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 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.
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.
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 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, and maps embedded on third-party websites via the Google Maps API. 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.
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.
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.
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.
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.