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History and Utility of Zeolite Framework-Type Discovery from a Data-Science Perspective
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How Hydrogen and Oxygen Vapor Affect the Tribochemistry of Silicon- and Oxygen-Containing Hydrogenated Amorphous Carbon under Low-Friction Conditions: A Study Combining X-ray Absorption Spectromicroscopy and Data Science Methods
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Supramolecular Catalyzed Cascade Reduction of Azaarenes Interrogated via Data Science
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Data science enabled discovery of a highly soluble 2,2′-bipyrimidine anolyte for application in a flow battery
Non-linear threshold analysis enabled discovery of a highly soluble (1.3 M) bipyrimidine anolyte. Stable cycling in a full flow battery cell for 75 cycles.
Performance Portability of an SpMV Kernel Across Scientific Computing and Data Science Applications.
Abstract not provided.
Enhancing the Impact of Science Data: Toward Data Discovery and Reuse
The amount of data produced in support of scientific research continues to grow rapidly. Despite the accumulation and demand for scientific data, relatively little data are actually made available for the broader scientific community. We surmise that one root of this problem is the perceived difficulty of electronically publishing scientific data and associated metadata in a way that makes it discoverable. We propose exploiting Semantic Web technologies and best practices to make metadata both discoverable and easy to publish. We share experiences in curating metadata to illustrate the cumbersome nature of data reuse in the current research environment. We also make recommendations with a real-world example of how data publishers can provide their metadata by adding limited additional markup to HTML pages on the Web. With little additional effort from data publishers, the difficulty of data discovery, access, and sharing can be greatly reduced and the impact of research data greatly enhanced.
Enhancing Scientific Research with FAIR Digital Objects in the National Science Data Fabric
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Correction: CitSci.org: A New Model for Managing, Documenting, and Sharing Citizen Science Data
The Funding section incorrectly states that “the authors received no specific funding for this work.” The authors would like to correct the statement as follows: Funding: YW was supported by the NSF grant 1430508. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Reproducible big data science: A case study in continuous FAIRness
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A Client/Server Architecture for Supporting Science Data Using EPICS Version 4
The Phase 1 grant that serves as a precursor to this proposal, prototyped complex storage techniques for high speed structured data that is being produced in accelerator diagnostics and beam line experiments. It demonstrates the technologies that can be used to archive and retrieve complex data structures and provide the performance required by our new accelerators, instrumentations, and detectors. Phase 2 is proposed to develop a high-performance platform for data acquisition and analysis to provide physicists and operators a better understanding of the beam dynamics. This proposal includes developing a platform for reading 109 MHz data at 10 KHz rates through a multicore front end processor, archiving the data to an archive repository that is then indexed for fast retrieval. The data is then retrieved from this data archive, integrated with the scalar data, to provide data sets to client applications for analysis, use in feedback, and to aid in identifying problem with the instrumentation, plant, beam steering, or model. This development is built on EPICS version 4 , which is being successfully deployed to implement physics applications. Through prior SBIR grants, EPICS version 4 has a solid communication protocol for middle layer services (PVAccess), structured data representation and methods for efficient transportation and access (PVData), an operational hierarchical record environment (JAVA IOC), and prototypes for standard structured data (Normative Types). This work was further developed through project funding to successfully deploy the first service based physics application environment with demonstrated services that provide arbitrary object views, save sets, model, lattice, and unit conversion. Thin client physics applications have been developed in Python that implement quad centering, orbit display, bump control, and slow orbit feedback. This service based architecture has provided a very modular and robust environment that enables commissioning teams to rapidly develop and deploy small scripts that build on powerful services. These services are all built on relational database data stores and scalar data. The work proposed herein, builds on these previous successes to provide data acquisition of high speed data for online analysis clients.
Telematics and Data Science: Informing Energy-Efficient Mobility: October 25, 2016 - October 31, 2017
The University of Connecticut (UCONN) is exploring the possibility of adding electric vehicles (EVs) - including battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), or both-to its vehicle fleet. This report presents results of the UCONN fleet EV Suitability pilot program and offers recommendations for transitioning fleet vehicles to EVs as well as implementing adequate charging infrastructure.
Rapid Response Data Science for COVID-19
This report describes the results of a seven day effort to assist subject matter experts address a problem related to COVID-19. In the course of this effort, we analyzed the 29K documents provided as part of the White House's call to action. This involved applying a variety of natural language processing techniques and compression-based analytics in combination with visualization techniques and assessment with subject matter experts to pursue answers to a specific question. In this paper, we will describe the algorithms, the software, the study performed, and availability of the software developed during the effort.
Extending Theory-Guided Data Science to Consider Social Science Domains.
Abstract not provided.
Performance Portability of an SpMV Kernel Across Scientific Computing and Data Science Applications .
Abstract not provided.