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Ethical Standards I

Ethical Standards I

The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
The Code of Ethics provisions outline the nurse's duty to the patient, the healthcare team, the profession, and society. The Code's fundamental principles include advocacy,...

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Related Experiment Video

Updated: May 5, 2026

Brain Imaging Investigation of the Impairing Effect of Emotion on Cognition

Brain Imaging Investigation of the Impairing Effect of Emotion on Cognition

Published on: February 1, 2012

Assessing metadata privacy in neuroimaging.

Emilie Kibsgaard¹, Anita Sue Jwa², Christopher J Markiewicz²

¹Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark.

Imaging Neuroscience (Cambridge, Mass.)

|February 25, 2026

Summary

This summary is machine-generated.

Research data sharing enhances science but risks privacy. A study found neuroimaging data generally well-protected, with demographics posing the main privacy risks. Mitigation strategies are proposed for safer data sharing.

Keywords:

BIDS data privacy evaluation data sharing metaprivBIDS neuroimaging

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Last Updated: May 5, 2026

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Published on: February 1, 2012

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Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

Published on: November 27, 2019

Area of Science:

Neuroscience
Data Science
Bioethics

Background:

Sharing research data is crucial for scientific advancement.
However, privacy risks, including reidentification and potential harm, are significant concerns.
Balancing data accessibility with participant privacy is an ongoing ethical and legal challenge.

Purpose of the Study:

To assess privacy risks in openly available neuroimaging datasets.
To evaluate the effectiveness of privacy metrics in identifying vulnerabilities.
To propose practical measures for enhancing the safety of shared research data.

Main Methods:

Reviewed metadata from heterogeneous neuroimaging studies on OpenNeuro.
Utilized the metaprivBIDS software to compute privacy metrics (k-anonymity, l-diversity, etc.).
Analyzed demographic and clinical score data for reidentification risks.

Main Results:

Privacy was generally well maintained across datasets, with rare serious vulnerabilities.
Nearly all datasets exhibited minor issues requiring mitigation.
Demographic variables (age, sex, race, location) posed higher reidentification risks than clinical scores.

Conclusions:

Open neuroimaging data sharing is largely safe, but not without risks.
Demographic data requires careful handling to prevent reidentification.
Implementing practical mitigation strategies can facilitate safer data sharing practices.