[Paper Review] Enabling Global Image Data Sharing in the Life Sciences

Peter Bajcsy, Sreenivas Bhattiprolu+21 more|arXiv (Cornell University)|Jan 23, 2024

Scientific Computing and Data Management5 citations

TL;DR

This white paper outlines common and emerging use cases for global image data sharing in biomedicine, discusses needed frameworks (technical, legal, ethical, resourcing), and presents a pathway toward an ideal shared-data ecosystem within a decade.

ABSTRACT

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). In the second half of this paper, we paint an ideal world scenario for how global image data sharing could work and benefit all life sciences and beyond. As this is still a long way off, we conclude by suggesting several concrete measures directed toward our institutions, existing imaging communities and data initiatives, and national funders, as well as publishers. Our vision is that within the next ten years, most researchers in the world will be able to make their datasets openly available and use quality image data of interest to them for their research and benefit. This paper is published in parallel with a companion White Paper entitled Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data, which addresses challenges and opportunities related to producing well-documented and high-quality image data that is ready to be shared. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.

Motivation & Objective

• Motivate coordinated collaboration to realize the value and infrastructure for global image data sharing in life sciences.
• Identify common and emerging use cases for biological and medical image data, including AI-driven analyses.
• Discuss technical, resourcing, legal, and ethical requirements for shared image data frameworks.
• Articulate an ideal future scenario and practical measures for institutions, communities, funders, and publishers.

Proposed method

• Present common and emerging use cases of image data and AI applications relevant to life sciences.
• Discuss the technical, resourcing, legal, and ethical aspects needed to enable sharing at scale.
• Outline an ideal world scenario for global image data sharing and the benefits across life sciences.
• Suggest concrete actions for institutions, imaging communities, data initiatives, and national funders.
• Position the companion paper on harmonizing generation and pre-publication stewardship as part of the broader effort.

Experimental results

Research questions

• RQ1 What are the most impactful use cases for shared image data in biology and medicine?
• RQ2 What technical, legal, ethical, and resourcing frameworks are required to enable global image data sharing?
• RQ3 What would an ideal, globally shared image data ecosystem look like in the next decade?
• RQ4 What concrete measures can institutions, communities, funders, and publishers implement to move toward that goal?

Key findings

• Identification of common and emerging use cases that would benefit from shared image data and AI techniques.
• A discussion of necessary frameworks across technical, resourcing, legal, and ethical dimensions for data sharing.
• A vision of an ideal world where open image datasets are routinely accessible to researchers worldwide within ten years.
• Concrete measures directed at institutions, imaging communities, data initiatives, national funders, and publishers to advance readiness and stewardship.