There is a broad consensus that modern science is undergoing profound structural changes afforded by the rise of digital technology, and that this change is occuring on multiple levels of the scientific work process at once (Nielsen 2012; Nentwich and König 2012). The abundance of massive storage capacities, high volumes of processing power, and ubiquitous network access enables new forms of research which are contingent on large quantities of digital data and its efficient computational analysis (Weinberger 2011). This development is underscored by the rise of data science, that is, science that is driven by the analysis of large quantities of data from a wide range of sources such as sensors, scanners, MRI, telescopes, but also human-generated data from social media and digital libraries, and interrogated through statistical procedures, machine learning algorithms, and other computational instruments, allowing researchers to discover previously unrecognized patterns. Such approaches are innovative in the sense that they surpass the capabilities of traditional research in making observations of changes in very complex systems as they unfold, and in that they potentially allow predictions regarding the future behavior of such systems (Golder and Macy 2012). Whereas research has in the past been based upon comparably scarce evidence, the promise of data science is that it will be both scalable and reproducible on a previously unimaginable level, providing novel insights into a wide array of areas, from climatology to social science (Lazer et al. 2009) (Fig. 1).
Beyond innovation of research methods, other aspects of how science is undertaken are also changing visibly, both as a result of technological shifts and because of economic and cultural changes in how research is financed and organized (cf. several contributions in this volume). From teaching and funding to publishing and peer review, it seems that a variety of aspects of how scientists work are changing, and that communication is at the forefront of this change, a change brought about primarily by the proliferation of technologies which are themselves the result of publicly funded scientific research. These technologies not only make it easier, cheaper, and quicker for scientists to exchange information with peers around the globe, they also have the potential to blur the line between internal communication among researchers and communication with the wider public. New formats must be adopted for scholarly use to fit the needs of academics while established genres evolve as a result of new technologies for the production and dissemination of scholarly publications (Cope and Kalantzis 2009).
Scientists have, of course, always been avid communicators. From Darwin’s notebooks to the Large Hadron Collider, getting complex scientific issues across both to colleagues and laypersons has been at the top of the agenda for researchers for as long as modern science has existed. Successful communication is integral to scholarship because it allows scientific knowledge to proliferate, enable practical applications, and become entrenched societal knowledge, but also because frequently the outcomes of scientific research have far-reaching societal implications and are highly controversial (e.g., climate research, nuclear energy, genetics). Scientists must be able to explain what they do to a broader public to garner political support and funding for endeavors whose outcomes are unclear at best and dangerous at worst, a difficulty which is magnified by the complexity of scientific issues. They do so in an increasingly challenging environment, engaging with a public that has access to a wide range of sources of (by scientific standards) often dubious quality, many of them online (Puschmann and Mahrt 2012; König 2011). This public is increasingly critical and unimpressed by scientific authority and simple promises of scientific progress as an enabler of economic growth and societal welfare, and must be both won over and brought on board, rather than talked down to. Civil society expects to be engaged in a dialog with science, rather than being lectured. The affordances of social media (blogs, wikis, social networking sites) should accordingly be regarded as supporting a general long-term shift towards a more egalitarian relationship between experts and the lay public, rather than driving it (Fig. 2).
Intra-scientific discourse is changing as well, as a result of the move from paper to digital, which seems almost completed in much of the hard sciences. The majority of formal publishing in the STM disciplines takes place in academic journals and conference proceedings, with pre-prints, post-prints, reports, technical manuals, posters, and other formats also playing an important role (Borgman 2007). Increasingly, traditional academic genres (journal articles, conference papers, scholarly monographs) are published online, rather than in print, and disseminated through a variety of channels (email, blogs, online book reviews, social media). Preprint archives such as arXiv
Footnote 1 and Social Science Research Network (SSRN)Footnote 2 have proliferated in a variety of disciplines and continue to grow in popularity. Beyond Open Access, there is an increased push for adding features that make use of the affordances of digital publishing, such as interactive charts and figures, and towards providing raw data along with papers to encourage follow-up research, for example on sites such as Figshare.