Philip R.O Payne1, Nigam H. Shah2, Jessica D. Tenenbaum3, Lara Mangravite4
1Institute for Informatics, Washington University School of Medicine
2Center for Biomedical Informatics Research, Stanford University School of Medicine
3Department of Biostatistics and Bioinformatics, Duke University School of Medicine
4Sage Bionetworks
Email: prpayne@wustl.edu, nigam@stanford.edu, jessie.tenenbaum@duke.edu, lara.mangravite@sagebase.org
Pacific Symposium on Biocomputing 23:240-246(2018)
© 2018 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.
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.