Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.5K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.5K
Real Time RT-PCR02:57

Real Time RT-PCR

62.6K
Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...
62.6K
Ratio Level of Measurement00:54

Ratio Level of Measurement

19.7K
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....
19.7K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

33.8K
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...
33.8K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

10.9K
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...
10.9K
Interval Level of Measurement00:55

Interval Level of Measurement

16.7K
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...
16.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Urban vibrancy: An analogy of biodiversity, retail diversity, and activity-based urban diversity measures.

PNAS nexus·2026
Same author

COVID-19 is linked to changes in the time-space dimension of human mobility.

Nature human behaviour·2023
Same author

Rapid indicators of deprivation using grocery shopping data.

Royal Society open science·2021
Same author

Modelling urban vibrancy with mobile phone and OpenStreetMap data.

PloS one·2021
Same journal

Desert lizards modulate nutritional responses to match seasonal biological needs.

Royal Society open science·2026
Same journal

Multi-generational fidelity, ecological and social determinants of roosting in a cooperatively breeding bird (<i>Argya squamiceps</i>).

Royal Society open science·2025
Same journal

Multifaceted polarization and information reliability in climate change discussions on social media platforms.

Royal Society open science·2025
Same journal

Comparing the kinematics related to inflicted head injury between violent shaking of a 6-week-old and a 1-year-old infant surrogate.

Royal Society open science·2025
Same journal

Partner choice increases observed reciprocity-based cooperation but decreases unobserved stake-based cooperation.

Royal Society open science·2025
Same journal

Importation models for travel-related SARS-CoV-2 cases reported in Newfoundland and Labrador during the COVID-19 pandemic.

Royal Society open science·2025
See all related articles

Related Experiment Video

Updated: Oct 29, 2025

Simultaneous Quantification of T-Cell Receptor Excision Circles TRECs and K-Deleting Recombination Excision Circles KRECs by Real-time PCR
14:14

Simultaneous Quantification of T-Cell Receptor Excision Circles TRECs and K-Deleting Recombination Excision Circles KRECs by Real-time PCR

Published on: December 6, 2014

16.9K

Quantifying the differences in call detail records.

Federico Botta1

  • 1Department of Computer Science, University of Exeter, Exeter, UK.

Royal Society Open Science
|July 8, 2021
PubMed
Summary
This summary is machine-generated.

Mobile phone data, including call detail records, reveal behavioral differences. Analyzing these interactions over time is crucial for computational social science research.

Keywords:
call detail recordsdata sciencemobile phone data

More Related Videos

Recording Mouse Ultrasonic Vocalizations to Evaluate Social Communication
10:28

Recording Mouse Ultrasonic Vocalizations to Evaluate Social Communication

Published on: June 5, 2016

22.9K
Author Spotlight: Exploring the Impact of Trauma on Cellular Aging
11:44

Author Spotlight: Exploring the Impact of Trauma on Cellular Aging

Published on: March 22, 2024

2.4K

Related Experiment Videos

Last Updated: Oct 29, 2025

Simultaneous Quantification of T-Cell Receptor Excision Circles TRECs and K-Deleting Recombination Excision Circles KRECs by Real-time PCR
14:14

Simultaneous Quantification of T-Cell Receptor Excision Circles TRECs and K-Deleting Recombination Excision Circles KRECs by Real-time PCR

Published on: December 6, 2014

16.9K
Recording Mouse Ultrasonic Vocalizations to Evaluate Social Communication
10:28

Recording Mouse Ultrasonic Vocalizations to Evaluate Social Communication

Published on: June 5, 2016

22.9K
Author Spotlight: Exploring the Impact of Trauma on Cellular Aging
11:44

Author Spotlight: Exploring the Impact of Trauma on Cellular Aging

Published on: March 22, 2024

2.4K

Area of Science:

  • Computational Social Science
  • Human Behavior Analysis
  • Data Science

Background:

  • Mobile phone data, such as call detail records (CDRs), are increasingly utilized to study collective human behavior.
  • Interactions via mobile networks include SMS, calls, and data usage, each potentially reflecting distinct behavioral patterns.
  • Existing research often aggregates these interaction types, potentially overlooking significant behavioral nuances.

Purpose of the Study:

  • To investigate the differences and limitations inherent in various mobile phone interaction data types.
  • To analyze the relationships between different forms of mobile phone interactions and how these relationships evolve.
  • To provide insights for researchers utilizing mobile phone data in computational social science.

Main Methods:

  • Analysis of a large-scale mobile phone dataset.
  • Examination of Call Detail Records (CDRs) encompassing SMS, call logs, and data usage.
  • Temporal analysis of interaction patterns and their interrelationships.

Main Results:

  • Significant differences were observed in the behavioral patterns reflected by different mobile phone interaction types (e.g., SMS vs. calls).
  • The relationships between various interaction types are dynamic and change over time.
  • Call Detail Records (CDRs) present both opportunities and limitations for behavioral analysis due to inherent data characteristics.

Conclusions:

  • Researchers must carefully consider the distinct nature of different mobile phone interaction data when conducting behavioral studies.
  • Understanding the temporal dynamics of interaction relationships is essential for accurate computational social science research.
  • The findings highlight the need for nuanced approaches when interpreting mobile phone data for behavioral insights.