A Capability Maturity Model for Research Data Management
CMM for RDM » 0. Introduction » 0.1 Research Lifecycle and Data Management Lifecycle
Last modified by Arden Kirkland on 2014/06/30 09:26
From version 1.1
edited by Arden Kirkland
on 2013/10/20 17:44
To version 2.1
edited by Arden Kirkland
on 2013/10/20 17:48
Change comment: added links to previous and next pages

Content changes

... ... @@ -9,3 +9,7 @@
9 9 For example, in the United States, national research centers such as [[(% style="font-family: sans-serif; font-size: 14px; font-style: normal; line-height: 19.59375px; text-align: start;" %)National Center for Atmospheric Research (% style="font-size: 14px;" %)(NCAR)>>url:http://ncar.ucar.edu/||rel="__blank"]](%%) and [[(% style="font-size: 14px; font-size: 14px" %)National Oceanic and Atmospheric Administration (NOAA)>>url:http://www.noaa.gov/||rel="__blank"]](%%) regularly collect data about the global ecosystems and process them into data products for scientific research and learning. The research lifecycle and data management lifecycle at this level will be different from those at the individual project level where teams of scientists have specific goals to solve specific problems. The scale of data and kinds of requirements for data management will vary along the stages of the whole research lifecycle. National research centers are publicly funded agencies and have the obligation of preserving and providing access to ecosystems data they collected. Hence generating data products and providing ways to discover and obtain data is crucial for them. Another example is the type of research projects carried out at academic institutions. These research projects may be funded by federal funding agencies or private foundations and can be collaborative among institutions or within a department/college of an institution. The data collected and generated from these projects are specialized and subject to the control and regulation of different data policies and compliance, which creates a different set of issues and requirements for data management and use/reuse from those generated by the national research centers.
10 10
11 11 Regardless of the context and nature of research, research data need to be stored, organized, documented, preserved (or discarded), and made discoverable and (re)usable. The amount of work and time involved in these processes is daunting, both intellectually intensive and costly. The personnel performing these tasks must be highly trained in technology and subject fields and able to effectively communicate between different stakeholders. In this sense, the lifecycle of research and data management is not only a technical domain but also a domain requiring effective management and communication. To be able to manage research data at community, institution, and project levels without reinventing-the-wheel, it is critical to build technical, communication, personnel, and policy capabilities at project and institutional levels and gradually evolve the maturity levels.
12 +
13 +
14 +[[<~~-~~-Previous Page>>doc:Introduction]] [[Next Page ~~-~~->>>doc:Background of the Capability Maturity Model]]
15 +

XWiki Enterprise 5.1-milestone-1 - Documentation