Elsevier Fingerprint Engine
Elsevier develops critical solutions for research institutions and funding agencies using the Elsevier Fingerprint Engine™ as an enabling technology.
It is available as a hosted or installed solution, via Web Services or embedded in Elsevier tools.
What is Fingerprinting?
A back-end software system, the Elsevier Fingerprint Engine mines the text of scientific documents – publication abstracts, funding announcements and awards, project summaries, patents, proposals/applications, and other sources – to create an index of weighted terms which defines the text, known as a Fingerprint™ visualization.
By aggregating and comparing Fingerprints, the Elsevier Fingerprint Engine enables institutions to look beyond metadata and expose valuable connections among people, publications, funding opportunities and ideas.
Covering a wide range of subject areas with a collection of thesauri
The Elsevier Fingerprint Engine uses a variety of thesauri to support applications pertaining to different subject areas. By applying a wide range of thesauri, Elsevier can develop solutions for researchers in but not limited to: the life sciences, engineering, earth and environmental sciences, arts and humanities, social sciences, mathematics and agriculture. Thesauri provided by the institution can also be incorporated.
How it works
The Elsevier Fingerprint Engine creates Fingerprints via a three-step process:
- The Elsevier Fingerprint Engine applies a variety of Natural Language Processing (NLP) techniques to mine the text of scientific documents including publication abstracts, funding announcements and awards, project summaries, patents, proposals, applications and other sources
- Key concepts that define the text are identified in thesauri spanning all the major disciplines
- The Elsevier Fingerprint Engine creates an index of weighted terms that defines the text, known as a Fingerprint.
Applying Fingerprints to inform decision making
By aggregating and comparing Fingerprints of people, publications, funding opportunities and ideas, the Elsevier Fingerprint Engine reveals insightful connections with practical applications. Here are some examples of how Fingerprints can bring scholarly business intelligence to your data.
Creating expertise profiles to enable collaboration
- Pure aggregates the Fingerprints of individual documents to create unique Fingerprints that reveal your researchers’ distinctive expertise. In mid 2014 Pure will match the Fingerprints of funding opportunities in SciVal® Funding to your researchers’ Fingerprints, recommending appropriate funding opportunities and suggested collaborators.
Comparing Fingerprints to find reviewers
- Reviewer Finder compares document Fingerprints with researcher Fingerprints, making it easier to identify reviewers and raise awareness about potential conflicts of interest.
Helping researchers find suitable journals for publishing articles
- Elsevier Journal Finder helps researchers find journals that could be best suited for publishing their articles. Journal Finder matches abstracts to Elsevier journals, scanning Elsevier's 2,200+ titles in the Health Sciences, Life Sciences, Physical Sciences and Social Sciences.