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Go beyond abstracts and open-source data and get the full story. Datasets from full-text scientific literature give you comprehensive, direct access to the most impactful research for your industry. Unlock the full potential of your AI and advanced analytics projects.
Using data from open-source scientific literature for your R&D or AI projects can lead to gaps in your data pipeline and hamper these projects during development. In most industries, the majority of relevant scientific literature is not open source or available through free channels. This includes some of the most frequently cited scientific research being published today.
Pharmaceuticals: 63%
Medical Devices: 69%
Chemicals and Materials: 77%
Energy and Natural Resources: 74%
*Source: Scopus. These numbers represent relevant published scientific literature between 2017-2021.
Full-text literature has been shown to provide richer data for a more complete research picture. If your data pipeline pulls only from abstracts, you will miss co-occurrences that only appear in the full text. It can often take years for co-occurrences to appear in the abstracts of other articles, which means you could be missing crucial insights in your data.
When it comes to using scientific literature in your data pipeline, you can only get the full story from the full study. With direct access to full-text scientific datasets from Elsevier(opens in new tab/window), you can proceed with confidence that you aren’t missing the data and insights that can mean the difference between data science success and failure.
