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   https://doi.org/10.1002/cpe.6086  

   Valentine, David; Zaslavsky, Ilya; Richard, Stephen; Meier, Ouida; Hudman, Gary; Peucker‐Ehrenbrink, Bernhard; Stocks, Karen (November 2020, Concurrency and Computation: Practice and Experience)

   Abstract EarthCube Data Discovery Studio (DDStudio) is a crossdomain geoscience data discovery and exploration portal. It indexes over 1.65 million metadata records harvested from 40+ sources and utilizes a configurable metadata augmentation pipeline to enhance metadata content, using text analytics and an integrated geoscience ontology. Metadata enhancers add keywords with identifiers that map resources to science domains, geospatial features, measured variables, and other characteristics. The pipeline extracts spatial location and temporal references from metadata to generate structured spatial and temporal extents, maintaining provenance of each metadata enhancement, and allowing user validation. The semantically enhanced metadata records are accessible as standard ISO 19115/19139 XML documents via standard search interfaces. A search interface supports spatial, temporal, and text‐based search, as well as functionality for users to contribute, standardize, and update resource descriptions, and to organize search results into shareable collections. DDStudio bridges resource discovery and exploration by letting users launch Jupyter notebooks residing on several platforms for any discovered datasets or dataset collection. DDStudio demonstrates how linking search results from the catalog directly to software tools and environments reduces time to science in a series of examples from several geoscience domains. URL: datadiscoverystudio.org 

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   Bukkapatnam, Satish T.S. (January 2023, IISE Transactions)

   This article presents an overview of the emerging themes in Autonomous Materials Discovery andManufacturing (AMDM). This interdisciplinary field is garnering a growing interest among the sci-entists and engineers in the materials and manufacturing domains as well as those in the ArtificialIntelligence (AI) and data sciences domains, and it offers immense research potential for the indus-trial systems engineering (ISE) and manufacturing fields. Although there are a few reviews relatedto this topic, they had focused exclusively on sequential experimentation techniques, AI/machinelearning applications, or materials synthesis processes. In contrast, this review treats AMDM as acyberphysical system, comprising an intelligent softwarebrainthat incorporates various computa-tional models and sequential experimentation strategies, and a hardwarebodythat integratesequipment platforms for materials synthesis with measurement and testing capabilities. Thisreview offers a balanced perspective of the software and the hardware components of an AMDMsystem, and discusses the current state-of-the-art and the emerging challenges at the nexus ofmanufacturing/materials sciences and AI/data sciences in this nascent, exciting area 

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4. A comparative genomics multitool for scientific discovery and conservation

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   null (Ed.)

   The Zoonomia Project is investigating the genomics of shared and specialized traits in eutherian mammals. Here we provide genome assemblies for 131 species, of which all but 9 are previously uncharacterized, and describe a whole-genome alignment of 240 species of considerable phylogenetic diversity, comprising representatives from more than 80% of mammalian families. We find that regions of reduced genetic diversity are more abundant in species at a high risk of extinction, discern signals of evolutionary selection at high resolution and provide insights from individual reference genomes. By prioritizing phylogenetic diversity and making data available quickly and without restriction, the Zoonomia Project aims to support biological discovery, medical research and the conservation of biodiversity. 

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