to see how few people actually got some kind of help out of this data set. The dark gray nodes are still the receivers, but the light gray ones are types of help this time, and they’re much more varied in size than the giver nodes were. This allows us to see what kinds of help were most commonly received. The connections between receivers are established through kinds of help here, instead of through who gave help to them. So while it shows us nothing about human relations, it provides a different view of what actually happened when givers offered help to the recipients. (Who knows what happened with poor Herald up in no-man’s-land there. Apparently he needed his own special brand of help.)

Looking at these different kinds of connections between people reminds me a lot of Kieran Healy’s article, Using Metadata to find Paul Revere. Healy took a simple set of metadata with the names of colonial U.S. people like, of course, Paul Revere, and the different organizations those people were a part of, and used the metadata in order to create various connections between those people. One data set showed how the organizations were connected by how many members each had, how the people were connected by their organizations, and several other snippets of interesting info. By creating a network of the metadata, Healy was able to pinpoint Paul Revere as one of the centers of all this activity, for he was connected to a large number of people and organizations. What we’ve been able to do with Palladio and Google Fusion Tables is similar. Particularly with my Palladio examples, we can see how almost any metadata can be rearranged and viewed from different angles, in order to discover and display different relationships between people, places, and other quantifiable things.

This past week, we were revealed to the simplicity and complexity behind networks and the visualization of networks. Weingart’s article, “Demystifying Networks,” begins with a real simplification of a network: “a net-like arrangement of threads, wires, etc.” which later incorporated “stuff and relationships.” Moreover, network visualizations explore these relationships in many dimensions: degree, attributes, directionality, and more (Powerpoint about Social Networks).

As a class, we used Palladio to gain first-hand experience with creating network visualization. Using this sample data pertaining to Ralph Neumann, we utilized the Graph function of Palladio.

First, I set the Source to “Giver” and the Target to “Recipient.” Highlighting the “Giver,” however, was not useful, because it didn’t distinguish any of the dots, signifying that everyone was a Giver.

Instead, I highlighted the Recipients. As a result, a few Recipient dots were highlighted, pointing out who received help.

Then, I changed the Target to “Time Step Start” and checked “size nodes,” and the following was revealed.

Here, we can easily see the amount of people that were helped according to who is connected to the larger center dot with a number. However, here we have a questionable large dot on the bottom that has no links.

Still playing with Sources and Targets, I changed the Source to “Recipient.” This graph visualizes who was present to receive help at the same moment of time. But again, we have a dot at the top without any information. This may indicate that there is missing data in our dataset.

Lastly, I experimented with the Timespan filter. This timespan allows us to limit the graph to only visualize those who received help within this period “0012-01-1 and 0012-12-29.”

Experimenting with Palladio raised a lot of concerns for me; one being, I should definitely know my data before plugging it into a tool. I am not familiar with how the data was collected or what the numbers represent, which limit my ability to make any conclusions. Secondly, I would prefer if there were more visual options to customize, such as color of nodes or adding arrows to signify direction. Lastly, all the images pasted here are screenshots throughout the process. The download option saves as a .json file that can be reopened in Palladio but not reposted onto a blog like this. A screenshot does not do Palladio justice, because resizing impedes the lengths of lines and size of the font. All in all, it is a groundbreaking tool in network visualization, definitely considering that the data uploaded will not be datamined for other purposes.

Palladio is an interesting mapping application that is used for examining spatial history. Above, you can see two images that show a little bit of Palladio’s capabilities. The top image shows locations marked where specific photographs were taken. As you can see in that image, a large amount of the photographs wee taken in California, near Michigan, and throughout the rest of the United States. The image below that top image shows a more detailed look at the area in California, near San Francisco, where Photographs were taken. This allows students and researchers to more accurately find where the photographs were taken. I think that it might also be helpful to mention that the maps shown above shows the streets, and mark locations like cities, and shows borders between states. Below, I included another image that shows a satellite view of the area and as well as marked streets and cities, just to show the different tools Palladio offers.

I mentioned in the beginning of this post that Palladio can be used for examining spatial history, which is pretty much the relationship between a physical space and time. Well, if you notice in all of those previous images there is a button in the bottom left corner that says “Timeline”. With that you can open up a timeline and look at how the dates of the photographs relate to any other set of data. I’m interested in the relationships between the dates and the cities the photos were taken, so I can tell Palladio what I want and I get a visual timeline like the one below.

As you can see in the image, I went over Wheaton, Illinois and it shows me when the photos were taken there over the course of over 10 years.

The class was introduced to Palladio and Google Fusion tables recently. These are two applications used to create and digitalize maps. First we downloaded the cushman collection data onto our computers and then uploaded them onto the applications. Both Palladio and Google Fusion tables are somewhat similar but I feel that each have different features that the other does not have. I myself prefer the Google Fusion Tables just because it has an actual street view which is always more helpful because you get to see the literal street and the surroundings.

At first Dr. S guided the entire class through the process and how to use the applications and how to make a map. It took me a little while but once I got to making a map it was pretty cool. I just played around with the features but since it was my very first time working with these tools I got a little lost and confused. I started with creating a map of the United States. There was a feature on Palladio that allowed me to zoom in and listed the city and states. Each city and state was marked by a little red dot that you could click on to show the city and state. Palladio does not have a street view available so that would be the only thing I like less about it.

The readings we did last week on spatial mapping made me question how accurate these mapping applications really were. The reading gave me the impression that when we digitalize maps, we distort them and they are no longer conveying the original message. I can kind of see that with the applications because they are not really spot on.

For my visualizations, I used Google Fusion Tables (the one on top) and Palladio (the one on the bottom) to graph the Cushman-Collection. Although both programs are mapping tools, you can see that there are distinct similarities and variations between the two.

Of similarities, both are maps that display geocoordinates, placing dots at the location where photographs were taken. In this case, while Palladio offers more color customization, GFT is a bit easier to read, using red dots with a black outline. You can zoom in and out of the maps to get a better sense of location.

It is also here that you can see the most difference. First of all, when clicking a dot on GFT, you quickly pull up all of the information associated to that location, from URL to date and description. As you can see in the first picture, my mouse if hovered over a dot near the top of the picture. Everything related to Buthart’s Gardens is displayed. On the other hand, Palladio is much more specialized in the data it presents, allowing more of a comparison of specific categories. The timeline at the bottom shows when pictures were taken and the chunks on the bars depict approximate how many of the pictures fit a certain category. For example, in the bottom picture, my mouse is hovered over one of the chunks in the bars, which shows the pictures that are “snapshots.”

While we are on the subject of comparisons, I’d like to add that both maps are useful tools in spatial visualization. As Jenna Hammerich, explains in her article, “Humanities Gone Spatial,” spatial visualization allows us to view spatial relationships and change over time in ways that we couldn’t before. While both tools map out locations, they don’t solely exist as mapping tools. Both programs allow for the creation of graphs and other such useful information to compare information. I’d say that Palladio appears to be more appealing than GFT in that aspect, nonetheless. It allows side-by-side visualization of graphs along with the maps. However, GFT is certainly much more trustworthy than Palladio, as Palladio constantly freezes up and has a long loading time.

As useful as maps are from both programs, there is an inaccuracy. As Zephyr Frank states in his article, “Spatial History as Scholarly Practice,” maps are usually altered and therefore are not exact representations of the actual locations. You can tell when you view a closeup of both photographs that there are differences in locations. Sidney (the top-most point on the upper left hand corner) and Butchart’s Garden (the second top-most point on the upper left hand corner) are located farther apart in GFT than they are in Palladio.  It is difficult to say which is more accurate. Of course, in this case, I doubt either program was attempting to misconstrue data to make it aesthetically more pleasing, but the trouble of accuracy remains. I would trust GFT more, seeing that GFT comes from Google, which uses precise satellite images to create maps. As a bigger company, it would make sense that it has more funds to ensure that its products are accurate. That’s not saying that Palladio completely inaccurate. It’s merely that it is less so.

In class, we have been talking about the difference between maps and “Spacial Maps”. Essentially, Spacial maps are not your ordinary 2D map but instead have many different layers to it. Spacial Maps can be interactive and show such things as, how many people migrated to an area at a certain time, the distance that a herd of animals traveled, or even where a photograph was taken. In an article by  Jenna Hammerich that we read in class, she talks about how this technology can answer historical questions  such as  “Why did African American families settle almost exclusively on the near north side of St. Louis in the 1940s?”. Below is a link to an example of a Spacial Map made by the Humanities Team at Standford:

https://www.hastac.org/news/check-out-latest-version-palladio-dataviz-platform-humanities-design-stanford

I used the website palladio.designhumanities.org to create my own spacial maps using the Cushman Collection data set.

This is one map that I created. The “dots” are all based off of a data set and represent where each photograph was taken.  To make this map, I simply uploaded the data set and added the layers of geocoordinates and land.

In the map above (I know it is a little blurry that’s how my screenshots kept coming out whenever I saved them) I used the “size point” future on the dots instead. With the size point feature, the dots appear larger where more photographs are documented. I also changed the view of the map to ‘streets’ so that I could see actual state / city names instead of just land. Large dots appear frequently in California so I can learn that a majority of the photos in the Cushman Collection were taken on the West coast of the United States.

Taking it a step further, I focused in on the map in California so I could see which cities actually had the most documented photos. From this, I was able to see that there were a lot of photos scattered along the Bay such as San Francisco and Oakland. However, Salinas, Fresno, and Los Angeles had the largest dot sizes telling me that those cities individually had a lot of photographs taken there.

This map, that I found on http://ed101.bu.edu/StudentDoc/Archives/ED101fa10/mogavero/maps.html is a economic activity map. This map is very similar to the Spacial maps that we have gone over. It has a legend in accordance with colors that shows where in the south there were farmers, forests, manufacturing and trade, livestock, fishing, mining, and places of no activity.

So for this blog post, I delved into two different digital map making tools in order to explore what kinds of things can be learned from maps: Palladio and Google Fusion Tables. The data set that acted as the centerpiece for my maps was the Cushman Collection, a spreadsheet archive of photographs from around the U.S, dating from 1938 to 1969 and taken by Charles W. Cushman. (The website where the photographs originally came from can be found here.) In creating different maps of this data, I was able to discover various things about the photographs, particularly some of the most recurrent locations where they were taken.

 This map at the left was the first I made in Palladio. It’s rather difficult to see, but each pink dot on the map represents a place in the U.S. where a particular photograph – or a group of photographs – was taken. Obviously some areas have more condensed dots than others, like the West Coast. These dots act as markers of the places Cushman traveled and documented with his camera. Behind each little spot of pink is a bit of spacial history, specific to the year any given photograph was taken.

The second map I made in Palladio was a little different. I made a slight change to the way my lovely pink dots were displayed, adding the “size points” feature so that areas with more photographs documented had larger dots. This gives a much more dramatic visualization of how many pictures were taken in places like California, versus how few came from places like the Northern states. Here we’re provided with a means of comparing the spacial history that these photographs show, and we can easily see how much these pictures might tell us about certain parts of the country over others.

 Now then, moving on to Google Fusion Tables. The first map I created with this tool was very similar to my first Palladio map. I wanted to see what a simple layout of picture locations might look like between the two different tools. Instead of dots here, I used little location markers, like the ones you would see on a Mapquest search, but the principle is the same. In this case, each marker on the map stands for an individual photo, rather than some acting as representations of many photos. Clicking on one marker would tell me exactly what photo it represented, and the metadata that went along with that photo. We can still see here that some locations in the U.S. have a much more dense collection of pictures than others, but the impact seems greater when Google Fusion Tables provides so many more markers than Palladio does.

Out of curiosity, I tried creating a different kind of map with Google Fusion Tables than my previous three. It’s called a “heat map”, and from what I gather, it’s supposed to represent the densest parts of a data set on the map. Again, the image I took of my map is rather small, but there’s a very obvious red and yellow circle around the North Eastern part of the country, meaning that a vast majority of the Cushman photographs probably come from there. Other places on the map have lighter green splotches on them, showing that there are indeed many photographs from there, but not nearly the amount as are in that big circle. While this map does give a better idea of the hot spots in the U.S. for these photographs, it does a disservice to the data as a whole, because it leaves out locations that didn’t have enough photos to be included. The Northern states and Texas aren’t included at all here, when it was clear that there were photos from these places on the other maps. So this kind of map eliminates some of the important spacial history to these photographs that the other maps represent more accurately.

Observing these different maps, and the perks and drawbacks to each, reminds me of the article we read for class by Patricia Seed. In her writing, she drives home the point that maps are more than just pictures, they’re visualizations for conveying meaning. That comes from the spacial history of maps – from being able to see things that have occurred in the past, connections between different parts of the world, and pretty much anything else under the sun. To treat maps as mere pictures is to lose the most important element of them. But in order to convey the appropriate meaning, a map has to be suited for the job. Some maps, like the heat map I showed above, or maps that have been tampered with upon going digital, don’t display the spacial history of their data like they’re supposed to. And that can sometimes leave out the most important details of the data. Maps must be made and treated with respect and care, otherwise the stories they tell may be lost.

A few days ago we entered the Cushman Collection from Indiana University into Palladio. While in the Palladio I was able to make a map showing where each item from the collection is. I was also able to create a timeline showing when each item was added to the collection and at the same time see the map show the increase and spread of the items based on what date was selected on the timeline.

Advances in technology have made digitizations of maps possible; despite it having pros and cons, this progress has also segued into the concept of spatial history.

Programs such as Google Fusion Tables and Palladio have mapping functions. We were fortunate enough to experiment with a cleaned up comma delimitated (.csv) file, the Cushman Collection.

Using Google Fusion Tables, Google automatically detects the geocoordinates column and places dots for every photo taken at that location. The perks, in my opinion, of Google Fusion Tables is you receive an expanded amount of information when you hover over a dot. In addition, you can utilize street view, which could be helpful in comparing the location up to the date Google maps took the picture with the photo taken in the past.

On the other hand, Google Fusion Tables’ map function only has two overlays: “map” and “satellite.” So the two images here display the only two options available to view. (Above is Satellite view and below is Map view)

Google has become so prominent in our society today that it doesn’t get questioned often. However, Patricia Seed, in “A Map is Not a Picture…,” calls us to reexamine our standards and how credible maps are. The borderlines above seem irregularly straight, so I searched for another U.S. map displaying the state borderlines.

This picture is provided by Wikipedia (click the picture for direct link). If we closely examine Wyoming, for example, the borderline on the West is slanted, but on Google Fusion Tables, it is completely straight.

Palladio can look almost exactly identical with Google Fusion Tables’ map, by using the Streets and Terrains tiles. Palladio, for one, has more options for view-ability of the map, but it lacks the street view and amount of information shown when hovering over a dot, like Google Fusion Tables. Palladio only features a number description over a dot.

Additionally, Palladio offers more functions with the map function. The Timeline below displays a bar graph of how many photos is taken in that time and the colors represent the different types of pictures.

Above, I highlighted the timeline between ~1947-1949 to only display the photos taken at the time as the dots on the map. This function is especially useful when examining the correlation of time, location, and types of photos taken.

Playing with these two mapping tools, Google Fusion Tables and Palladio, have caused me to be more aware of mapping in general. Though maps can be manipulated to be aesthetically pleasing, examining them closer is the only way to really decipher any problems or information.

We have learned how to use Palladio today and it was interesting in making the maps.  This map is based off of the Cushman Collections on where the photographs were taking place.  Most of them seem to be around the United States, mainly in places like San Francisco and Chicago.

This is the picture of the map that I created today in Digital Humanities using Padillo. The map is of the United States and shows the city and states. On Padillo you can play around with different options and functions to create any kind of map that you want with the details and features that you like.

I imagine it would be fun yelling the word “Palladio” at a bar or in a random area for no reason. Palladio isn’t just a cool sounding fun word, it is a mapping program online that I used to make the map below! 

Palladio was very simple to use and with the sample information we had I charted the years 1940-1942 and 1950-1952 (that highlights on the time line).

Using the Cushman Collection, we uploaded it to Palladio and explored the Map feature. Above, I created two map layers: Street and Satellite view. Then, I limited the points to display the photos taken between 1940-01-01 to 1942-01-05 by using the Timeline feature. This function is useful, because when you hover over the points, more details show.

Still, lots more to explore.

Technically, yes; a map is a visual image, but subsequently, it is not and should not be treated as a picture. Patricia Seed is very vocal in the argument of “a map is not a picture.” Print has been the “gold standard” of maps, thought to be the most trusted and reliable map, because it has to be accurate to be printed, right? Not right. The digitization of maps that makes reproduction possible has its kinks: subtle changes in axes and in color; however, Seed points out that alterations are sometimes intentional. Maps have been treated as pictures – sellers can only sell what is aesthetically pleasing, thus encouraging them to straighten lines, heighten color contrast, brighten, or darken. On the other hand, digitization has changed the map game for the better: introducing more detailed study of maps, better and easier magnification, and, of course, portability. It is just ironic that digitization is the cause of the loss of fidelity for maps. “Maps cannot be treated as illustrations” – they must maintain their intellectual integrity, or the consequences may be serious (the example used is poor planning of irrigation systems).

Take this map, for example; this print of a map is for purchase from the David Rumsey Map Collection. The note entails that the map was “hand colored;” chances are, the colors have been heightened or contrasted in this digital photo and will be in the reproduction. We would not know, though, because it does not state anything about the possibility of being photoshopped. Seed suggests that sellers clearly state “after the original” to explicitly show that it has been altered – changing intellectual property for aesthetics can be harmful.

This past week in class, we dedicated our time making exhibits and learning about the ways of Omeka. For me, this was a completely new experience and I had never heard of Omeka before let alone know such exhibits existed online. This is one of my favorite activities that we have done in class and I learned a lot from it mostly because it was very hands on.

First, I learned that a lot goes into attributing a source. Dr. S gave us resources such as wikimedia.org where we could find images on our own and use it to build exhibits on Omeka. While in the past I would have thought that it’s as easy and copy and pasting, the Omeka site taught me that there are endless possibilities to attribute the source and even explain what the picture represents. For example, I used a photo that was a statue of two gladiators. While I definitely needed to attribute who sculpted the statue, where it was from, and what year it was made in, the interesting thing I learned was that the source I was using was actually just a PICTURE of the sculptures and not the physical sculptures themselves. Therefore, I then had to attribute the photographer who took the picture, when it was taken, and where the original picture came from. It was interesting to see how many layers of copyright there actually are.

Next, I really enjoyed creating an exhibit with my group on a subtopic. Like I said, this site was completely new to me and I didn’t even know that it was possible to do this. I thought it was really cool how each of member of my group created their own page yet all of our  pages came together in one exhibit. It was easy to navigate through and a great way to store a lot of knowledge. It was also really interesting to hear all of the other groups present their exhibits and see what their thought process was.

Overall, I thought the Omeka website was a very interesting learning tool and it is something that I would have never thought of. Omeka can be very useful if you are researching something and want to keep track of your attributes and citings as well as also present your information to the world.

-Jillian