Life-Cycle Decisions for Biomedical Data The Challenge of Forecasting Costs (2020) / Chapter Skim
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This report provides a comprehensive conceptual framework for cost-effective decision making that encourages data accessibility and reuse for researchers, data managers, data archivists, data scientists, and institutions that support platforms that enable biomedical research data preservation, discoverability, and use. The framework can be adapted by anyone responsible for managing data at any point in the data life cycle, but the analysis conducted during its application by researchers, data, data repository hosts, and funding institutions will vary greatly.
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. THE COST-FORECASTING FRAMEWORK The framework for forecasting costs presented in this report first describes the different data environments in which data may be placed (herein referred to as "data states"; Box 2.1)
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Table S.1 summarizes the steps necessary to understand the cost drivers that are important for FIGURE S.1 Conceptual diagram showing the three data states, the principal activities associated with each state, and how data may transition between states. Note that the transition arrows between states are bidirectional, indicating that data already existing in repositories can transition back into a primary research environment when new data are incorporated, data are a ggregated with other data, or data are used in new ways.
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. The data states are defined in Box 2.1: • Compare the above with the activities defined for each of the data states (see State 1: primary research environment Figure S.1)
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CREATING AN ENVIRONMENT CONDUCIVE TO COST FORECASTING OF SUSTAINABLE DATA MANAGEMENT Approaches to building and managing data repositories differ across institutions and among researchers, but regardless of where biomedical information resources are hosted, costs associated with personnel are likely to dominate total life-cycle costs. Storage, computing, and networking services also contribute to total cost.
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Strategies Efficient long-term data management and effective cost forecasting are more likely if data resource managers, cost forecasters, and institutions that support them apply the following strategies: • Create data environments that foster discoverability and interpretability through long-term planning and investment throughout the data life cycle. Data sharing is not equivalent to data reuse, and developing processes that allow efficient data preservation, archiving, and access to facilitate data reuse could benefit scientific discovery.
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Making people aware of and accountable for their costs -- and helping them understand that their actions generate costs for omeone -- might help researchers reduce resource consumption with more efficient workflows, experiment s design, and data tracking. The following activities, likely to be enabled at an agency or research-institution level, could advance practices and drive future improvements in the ability to forecast costs: • Recognize explicitly that scientific data constitute an asset and that data stewardship requires support.
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Chapter 7 of this report includes discussion of the following potential disruptors: • Biomedical data volume and variety: Sudden orders-of-magnitude increases in data collection in domains such as imaging and multiscale high-performance computing simulations have moved biomedical research into the realm of "big data." This has been observed, for example, given recent advances in genomics research. Biomedical research will experience growth that tends to add dimensions to the data space or to extend a dimension by an order of magnitude.
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The systematic collection of cost data related to the biomedical-research-data enterprise by an organization that owns that responsibility could provide evidence necessary to translate the cost-forecasting framework presented in this report into a set of tools that can be used by the biomedical-research and datapreservation community. This development could encourage institutions to focus on costs, facilitate future cost forecasting, and help refine cost-forecasting models.
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