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In recent years, intense usage of computing has been the main strategy of investigations in several scientific research projects. The progress in computing technology has opened unprecedented opportunities for systematic collection of experimental data and the associated analysis that were considered impossible only few years ago.

This paper focuses on the strategies in use: it reviews the various components that are necessary for an effective solution that ensures the storage, the long term preservation, and the worldwide distribution of large quantities of data that are necessary in a large scientific research project.

The paper also mentions several examples of data management solutions used in High Energy Physics for the CERN Large Hadron Collider (LHC) experiments in Geneva, Switzerland which generate more than 30,000 terabytes of data every year that need to be preserved, analyzed, and made available to a community of several tenth of thousands scientists worldwide.

The number of countries that have adopted International Financial Reporting Standards (IFRS) in some form has grown each year. However, the existing literature generally ignores the varied types and the complex timing of IFRS adoption. Our paper provides a cross-reference of IFRS adoption dates and types for 195 countries and territories around the world. This definitive data, including an extensive online dataset, was developed to help researchers better identify IFRS adoption events in the samples used in their empirical studies. Additionally, we highlight potential challenges in identifying IFRS adoption types and dates as well as provide areas of future research that can benefit from our dataset, which can be accessed online https://about.illinoisstate.edu/mktrimb/song-trimble-2022-dataset/.

3D scanning technologies are deployed toward developing a digital 3D model for Additive Manufacturing (AM) applications. It collects data, turning it into a 3D model that uses designated 3D printing processes. Many scanners, ranging from low-cost alternatives to professional series that are far more accurate and reliable, are now available to assist in bringing designs to reality. 3D scanning solutions enable the appropriate measurement of 3D physical parts into the virtual world, allowing factory production teams and corporate offices to share critical design information. These techniques are utilized everywhere in the design process, including product design and development, reverse engineering, quality control and quality assurance. The manufacturing sector can decrease costs while accelerating time to market and resolving quality issues. This study investigates the metrological need as per the advancements of 3D scanners. The procedural steps of the 3D scanners, along with specific metrological components and soft tools for 3D scanning, are discussed briefly. Finally, various 3D scanning applications are identified and discussed in detail. Because of the overall relative advantages of these non-contact measurement techniques, 3D metrological tools are crucial for modern production. Almost every sector aims for smaller, more complex components, which are more vulnerable to contamination or injury from even the slightest touch with a probe. The market is driven by global Research and Development (R&D) investment to develop game-changing technologies and solutions. Precision inspection and quality control are significant market drivers for industry progress. Smart factories will have lifetime access to 3D metrological data, allowing them to enhance quality and gain a competitive advantage in the marketplace.

As biomedical research data grow, researchers need reliable and scalable solutions for storage and compute. There is also a need to build systems that encourage and support collaboration and data sharing, to result in greater reproducibility. This has led many researchers and organizations to use cloud computing [1]. The cloud not only enables scalable, on-demand resources for storage and compute, but also collaboration and continuity during virtual work, and can provide superior security and compliance features. Moving to or adding cloud resources, however, is not trivial or without cost, and may not be the best choice in every scenario. The goal of this workshop is to explore the benefits of using the cloud in biomedical and computational research, and considerations (pros and cons) for a range of scenarios including individual researchers, collaborative research teams, consortia research programs, and large biomedical research agencies / organizations.

The availability of multiple publicly-available datasets studying the same phenomenon has the promise of accelerating scientific discovery. Meta-analysis can address issues of reproducibility and often increase power. The promise of meta-analysis is especially germane to rarer diseases like cystic fibrosis (CF), which affects roughly 100,000 people worldwide. A recent search of the National Institute of Health’s Gene Expression Omnibus revealed 1.3 million data sets related to cancer compared to about 2,000 related to CF. These studies are highly diverse, involving different tissues, animal models, treatments, and clinical covariates. In our search for gene expression studies of primary human airway epithelial cells, we identified three studies with compatible methodologies and sufficient metadata: GSE139078, Sala Study, and PRJEB9292. Even so, experimental designs were not identical, and we identified significant batch effects that would have complicated functional analysis. Here we present quantile discretization and Bayesian network construction using the Hill climb method as a powerful tool to overcome experimental differences and reveal biologically relevant responses to the CF genotype itself, exposure to virus, bacteria, and drugs used to treat CF. Functional patterns revealed by cluster Profiler included interferon signaling, interferon gamma signaling, interleukins 4 and 13 signaling, interleukin 6 signaling, interleukin 21 signaling, and inactivation of CSF3/G-CSF signaling pathways showing significant alterations. These pathways were consistently associated with higher gene expression in CF epithelial cells compared to non-CF cells, suggesting that targeting these pathways could improve clinical outcomes. The success of quantile discretization and Bayesian network analysis in the context of CF suggests that these approaches might be applicable to other contexts where exactly comparable data sets are hard to find.