Elsevier, a provider of scientific information and data analytics, is offering its scientific Datasets to enable researchers, data scientists and practice leaders to answer R&D questions with greater speed and precision, according to a recent press release from the New York-based company. 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 and Key Opinion Leader analysis.
“R&D-intensive businesses are excited by the possibilities of generative AI, predictive modeling and other areas at the vanguard of data science,” said Gino Ussi, president of corporate markets, Elsevier. “However, to deliver high-quality analytics and well-trained AI models, data scientists must still devote much of their time to sourcing quality data. This is laborious due to the volume and range of research literature and comes with risk if the data is not from a trusted, validated source. Elsevier’s Datasets address this challenge, drawing on our expertise in curating peer-reviewed science for more than 140 years and partnering with the research community.”
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
