Domain-specific Topic Model for Knowledge Discovery in Computational and Data-Intensive Scientific Communities.

Shortened time to knowledge discovery and adapting prior domain knowledge is a challenge for computational and data-intensive communities such as e.g., bioinformatics and neuroscience.

Recommender-as-a-Service with Chatbot Guided Domain-science Knowledge Discovery in a Science Gateway.

Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, datasets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task.

HonestChain: Consortium blockchain for protected data sharing in health information systems.

Healthcare innovations are increasingly becoming reliant on high variety and standards-compliant (e.g., HIPAA, common data model) distributed data sets that enable predictive analytics.

Real-time geotracking and cataloging of mass casualty incident markers in a search and rescue training simulation: Pilot study.

Objective: Search and rescue after mass casualty incidents relies on robust data infrastructure. Federal Emergency Management Agency (FEMA's) Task Force 1 (TF1) trains its volunteers to locate and virtually tag scene incidents using a global positioning satellite (GPS) device programmed with markers for each incident (Iron Sights). The authors performed a pilot study comparing Iron Sights™ to a Wi-Fi-based real-time incident geolocation and virtual tagging dashboard (Panacea™) in creating a dynamic common operating picture.

PGen: large-scale genomic variations analysis workflow and browser in SoyKB.

Background: With the advances in next-generation sequencing (NGS) technology and significant reductions in sequencing costs, it is now possible to sequence large collections of germplasm in crops for detecting genome-scale genetic variations and to apply the knowledge towards improvements in traits. To efficiently facilitate large-scale NGS resequencing data analysis of genomic variations, we have developed "PGen", an integrated and optimized workflow using the Extreme Science and Engineering Discovery Environment (XSEDE) high-performance computing (HPC) virtual system, iPlant cloud data storage resources and Pegasus workflow management system (Pegasus-WMS). The workflow allows users to identify single nucleotide polymorphisms (SNPs) and insertion-deletions (indels), perform SNP annotations and conduct copy number variation analyses on multiple resequencing datasets in a user-friendly and seamless way.