We asked Robin Padilla, the Product Director of Springer Nature Experiments to share the story behind this new and exciting product at Springer Nature. This one-stop platform combines portfolios across all of our research publishers to make searching for experiments across the life sciences easy and effective. Read on below for more details
1) What is Springer Nature Experiments?
Springer Nature Experiments is a research solution allowing researchers to quickly find and evaluate protocols and methods in the life sciences. This platform covers the content from the entire Springer Nature Protocols and Methods portfolio, namely: Nature Protocols, Nature Methods, Springer Protocols, and Protocol Exchange. Experiments was launched in October 2017 and is the newest offering from Springer Nature’s Database Research Group. It is unique in that it’s currently the only platform specifically optimized for finding and evaluating experimental procedures in the life sciences.
2) How was Springer Nature experiments developed?
The creation of Springer Nature in 2015 also created by far the largest collection of protocols and methods content in the STM publishing space. The combined portfolio contains nearly 60,000 protocols and methods articles and counting! The superb content quality and comprehensiveness of Nature Protocols, Nature Methods, Springer Protocols, and Protocol Exchange are without question. With this basis, we felt we could leverage our technological expertise to add even more value by operating beyond the content. Specifically, we wanted to create a platform to aid researchers searching for and evaluating experimental methods. Aside from the huge time saving potential, finding reliable and relevant procedures would also address problematic issues surrounding reproducibility, which have been very prominent in many research disciplines.
3) Explain a bit about how the platform works.
Springer Nature Experiments ingests content from the four portfolio titles mentioned above. We developed knowledge models (classification schemes) for two categories: scientific techniques and model organisms. We started with these two categories after extensive analysis and feedback from researchers on what the most important categories are for them. Using AI and machine learning methods, we then extracted techniques and organism data from the articles and process and applied this data to optimize searching and build advanced, topic specific filters. For example we have search filters for technique, organism, article type, and article source. Results can be sorted in many ways, such as by publication date, number of citations, number of downloads, and more.
In addition to highly advanced searching, we have article evaluation pages that display at-a-glance information about the article, which lets researchers quickly evaluate its content and relevance. For example, we display article keywords, which include author supplied and AI-extracted terms, a citation graph, the abstract, the latest three citations (real time data) and other key information.
4) What benefits does the platform provide?
Covering the largest and most comprehensive protocols and methods portfolio, Springer Nature Experiments delivers extremely high quality, reliable, and trustworthy content. The many advanced search options also save researchers valuable time compared to conventional search engines when finding and evaluating protocols and methods.
5) Can you touch on some future plans for Springer Nature Experiments?
Research solutions like Experiments are constantly evolving to best support scientific researchers. That said, we definitely have numerous design enhancements in the pipeline to make the user experience more seamless. This is especially true for the mobile site. The increased mobile device usage in research labs is a known trend and it’s something we’re definitely aiming to support. Additionally, we’re also developing new and improved knowledge models to create even more sophisticated searching and sorting options for Experiments users.