How can AI support the research community in times of crisis?

The Source
By: Guest contributor, Wed Apr 1 2020



Author: Guest contributor

Do you want to quickly grasp what has been published by Springer Nature on coronavirus and COVID-19 this year? Find our short report clustering the most recent papers here. We’re eager to find out how helpful this is to the research community and how we can make improvements.

Written by Markus Kaindl and Stephanie Preuss


In challenging times such as the current COVID-19 crisis, we are proud to witness researchers collaborating in an unprecedented way. To support the research community, Springer Nature and many other publishers have already decided to make thousands of publications on coronavirus freely available to accommodate the need for access to essential research

AI technology has become indispensable in providing valued scientific information and assisting in grasping the latest research quickly. We want to explore how we can help researchers get a quick and simple overview of all Springer Nature publications since 2020 through an AI-based snapshot.


One of Springer Nature’s missions is to open doors to discovery by reaching out to communities of researchers and supporting them with their most pressing challenges. The need for scientific paths to treat, cure, and develop a vaccine as fast as possible is extremely urgent. As a publisher, a valuable contribution we can make is facilitating access and rapid overviews to research. However, we think there is more we can do than just making relevant primary research freely available. Therefore, we have been exploring what added value a machine-generated overview of this research could contribute. This AI-based approach is a rapid prototype and in its early days of experimental development. Hence we’re planning an iterative improvement process. Nevertheless, we wanted to get the first results out as a minimal viable product to gather feedback from the research community both quantitatively and qualitatively and see how we can further support their work.


This minimal viable product is in its first iteration a web page that groups publications together based on their topic, provides snippets of text to grasp what they are about quickly, and then links directly to the Springer Nature publication. The selection of content was made with Dimensions from Digital Science combined with proprietary filters from Springer Nature. The export of 144 English publications dates from 1 Jan to 24 March 2020 and contains 58 original papers, 35 news snippets, 22 editorial notes, and 29 brief communications. Almost all of them are either from the medical and health area or the life sciences. At the time of export, these publications from 2020 had accrued roughly 6.4m downloads, were cited almost 600 times, mentioned in social media over 37k times, and shared via SharedIt over 9k times.

With this novel AI-based research overview, our rapid prototype provides structure to research by clustering in a meaningful way. We reuse parts of the natural language processing pipeline trialled successfully in the publication of our first machine-generated research book in 2019. If you want to know more about the technology, please see the detailed description in the introduction of the freely available book on Lithium-Ion Batteries. This result is once more a fruitful collaboration with Dr. Niko Schenk from the Applied Computational Linguistics Lab at Goethe University Frankfurt, Germany. Special thanks to Dr. Janis Müller, from the Institute of Molecular Virology at Ulm University, for his support and review of this report.

Explore the report now.

About Markus Kaindl

Markus Kaindl
Markus Kaindl works as Senior Manager in the Product and Platform Group. He is responsible for Springer Nature's internal analytics solution SN Insights based on Digital Science’s Dimensions. Previously he released the Linked Open Data knowledge graph SN SciGraph. His publishing engagement started in 2013 with big data consolidation for the Springer Book Archive, followed by major metadata migration projects due to mergers. With an M.A. degree in Computational Linguistics from Ludwig-Maximilians-University Munich in 2010, he was also involved in various text & data mining and natural language processing efforts dealing with document structuring, enrichment, and classification.

About Stephanie Preuss

Dr. Stephanie Preuss is Senior Editor and responsible for the German Languages Science book portfolio in biology, geosciences and chemistry. She has a passion for publishing innovations, driving projects like automated-translation and machine-generation of books. Her goal is to make AI-based services available to Springer Nature authors and editors and to learn how Springer Nature can contribute to research, professional and student communities offering these new services and content forms.


Author: Guest contributor

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