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Full elbow arthroplasty employing an augmented reality-assisted medical method.
Who decides what good data science looks like? And who gets to decide what "data ethics" means? The answer is all of us. Good data science should incorporate the perspectives of people who create and work with data, people who study the interactions between science and society, and people whose lives are affected by data science.Turning historical meteorological observations into usable data is a challenging process that is immeasurably enriched when it encompasses interdisciplinarity. Here, the McGill DRAW (Data Rescue Archives and Weather) project shows how climatologists, geographers, archivists, data scientists, and coders together built a citizen-science-based transcription platform to transform the McGill Observatory paper records into a traceable and sustainable database.Alan Turing and Bletchley Park are rightly recognized for their work on breaking the Enigma code. However, this was built on a foundation of work during the 1930s by the Polish cryptographer, Marian Rejewski. Often working alone, and with limited resources, he found ways to break early Enigma code. This article attempts to highlight the man and his invaluable contribution.ADR UK is helping to transform the way researchers access the UK's wealth of administrative data, enabling government policy to be informed by the best evidence available. Emma shares her insights into the ADR UK approach to making this happen, explaining why building trust is central to the ADR UK mission.With the rapid development of the fields of data science and artificial intelligence, a dichotomy presents itself more professionals are needed to fulfill the growing workfoce demand, and women continue to be underrepresented in all computer science-related jobs. Women AI Academy addresses both issues by inspiring, enabling, and targeting the employment of women in data science and artificial intelligence.The Scholexplorer API, based on the Scholix (Scholarly Link eXchange) framework, aims to identify links between articles and supporting data. This quantitative case study demonstrates that the API vastly expanded the number of datasets previously known to be affiliated with University of Bath outputs, allowing improved monitoring of compliance with funder mandates by identifying peer-reviewed articles linked to at least one unique dataset. Availability of author names for research outputs increased from 2.4% to 89.2%, which enabled identification of ten articles reusing non-Bath-affiliated datasets published in external repositories in the first phase, giving valuable evidence of data reuse and impact for data producers. Of these, only three were formally cited in the references. Further enhancement of the Scholix schema and enrichment of Scholexplorer metadata using controlled vocabularies would be beneficial. The adoption of standardized data citations by journals will be critical to creating links in a more systematic manner.Electromagnetic (EM) sensing is a widespread contactless examination technique with applications in areas such as health care and the internet of things. Most conventional sensing systems lack intelligence, which not only results in expensive hardware and complicated computational algorithms but also poses important challenges for real-time in situ sensing. To address this shortcoming, we propose the concept of intelligent sensing by designing a programmable metasurface for data-driven learnable data acquisition and integrating it into a data-driven learnable data-processing pipeline. Thereby, a measurement strategy can be learned jointly with a matching data post-processing scheme, optimally tailored to the specific sensing hardware, task, and scene, allowing us to perform high-quality imaging and high-accuracy recognition with a remarkably reduced number of measurements. We report the first experimental demonstration of "learned sensing" applied to microwave imaging and gesture recognition. Our results pave the way for learned EM sensing with low latency and computational burden.Learning from the rapidly growing body of scientific articles is constrained by human bandwidth. Existing methods in machine learning have been developed to extract knowledge from human language and may automate this process. Here, we apply sentiment analysis, a type of natural language processing, to facilitate a literature review in reintroduction biology. We analyzed 1,030,558 words from 4,313 scientific abstracts published over four decades using four previously trained lexicon-based models and one recursive neural tensor network model. We find frequently used terms share both a general and a domain-specific value, with either positive (success, protect, growth) or negative (threaten, loss, risk) sentiment. Sentiment trends suggest that reintroduction studies have become less variable and increasingly successful over time and seem to capture known successes and challenges for conservation biology. This approach offers promise for rapidly extracting explicit and latent information from a large corpus of scientific texts.Entropy is the natural tendency for decline toward disorder over time. Information entropy is the decline in data, information, and understanding that occurs after data are used and results are published. small molecule library screening As time passes, the information slowly fades into obscurity. Data discovery is not enough to slow this process. High-quality metadata that support understanding and reuse and cross domains are a critical antidote to information entropy, particularly as it supports reuse of the data-adding to community knowledge and wisdom. Ensuring the creation and preservation of these metadata is a responsibility shared across the entire data life cycle from creation through analysis and publication to archiving and reuse. Repositories can play an important role in this process by augmenting metadata through time with persistent identifiers and connections they facilitate. Data providers need to work with repositories to encourage metadata evolution as new capabilities and connections emerge.Traditionally, networks have been studied in an independent fashion. With the emergence of novel smart city technologies, coupling among networks has been strengthened. To capture the ever-increasing coupling, we explain the notion of interdependent networks, i.e., multi-layered networks with shared decision-making entities, and shared sensing infrastructures with interdisciplinary applications. The main challenge is how to develop data analytics solutions that are capable of enabling interdependent decision making. One of the emerging solutions is agent-based distributed decision making among heterogeneous agents and entities when their decisions are affected by multiple networks. We first provide a big picture of real-world interdependent networks in the context of smart city infrastructures. We then provide an outline of potential challenges and solutions from a data science perspective. We discuss potential hindrances to ensure reliable communication among intelligent agents from different networks. We explore future research directions at the intersection of network science and data science.
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