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

Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

144
The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
144
Sampling Methods: Overview01:06

Sampling Methods: Overview

452
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
452
Sampling Distribution01:12

Sampling Distribution

13.6K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
13.6K
Sampling Plans01:23

Sampling Plans

238
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
238
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

345
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
345
Cluster Sampling Method01:20

Cluster Sampling Method

12.3K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.3K

You might also read

Related Articles

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

Sort by
Same author

Negative temperature coefficient metamaterial absorber for uniform microwave heating.

Nature communications·2026
Same author

Global residual stress field inference method for die-forging structural parts based on fusion of monitoring data and distribution prior.

Visual computing for industry, biomedicine, and art·2025
Same author

Improvement of Heating Uniformity by Limiting the Absorption of Hot Areas in Microwave Processing of CFRP Composites.

Materials (Basel, Switzerland)·2021
Same author

Generation of Reference Softgauges for Minimum Zone Fitting Algorithms: Case of Aspherical and Freeform Surfaces.

Nanomaterials (Basel, Switzerland)·2021
Same author

Traceable Reference Full Metrology Chain for Innovative Aspheric and Freeform Optical Surfaces Accurate at the Nanometer Level.

Sensors (Basel, Switzerland)·2021
Same author

Transfer Learning Under Conditional Shift Based on Fuzzy Residual.

IEEE transactions on cybernetics·2020
Same journal

Intimate encapsulation of non-planar electrodes via a viscoplastic interlayer.

National science review·2026
Same journal

The emerging Antarctic amplification.

National science review·2026
Same journal

Reconstructing vegetation biomass in the Middle Jurassic Yanliao Biota from insect fossil assemblages.

National science review·2026
Same journal

Industrial electrocatalytic C-C coupling reaction of C<sub>1</sub> liquid molecules for efficient ethanol synthesis.

National science review·2026
Same journal

Intrinsic auxetic piezoelectricity in bulk ferroelectrics.

National science review·2026
Same journal

Electrochemical in-biosensing computing.

National science review·2026
See all related articles

Related Experiment Video

Updated: Aug 21, 2025

Author Spotlight: Innovative Device Development for Advancing Dendroecology and Wood Anatomy Research
07:05

Author Spotlight: Innovative Device Development for Advancing Dendroecology and Wood Anatomy Research

Published on: September 27, 2024

2.8K

Sampling via the aggregation value for data-driven manufacturing.

Xu Liu1, Gengxiang Chen2, Yingguang Li2

  • 1School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China.

National Science Review
|November 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to value data sample sets, reducing the need for extensive data labeling in industrial applications. The approach models data redundancy to create smaller, more informative datasets for data-driven modeling.

Keywords:
data samplingdata valuedata-driven modellingintelligent manufacturing

More Related Videos

Automating Aggregate Quantification in Caenorhabditis elegans
07:50

Automating Aggregate Quantification in Caenorhabditis elegans

Published on: October 14, 2021

2.8K
A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars
05:32

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars

Published on: August 4, 2018

12.7K

Related Experiment Videos

Last Updated: Aug 21, 2025

Author Spotlight: Innovative Device Development for Advancing Dendroecology and Wood Anatomy Research
07:05

Author Spotlight: Innovative Device Development for Advancing Dendroecology and Wood Anatomy Research

Published on: September 27, 2024

2.8K
Automating Aggregate Quantification in Caenorhabditis elegans
07:50

Automating Aggregate Quantification in Caenorhabditis elegans

Published on: October 14, 2021

2.8K
A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars
05:32

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars

Published on: August 4, 2018

12.7K

Area of Science:

  • Industrial applications
  • Data science
  • Machine learning

Background:

  • Data-driven modeling offers significant industrial potential.
  • High costs and time associated with data labeling hinder its widespread adoption.
  • Existing methods struggle to quantify the collective value of data samples.

Purpose of the Study:

  • To develop a novel method for representing the value of data sample sets.
  • To address the challenge of selecting informative yet smaller datasets.
  • To reduce data labeling efforts in data-driven modeling.

Main Methods:

  • Defined 'aggregation value' to model redundant information as overlapping data values.
  • Formulated data sampling as a submodular function maximization problem.
  • Validated the approach on multiple manufacturing datasets.

Main Results:

  • The proposed method generated superior and stable performance compared to existing techniques.
  • Demonstrated effective selection of informative sample sets.
  • Showcased potential for significant reduction in data labeling requirements.

Conclusions:

  • The novel aggregation value representation effectively quantifies sample set value.
  • The method offers a viable solution for creating smaller, high-value datasets.
  • This research has strong implications for data-scarcity scenarios and efficient industrial AI.