GSA Annual Meeting in Denver, Colorado, USA - 2016

Paper No. 156-1
Presentation Time: 9:00 AM-6:30 PM


STALL, Shelley1, HANSON, Brooks1 and WYBORN, Lesley A.I.2, (1)American Geophysical Union, 2000 Florida Avenue, NW, Washington, DC 20009, (2)National Computational Infrastructure, Australian National University, 56 Mills Road, Acton, 2600, Australia,

There is a growing appreciation that the collective output of publicly funded research programs is truly a ‘Big Data’ asset that is of enduring value, and if carefully managed, curated and archived, can be reused and/or repurposed to answer the key research questions of today and those of the future. As a result, funders are increasingly mandating that data collected by publicly funded research be properly captured, documented, curated, and made accessible. Data management plans are now required as part of grant applications. Such mandates are posing challenges for researchers and repositories managers, many of whom have little experience in managing data throughout its full life cycle, including publication and reporting back to funding agencies. In response to these mandates and a broad recognition of the importance of geoscience data, the American Geophysical Union (AGU) has developed an assessment program that will help data repositories, large and small, domain-specific to general, use best practices to improve data management.

AGU has partnered with the CMMI® Institute to adapt their Data Management Maturity (DMM)SM framework to the needs of the Earth and space sciences. The new DMMSM model was developed by a large number of experts in data management. The DMMSM is comprised of 25 process areas organized into 5 categories: strategy, governance, data quality, operations, and platform and architecture. These process areas serve as the principal means to communicate the goals, practices, and example work products of the model. Accomplishment of process area practices allows an organization, and those within it, to build capabilities in data management.

An AGU data management assessment using the DMMSM involves identifying achievements and weaknesses in an organization, compared with leading practices for data management. Recommendations help improve quality and consistency for the assessed organization and support improvement in the community across the data lifecycle. During 2015 two repositories took part in pilot studies to test the process. Both groups reported that they found strong value in how the assessment improved data management and supported their organizational plans and goals.