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

Stereotype Content Model02:16

Stereotype Content Model

14.8K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.8K
Mass Analyzers: Overview01:13

Mass Analyzers: Overview

784
The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
784
Mass Analyzers: Common Types01:19

Mass Analyzers: Common Types

687
The quadrupole mass analyzer consists of four cylindrical metal rods arranged in a diamond carrying a DC voltage and a radio-frequency AC voltage. The motion of ions through the quadrupole depends on the field strength, causing only ions of a certain m/z to resonate successfully and strike the detector at a given field strength. Though the transmission rate for these analyzers is high, the exact elemental composition of the sample is not determined because of low resolution; however, they are...
687
Molecular Models02:00

Molecular Models

39.9K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
39.9K
High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

1.5K
The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...
1.5K
Models, Theories, and Laws01:16

Models, Theories, and Laws

6.3K
Scientists frequently use models to help them comprehend a specific collection of phenomena. In physics, a model is a condensed version of a physical system that is too complex to study thoroughly. One such example is the light wave model; unlike water waves, light waves are typically invisible to us. Nonetheless, it is helpful to think of light as being composed of waves, since investigations show that light behaves like water waves. Since it is impossible to visually see what is genuinely...
6.3K

You might also read

Related Articles

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

Sort by
Same author

Towards a new vision of PaNET: enhancing reasoning capabilities for better photon and neutron data discovery.

Journal of synchrotron radiation·2025
Same author

Community-driven governance of FAIRness assessment: an open issue, an open discussion.

Open research Europe·2024
Same author

"Be sustainable": EOSC-Life recommendations for implementation of FAIR principles in life science data handling.

The EMBO journal·2023
Same author

The Translational Data Catalog - discoverable biomedical datasets.

Scientific data·2023
Same author

FAIR in action - a flexible framework to guide FAIRification.

Scientific data·2023
Same author

The FAIR Cookbook - the essential resource for and by FAIR doers.

Scientific data·2023
Same journal

Dataset of Optimized Structures of Aliphatic Chains Chemisorbed on Si(110) and Si(111) Surfaces via First-Principles Methods.

Scientific data·2026
Same journal

EURO-PROBE - Manual segmentations of the prostate and intraprostatic urethra on T2-weighted MRI.

Scientific data·2026
Same journal

Chromosome-Level Genome Assembly of Southern Africa Mozambique Tilapia (Oreochromis mossambicus) using PacBio HiFi and Omni-C sequencing.

Scientific data·2026
Same journal

Ovarian Stainology: Database of evidence-based immunohistochemical antigen expression in ovarian tumors.

Scientific data·2026
Same journal

A dataset of small protein conformational ensembles from all-atom molecular dynamics simulations.

Scientific data·2026
Same journal

A real-world Fitbit-derived dataset of activity, sleep, and heart rate with matched clinical factors in on-treatment lung cancer patients.

Scientific data·2026
See all related articles

Related Experiment Video

Updated: Aug 27, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.0K

Machine actionable metadata models.

Dominique Batista1, Alejandra Gonzalez-Beltran1,2, Susanna-Assunta Sansone1

  • 1Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK.

Scientific Data
|September 30, 2022
PubMed
Summary
This summary is machine-generated.

Community-developed minimum information checklists improve data reproducibility and reuse. This study introduces machine-readable models to quantify metadata quality and standardize reporting for complex life science experiments.

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

527
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

426

Related Experiment Videos

Last Updated: Aug 27, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.0K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

527
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

426

Area of Science:

  • Life Science
  • Data Science
  • Bioinformatics

Background:

  • Community-developed minimum information checklists enhance metadata reporting for data reproducibility and reuse.
  • Current reporting guidelines are primarily narrative, limiting machine readability and quantitative assessment.
  • There is a need for machine-readable versions to support FAIR data principles and standardized metadata authoring.

Purpose of the Study:

  • To develop and present new functionalities for creating and improving machine-readable metadata models.
  • To enable quantitative and verifiable measures of metadata quality against community requirements.
  • To encourage the creation of standards-driven templates for authoring metadata, especially for complex experiments.

Main Methods:

  • Application of a new approach to an exemplar set of life science reporting guidelines.
  • Development of functionalities supporting the creation and enhancement of machine-readable models.
  • Focus on compositional metadata elements and modular, interoperable community standards.

Main Results:

  • Demonstrated a method for creating machine-readable metadata models from existing reporting guidelines.
  • Successfully applied the approach to life science reporting guidelines, highlighting its utility.
  • Identified challenges and discussed potential solutions for implementing machine-readable standards.

Conclusions:

  • The developed functionalities facilitate the creation of machine-readable metadata models.
  • This approach supports the FAIR data principles by enabling quantitative assessment of metadata.
  • Promotes the development of modular, interoperable, and standards-driven metadata for complex research.