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Data from: Automatic recognition of self-acknowledged limitations in clinical research literature
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- ee methods: a rule-based approach based on document structure, supervised machine learning, and a semi-supervised method that uses self-training to expand
Data from: On the variety of methods for calculating confidence intervals by bootstrapping
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- and accelerated method as giving generally good performance; although researchers should be warned that coverage of bootstrap confidence intervals
Data from: A machine learning method to monitor China’s AIDS epidemics with data from Baidu Trends
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- in China. Methods: A machine learning method, artificial neural networks (ANNs), is used to forecast AIDS occurrences and deaths. Search trend data rela
Data from: Establishment of a refined oral glucose tolerance test in pigs, and assessment of insulin, glucagon and glucagon-like peptide-1 responses
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- Diabetes mellitus is increasing worldwide and reliable animal models are important for progression of the research field. The pig is a commonly use
Data from: User experience methods and maturity in academic libraries
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- This article presents a mixed-methods study of the methods and maturity of user experience (UX) practice in academic libraries. The authors apply
ween 2005 and 2013: a reflection of the development of medical education research?
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- ers of German-speaking researchers at AMEE conferences as a means of measuring said development. Methods: The AMEE conference proceedings from the categories
Data from: EpiRADseq: scalable analysis of genome-wide patterns of methylation using next-generation sequencing
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- 1. Research addressing the role of epigenetics in a diversity of experimental and natural systems is rapidly accumulating. Diverse methods hav
Data from: Prolific observer bias in the life sciences: why we need blind data recording
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- effect sizes and more significant p-values. We discuss methods to minimize bias and urge researchers, editors, and peer reviewers to keep blind protocols
Data from: Accurate inference of tree topologies from multiple sequence alignments using deep learning
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- ning techniques have made inroads on a number of both new and longstanding problems in biological research. Here we designed a deep convolutional neural network
