SN Insights: Taking Research Analytics to the Next Level
SN Insights breaks down research data silos by providing clear and intuitive analytics drawn from millions of Linked Open Data connections in SN SciGraph. While SN Scigraph is a vast data repository, SN Insights is an application that complements it by interpreting its rich ecosystem of datasets, curated by Springer Nature and Digital Science.
Senior Manager for Semantic Data at Springer Nature, Markus Kaindl, talks here about how the idea for SN Insights developed; how it will improve visibility of the global research landscape; and how he’s expecting it to evolve after launch.
Where did the idea for SN Insights originate?
SN Insights started life as an internal analytics dashboard called ‘SN SciGraph Analytics’, which collated information across the entire Springer Nature journals portfolio. It combines the functionality of Digital Science’s Dimensions with Springer Nature publications data. This pairing can be used to drill deep into research output (by discipline, author and institution) to generate detailed data profiles and clear, visual reports. Our goal is to scale up the power, functionality, and volume of data SN Insights is built on to deliver a comprehensive view to the research community.
The main driver for SN Insights was offering complete transparency of data associated with our 13M publications to the global scholarly community. This is a fundamental expectation of the research community today, and it’s an expectation that we are pushing to meet.
What does SN Insights actually do?
SN Insights provides detailed statistics for all Springer Nature content, and across all publication types (journals and books, both open access and subscription). Some of the key data points:
Our ultimate goal is that librarians and research officers are able to use SN Insights to:
· assess the impact of individual researchers on the global scientific landscape;
· uncover emerging research trends at the faculty or department level;
· identify gaps in content that they can respond to with the right level of investment.
Thus, SN Insights will be able to help reviewers get a detailed picture of everything that is happening in a specific field of research. It will help editors more easily identify potential journal and book reviewers in their field, and assess the quality of competing journals. And for librarians, it will enable a deeper knowledge of their institution’s content portfolio also helps make marketing and investment decisions more data-driven.
What is our goal for SN Insights to help institutions assess their standing in the global scientific landscape?
SN Insights is a focused version of Dimensions, containing only information and analytics relating to Springer Nature content. Publications data is connected to grants, patents and clinical trials, helping users drill deeper into research areas and apply sorting and faceting according to their exact information needs. The platform contains all citations from other publications and of course all references to publications. The volume of publications data in SN Insights is large enough to show trends in individual disciplines, especially in areas where Springer Nature is prolific, such as Chemistry and Computer Science Proceedings.
Using this data and the analytics functionality in SN Insights, we identified a growing trend at Cornell University towards open access and tied that back specifically to the domain of physics. We were able to get an even more precise correlation between the department publishing in a specific physics journal from Springer Nature (Journal of High Energy Physics) and funding they had acquired due to their growing cooperation with CERN in Switzerland and other European partners.
What kind of new analysis can SN Insights bring?
With SN Insights, a level of detail and dataset integration can be seen that just didn’t exist before – and that’s unique to this application. Connections between wide-ranging data points can show the impact Springer Nature publications are having on an institution’s research pipeline, when it comes to activities like applying for grants, filing patents and the progress of clinical trials. If you add additional impact metrics (usage statistics, downloads etc) to this view it becomes even more useful. You could pull this data from content platforms, but you wouldn’t be able to dynamically filter it in the same aggregated way that you can in SN Insights. If you’re extracting data from lots of individual sources and in different formats, it’s much more difficult to visualise in its entirety and accurately analyse. SN SciGraph removes the data silo problem and SN Insights analyses and presents that data in a clear way as aggregated lists, timelines and heatmaps.
How do you see SN Insights evolving over the next few years?
We plan to enrich SN Insights with lots more proprietary data. The type of data we add will depend heavily on user feedback from demoing and beta-testing, so we’re really looking for the research community’s input here. We’re also going to be improving data quality and providing data in a format that’s consistent with other reporting tools and repositories for a more efficient, smoother experience. Among some of the new features we’ll be adding soon is author distribution mapping to make author data more visually appealing and intuitive.
SN Insights is not currently available to license. For more information on SN Insights, contact Markus Kaindl.