A Capability Maturity Model for Research Data Management

Changes for document 3.2 Ability to Perform

Last modified by Arden Kirkland on 2014/06/06 12:56
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edited by Jian Qin
on 2014/05/28 23:33
To version 34.1
edited by Jian Qin
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31 31 \\There are efforts to automate the generation of metadata via software tools, though this capability is not fully realized for most research communities. An example of an ability to perform issue is ensuring flexible data services for virtual datasets ([[DataONE, 2011>>||anchor="DataONE"]]).
32 32 \\A best practice in many contexts is to conceptualize metadata creation as a shared responsibility, that is facilitated by librarian support ([[Riley, 2014>>||anchor="Riley"]]). For example, the ICPSR data repository asks researchers to provide descriptive study information, but also devotes significant staff resources to enhancing researcher metadata to make it more fully interoperable with DDI (Data Documentation Initiative) metadata (a social science metadata standard), and transforming data into multiple data formats (for three common statistical software platforms) to make it widely accessible.
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34 -(% style="font-family: sans-serif; font-size: 13.63636302947998px; font-style: normal; line-height: 19.600000381469727px; text-align: start;" %)Researcher interest in documentation of data is greatest when it assists with everyday project data management ([[(%%)Jahnke & Asher, 2012>>||anchor="Jahnke" style="font-family: sans-serif; font-size: 13.63636302947998px; font-style: normal; line-height: 19.600000381469727px; text-align: start;"]](% style="font-family: sans-serif; font-size: 13.63636302947998px; font-style: normal; line-height: 19.600000381469727px; text-align: start;" %)). A best practice is to integrate metadata creation into researcher workflows during the active phase of research projects, leveraging researcher interest in project data management ([[(%%)Jahnke & Asher, 2012>>||anchor="Jahnke" style="font-family: sans-serif; font-size: 13.63636302947998px; font-style: normal; line-height: 19.600000381469727px; text-align: start;"]](% style="font-family: sans-serif; font-size: 13.63636302947998px; font-style: normal; line-height: 19.600000381469727px; text-align: start;" %)).
34 +(% style="font-family: sans-serif; font-size: 13.63636302947998px; font-style: normal; line-height: 19.600000381469727px; text-align: start;" %)Researcher interest in documentation of data is greatest when it assists with everyday project data management ([[Jahnke & Asher, 2012>>||anchor="Jahnke" style="font-family: sans-serif; font-size: 13.63636302947998px; font-style: normal; line-height: 19.600000381469727px; text-align: start;"]]). A best practice is to integrate metadata creation into researcher workflows during the active phase of research projects, leveraging researcher interest in project data management ([[Jahnke & Asher, 2012>>||anchor="Jahnke" style="font-family: sans-serif; font-size: 13.63636302947998px; font-style: normal; line-height: 19.600000381469727px; text-align: start;"]]).
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36 36 == 3.2.4 (% style="font-size: 22.727272033691406px;" %)Arrange staffing for creating metadata(%%) ==
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