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Data Collection by Observations01:08

Data Collection by Observations

11.9K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
11.9K
Quartile01:15

Quartile

4.2K
Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
4.2K
Data Collection II01:29

Data Collection II

8.1K
The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
8.1K
Data Collection I01:30

Data Collection I

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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
6.2K
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.9K
Histogram01:05

Histogram

12.9K
The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
12.9K

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Updated: Jun 25, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Working with benchmark datasets in the Cuby framework.

Jan Řezáč1, Outi Vilhelmiina Kontkanen1, Martin Nováček1

  • 1Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 160 00 Prague, Czech Republic.

The Journal of Chemical Physics
|May 22, 2024
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Summary

The Cuby framework streamlines computational chemistry by providing tools for efficient dataset management and integration with various software. It supports advanced workflows and includes new benchmark databases for method development.

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Area of Science:

  • Computational Chemistry
  • Data Science in Chemistry

Background:

  • Computational chemistry method development requires robust benchmark datasets for validation.
  • The increasing size and number of these datasets necessitate efficient data handling tools.

Purpose of the Study:

  • To review the capabilities of the Cuby framework for managing computational chemistry benchmark datasets.
  • To demonstrate advanced workflows for large-scale computations and data reuse.
  • To highlight the integration of new benchmark databases within Cuby.

Main Methods:

  • Review of Cuby framework functionalities for dataset manipulation.
  • Examples of advanced computational workflows using Cuby.
  • Integration and utilization of NCIAtlas and GMTKN55 benchmark databases.

Main Results:

  • Cuby offers comprehensive tools for working with computational chemistry datasets.
  • The framework supports efficient handling of large-scale computations on high-performance computing resources.
  • Cuby facilitates the reuse of previously computed data, enhancing workflow efficiency.
  • NCIAtlas and GMTKN55 databases are now accessible through Cuby.

Conclusions:

  • Cuby is a valuable framework for computational chemists, enabling efficient management and utilization of benchmark data.
  • Its advanced features and database integrations support the development and validation of computational chemistry methods.