Research papers in the biomedical field come with large and complex data sets that are shared with the scientific community as unstructured data files via public data repositories. Examples are sequencing, microarray, and mass spectroscopy data. The papers discuss and visualize only a small part of the data, the part that is in its research focus. For labs with similar but not identical research interests different parts of the data might be important. They can thus download the full data, preprocess it, integrate it with data from other publications and browse those parts that they are most interested in. This requires substantial work as well as programming and analysis expertise that only few biological labs have on board. In contrast, providing access to published data over web browsers makes all data visible, allows for easy interaction with it, and lowers the barrier to working with data from others.

Online learners can study at convenient times and collaborate with others online, but usually can not come to physical labs. We investigated the use of simulated electronics laboratories to increase access and decrease trips to a physical lab. Students were assigned to either physical labs or to a combination of simulated and physical labs. Students using combined virtual and physical labs performed as well as those using only physical labs on both written and physical lab tests. This evidence supports the use of virtual labs to replace some physical labs, including the use of virtual labs for online learners.

Many interesting geospatial datasets are publicly accessible on web sites and other online repositories. However, the sheer number of datasets and locations, plus a lack of support for cross-repository search, makes it difficult for researchers to discover and integrate relevant data. We describe here early results from a system, Klimatic, that aims to overcome these barriers to discovery and use by automating the tasks of crawling, indexing, integrating, and distributing geospatial data. Klimatic implements a scalable crawling and processing architecture that uses an elastic container-based model to locate and retrieve relevant datasets and to extract metadata from headers and within files to build a global index of known geospatial data. In so doing, we create an expansive geospatial virtual data lake that records the location, formats, and other characteristics of large numbers of geospatial datasets while also caching popular data subsets for rapid access. A flexible query interface allows users to request data that satisfy supplied type, spatial, temporal, and provider specifications; in processing such queries, the system uses interpolation and aggregation to combine data of different types, data formats, resolutions, and bounds. Klimatic has so far incorporated more than 10,000 datasets from over 120 sources and has been demonstrated to scale well with data size and query complexity.

Intending to enable a broader collaboration with the scientific community while maintaining privacy of the data stored and generated in Living Labs, this paper presents the Shareable Data Publishing and Access Service for Living Labs, implemented within the framework of the H2020 VITALISE project. Building upon previous work, significant enhancements and improvements are presented in the architecture enabling Living Labs to securely publish collected data in an internal and isolated node for external use. External researchers can access a portal to discover and download shareable data versions (anonymised or synthetic data) derived from the data stored across different Living Labs that they can use to develop, test, and debug their processing scripts locally, adhering to legal and ethical data handling practices. Subsequently, they may request remote execution of the same algorithms against the real internal data in Living Lab nodes, comparing the outcomes with those obtained using shareable data. The paper details the architecture, data flows, technical details and validation of the service with real-world usage examples, demonstrating its efficacy in promoting data-driven research in digital health while preserving privacy. The presented service can be used as an intermediary between Living Labs and external researchers for secure data exchange and to accelerate research on data analytics paradigms in digital health, ensuring compliance with data protection laws.

Background: As CMS started to reimburse videovisits during public health emergency, virtual diabetes care became routine for diabetes clinics. Since better diabetes care depends on review of data, at UPMC, we created a workflow to facilitate diabetes data download before virtual visits. Best practices for virtual diabetes data download is not known. Objective: To analyze the diabetes technology data download workflow efficiency in our clinic for virtual visits. Methods: At University of Pittsburgh endocrine clinic, staff reaches out to patients with diabetes who are scheduled for videovisits one week before their appointment with instructions on how to download glucometer, insulin pump and/or CGM data. We completed a retrospective chart review to understand the effectiveness of this workflow in obtaining data before visits. Results: August 2020 to January 2020, our clinic staff reached out to 1197 patients with diabetes via secure portal before their videovisits to obtain diabetes data download. Patients used Tidepool software to download their pumps and glucometers or cloud technology for CGM data sharing. 164(14%) patients were on insulin pump(with or without CGM), 159(13%) patients had a CGM alone and 874(73%) patients had a glucometer alone. 190(15%) patients uploaded their data to Tidepool, 150 (12%) patients shared their CGM data via cloud using their cell phone apps.873(73%) patients did not download their blood sugar data before appointment, most of these patients had glucometer only. The barriers to sharing data virtually were reported by 104 patients. 25/104 (25%) of patients had no access to a computer. 6 patients (5%) reported having a glucometer that does not have compatibility with Tidepool software. 35/104 (32%) of patients reported not having cable to download their data. Conclusion: The most common barrier for patients to share their diabetes technology data was lack of cables. Diabetes clinics should analyze their remote diabetes data workflows to troubleshoot barriers. Disclosure E. Karslioglu-french: None. D. Sistla: None. A. C. Meyer: None.

Virtual Globes such as Google Earth and NASA World Wind permit users to explore rich imagery and the topography of the Earth. While other online services such as map servers provide ways to view, query, and download geographic information, the public has become captivated with the ability to view the Earth’s features virtually. The National Snow and Ice Data Center began to display scientific data on Virtual Globes in 2006. The work continues to evolve with the production of high-quality Keyhole Markup Language (KML) representations of scientific data and an assortment of technical experiments. KML files are interoperable with many Virtual Globe or mapping software packages. This paper discusses the science benefits of Virtual Globes, summarizes KML creation methods, and introduces a guide for selecting tools and methods for authoring KML for use with scientific data sets.

Contribution: A novel approach to remote instruction in 802.11 technology is described using the virtual lab technology. Background: Lab-based education has been a staple of computing education for decades. By interacting with the technology, students are able to gain a much greater understanding of the subject through hands-on activities. Recently, virtual labs have provided a mechanism to allow both co-located and online students access to these lab environments without the time, space, and monetary constraints of traditional labs. Computer networking and security is one area where virtual labs provide a highly useful testbed for learning security concepts. One problem is implementing virtual educational labs pertaining to 802.11 technologies given the inherently physical location-based nature of the wireless medium. Intended Outcomes: This article builds on work by others by developing and testing a new hybrid physical/virtual lab to enable students to learn concepts pertaining to 802.11 networking technologies remotely in a virtual lab setting. Application Design: The virtual lab environment uses the ESXi virtualization technology coupled with arrays of USB 802.11 adapters to enable the use of 802.11 technologies remotely. Findings: The experimental results show the proposed system to be as effective as a traditional physical lab environment with regards to usefulness, ease of use, satisfaction, self-efficacy, and exercise completion time.

There has been an ongoing debate of which physical labs or virtual labs are better. To resolve this issue, a remote lab provides an online lab that can do real experiments to obtain real data from a distant physical lab. Instead of relying on a remote lab, this article suggests that students collect experimental data locally with low-cost data loggers and then model the data with a web tool that provides scaffold support like a remote lab or virtual lab. In this study, 32 tenth-grade students ran physics labs and collected data with NXT, smartphones, and digital video recorder. This study investigates how a web tool assists in data visualization, hypothesis generation, hypothesis testing, and regulation of the discovery process. Results indicated the students became more sensitive in applying strategies of parameter tuning and backtracking. Questionnaire responses indicated the students found such physical labs to be satisfying.

Virtual screening methods are commonly used nowadays in drug discovery processes. However, to ensure their reliability, they have to be carefully evaluated. The evaluation of these methods is often realized in a retrospective way, notably by studying the enrichment of benchmarking data sets. To this purpose, numerous benchmarking data sets were developed over the years, and the resulting improvements led to the availability of high quality benchmarking data sets. However, some points still have to be considered in the selection of the active compounds, decoys, and protein structures to obtain optimal benchmarking data sets.

AbstractBenchmarking is a crucial step in evaluating virtual screening methods for drug discovery. One major issue that arises among benchmarking datasets is a lack of a standardized format for representing the protein and ligand structures used to benchmark the virtual screening method. To address this, we introduce the Directory of Useful Benchmarking Sets (DUBS) framework, as a simple and flexible tool to rapidly created benchmarking sets using the protein databank. DUBS uses a simple input text based format along with the Lemon data mining framework to efficiently access and organize data to protein databank and output commonly used inputs for virtual screening software. The simple input format used by DUBS allows users to define their own benchmarking datasets and access the corresponding information directly from the software package. Currently, it only takes DUBS less than 2 minutes to create a benchmark using this format. Since DUBS uses a simple python script, users can easily modify to create more complex benchmarks. We hope that DUBS will be a useful community resource to provide a standardized representation for benchmarking datasets in virtual screening.