A Data Biosphere for Biomedical Research

Benedict Paten
Oct 16, 2017 · 5 min read

Introduction

Data Biosphere: Principles

Modular Components

Figure 1. The diagram illustrates a proposed architecture for a Data Biosphere, consisting of (from the bottom up): (i) Data Assets, such as large datasets of genome sequences or images, stored on Clouds that provide low-level services, such as storage, databases, and access control (grey); (ii) Data Access Services, which control access to data services and expose them via standardized APIs to multiple different services created by many groups (blue); (iii) Indexing and Search capabilities to make it easy for researchers to find data and build cohorts (pink); (iv) Workspaces, which are analytical sandboxes where researchers can perform analyses on cuts of data and share them with collaborators (red); (v) Analytical engines, which allow users to deploy workflows and perform exploratory analyses (green); (vi) Repositories for sharing workflows and notebooks (orange); and (vii) Specialized Portals and user interfaces to support ad-hoc use cases and leverage the underlying services (yellow and peach).

Data Environments

Figure 2. The diagram illustrates the role of Data Environments: (i) data assets are stored on one or more clouds; (ii) Data Environments are stood up and operated to enable researchers to access and analyze these data assets; (iii) each Data Environment assembles Components that meet the needs of its community of researchers. A given data asset can be accessed by multiple Environments (as represented in by data asset A in this Figure), and a given Environment can access multiple assets (as represented by Data Environment #3 in this Figure).

Creating a Data Biosphere

Conclusion

Benedict Paten

Written by

UC Santa Cruz Genomics Institute

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