Related Experiment Video
Updated: Sep 2, 2025

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
Published on: November 22, 2019
Identifying and addressing data asymmetries so as to enable (better) science
Stefaan Verhulst1, Andrew Young1
1The Governance Lab, An Action Research Centre at New York University's Tandon School of Engineering, New York, NY, United States.
Addressing data asymmetries is crucial for equitable open science. This study offers solutions for responsible data sharing, promoting data liquidity and unlocking public value from research data.
Area of Science:
- Data Science
- Open Science
- Research and Development
Background:
- Societal need for sophisticated assessment of data asymmetries and resulting power inequalities.
- Analytical gap in understanding global data asymmetries, especially concerning privately-held data in open science.
- Existing efforts to address data asymmetries require structured analysis and actionable solutions.
Purpose of the Study:
- To fill the analytical gap regarding global data asymmetries, focusing on privately-held data for open science.
- To provide a taxonomy of data asymmetries and analyze their societal and institutional impacts.
- To outline practical solutions and a toolbox for open science practitioners and data users.
Main Methods:
- Exploration of data liquidity and portability concepts for responsible data exchange ecosystems.
- Examination of operational models and governance frameworks for cross-sector data collaboratives.
- Analysis of case studies on efforts to address science data asymmetries using a repurposable analytical framework.
Main Results:
- A taxonomy of data asymmetries and their impacts is presented.
- New operational models and governance frameworks for data collaboratives are explored.
- The professionalization of data steward roles is highlighted as a key solution.
Conclusions:
- Recommended actions to build an evidence base for addressing data asymmetries.
- Emphasis on unlocking public value through enhanced science data liquidity and responsible reuse.
- Call for a more sophisticated societal approach to data governance in research and development.
Related Concept Videos
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Bias in Epidemiological Studies
Bias
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Data: Types and Distribution
Distributions in...
Systematic Error: Methodological and Sampling Errors
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Statistical Analysis: Overview
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...

