Democratizing Health Data for Translational Research

Abstract

There is an expanding and intensive focus on the accessibility, reproducibility, and rigor of basic, clinical, and translational research. This focus complements the need to identify sustainable ways to generate actionable research results that improve human health. The principles and practices of open science offer a promising path to address both issues by facilitating: 1) increased transparency of data and methods which promotes research reproducibility and rigor; and 2) cumulative efficiencies wherein research tools and the output of research are combined to accelerate the delivery of new knowledge. While great strides have been in made in terms of enabling the open science paradigm in the biological sciences, progress in sharing of patient-derived health data has been more moderate. This lack of widespread access to common and well characterized health data is a substantial impediment to the timely, efficient, and multi-disciplinary conduct of translational research, particularly in those instances where hypotheses spanning multiple scales (from molecules to patients to populations) are being developed and tested. To address such challenges, we review current best practices and lessons learned, and explore the need for policy changes and technical innovation that can enhance the sharing of health data for translational research.

The cultivation of supply side data science in medical imaging: an opportunity to define the future of global health

“…Ultimately, through all the complexities of translating innovation, synergies, biases, healthcare inequalities, definitions of success and leadership, and imaginative ideas we have not thought of yet, the solution to bolster the next generation may be quite simple in concept: democratization of medical imaging data across the imaging community -making data accessible at its highest fidelity [11,27]. A large step in that direction could be as simple as evolving expectations for hardware vendors to publicly describe their raw data, or as complex as creating standard data formats across modalities or innovating new data sharing networks and spaces.…”

“…Specifically, there exists today an unprecedented opportunity for establishing a concept/role for data that is unique from the hardware that creates it. The pivot could bolster all elements of research, discovery, translation, and inclusion and ultimately lay a foundation for meaningful growth in the coming decades [11].…”

DNA-SeAl: Sensitivity Levels to Optimize the Performance of Privacy-Preserving DNA Alignment

“…Global Alliance [13] developed an ecosystem of worldwide databases that can be remotely accessed. Despite these efforts, patientderived health data are not widely shared [14]. Recently, several ecosystems addressing the privacy and performance challenges of accessing genomic data in the cloud have been developed.…”

Sensitivity Levels: Optimizing the Performance of Privacy Preserving DNA Alignment

“…Some more research have been done in order to develop privacy-preserving environments for genomic data computations, which include, but are not limited to, performing Genome-Wide Association Studies without revealing private data [15], and the development of methods to share aggregated data while preserving privacy [16]. Such works have been enabling the open science paradigm in the biological sciences, and progress in sharing of patient-derived health data has been more moderate [17]. However, moving genomic data to the clouds, among other possibilities, raises privacy challenges.…”