Science has entered the era of Big Data with new challenges related to data governance, stewardship, and management. The existing data governance practices must catch up to ensure proper data management. Existing data governance policies and stewardship best practices tend to be disconnected from operational data management practices and enforcement and mainly exist in well-meaning documents or reports. These governance policies are, at best, partially implemented and rarely monitored or audited. In addition, existing governance policies keep adding additional data management steps that require a human, ‘a data steward’, in the loop, and the cost of data management can no longer scale proportionately with the current and future increased data volume and complexity. The goal for developing an updated data governance framework is to modernize scientific data governance to the reality of Big data and align it with the current technology trends such as cloud computing and AI. The goals of this framework are two folds. One is to ensure thoroughness that the governance adequately covers the entire data life cycle. Two, provide a practical approach that offers a consistent and repeatable process for different projects. Three core principles ground this framework. First, focus on just enough governance and prevent data governance from becoming a roadblock toward the scientific process. Remove any unnecessary processes and steps. Second, automate data management steps where possible. Actively remove steps that require  ‘human in the loop’ within the management process to be efficient and scale with increasing data. Third, all the processes should continually be optimized using quantified metrics to streamline the monitoring and auditing workflows. 

This paper presents the process of developing 3D printed task trainers and simulators for healthcare education. The process is a collaborative effort between the College of Nursing at the University of Alabama in Huntsville (UAH) and the UAH Systems Management and Production Center. The three year collaboration led to development of a five-phase feedback and development process which is continually refined and now used for the development of all 3D printed training simulations in the interprofessional center. The first phase consists of nursing faculty and center engineers conducting a needs assessment. The output of this phase is a prioritized list of potential simulations. The second phase consists of a team of engineering students developing the 3D training simulation. The third phase is evaluating the simulation. A number of iterations are generally necessary to have a simulation that satisfied nursing requirements. The nursing faculty is actively involved throughout the process to assure the desired characteristics and fullest medical design to include texture, elasticity, density, strength, color and realism. The fourth phase is implementing the simulation into the program. The fifth phase is to document and evaluate the process. During the past several years a number of 3-D products have been developed and are now implemented. As an example, a 3D printed brain with multiple sclerosis (MS) is discussed in this paper. The 3D printed brain is being used to assist nurses better understand and more easily visualize lesions in MS patients. The 3D printed trainers are being used to provide nursing students with much needed hands-on experiences, to allow students to practice specific skills at a lower cost and to gain these skills in a safe setting. Included in this paper are a description of the process used to develop the training simulators, the use of simulators in nurse training the results and benefits.

Whitepaper #098 submitted to the Planetary Science and Astrobiology Decadal Survey 2023-2032. Topics: interior evolution and volcanism; Mercury and/or the Moon; solar system formation, dynamics processes, and chronology

A mammogram is an X-ray image of the human breast. It is used for the detection and diagnosis of changes in breast tissues. Asymptomatic women may be encouraged to undergo screening mammography on a regular basis after reaching a certain age. In most cases, mammograms reveal information that could yield the detection of suspicious lesions by breast radiologists (BRs). Hopefully, the detection is achieved before the lesions have advanced to a late stage of cancer, which makes it difficult to cure.

In this chapter we discuss the wide range of challenges in user-generated Internet of Things applications, as being worked on among the large consortium of the DiY Smart Experiences (DiYSE) project (DiYSE, ITEA2 08005). The chapter starts with a discussion on the context of ‘DiY’ as a phenomenon to be leveraged, and eco-awareness as an example application area. The main body of the chapter is devoted to the technical outline of the DiYSE architecture, starting at the lower Internet of Things layers of sensors, actuators and middleware, over the role of semantics in device and service interoperability, up to requirements for the service framework and the application creation process. Furthermore, the chapter adds considerations concerning tangible interaction in the smart space, assumed in Di- YSE both for the context of experiencing as well as shaping the user experience. With the chapter, we thus take a holistic view, sampling the range from lowerlayer technical implications of enabling DiY creation in the Internet of Things, up to the human-level aspects of creative communities as well as tangible interaction.

The Burst and Transient Source Experiment (BATSE) on the Compton Gamma Ray Observatory detected gamma-ray bursts (GRBs) with a real-time burst detection (or "trigger") system running onboard the spacecraft. Under some circumstances, however, a GRB may not have activated the onboard burst trigger. For example, the burst may have been too faint to exceed the onboard detection threshold, or it may have occurred while the onboard burst trigger was disabled for technical reasons. This paper describes a catalog of 873 "non-triggered" GRBs that were detected in a search of the archival continuous data from BATSE, recorded between 1991 December 9.0 and 1997 December 17.0. For each burst, the catalog gives an estimated source direction, duration, peak flux, and fluence. Similar data are presented for 50 additional bursts of unknown origin that were detected in the 25--50 keV range; these events may represent the low-energy "tail" of the GRB spectral distribution. This catalog increases the number of GRBs detected with BATSE by 48% during the time period covered by the search.

We evaluate the significance of the line candidates identified by a visual search of burst spectra from BATSE's Spectroscopy Detectors. None of the candidates satisfy our detection criteria: an F-test probability less than 10^-4 for a feature in one detector and consistency among the detectors which viewed the burst. Most of the candidates are not very significant, and are likely to be fluctuations. Because of the expectation of finding absorption lines, the search was biased towards absorption features. We do not have a quantitative measure of the completeness of the search which would enable a comparison with previous missions. Therefore a more objective computerized search has begun.