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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

174
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
174
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

683
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
683
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

789
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...
789
Clinical Trials01:16

Clinical Trials

8.4K
Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
8.4K
Hazard Ratio01:12

Hazard Ratio

247
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
247
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

419
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
419

You might also read

Related Articles

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

Sort by
Same author

Evaluating the quality of tabular synthetic data in health care.

PLOS digital health·2026
Same authorSame journal

Using an Open Science Checklist in Grant Proposal Reviews to Predict Reproducibility of Funded Publications.

Journal of clinical epidemiology·2026
Same author

Selecting medical research data platforms for translational biomedical research: a five-tier overview and requirement-weighted assessment framework.

Frontiers in digital health·2026
Same author

A Molecular "Thermometer" for Measuring Effective Non-Local Exchange.

Journal of computational chemistry·2026
Same author

Using routinely collected data for research purposes: challenges and mitigation strategies.

BMJ (Clinical research ed.)·2026
Same author

Concerning the harm-benefit ratio of escitalopram for pediatric generalized anxiety disorder. A critical viewpoint on the evidence and approval process.

The International journal of risk & safety in medicine·2026
Same journal

AI-enabled GRADE: How the GRADE Working Group will use automation to rate the certainty of evidence of intervention effects.

Journal of clinical epidemiology·2026
Same journal

Harms Reporting Was Frequently Incomplete or Discordant in Biomedical Randomized Trials Published in 2023: A Meta-epidemiological Study.

Journal of clinical epidemiology·2026
Same journal

A comparison of five statistical methods used to analyse longitudinal EORTC QLQ-C30 quality of life scores in randomised controlled trials: a simulation study.

Journal of clinical epidemiology·2026
Same journal

Sample Size Determination for Decision-centered Pragmatic Trials.

Journal of clinical epidemiology·2026
Same journal

Many multicenter randomized controlled trials do not account for center effect: a methodological review.

Journal of clinical epidemiology·2026
See all related articles

Related Experiment Video

Updated: Sep 12, 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

FAIRification of biomedical research data.

Ka Hin Tai1, Marcel Müller2, Ulrich Mansmann2

  • 1Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000, Rennes, France.

Journal of Clinical Epidemiology
|August 7, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a three-dimensional FAIRification framework to address challenges in biomedical data sharing. It emphasizes proactive planning and technical/legal support for enhanced data management and reuse.

Keywords:
Biomedical researchData sharingFAIR principleMetadata standardsOpen scienceResponsible research

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K
Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management
08:01

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Published on: November 30, 2022

4.6K

Related Experiment Videos

Last Updated: Sep 12, 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
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K
Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management
08:01

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Published on: November 30, 2022

4.6K

Area of Science:

  • Biomedical research
  • Data management and stewardship

Background:

  • Ethical, legal, and technical barriers hinder biomedical research data sharing.
  • The Findable, Accessible, Interoperable, and Reusable (FAIR) principles aim to improve data management.

Purpose of the Study:

  • To propose a comprehensive framework for FAIRification across scientific, technical, and legal/ethical dimensions.
  • To guide researchers in overcoming challenges in biomedical data sharing and promoting data reuse.

Main Methods:

  • Advocating for prospective FAIRification with emphasis on early planning for data sharing.
  • Utilizing reflective questions to guide researchers in assessing resources, feasibility, and stakeholder needs.
  • Detailing technical preparation, including documentation, metadata, and repository hosting.

Main Results:

  • A proposed framework for FAIRification encompassing scientific, technical, and legal/ethical aspects.
  • Emphasis on proactive planning, documentation, and secure repository use for effective data sharing.
  • Highlighting the importance of ongoing support and maintenance for sustained data reuse.

Conclusions:

  • Implementing a proactive, multi-dimensional FAIRification strategy is crucial for overcoming biomedical data sharing barriers.
  • Careful planning, technical preparation, and administrative oversight are essential for ensuring data confidentiality, security, and reusability.
  • Continued researcher support and data maintenance are vital for maximizing the value of shared biomedical data.