Computer-supported analysis of scientific measurements

In the past decade, large-scale databases and knowledge bases have become available to researchers working in a range of scientific disciplines. In many cases these databases and knowledge bases contain measurements of properties of physical objects which have been obtained in experiments or at observation sites. As examples, one can think of crystallographic databases with molecular structures and property databases in materials science. These large collections of measurements, which will be called measurement bases, form interesting resources for scientific research. By analyzing the contents of a measurement base, one may be able to find patterns that are of practical and theoretical importance. With the use of measurement bases as a resource for scientific inquiry questions arise about the quality of the data being analyzed. In particular, the occurrence of conflicts and systematic errors raises doubts about the reliability of a measurement base and compromises any patterns found in it. On the other hand, conflicts and systematic errors may be interesting patterns in themselves and warrant further investigation. These considerations motivate the topic that will be addressed in this thesis: the development of systematic methods for detecting and resolving con icts and identifying\ud systematic errors in measurement bases. These measurement analysis (MA) methods are implemented in a computer system supporting the user of the measurement base

Situational Awareness Support to Enhance Teamwork in Collaborative Environments

Modern collaborative environments often provide an overwhelming amount of visual information on multiple displays. The multitude of personal and shared interaction devices leads to lack of awareness of team members on ongoing activities, and awareness of who is in control of shared artefacts. This research addresses the situational awareness (SA) support of multidisciplinary teams in co-located collaborative environments. This work aims at getting insights into design and evaluation of large displays systems that afford SA and effective teamwork

FABRIC: A National-Scale Programmable Experimental Network Infrastructure

FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities

Analysis and Synthesis of Metadata Goals for Scientific Data

The proliferation of discipline-specific metadata schemes contributes to artificial barriers that can impede interdisciplinary and transdisciplinary research. The authors considered this problem by examining the domains, objectives, and architectures of nine metadata schemes used to document scientific data in the physical, life, and social sciences. They used a mixed-methods content analysis and Greenberg’s (2005) metadata objectives, principles, domains, and architectural layout (MODAL) framework, and derived 22 metadata-related goals from textual content describing each metadata scheme. Relationships are identified between the domains (e.g., scientific discipline and type of data) and the categories of scheme objectives. For each strong correlation (\u3e0.6), a Fisher’s exact test for nonparametric data was used to determine significance (p \u3c .05). Significant relationships were found between the domains and objectives of the schemes. Schemes describing observational data are more likely to have “scheme harmonization” (compatibility and interoperability with related schemes) as an objective; schemes with the objective “abstraction” (a conceptual model exists separate from the technical implementation) also have the objective “sufficiency” (the scheme defines a minimal amount of information to meet the needs of the community); and schemes with the objective “data publication” do not have the objective “element refinement.” The analysis indicates that many metadata-driven goals expressed by communities are independent of scientific discipline or the type of data, although they are constrained by historical community practices and workflows as well as the technological environment at the time of scheme creation. The analysis reveals 11 fundamental metadata goals for metadata documenting scientific data in support of sharing research data across disciplines and domains. The authors report these results and highlight the need for more metadata-related research, particularly in the context of recent funding agency policy changes