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

Mass Spectrometry: Overview01:19

Mass Spectrometry: Overview

6.9K
Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass.  One common type of ionization, known as electrospray ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave...
6.9K
Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

1.5K
Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and signal-to-noise ratio for the analyte. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.
Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called collision-induced...
1.5K
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

1.1K
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
1.1K
Drug Discovery: Overview01:26

Drug Discovery: Overview

9.6K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
9.6K
Inductive Effects on Chemical Shift: Overview01:27

Inductive Effects on Chemical Shift: Overview

1.6K
The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...
1.6K
Chemical Shift: Internal References and Solvent Effects01:17

Chemical Shift: Internal References and Solvent Effects

919
In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
The internal reference compound generally used in NMR spectroscopy is tetramethylsilane (TMS). TMS is preferred because it is chemically inert, soluble in NMR solvents, and easily removable. Also, the highly shielded methyl protons in TMS yield an intense...
919

You might also read

Related Articles

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

Sort by
Same author

Band Gap Prediction of Two-Dimensional Materials Using a Gradient-Boosted Feature Selection Approach.

Journal of chemical information and modeling·2026
Same author

Machine-Learning Predictions of Photoluminescence in Molecules Exhibiting Thermally Activated Delayed Fluorescence with Implicit Experimental Validation.

Journal of chemical information and modeling·2026
Same author

A swarm intelligence approach to density function reconstruction from moments using entropy optimization.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Automatic Generation of a Mechanical Properties Question-Answering Data Set for Language Model Benchmarking: A Comparative Study of BERT, XLNet, and LLaMA Models.

Journal of chemical information and modeling·2026
Same author

Charge-triggered switching mechanism in selenium selector enabling ultralow leakage current.

Nature materials·2026
Same author

A dataset of Curie and Néel temperatures auto-generated with ChemDataExtractor and the Snowball algorithm.

Scientific data·2025

Related Experiment Video

Updated: Oct 20, 2025

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

989

ChemDataExtractor 2.0: Autopopulated Ontologies for Materials Science.

Juraj Mavračić1,2, Callum J Court1, Taketomo Isazawa1

  • 1Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.

Journal of Chemical Information and Modeling
|September 16, 2021
PubMed
Summary

This study introduces an automated framework for extracting complex chemical and physical property relationships from scientific literature, enabling ontology population with high precision.

More Related Videos

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.0K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.3K

Related Experiment Videos

Last Updated: Oct 20, 2025

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

989
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.0K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.3K

Area of Science:

  • Chemistry
  • Materials Science
  • Data Science

Background:

  • Increasing data in scientific publications necessitates automated extraction techniques.
  • Shift from extracting individual properties to higher-level relationships in physical sciences.
  • Need for methods to integrate primary literature into data-driven scientific frameworks.

Purpose of the Study:

  • To present a framework for automated ontology population through direct extraction of property networks.
  • To develop a model for extracting chemical and physical properties, including hierarchical and nested data.
  • To demonstrate the framework's capability in extracting complex crystallographic information.

Main Methods:

  • Exploiting data-rich sources like tables within scientific documents.
  • Developing a novel model for hierarchical data organization and extraction.
  • Utilizing automatically generated parsers and interdependency resolution.
  • Applying the framework to extract crystallographic hierarchies from scientific articles.

Main Results:

  • Successfully extracted 18 interrelated submodels of nested crystallographic data.
  • Achieved an overall precision of 92.2% across 26 different scientific journals.
  • Demonstrated the framework's effectiveness in handling complex, linked data.

Conclusions:

  • The developed framework and toolkit (ChemDataExtractor 2.0) enable automated population of ontologies.
  • This approach facilitates the seamless integration of primary scientific literature into data-driven systems.
  • Offers a significant advancement in extracting and organizing scientific data for broader use.