4. The researcher perspective

Students are one of several groups of people using the campus, and others may have some similar interests, whether it be visitors, administrators, managers or lecturers. The need for a comfortable and healthy environment, effective use of rooms and facilities, timely access to relevant information are all common concerns.

So what specifically would researchers look for in an intelligent campus?

  • communicating and collaborating with other researchers, particularly in support of cross disciplinary challenges
  • collection, storing and sharing of data in support of their research
  • effective interpretation and presentation of data
  • publishing and promoting research outputs
  • efficient and effective use of facilities
  • streamlining the processes in the research lifecycle
  • engaging with businesses and the community to maximise impact

How can an intelligent campus help?

How can collecting, integrating and analysing data from different devices help researchers in achieving some of these objectives? Some are very similar to the arguments for learners, for example in relation to facilities or people:

  • timetabling and using different workspaces flexibly for research and collaboration
  • getting the best use out of specialist and possibly expensive equipment
  • knowing who is where – colleagues in the cafe or potential collaborators at the conference or industry partners passing by

Real time information on actual usage or location rather than what was planned could be of benefit. Other potential research applications could include working in more sophisticated ways with data or processes across the research lifecycle.

Data in research

Many research disciplines collect, analyse and interpret data as evidence for testing theories or creating new ones. The availability of different sources and methods for using data open up opportunities that may have been used in specialist fields but could now be more widespread. Potential applications include:

  1. smart experiments – to what extent can data be self collecting, self organising or even self analysing?
  2. what changes in the environment can be monitored and reported to identify patterns for research – movement or even behaviour of people around a space that is being observed
  3. can nearby users be asked to share data from their mobile phone for a real time experiment?
  4. the creation of experiment zones on campus for people to pass through and interact with
  5. wearable devices are already used in healthcare applications, are there broader applications?

As an example, Southampton University have given open access to a wide variety of data from on and around the campus, from location information to the shape of rooms, local transport and food outlets to student statistics. Imagine what combinations of this data could reveal in terms of researching say behavioural patterns, transport usage or eating habits? Nottingham University’s Smart Campus – Smart Cities research area uses data such as this to observe, test and evaluate user behaviours, including intra and inter campus mobility and the smart, green, more efficient movement of people and goods.

Another example is Glasgow University whose smart campus initiative aims to embed smart technology enabling research and development of areas including new materials, design, sensors and urban informatics, in addition to the areas already identified such as health, transport, energy and the environment.

Whilst the collection of data from different sources is perhaps the most obvious application, cross referencing or interpreting the data could be potentially even more beneficial in terms of achieving real impact from the research. This could lead to attempts to unearth real meaning in the data, in other words looking for the narrative underlying the data. As an example, what if the best anaesthetist had the highest death rate because they were given the most demanding cases? Sophisticated analysis is already conducted by humans and computer applications alike, but the opportunity with “smart” research could be in linking data sets together in real time, even ones that are seemingly unrelated, to discover new insights. As with all research, confidence in the ethics is crucial, with robust algorithms and awareness of potential bias or misinterpretation.

The research lifecycle

Streamlining processes and having more efficient and effective access to timely and relevant information is another key feature. For research this could include:

  1. harnessing and sharing ideas for new research and collaboration
  2. links with the open science agenda – making data and research outputs publicly accessible
  3. real-time, on demand access to other professional services providing expertise on grants and contracts or commercialisation and impact such as through a chat bot or connecting to live online help
  4. access to marketplaces to rapidly test prototypes and innovations
  5. links to crowd sourced funding for conducting popular research or commercialising it
  6. hosting flexible and responsive conferences with live research

Although in its infancy, researchers are beginning to explore the potential offered by intelligent campus concepts. For now this is primarily in the scope of collecting and using data in research, however, the application of these same principles in streamlining and enhancing the processes underlying the research lifecycle are an obvious next step.

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