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Related Concept Videos

Measures of Central Tendency02:16

Measures of Central Tendency

The "center" of a data set is also a way of describing location. The two most widely used measures of the "center" of the data are the mean (average) and the median. The words "mean" and "average" are often used interchangeably. The substitution of one word for the other is common practice. The technical term is "arithmetic mean" and "average" is technically a center location. However, in practice among non-statisticians, "average" is commonly accepted for "arithmetic mean."
Measurement: Derived Units03:02

Measurement: Derived Units

The International System of Units or SI system, by international agreement, has fixed measurement units for seven fundamental properties: length, mass, time, temperature, electric current, amount of substance, and luminosity. These are called the SI base units.
Quantifying Work02:30

Quantifying Work

As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system.
Ratio Level of Measurement00:54

Ratio Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
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Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...

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Related Experiment Video

Updated: Jun 18, 2026

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
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Article-Level Metrics.

Armen Yuri Gasparyan1, Marlen Yessirkepov2, Alexander A Voronov3

  • 1Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, UK. a.gasparyan@gmail.com.

Journal of Korean Medical Science
|March 23, 2021
PubMed
Summary
This summary is machine-generated.

Article-level metrics, including views, downloads, citations, and social media mentions, help evaluate research impact in the digital age. These metrics, alongside expert evaluation, offer a nuanced view beyond traditional journal metrics.

Keywords:
Access to InformationBibliography as TopicBibliometricsOpen Access PublishingPeriodicals as Topic

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Area of Science:

  • Bibliometrics and scientometrics
  • Scholarly communication analysis
  • Research impact assessment

Background:

  • The rise of digitization and Open Access has increased the need for effective research evaluation tools.
  • Traditional journal-level metrics are often misused, necessitating alternative assessment methods.
  • Digital Object Identifiers (DOIs) enable tracking and quantifying research activities across various platforms.

Purpose of the Study:

  • To provide a comprehensive overview of current article-level metrics (ALMs).
  • To discuss the advantages and limitations of various ALMs.
  • To highlight the complementary nature of quantitative and qualitative research evaluation.

Main Methods:

  • Review of existing literature on article-level metrics.
  • Analysis of different types of ALMs, including usage data, citations, and altmetrics.
  • Discussion of the role of expert evaluation in validating ALMs.

Main Results:

  • Article views, downloads, citations, and social media metrics are key indicators of research influence.
  • ALMs offer a more granular assessment of research impact compared to journal metrics.
  • Quantitative metrics should be interpreted alongside qualitative expert judgment to mitigate manipulation risks.

Conclusions:

  • Article-level metrics provide valuable insights into research influence but require careful interpretation.
  • Contextual factors, such as disciplinary and geographical variations in citation and social media activity, must be considered.
  • A combination of diverse metrics and expert evaluation is crucial for robust research assessment.