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

Bioequivalence Data: Statistical Interpretation01:16

Bioequivalence Data: Statistical Interpretation

354
The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
354
Statgraphics01:10

Statgraphics

483
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
483
Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

1.3K
SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
1.3K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.7K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.7K

You might also read

Related Articles

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

Sort by
Same author

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

Frontiers in artificial intelligence·2026
Same author

Reactive Stroma and Acinar Morphology in Prostate Cancer: Implications for Progression and Prognostic Assessment.

The Prostate·2024
Same author

Zombie cheminformatics: extraction and conversion of Wiswesser Line Notation (WLN) from chemical documents.

Journal of cheminformatics·2024
Same author

A trust framework for digital food systems.

Nature food·2023
Same author

Prostate cancer tissue classification by multiphoton imaging, automated image analysis and machine learning.

Journal of biophotonics·2023
Same author

Robustness under parameter and problem domain alterations of Bayesian optimization methods for chemical reactions.

Journal of cheminformatics·2022
Same journal

Repurposing bleomycin against Acinetobacter baumannii HisG: computational, biophysical, and antibacterial evidence.

Journal of computer-aided molecular design·2026
Same journal

Topological data analysis for antibody-drug conjugate payload discovery: a computational framework for mechanistic classification and target validation.

Journal of computer-aided molecular design·2026
Same journal

Commentary on the fundamentals and development of artificial intelligence models in the life sciences and best research practices.

Journal of computer-aided molecular design·2026
Same journal

RANQSAR: a standalone open-source application for reproducible machine learning-based QSAR analysis.

Journal of computer-aided molecular design·2026
Same journal

Integrating evolutionary and compositional features with ML and DL for robust and interpretable druggable protein prediction.

Journal of computer-aided molecular design·2026
Same journal

QUAD: a composite risk framework integrating uncertainty, applicability domain, and model disagreement for reliable QSAR predictions.

Journal of computer-aided molecular design·2026
See all related articles

Related Experiment Video

Updated: Apr 26, 2026

The Use of Induced Somatic Sector Analysis ISSA for Studying Genes and Promoters Involved in Wood Formation and Secondary Stem Development
09:54

The Use of Induced Somatic Sector Analysis ISSA for Studying Genes and Promoters Involved in Wood Formation and Secondary Stem Development

Published on: October 5, 2016

8.1K

Scientific and technical data sharing: a trading perspective.

Jeremy G Frey1, Colin L Bird

  • 1Chemistry, University of Southampton, Southampton, SO17 1BJ, UK, J.g.frey@soton.ac.uk.

Journal of Computer-Aided Molecular Design
|August 13, 2014
PubMed
Summary
This summary is machine-generated.

Open data sharing accelerates scientific progress, but researchers hesitate. A trading model with trusted brokers could foster better scientific data exchange and collaboration.

More Related Videos

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

1.8K
TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis
07:44

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Published on: June 8, 2020

14.5K

Related Experiment Videos

Last Updated: Apr 26, 2026

The Use of Induced Somatic Sector Analysis ISSA for Studying Genes and Promoters Involved in Wood Formation and Secondary Stem Development
09:54

The Use of Induced Somatic Sector Analysis ISSA for Studying Genes and Promoters Involved in Wood Formation and Secondary Stem Development

Published on: October 5, 2016

8.1K
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

1.8K
TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis
07:44

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Published on: June 8, 2020

14.5K

Area of Science:

  • Scientific research
  • Data management
  • Collaboration

Background:

  • Open data sharing is crucial for scientific advancement.
  • Researchers often show reluctance to share data due to professional priorities.
  • Existing data sharing mechanisms face challenges.

Purpose of the Study:

  • To analyze data exchange processes from a trading environment perspective.
  • To explore how a trading model could enhance collaboration in data-rich scientific disciplines.
  • To propose a framework for setting up effective data exchanges.

Main Methods:

  • Appraisal of current data exchange processes.
  • Conceptualization of a trading environment for scientific data.
  • Analogy to commodity markets for trusted brokering.

Main Results:

  • Reluctance to share data stems from accommodating original researchers' priorities.
  • A trading environment with trusted brokers can potentially overcome current data sharing challenges.
  • This model may promote collaboration in data-intensive research.

Conclusions:

  • A trading perspective offers a novel approach to scientific data sharing.
  • Encouraging debate on a trading model for data exchange is vital.
  • Implementing such a model could foster more open scientific collaboration.