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相关概念视频

Introduction to R01:11

Introduction to R

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R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
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Methods of Documentation I: Source-Oriented Records01:18

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Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
In an SOR, each discipline involved in patient care maintains a separate medical record section. This record-keeping method enables easy tracking of patient progress and ensures healthcare staff have access to up-to-date information.
Key Attributes include the following:
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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Guidelines and Strategies for Safe Computer Charting01:18

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The guidelines and strategies provided by the American Nurses Association (ANA) and the Canadian Nurses Association (CNA) offer essential principles for ensuring safe and secure computer charting systems in healthcare settings. Let's break down each recommendation:
Maintain Confidentiality and Security:
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Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
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Protein Folding Quality Check in the RER01:29

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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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相关实验视频

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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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数据文件的人类可读性

Michael Robert Gryk1

  • 1Associate Professor, Department of Molecular Biology and Biophysics, UCONN Health (US), Doctoral Student, University of Illinois, Urbana-Champaign (US).

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概括
此摘要是机器生成的。

这项研究提出了一种衡量机器可解读数据格式,如XML和JSON的人类可读性的指标. 这项研究旨在评估这些标准是否平衡机器解释性与人类理解.

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科学领域:

  • 数据科学数据科学数据科学
  • 信息科学 信息科学 信息科学
  • 计算机科学 计算机科学

背景情况:

  • 大数据和FAIR数据原则需要机器可解释的数据格式.
  • 人类可读性是数据格式标准化的关键,但往往未被量化,属性.
  • 像STAR,W3C PROV和XML开发等现有标准强调了人类可读性的需要.

研究的目的:

  • 在结构化数据档案格式中定义和测量人类可读性.
  • 为了比较在不同格式,如JSON和XML中表示的数据的人类可读性.
  • 评估当前的数据标准是否实现机器解释性和人类可读性.

主要方法:

  • 审查影响可读性的书面文本方面.
  • 调整结构化数据的教育可读性指标.
  • 应用拟议的指标来比较各种格式 (如JSON,XML) 的数据.

主要成果:

  • 一种用于估计结构化数据人类相对可读性的指标.
  • 基于新的可读性指标的数据格式的比较分析.
  • 洞察数据标准中机器可解释性和人类可读性之间的权衡.

结论:

  • 数据格式中的人类可读性可以通过使用拟议的指标来估计和比较.
  • 该研究为评估数据格式设计的框架提供了一个框架,超出了单纯的机器可解释性.
  • 进一步的研究可以完善该指标,并将其应用于更广泛的数据标准.