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

Contact-dependent Signaling01:19

Contact-dependent Signaling

Contact-dependent signaling, as the name suggests, requires that communicating cells be in direct contact with each other. This is achieved either through receptor-ligand interactions or by specialized cytoplasmic channels that allow the flow of small molecules between cells. In animal cells, channels called gap junctions facilitate contact-dependent signaling in certain tissues, whereas, plasmodesmata perform a similar function in plants.
Gap Junctions
In animal cells, gap junctions are formed...
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

You might also read

Related Articles

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

Sort by
Same author

Measurement of Direct-Photon Cross Section and Double-Helicity Asymmetry at sqrt[s]=510  GeV in p[over →]+p[over →] Collisions.

Physical review letters·2023
Same author

Letter from A. J. Miller, M. D.

The Chicago medical journal·2023
Same author

Modes and Mechanisms of Pacific Decadal-Scale Variability.

Annual review of marine science·2022
Same author

Growth and Sporulation Potential of Clostridium perfringens in Aerobic and Vacuum-Packaged Cooked Beef.

Journal of food protection·2019
Same author

Combined Water Activity and Solute Effects on Growth and Survival of Listeria monocytogenes Scott A.

Journal of food protection·2019
Same author

Microbiology and Composition of Snack Sausages.

Journal of food protection·2019

Related Experiment Video

Updated: Jul 12, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Informal contacts in science: a probabilistic model for communication processes.

B C Griffith, M J Jahn, A J Miller

    Science (New York, N.Y.)
    |July 9, 1971
    PubMed
    Summary

    Most scientific contacts are infrequent and random, following a Poisson distribution. However, highly productive scientists have significantly more connections, forming a distinct group.

    Area of Science:

    • Scientometrics
    • Network Science
    • Sociology of Science

    Background:

    • Understanding scientist collaboration patterns is crucial for scientific progress.
    • Previous research suggests collaboration networks can be complex and non-uniform.

    Purpose of the Study:

    • To analyze the contact patterns among scientists within specific research specialties.
    • To identify if contact distributions deviate from random processes, particularly for highly productive individuals.

    Main Methods:

    • Statistical analysis of contact frequency data within scientific research fields.
    • Comparison of observed contact patterns against theoretical distributions, such as the Poisson distribution.

    Main Results:

    More Related Videos

    A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts
    08:43

    A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

    Published on: December 1, 2018

    Related Experiment Videos

    Last Updated: Jul 12, 2026

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts
    08:43

    A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

    Published on: December 1, 2018

  • The majority of scientist contacts within specialties are infrequent and follow a Poisson distribution, indicating a random process.
  • Extremely productive scientists exhibit a separate, distinct contact distribution with a significantly higher number of connections.

    • Conclusions:

    • Scientific contact networks are not entirely random; highly productive scientists act as central hubs.
    • These findings have implications for understanding knowledge diffusion and innovation within scientific communities.