DHO:Discovery - Digital Humanities Observatory


DHO:Discovery enables serendipitous discovery of related knowledge. By allowing for cross-collection browsing and searching we enable you to draw connections between data that would previously have existed in isolated silos of information. The Discovery interface also enables the leverage of advanced data visualisations that can allow for you to visually seek out patterns and linkage between data for further exploration.

These visualisations are experimental in nature and by definition. As a result, all features may not work in all browsers.

This component of DHO:Discovery is possibly the most exciting component of the website and also the most experimental. Data visualisation is a highly subjective experience, and while it can be powerful what works for one person does not work for another. This is why we provide a variety of visualisations to choose from. Not all will work in all browsers nor with all collections. They will work with most and also provide you with innovative ways about thinking about how data can be explored. We encourage feedback and welcome your contributions and comments as this part of DHO:Discovery evolves.

We currently provide the following visualisations:


Exhibit Visualisations

Exhibit Visualisations

Exhibit visualisations are most useful for exploring one or two collections in greater detail. Exhibit-based visualisations allow you to choose specific collections of interest and to view their objects by focusing on particular data to sort and discover.

The thumbnail view provides small images of all objects in the collection and provides limited information about each. You can click to choose objects to find ones that interest you.

The timeline view takes the date of each object and plots these in a scrollable timeline so that you can visually inspect the objects temporally.

The List View provides a table listing of all items in the collection. You can sort any of the columns by clicking on the column headings.

The Exhibit framework for lightweight data presentation is freely available from MIT's SIMILE project. The use of Exhibit allows you can choose to combine collections within visualisations and conduct faceted browsing within the Exhibit environment.

Exhibit Visualisations

Google Visualisations

Google Visualisations

Google Visualisations are useful for exploring specific collections in greater detail. They allow you to choose a collection or an author of interest and to view related objects subject terms visually to appreciate the breadth of coverage or to discover objects in related collections.


  1. The Pie Chart provides a proportional visualisation of the subjects related to the collection or author you have chosen.
  2. The Line Chart displays subject terms horizontally with frequencies indicated on the vertical axis.
  3. The TermClouds display subject terms, where the size of each word is determined by the frequency - the number of objects related to the collection or author you specify. You can click on any term to view the related objects in a table.
  4. The Subject term table provides you with a raw data table of the results of your search. This table can be sorted by clicking on the column headings and can also be exported for your own use.

The Google Visualization API lets you access multiple sources of structured data that you can display, choosing from a large selection of visualizations. Google Visualization API enables you to expose your own data, stored on any data-store that is connected to the web, as a Visualization compliant datasource. Thus you can create reports and dashboards as well as analyze and display your data through the wealth of available visualization applications. The Google Visualization API also provides a platform that can be used to create, share and reuse visualizations written by the developer community at large.

Google Visualisations

Image Galleries

Google Maps

There are a multitude of image galleries available online, each allowing the user to view and arrange collections of pictures in new and exciting ways. Choosing a small selection of these as models, the DHO has developed several galleries that use the latest javascript, HTML5 and CSS3 tools to browse image and video content indexed in DHO: Discovery. As the latest technologies are used, it is recommended that you use an up-to-date browser such as Firefox 13.0.1 or Chrome 20 to access these visualisations.

Image Galleries

Google Maps

Google Maps

Google Maps (formerly Google Local) is a web mapping service application and technology provided by Google, that powers many map-based services, including the Google Maps website, Google Ride Finder, Google Transit,[1] and maps embedded on third-party websites via the Google Maps API.[2] It offers street maps, a route planner for traveling by foot, car, bike (beta) or public transport and an urban business locator for numerous countries around the world. Google Maps satellite images are not updated in real time; they are several months or years old.[3]

Google Maps

D3 Bubble Visualisation

D3 Bubble Visualisation

D3.js is a JavaScript library for manipulating documents based on data. It binds arbitrary data to a Document Object Model (DOM), and then applies data-driven transformations to the document. In this example, the library is used to create Bubble charts. These encode data in the area of circles. Although less perceptually-accurate than bar charts, they can pack hundreds of values into a small space. This visualisation is based on work by Jeff Heer.

D3 Bubble Visualisation

D3 Sunburst Visualisation

D3 Sunburst Visualisation

D3.js is a JavaScript library for manipulating documents based on data. It binds arbitrary data to a Document Object Model (DOM), and then applies data-driven transformations to the document. In this example, the library is used to create sunburst charts. A sunburst is an adjacency diagram. Rather than drawing a link between parent and child in a hierarchy, nodes are drawn as solid areas (in this case wedges), and their placement relative to adjacent nodes reveals their position in the hierarchy. This visualisation is derived from work by the Stanford Visualization Group.

D3 Sunburst Visualisation

D3 Dendrogram Visualisation

D3 Dendrogram Visualisation

D3.js is a JavaScript library for manipulating documents based on data. It binds arbitrary data to a Document Object Model (DOM), and then applies data-driven transformations to the document. In this example, the library is used to create dendrograms. A dendrogram (or cluster layout) is a node-link diagram that places leaf nodes of the tree at the same depth. This visualisation is derived from work by the Stanford Visualization Group.

D3 Dendrogram Visualisation

D3 Circle Packing Visualisation

D3 Circle Packing Visualisation

D3.js is a JavaScript library for manipulating documents based on data. It binds arbitrary data to a Document Object Model (DOM), and then applies data-driven transformations to the document. In this example, the library is used to create circle packing charts. Enclosure diagrams are also space-filling, using containment rather than adjacency to represent the hierarchy. As with adjacency diagrams, the size of any node in the tree is quickly revealed. This visualisation is derived from work by the Stanford Visualization Group.

D3 Circle Packing Visualisation

Wordle-style Word Cloud

Wordle-style Word Cloud

Wordle™ is a Java applet developed by Jonathan Feinberg to create colourful word clouds based on the frequency of words within a webpage. Jason Davies replicated this applet using HTML and Mike Bostock's D3 javascript library. We have taken Jason Davies's script and modified it slightly, adding a customised feed from DHO: Discovery. Searching for a term, the user can generate a word cloud based on subject terms and titles related to the objects in the results set. The size of the words displayed reflect the frequency of that term. As the latest technologies are used, it is recommended that you use an up-to-date browser such as Firefox 13.0.1 or Chrome 20 to access these visualisations.

Wordle-style Word Cloud

Raphaël

Raphaël

Raphaël ['ræfeɪəl] is a cross-browser JavaScript library used for drawing online vector graphics. 'Vector graphics' is the use of geometrical primitives such as points, lines, curves, and shapes or polygon(s), which are all based on mathematical expressions, to represent images in computer graphics. Raphaël uses the SVG W3C Recommendation and VML as a base for creating its images. It’s goal is to provide an adapter that will make drawing vector art easy and compatible across all browsers.
This small JavaScript library has been used to graph the distribution of objects within collections across time against subject terms or creators.

Raphaël

europeana4D

Europeana4D

The amount of online data that supplies geo-spatial and temporal metadata has grown rapidly in recent years. Social networks like Twitter, Flickr, and YouTube are popular providers of masses of data that are hard to browse. The europeana 4D interface – e4D – enables comparative visualisation of multiple queries and supports data annotated with time span data. Built in the context of the European project EuropeanaConnect, it is based on a client-server architecture that charges the client with the main functionality of the system. Researchers, data-journalists, and the broad public alike can use this open source framework to explore complex data – answering both time and space-related issues. To enhance understanding of data from historical contexts, the tool also supports multiple historical maps.

europeana4D

Treemap Visualisations

Tree Map

The Tree Map is most useful for getting an overview of the types and number of objects in collections or in particular subjects. They are typically very space efficient means of giving an overview of the contents of collections.

The Tree Map that we provide allows you to view the entire contents of DHO:Discovery as a grid. This size of squares in the grid is proportional to the number of particular objects in a collection or subject area. More objects results in larger squares.

The tab at the top of the tree map allows you to view by collections or by subject terms. You can quickly appreciate popular subjects (they will have a larger squared area) or particularly prominent subjects within particular collections. You can quickly identify areas of interest to you. Clicking the area with a heading of interest will make that the target and allow you to click on it and display a list of objects matching that criteria.

Treemaps are a complex but powerful information visualization technique. They were introduced in the work of Ben Shneiderman in 1992. Our layout algorithm is based on Bruls, Huizing, and & van Wijk, 2000. A tree map is a visualization of hierarchical structures. It is very effective in showing attributes of leaf nodes using size and color coding. Tree maps enable users to compare nodes and sub-trees even at varying depth in the tree, and help them spot patterns and exceptions.

Treemap Visualisations

Tag Cloud

Tag Cloud

The Tag Cloud is a visualization of subject frequencies. In our cloud of subject terms - the more objects with that subject term, the larger that word will be in the cloud. The Tag Cloud highlights the most popular 25 subject terms in all collections. When you click on a subject term DHO:Discovery will list all objects tagged with that term. You can control the 3 dimensional tag cloud by dragging your pointer around the cloud. Speed of rotation will be dependent on how fast you scroll.

Tag Clouds vary the size or font weight of the word based on the number of objects associated with that word. For instance, most of the collections in DHO:Discovery pertain to ‘Irish’ subject matter, and thus you will be likely to see frequently-occurring words like "Ireland" and "Irish" drawn in a larger size than words like "open" or "restaurant".

Tag clouds have several benefits: they are extremely simple, easy to read, and by their nature don't suffer from the labeling problems of bar charts, tree maps or bubble charts. Yet there is some controversy around tag clouds, partly due to their strong association with trendy web sites.

In tag clouds long words are emphasized over short words, and words whose letters contain many ascenders and descenders may receive undue attention as well. Indeed, recent work from Centre for User Experience at IBM suggests that in some circumstances tag clouds are no more effective than simple lists. However, this is a visualisation that may be of unique value to particular users. The legibility and potential data density of tag clouds make them well-suited to large texts and collections of tags. We have implemented this Tag Cloud in 3D, so if you view with 3D glasses, you will see the tags floating within a 3D space.

Tag Cloud

Node-Link Visualisations

Alternative Tree Map

Node-Link Diagrams are an efficient means to represent the shape and structure of collections. By using a node-link diagram, you can quickly see and navigate through a hierarchy of subjects within a particular collection. When centering a particular collection you can see what subjects are contained in that collection. Likewise when centering a subject term, you can quickly see what collections that subject term occurs within.

Node-Link Visualisations