Elsevier announced a new offering of enriched and authoritative scientific Datasets to power data applications that solve R&D challenges. Elsevier’s Datasets enable researchers, data scientists and practice leaders to answer R&D questions with greater speed and precision across many industries, including life sciences, energy, chemicals and materials, and technology. Use cases span a variety of data science and analytical projects including identifying disease targets using natural language processing, predicting molecule efficacy and toxicity using neural networks, predictive modeling, Key Opinion Leader (KOL) analysis and more.
Pharma, chemicals, energy, applied materials and technology companies can extract scientific insights by integrating data from Elsevier into private, secure computational ecosystems, including custom applications and third-party tools. Application-ready Datasets for chemistry, biology and 22 other disciplines come from a variety of sources, including:
19 million full-text articles from peer-reviewed journals
17 million author profiles
1.8 billion cited references
333 million chemical substances and reactions
86 million bioactivities and biomedical records
35 million chemical patents
Elsevier’s Datasets accelerate discovery and innovation in multiple domains. Leaders in pharmaceuticals, chemicals, technology and other industries are licensing Elsevier data for a variety of use cases. For example, in drug discovery, Datasets are used for target selection and discovery, confirming or identifying lead candidates, and in performing protein-ligand binding QSAR modeling. Pharmaceutical companies can also benefit from applying Datasets to pharmacovigilance, clinical trial design and to inform market access strategy. In materials science and materials informatics, Datasets support selecting the right material for a given application or product design based on property prediction and analysis of relevant datasets. Spanning all disciplines, Datasets enable KOL identification and rising star selection; predictive modeling (e.g., material property predictions or drug-drug interactions); training sets; knowledge graph creation; enterprise, federated and/or semantic search; business intelligence dashboards; and algorithm and neural network training.
Datasets are delivered flexibly via APIs or flat files. Elsevier has a team of domain and data science experts who can support customers’ data projects, and ontology management, text analytics and semantic search tools to help find, manage and share data.