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Related Concept Videos

Random and Systematic Errors01:20

Random and Systematic Errors

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Systematic Sampling Method01:17

Systematic Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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.
Systematic sampling is one of the simplest methods...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Chemical Equilibria: Systematic Approach to Equilibrium Calculations01:21

Chemical Equilibria: Systematic Approach to Equilibrium Calculations

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Equilibrium calculations for systems involving multiple equilibria are often complex. For example, to calculate the solubility of a sparingly soluble salt in an aqueous solution in the presence of a common ion, one must consider all the equilibria in this solution. Calculations for these systems can be complicated and tedious, so a systematic approach with a series of steps is often helpful. The process is detailed below.
The first step is to identify all the chemical reactions involved, The...
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Related Experiment Video

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Computational Reconstruction of Pancreatic Islets as a Tool for Structural and Functional Analysis
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Systematic benchmarking of omics computational tools.

Serghei Mangul1,2, Lana S Martin3, Brian L Hill4

  • 1Department of Computer Science, University of California Los Angeles, 580 Portola Plaza, Los Angeles, CA, 90095, USA. smangul@ucla.edu.

Nature Communications
|March 29, 2019
PubMed
Summary
This summary is machine-generated.

Benchmarking computational omics software is crucial for reliable biological research. Standardized practices enhance reproducibility and transparency in omics data analysis, benefiting scientific discovery.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics
  • Proteomics
  • Metabolomics

Background:

  • Computational omics methods are integral to modern biological research.
  • Growing reliance on omics software necessitates systematic evaluation.
  • Benchmarking ensures the reliability and effective use of these powerful tools.

Purpose of the Study:

  • To review current benchmarking practices in computational omics.
  • To identify challenges, benefits, and limitations of omics benchmarking.
  • To propose principles for sustainable and reproducible benchmarking studies.

Main Methods:

  • Systematic review of 25 recent computational omics benchmarking studies.
  • Analysis of benchmarking methodologies across diverse biological domains.
  • Identification of common challenges and best practices.

Main Results:

  • Varied benchmarking approaches exist across different omics fields.
  • Key challenges include lack of standardization and reproducibility.
  • Advantages include improved method selection and data interpretation.

Conclusions:

  • Standardized benchmarking is essential for advancing computational biology.
  • Proposed principles aim to enhance sustainability and reproducibility.
  • Increased transparency in omics data and results is a primary outcome.