Biomedical research results in the collection and storage of increasingly large and complex data sets. Preserving those data so that they are discoverable, accessible, and interpretable accelerates scientific discovery and improves health outcomes, but requires that researchers, data curators, and data archivists consider the long-term disposition of data and the costs of preserving, archiving, and promoting access to them.
Life Cycle Decisions for Biomedical Data examines and assesses approaches and considerations for forecasting costs for preserving, archiving, and promoting access to biomedical research data. 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.
Table of Contents
|2 Framework Foundation: Data States and Associated Activities||26-35|
|3 Cost and the Value of Data||36-47|
|4 The Cost-Forecasting Framework: Identifying Cost Drivers in the Biomedical Data Life Cycle||48-82|
|5 Applying the Framework to a New State 2 Data Resource||83-104|
|6 Applying the Framework to a New Data Set||105-117|
|7 Potential Disruptors to Forecasting Costs||118-128|
|8 Fostering the Data Management Environment||129-136|
|Appendix A: Meetings and Presentations||139-145|
|Appendix B: Active Data Management Plans as a Planning Tool||146-148|
|Appendix C: Identifying Salary Ranges for Jobs Relevant to the Data Life Cycle||149-158|
|Appendix D: Soft Costs for Digital Preservation||159-161|
|Appendix E: Template to Map Cost Drivers to Data Resource Properties||162-168|
|Appendix F: Comparison of the Contents Across the Three Data States||169-173|
|Appendix G: Committee Biographical Information||174-179|
|Appendix H: Acronyms||180-182|
Members of the committee discuss the key findings and conceptual framework presented in the report.
Dr. Maryann Martone walks a user through the cost driver template for forecasting the costs associated with a biomedical information resource.
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