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How to... use digital tools for research

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How researchers analyse linked data

The ability to link different data sets, and to have access to a large volume of data, changes the methods researchers can use. In particular, it necessitates a different approach to the study of text. Many researchers (in the humanities at least) are used to reading every word of text, and even if they skim, they usually do so in a linear fashion.

However, when dealing with the large corpora of data that digital collections make possible, and powerful new software tools, this approach is both limited and impractical. The best way to undertake digital research is to look for patterns in data.

Visualization tools

Visualization is a good way of creating patterns, and there are a number of software tools that help.

, for example, takes data in text form, and, providing it is formatted in the correct way (i.e. with a left-hand column which states values, which it interprets as dimensions, whilst numbers are perceived as measurements), turns it into a visualization of the user's choice. Similar tools include and .

uses visualization techniques and GIS to create new models of cities, virtual urban environments. Currently it has three projects: one which links publicly available data to non-proprietary maps; one which is specifically concerned with pollution data, and one in which the user can build their own simulation.


Social scientists, like economists, want to be able to forecast scenarios in a number of areas such as housing, health care, education and transport. A good way of doing this is to create simulation tools, to which variables can be applied and outputs generated.

Social simulation is thus an expanding field, creating a demand for tools, services and research communities. The has been set up to cater for this demand.

Text mining

Text mining offers the opportunity to describe a piece of text in statistical terms. For example, text can be analysed and repetitions identified. Each repetition can be given a unique identifier, and a map created of where the repetitions occur in the text.

The project, applies text mining to historical sources, and in particular to the history of crime, using a form of compression analysis. Dr Tim Hitchcock explains how text mining can help research nineteenth century domestic violence, allusions to which are scarce as it was not then classed as a crime.

There were, however, a total of 1,200 trials involving spouse murder, which themselves define a context for domestic violence. Thus by studying accounts of these trials, it is possible to build up a model of what domestic violence looked like, use the model to define other cases, then refine the model and apply it to newspapers and novels.

Text mining can also be used to ease the burden of producing systematic reviews, made more difficult by the deluge of information (Ananiadou et al., 2007). Text mining techniques can be used in query expansion, document screening (topic clustering), and synthesizing (sentences selected from documents based on the most significant terms and classification techniques).

Qualitative longitudinal analysis

Perhaps one of the most interesting methods of analysis associated with linked data (although not uniquely) is the combination of qualitative with longitudinal methods: the ability to measure change over time.

Qualitative longitudinal (QL) analysis combines the richness of qualitative data ("'), with longitudinal research, with its dynamic view of social processes (University of Leeds, n.d.).

is the first major longitudinal study to be funded in the UK. Run by the University of Leeds, it explores how personal and family relationships change over time.

A major feature of the project is Timescapes' and , which comprises data generated by the various projects (seven in all, encompassing all generations). The archive is open to all, the idea being to benefit the growing international community of QL researchers.

The existence of so much rich data at one location also benefits those using secondary analysis – innovative analytical strategies and different perspectives brought to bear on existing data, seen with new eyes.

However, to conduct secondary analysis successfully, it is important to have to hand not just the participants' data, but also other relevant information such as the interview schedules and the motivation behind the questions, as well as field notes and data tables for each project, and details of the researchers (Baker, 2010).

Longitudinal research is a very powerful form of research, according to Timescapes' director Bren Neale, because we can see how change happens. However it requires the build-up of rich, huge data sets, and these are best hosted in a digital archive with good search and retrieve facilities, that can by definition be open to all.

To read more about QL, and secondary analysis, see .