Leveraging machine learning to overcome the challenges of searching science data

Leveraging machine learning to overcome the challenges of searching science data

July 5, 2018

Scientific data sets are increasing in both size and complexity. Consequently, the ability to label, filter, and search information is becoming laborious, time-consuming and at times even an impossible task for the scientists. Hence, there is a dependence on automated tools. Responding to this need, researchers are developing tools that will make raw data searchable and shareable.

A team of researchers from Lawrence Berkeley National Laboratory (Berkeley Lab) and the University of California (UC) Berkeley are developing innovative machine learning tools. Their goal is to extract contextual information from scientific datasets and automatically generate metadata tags for each file with the help of these tools. Scientists can then search these files via a web-based search engine for scientific data, developed by the Berkeley team, called Science Search.

Click here to continue reading how machine-learning tools can help mine the scientific ecosystem.

Brought to you by Scope e-Knowledge Center, an SPi Global Company, a trusted global partner for Digital Content Transformation Solutions, Knowledge Modeling (Taxonomies, Thesauri and Ontologies), Abstracting & Indexing (A&I), Metadata Enrichment and Entity Extraction.

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