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Related Concept Videos

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Coefficient of Correlation

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
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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.
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Impact loading occurs when a moving object collides with a stationary structure, such as a rod with a uniform cross-sectional area fixed at one end. Under these conditions, the rod absorbs the kinetic energy from the striking object, leading to deformation and subsequent stress development. As the rod returns to its original position and reaches maximum stress, the absorbed energy, initially manifested as kinetic energy, transforms entirely into strain energy.
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Data Publications Correlate with Citation Impact.

Florian Leitner1, Concha Bielza2, Sean L Hill3

  • 1Computational Intelligence Group, Department for Artificial Intelligence, Universidad Politécnica de MadridMadrid, Spain; Data Catalytics S.L.Madrid, Spain.

Frontiers in Neuroscience
|September 29, 2016
PubMed
Summary
This summary is machine-generated.

Publishing research data openly in neuroscience and molecular biology significantly increases citation impact. A new metric, the data article citation index (DAC-index), identifies prolific data authors, encouraging open data sharing.

Keywords:
DAC-indexcitationsdata article citation indexdata publicationsdata sharingopen data

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Area of Science:

  • Neuroscience
  • Molecular Biology
  • Bibliometrics
  • Data Science

Background:

  • Large datasets are transforming neuroscience and molecular biology research.
  • Open data sharing presents a significant challenge in these fields.
  • Citation impact is a key metric for research dissemination.

Purpose of the Study:

  • To compare the citation impact of data publications in neuroscience and molecular biology.
  • To introduce a metric for identifying prolific authors of data-related publications.
  • To encourage open data sharing practices.

Main Methods:

  • Comparative citation analysis of publications tagged with data-related terms.
  • Utilized NCBI MeSH (Medical Subject Headings) for data term identification.
  • Developed the data article citation index (DAC-index).
  • Ensured study reproducibility using an R Markdown script and citation datasets.

Main Results:

  • Data publications achieve significantly higher citation impact than average publications in both fields.
  • The DAC-index can effectively identify prolific authors in data-related research.
  • The study provides a reproducible framework for analyzing data publication impact.

Conclusions:

  • Openly publishing research data enhances scientific visibility and impact.
  • The DAC-index offers a valuable tool for recognizing contributions to open data.
  • Promoting open data sharing is crucial for advancing scientific discovery.