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

Labeling Emotion01:20

Labeling Emotion

257
Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
257
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

36.3K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
36.3K
Labeling DNA Probes03:31

Labeling DNA Probes

8.4K
DNA probes are fragments of DNA labeled with a reporter tag to enable their detection or purification. The resulting labeled DNA probes can then hybridize to target nucleic acid sequences through complementary base-pairing, and may be used to recover or identify these regions.
Radioisotopes, fluorophores, or small molecule binding partners like biotin or digoxigenin, are the most widely used reporter tags for labeling DNA probes. These labels can be attached to the probe DNA molecule via...
8.4K
Aggregates Classification01:29

Aggregates Classification

397
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
397
Classification of Systems-II01:31

Classification of Systems-II

248
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
248
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

31.4K
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...
31.4K

You might also read

Related Articles

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

Sort by
Same author

Benchmarking machine learning approaches for polarization mapping in ferroelectrics using 4D-STEM.

Scientific reports·2026
Same author

The future of fundamental science led by generative closed-loop artificial intelligence.

Frontiers in artificial intelligence·2026
Same author

Large language models in food and nutrition science: Opportunities, challenges, and the case of FoodyLLM.

Current research in food science·2026
Same author

The development and evaluation of a quality assessment framework for reuse of dietary intake data: an FNS-Cloud study.

Frontiers in nutrition·2025
Same author

Cortico-Muscular Phase Connectivity During an Isometric Knee Extension Task in People with Early Parkinson's Disease.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2025
Same author

Using Machine Learning Methods to Assess Module Performance Contribution in Modular Optimization Frameworks.

Evolutionary computation·2024
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Sep 24, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K

A catalogue with semantic annotations makes multilabel datasets FAIR.

Ana Kostovska1,2, Jasmin Bogatinovski1,3, Sašo Džeroski1,2

  • 1Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia.

Scientific Reports
|May 4, 2022
PubMed
Summary
This summary is machine-generated.

Multilabel classification (MLC) benchmarking needs trustworthy datasets. This study introduces an ontology-based online catalogue of MLC datasets, adhering to FAIR and TRUST principles for enhanced data management and discoverability.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K
Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level
08:29

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Published on: April 19, 2019

6.3K

Related Experiment Videos

Last Updated: Sep 24, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K
Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level
08:29

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Published on: April 19, 2019

6.3K

Area of Science:

  • Machine Learning
  • Data Science
  • Computer Science

Background:

  • Multilabel classification (MLC) is gaining traction in machine learning.
  • Robust benchmarking is crucial for advancing MLC research.
  • Existing datasets lack standardized management and description.

Purpose of the Study:

  • To establish a trustworthy and standardized benchmarking system for MLC.
  • To create a centralized, accessible resource for MLC datasets.
  • To promote adherence to data management principles in AI research.

Main Methods:

  • Developed an ontology-based online catalogue for MLC datasets.
  • Incorporated FAIR (Findable, Accessible, Interoperable, Reusable) and TRUST principles.
  • Collected and described MLC datasets with meta-features and provenance.

Main Results:

  • An ontology-based online catalogue of MLC datasets is now available.
  • Datasets are described with meta-features, semantic descriptions, and provenance.
  • The catalogue facilitates findable, accessible, interoperable, and reusable data.

Conclusions:

  • The MLC data catalogue supports robust and trustworthy benchmarking.
  • Adherence to FAIR and TRUST principles enhances data management in MLC.
  • This resource will aid the further development of the multilabel classification field.