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

Blinding01:11

Blinding

4.0K
Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
4.0K
Deindividuation00:57

Deindividuation

31.1K
Deindividuation is a form of social influence on an individual’s behavior such that the individual engages in unusual or non-normal behavior while in a group setting. Why? Because in these group settings, the individual no longer sees themselves as an individual anymore, disinhibiting their behavior and personal restraint.
31.1K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

522
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
522
Censoring Survival Data01:09

Censoring Survival Data

630
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
630
Masking and Demasking Agents01:19

Masking and Demasking Agents

4.0K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
4.0K
Ethical Standards II01:23

Ethical Standards II

1.4K
Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
Nurses are entrusted with upholding various ethical principles and standards. Nurses forge solid therapeutic relationships using trust, empathy, autonomy, confidentiality, and professional competence.
Confidentiality is crucial, embodying respect for individual privacy...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Emerging and evolving values in the changing landscape of genomics.

Frontiers in genetics·2025
Same author

Extended Cohort for E-health, Environment and DNA (EXCEED) COVID-19 focus.

Wellcome open research·2024
Same author

Genome-wide association study of thyroid-stimulating hormone highlights new genes, pathways and associations with thyroid disease.

Nature communications·2023
Same author

GA4GH: International policies and standards for data sharing across genomic research and healthcare.

Cell genomics·2022
Same author

Adding dynamic consent to a longitudinal cohort study: A qualitative study of EXCEED participant perspectives.

BMC medical ethics·2021
Same author

How Can We Not Waste Legacy Genomic Research Data?

Frontiers in genetics·2020

Related Experiment Videos

What Does Anonymization Mean? DataSHIELD and the Need for Consensus on Anonymization Terminology.

Susan E Wallace1

  • 1Department of Health Sciences, University of Leicester , Centre for Medicine, Leicester, United Kingdom .

Biopreservation and Biobanking
|May 25, 2016
PubMed
Summary

Consistent terminology for data anonymization is crucial for biomedical research. Clear definitions protect confidentiality, build public trust, and support international data sharing initiatives.

Related Experiment Videos

Area of Science:

  • Biomedical research
  • Data privacy
  • Information governance

Background:

  • Anonymization is standard for protecting confidentiality in biomedical data sharing.
  • Inconsistent terminology (e.g., coding, pseudonymization, deidentified) creates confusion.
  • This ambiguity is a long-standing issue in research data management.

Purpose of the Study:

  • To highlight the historical problem of inconsistent terminology in data anonymization.
  • To argue that this ambiguity risks damaging international research collaborations and data sharing.
  • To emphasize the need for clear, consistent terminology to maintain public trust in research.

Main Methods:

  • Analysis of historical use of anonymization terms.
  • Examination of initiatives like DataSHIELD and legal templates reliant on anonymization.
  • Argumentative approach based on potential negative impacts of terminological inconsistency.

Main Results:

  • Inconsistent terminology in anonymization hinders progress in data sharing initiatives.
  • Vague notions of anonymization can erode public trust in research and institutions.
  • Lack of clarity can lead to unmet participant expectations regarding data protection.

Conclusions:

  • Consistent, internationally recognized terminology for anonymization is essential.
  • Clear definitions ensure all parties understand data identifiability and access levels.
  • Standardized terms will facilitate secure and effective cross-national research and data sharing.