Subscribers access 12 valuable engineering databases to advance their research, influence stakeholders and keep abreast of breakthroughs most relevant to their critical initiatives.
The National Technical Information Service (NTIS) database is the premier source of unclassified reports from US and international government agencies, including the National Aeronautics and Space Administration (NASA), the United States Department of Energy and Defense, the Japan Ministry of International Trade and Industry and the United Kingdom Department of Trade.
Reaxys provides rapid and easy access to experimental facts to empower chemistry research, chemical discovery. It is also the leading database for bioactivity data.
Find state-of-the-art routes to synthesize molecules with superior properties and discover novel molecules with improved activities against specified targets.
Stay current with updates on new chemistry publications and patents
Find the most reliable vendors of starting materials and intermediates
Search integrated in-house and external data
We can help you mine text from scientific documents—including publication abstracts, funding announcements and awards, project summaries, technical papers, patent abstracts, proposals, applications and other sources—to create an index of weighted terms.
Elsevier offers an intelligent, AI-driven semantic search and categorization platform based on the Elsevier Fingerprint Engine® (FPE), a text-mining toolkit that successfully powers projects at both Government agencies and Elsevier itself, including Expert Lookup and Pure. The Fingerprint technology applies a variety of natural language processing (NLP) techniques to mine text from scientific documents and create an index of weighted terms - the “Fingerprint Index” - which defines that text. These representations are ideal for visualization, semantic search, ranking and recommendations and categorization, all of which help to optimize information processes across an institution or government organization. Recent releases of Elsevier’s Fingerprint technology have featured improved processing speed and machine learning techniques to accurately mine increasing volumes of text.
By combining Elsevier’s Fingerprint technology with state-of-the art supervised machine-learning algorithms, Elsevier offers a platform that automates the assignment of various categories to text assets. This includes research grant descriptions or publications with categories from the Common Alzheimer Disease Research Ontology (CADRO). This platform embraces existing labeled datasets to produce fingerprints and infer a classification model. This model can be used to categorize subsequent content correction and training modules, which enables users to improve performance or to build a text classification capability for a specific use-case from scratch.