“…Namely, we suggest that future research in animal sciences teaching and learning 1) develop animal-science-specific expertise on a greater variety of pedagogies, 2) validate improved methods for assessing transferable skills, 3) expand pedagogical knowledge of emerging topics (e.g., sustainability, data science, welfare science, social science), and 4) deepen and broaden animal sciences’ teaching and learning identity through theory-building work and collaborations across instructors, disciplines, and institutions.…”
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“…We propose that the successful clinical neuroepidemiological investigators of the future must be able to link comorbidities, environmental exposures, lifestyles, genomics, e.g. host susceptibility, with knowledge of modern technology of neurosciences and measurement of brain disease and data science. We will describe our experience at the University of Pittsburgh in leading a new training program in Population Neuroscience of ADRD. …”
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“…Areas of concentration include promoting caregiving mastery (Emory University), integrating the use of technology in care support to improve assessments and interventions in care provision (Oregon Health & Science University), developing behavioral interventions to reduce isolation and promote social connectedness in caregivers (University of Rochester), promoting health in racial/ethnic minorities (University of Illinois at Chicago), and using insights from data science and behavioral economics to improve palliative care delivery and long-term support facilities for persons with dementia and their caregivers (University of Pennsylvania). …”
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“…The 2020 International Conference on Intelligent Biology and Medicine (ICIBM 2020) provided a multidisciplinary forum for computational scientists and experimental biologists to share recent advances on all aspects of intelligent computing, informatics and data science in biology and medicine. ICIBM 2020 was held as a virtual conference on August 9–10, 2020, including four live sessions with forty-one oral presentations over video conferencing. …”
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“…We develop three computational formulations of textual coherence drawing upon the state-of-the-art methods in natural language processing and data science. Two real-world datasets from widely different domains which have fake/legitimate article labellings are then analyzed with respect to textual coherence. …”
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“…Data mining is employed to extract useful information and to detect patterns from often large data sets, closely related to knowledge discovery in databases and data science. In this investigation, we formulate models based on machine learning algorithms to extract relevant information predicting student retention at various levels, using higher education data and specifying the relevant variables involved in the modeling. …”
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“…At the same time, the dataset can also be relevant for researchers in fields such as data science, epidemiology, virology and earth modelling.…”
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“…Accumulating knowledge of T cell development and function coupled with advancements in genetics and data science has enabled the use of a patient’s own (autologous) T cells for ACT (TIL ACTs). …”
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“…In recent years, the development of data science made data mining a popular research area. …”
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“…Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as a real physical notion so as to formulate it from first principles, however, seems to have gone unnoticed. …”
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“…This retrospective population-based cohort study used data from Taiwan’s Health and Welfare Data Science Center (2004–2012). A total of 298,377 patients received surgeries for FF between 2006 and 2010; of them, 1209 (937 EIBP and 272 LIBP) received first-time bisphosphonates (oral alendronate, 70 mg, once a week). …”
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“…In this paper, we provide a detailed review of CRISP-DM and summarize extensions of this model into a novel framework we call Generalized Cross-Industry Standard Process for Data Science (GCRISP-DS). This framework is designed to allow dynamic interactions between different phases to adequately address data- and model-related issues for achieving robustness. …”
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“…A fundamental issue of network data science is the ability to discern observed features that can be expected at random from those beyond such expectations. …”
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“…In the recent years, data science methods have been developed considerably and have consequently found their way into many business processes in banking and finance. …”
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“…We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. …”
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“…This article also explores briefly new roles of data science in connection with responsibility and altruism. …”
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