The big deal about big data in the world of research

Academic information solutions providers such as Elsevier sit on vast databases of high-quality scientific, technical and medical research content that has been collected, curated, aggregated, disseminated and published for over a century.

With the transformational changes brought on by the digital age, our role as an academic publisher has expanded.

The convergence of cloud computing, big data and social networking is creating new expectations, possibilities and opportunities for publishers and the researcher community.

While providing high-quality content will always be crucial, it is no longer enough.

Our job doesn’t end with publishing articles in journals; it actually begins there.

Today we must leverage big data applications to add value to that content and develop better, faster, more efficient tools and solutions. A significant part of a publisher’s role now is to provide the right content, to the right audience, in the right context, when and how they want it.

Elsevier has embraced the evolution of technology by building the necessary digital infrastructure for effective management and facilitation of scientific research.

New capture, search, discovery and analysis tools such as Scopus can, thanks to big data, now draw insight from the increasing pools of unstructured data.

Smart tools

It is a now a necessary responsibility for those of us in scholarly publishing to help researchers find relevant data quickly through smart collection tools, recommended reading lists and data banks that offer a variety of sort-and-search applications.

Scholarly publishing is also not just about giving our customers what they want; it’s also about anticipating their needs.

Today, Elsevier is able to recommend articles to researchers they might otherwise not have discovered.

Through big data predictive analytics, we now have the ability to proactively play “matchmaker” by recommending and promoting relevant research and related information from a broad range of global sources.

Examples of where big data is used to drive science research innovation at Elsevier include:

1. Enhanced content: We are adding enhanced functionality to static content with our Article of the Future, to provide a dynamic and interactive reading experience by incorporating tagged and searchable audio files, videos, interactive images and figures, embedded maps, downloadable tables and sharing capabilities.

2. Re-use of content: allows users to interact with content in new, insightful ways. One example of how we’re re-using content lies in text and data mining (TDM). We offer application programming interfaces (APIs) that allow researchers to explore patterns across large content databases and derive meaningful analyses from these correlations.

3. Solutions content is tailored content that helps researchers find what they’re looking for more quickly by delivering not just information, but answers. We are creating digital solutions such SciVal that take advantage of big data, allowing researchers to easily discover evidence-based insights from massive data sets in ways that were not previously possible.

As with all new technology, we do not and cannot know what can be fully achieved with big data solutions yet.

As the century advances, one thing’s certain: big data is truly entrenched in transforming academic publishing processes for advancing research.

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