Robust Data Management

Mobility gains more and more importance from a technological as well as social perspective. On the one hand, mobility is required from the personal and professional environment in order to keep pace with the developments in a global world. On the other hand, the existence of wireless networks and the success of cell-phones enable a wide usage of mobile communication infrastructure. While mobile devices (especially cell-phone) are becoming more and more a general vehicle to perform a wide spectrum of applications like internet browsing, etc. many, many issues are still unsolved in order to provide a technologically solid and well accepted mobile infrastructure. In the following, we focus on the term of robustness as a mean to achieve this goal. No only the general possibility to communicate via mobile devices using wireless networks is the question, but the reliable, secure, and finally simple way of doing it must be the core research in the context of mobile environments

Preserving Scientific Research Data at Middlebury College

Middlebury College is a small undergraduate college which nevertheless produces significant volumes of research data created by both faculty and students. Many funding agencies now require robust data management planning, including data archiving, but not all the data locally produced with such funding fits naturally into national, discipline specific repositories. Thus, we are attempting to create a local repository to curate this data. We began a pilot with the repository software that was already available on campus, but it has become clear that a more flexible platform is needed, so a second phase of the pilot has begun

Beyond Data Capture: Using REDCap™ to Facilitate Web-Based Therapeutic Intervention Research

Background Limited guidelines to assist nurse researchers who use web-based interventions are available. Nurses must develop the supporting technology enabling participants to complete study activities and collected data while maintaining data security and participant confidentiality. Objectives To describe how the authors used advanced Research Electronic Data Capture (REDCapTM) functionality to support the data management infrastructure of an interactive, web-based therapeutic intervention. Methods The data management infrastructure for the WISER intervention pilot study consisted of two components: a website for presentation of the intervention and participant account management and a REDCap project for data capture and storage. REDCap application programming interface (API) connected these two components using HTML links and data exchanges. Results We completed an initial pilot study of WISER with 14 participants using the REDCap-based infrastructure. Minimal technical difficulties were encountered. Discussion REDCap is cost-effective, readily available, and through its advanced functionality is able to facilitate confidential, secure interactions with participants, robust data management, and seamless participant progression in web-based intervention research

Building the case for actionable ethics in digital health research supported by artificial intelligence

The digital revolution is disrupting the ways in which health research is conducted, and subsequently, changing healthcare. Direct-to-consumer wellness products and mobile apps, pervasive sensor technologies and access to social network data offer exciting opportunities for researchers to passively observe and/or track patients ‘in the wild’ and 24/7. The volume of granular personal health data gathered using these technologies is unprecedented, and is increasingly leveraged to inform personalized health promotion and disease treatment interventions. The use of artificial intelligence in the health sector is also increasing. Although rich with potential, the digital health ecosystem presents new ethical challenges for those making decisions about the selection, testing, implementation and evaluation of technologies for use in healthcare. As the ‘Wild West’ of digital health research unfolds, it is important to recognize who is involved, and identify how each party can and should take responsibility to advance the ethical practices of this work. While not a comprehensive review, we describe the landscape, identify gaps to be addressed, and offer recommendations as to how stakeholders can and should take responsibility to advance socially responsible digital health research

Southern Hemisphere automated supernova search

The Perth Astronomy Research Group has developed an automated supernova search program, using the 61 cm Perth–Lowell reflecting telescope at Perth Observatory in Western Australia, equipped with a CCD camera. The system is currently capable of observing about 15 objects per hour, using 3 min exposures, and has a detection threshold of 18th–19th magnitude. The entire system has been constructed using low‐cost IBM‐compatible computers. Two original discoveries (SN 1993K, SN 1994R) have so far been made during automated search runs. This paper describes the hardware and software used for the supernova search program, and shows some preliminary results from the search system

Confronting the “second wave of the tsunami”: stabilizing communities in the wake of foreclosures

Foreclosure ; Community development

Ivar, an interpretation‐oriented tool to manage the update and revision of variant annotation and classification

The rapid evolution of Next Generation Sequencing in clinical settings, and the resulting challenge of variant reinterpretation given the constantly updated information, require robust data management systems and organized approaches. In this paper, we present iVar: a freely available and highly customizable tool with a user‐friendly web interface. It represents a platform for the unified management of variants identified by different sequencing technologies. iVar accepts variant call format (VCF) files and text annotation files and elaborates them, optimizing data organization and avoiding redundancies. Updated annotations can be periodically re‐uploaded and associated with variants as historically tracked attributes, i.e., modifications can be recorded whenever an updated value is imported, thus keeping track of all changes. Data can be visualized through variant‐centered and sample‐centered interfaces. A customizable search function can be exploited to periodically check if pathogenicity‐related data of a variant has changed over time. Patient recontacting ensuing from variant reinterpretation is made easier by iVar through the effective identification of all patients present in the database carrying a specific variant. We tested iVar by uploading 4171 VCF files and 1463 annotation files, obtaining a database of 4166 samples and 22,569 unique variants. iVar has proven to be a useful tool with good performance in terms of collecting and managing data from a medium‐throughput laboratory