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Related Concept Videos

P-value01:10

P-value

P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more unlikely...
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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...
Interval Level of Measurement00:55

Interval Level of Measurement

For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between the...
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.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated. For...
Statgraphics01:10

Statgraphics

Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
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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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

The value of data.

Barend Mons1, Herman van Haagen, Christine Chichester

  • 1Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.

Nature Genetics
|March 30, 2011
PubMed
Summary
This summary is machine-generated.

Data citation and semantic constructs are now vital in scientific communication. The challenge is balancing traditional publications with new metrics for citing data and its derived constructs.

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Area of Science:

  • Bibliometrics
  • Scientific Communication
  • Data Science

Background:

  • Data citation is increasingly recognized as a key component of scientific reproducibility.
  • Semantic constructs derived directly from datasets are emerging as new units of scholarly contribution.

Discussion:

  • Maintaining the value of traditional narrative publications alongside data-centric metrics is a significant challenge.
  • Developing robust citation metrics for both raw data and derived semantic constructs is crucial for fair attribution.

Key Insights:

  • Scientific communication is evolving to incorporate direct data referencing and semantic analysis.
  • New metrics are needed to quantify the impact and usage of datasets and their derived components.

Outlook:

  • Future research should focus on establishing standardized frameworks for data citation and metric development.
  • Integrating data citation into existing scholarly infrastructure will enhance research transparency and impact.