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Register a new userHarnessing the Benefits of Open Electronics in Science
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Freely and openly shared low-cost electronic applications, known as open electronics, have sparked a new open-source movement, with much un-tapped potential to advance scientific research. Initially designed to appeal to electronic hobbyists, open electronics have formed a global community of makers and inventors and are increasingly used in science and industry. Here, we review the current benefits of open electronics for scientific research and guide academics to enter this emerging field. We discuss how electronic applications, from the experimental to the theoretical sciences, can help (I) individual researchers by increasing the customization, efficiency, and scalability of experiments, while improving data quantity and quality; (II) scientific institutions by improving access and maintenance of high-end technologies, visibility and interdisciplinary collaboration potential; and (III) the scientific community by improving transparency and reproducibility, helping decouple research capacity from funding, increasing innovation, and improving collaboration potential among researchers and the public. Open electronics are powerful tools to increase creativity, democratization, and reproducibility of research and thus offer practical solutions to overcome significant barriers in science.
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Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift towards answering the question of how we can analyze and understand the massive amounts of data in front of us. Unfortunately, lack of standardized sharing mechanisms and practices often make reproducing or extending scientific results very difficult. With the creation of data organization structures and tools which drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries. Our proposed solution leverages these existing technologies and standards, and provides an accessible and extensible model for reproducible research, called science in the cloud (sic). Exploiting scientific containers, cloud computing and cloud data services, we show the capability to launch a computer in the cloud and run a web service which enables intimate interaction with the tools and data presented. We hope this model will inspire the community to produce reproducible and, importantly, extensible results which will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended.
Solid state electronics relies on the intentional introduction of impurity atoms or dopants into a semiconductor crystal and/or the formation of junctions between different materials (heterojunctions) to create rectifiers, potential barriers, and conducting pathways. With these building blocks, switching and amplification of electrical currents and voltages is achieved. As miniaturization continues to ultra-scaled transistors with critical dimensions on the order of ten atomic lengths, the concept of doping to form rectifying junctions fails and heterojunction formation becomes extremely difficult. Here it is shown there is no need to introduce dopant atoms nor is the formation of a heterojunction required to achieve the fundamental electronic function of current rectification. Ideal diode behavior or rectification is achieved for the first time solely by manipulation of quantum confinement in approximately 2 nanometer thick films consisting of a single atomic element, the semimetal bismuth. Crucially for nanoelectronics, this new quantum approach enables room temperature operation.
Data science has emerged from the proliferation of digital data, coupled with advances in algorithms, software and hardware (e.g., GPU computing). Innovations in structural biology have been driven by similar factors, spurring us to ask: can these two fields impact one another in deep and hitherto unforeseen ways? We posit that the answer is yes. New biological knowledge lies in the relationships between sequence, structure, function and disease, all of which play out on the stage of evolution, and data science enables us to elucidate these relationships at scale. Here, we consider the above question from the five key pillars of data science: acquisition, engineering, analytics, visualization and policy, with an emphasis on machine learning as the premier analytics approach.
Although the open-field test has been widely used, its reliability and compatibility are frequently questioned. Although many indicating parameters were introduced for this test, they did not take data distributions into consideration. This oversight may have caused the problems mentioned above. Here, an exploratory approach for the analysis of video records of tests of elderly mice was taken that described the distributions using the least number of parameters. First, the locomotor activity of the animals was separated into two clusters: dash and search. The accelerations found in each of the clusters were distributed normally. The speed and the duration of the clusters exhibited an exponential distribution. Although the exponential model includes a single parameter, an additional parameter that indicated instability of the behaviour was required in many cases for fitting to the data. As this instability parameter exhibited an inverse correlation with speed, the function of the brain that maintained stability would be required for a better performance. According to the distributions, the travel distance, which has been regarded as an important indicator, was not a robust estimator of the animals condition.
In the past decade, digital technologies have started to profoundly influence healthcare systems. Digital self-tracking has facilitated more precise epidemiological studies, and in the field of nutritional epidemiology, mobile apps have the potential to alleviate a significant part of the journaling burden by, for example, allowing users to record their food intake via a simple scan of packaged products barcodes. Such studies thus rely on databases of commercialized products, their barcodes, ingredients, and nutritional values, which are not yet openly available with sufficient geographical and product coverage. In this paper, we present FoodRepo (https://www.foodrepo.org), an open food repository of barcoded food items, whose database is programmatically accessible through an application programming interface (API). Furthermore, an open source license gives the appropriate rights to anyone to share and reuse FoodRepo data, including for commercial purposes. With currently more than 21,000 items available on the Swiss market, our database represents a solid starting point for large-scale studies in the field of digital nutrition, with the aim to lead to a better understanding of the intricate connections between diets and health in general, and metabolic disorders in particular.
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