Text & Data Mining
Harness the power of big data and analytics
Text & Data Mining (TDM) employs machine learning, complex algorithms, and artificial intelligence to perform sophisticated analyses on large amounts of data. TDM analyzes available texts and figures to extract relationships and trends that don’t typically surface via traditional techniques. ACS’s vast volumes of published research are a perfect source for TDM efforts.
Why choose ACS for Text & Data Mining (TDM)?
Regardless of job title, field, or industry, if you are ready to use information to predict trends and make evidence-based decisions, ACS publications are an excellent resource for analyses.
- Academic institutions: Accelerate data discovery and preparation. Using TDM with ACS publications, organizations can discover trends in published research more rapidly and reliably.
- Corporate organizations: Achieve a competitive edge by using TDM and ACS content to quickly and accurately locate information that can guide decision-making and advance R&D. Use TDM to enhance research processes and find cost-saving efficiencies.
- Government organizations: Gain fresh insight by discovering patterns and relationships that are often invisible without TDM. Equip legal, patent, and regulatory branches with data-driven evidence from proven ACS sources.
How to subscribe
Cost may vary based on type and breadth of data, duration of access, frequency, and delivery mechanism. Customers may opt to pair TDM with solutions like ACS All Access for added value.
For organizations interested in Text & Data Mining solutions, please contact your ACS representative directly.
Download Sample XML Files
In most cases, the files below are representative of the content we supply to TDM customers. To get started with TDM or to speak with us about additional customization, please contact your ACS representative.
- Journal Article: Download the full XML in JATS format for the ACS Omega journal article titled “Synthesis and Biological Evaluation of Three New Chitosan Schiff Base Derivatives”. View the full-text of this article online by visiting https://pubs.acs.org/doi/10.1021/acsomega.0c01342.