Science 4.0 is a movement resulting from the formation and use of the World Wide Web. Scientists are using the World Wide Web to collaborate and do science in new ways with significant implications. The term Science 4.0 is a special case of the World Wide Web and enhanced by an understanding of Web 2.0. Therefore it is helpful to understand what the Web 2.0 is in order to better understand Science 4.0. The term Web 2.0 was first used at the O’Reilly Media conference and implies a development in the use of the Web. This has been covered in an earlier article in the series (see Appendix). The Web 2.0 had several characteristics according to this definition including lightweight programming models. In this definition it is argued that lightweight programming models have the following properties
1. Simple to use
2. Easy to remix
3. Support syndication
Twitter and WordPress are two notable platforms that exemplify these principles. Both Twitter and WordPress enable third party developers to produce compatible applications and dramatically increase the range of tools available with these platforms. Both Twitter and WordPress facilitate syndication of content and both have simple intuitive interfaces that support end-users and developers alike. However these platforms are particularly focused on media. For the purposes of Science 4.0 such principles support the process of doing Science whether this be at the stage of data collection or the analysis of the results.
One interesting development with Science 4.0 properties is Wolfram Alpha which is described as an ‘answer engine’. The end user will enter an alphanumerical question into the answer engine and the standard version of Wolfram Alpha will use a combination of inbuilt datasets, analytical processes and knowledge to produce a best-fit answer.
By applying the principles above, we can envisage what an emerging Science 4.0 platform might look like. The Science 4.0 platform would operate on datasets which are a central feature of quantitative science. We have already seen how large datasets are becoming available (e.g the UK Government datasets) (see Appendix). A successful Science 4.0 platform would be simple to use, would enable syndication of analyses or discussion and would facilitate third-party application development. Researchers would be easily able to compare analytical approaches to the same datasets. The popularity of specific analytical approaches would however depend not only on the validity of the approach but also on other factors including the dialogue that follows. This in turn would be influenced by the applications that emerge to meet these demands.
Qualitative science however does not lend itself as easily to this approach. Interpretative Phenomenological Analysis for instance requires the context of the dialogue to be understood and included in the analytical process. The involvement of language makes the analysis a little more complicated although there is little reason not to suppose that a qualitative Science 4.0 platform could emerge.
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