A Capability Maturity Model for Research Data Management
CMM for RDM » 4. Data Dissemination » 4.3 Activities Performed

Changes for document 4.3 Activities Performed

Last modified by crowston on 2014/06/01 12:01
From version 10.1
edited by Jian Qin
on 2014/03/12 10:01
To version 11.1
edited by Arden Kirkland
on 2014/03/12 12:34
Change comment: slight changes in wording of beginning

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Document author
XWiki.JArdenKirklanQind

Content changes

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4 4 {{toc/}}
5 5 {{/box}}
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7 -(% style="font-size: 14px;" %)Policies regarding data dissemination institutionalize data dissemination and show the commitment and having enabling technologies adds the ability (% style="font-size: 14px; line-height: normal;" %)to perform this process.(% style="font-size: 14px;" %) Activities performed, therefore, describes the roles and procedures necessary to implement data dissemination, a key process area in RDM. Activities performed in this process area typically involve establishing plans and procedures, performing the work, tracking it, and taking corrective actions as necessary.
7 +(% style="font-size: 14px;" %)//**Activities Performed** describes the roles and procedures necessary to implement a key process area. Activities Performed typically involve establishing plans and procedures (i.e., the specific actions that need to be performed), performing the work, tracking it, and taking corrective actions as necessary.//
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11 +(% style="font-size: 14px;" %)Policies regarding data dissemination institutionalize data dissemination and show commitment, but enabling technologies add the actual ability (% style="font-size: 14px; line-height: normal;" %)to perform this process.(% style="font-size: 14px;" %)
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9 9 == 4.3.1 Identify and manage data products ==
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11 -Along a research lifecycle data products come in various forms and with different levels of processing. They can be categorized based on the nature of research as (% style="font-size: 14px;" %)observational, experimental, simulation, and derived (or compiled). ([[(%%)DataONE, 2011e>>||anchor="DataONE-e" style="font-size: 14px;"]](% style="font-size: 14px;" %)). The nature of research determines what types of data will be produced and what format these data will take ([[DataONE, 2011c>>||anchor="DataONE-c"]]). According to the level of processing, data products can range from raw data, calibrated data, derived/calculated data to visualized and interactable data. While data sharing policies define the classification of data to be shared, this process requires an operational list of criteria and procedures to identify individual datasets that can be shared and whether there is any restrictions associated with each of them.
15 +Along a research lifecycle data products come in various forms and with different levels of processing. They can be categorized based on the nature of research as (% style="font-size: 14px;" %)observational, experimental, simulation, and derived (or compiled). ([[DataONE, 2011e>>||anchor="DataONE-e" style="font-size: 14px;"]]). The nature of research determines what types of data will be produced and what format these data will take ([[DataONE, 2011c>>||anchor="DataONE-c"]]). According to the level of processing, data products can range from raw data, calibrated data, derived/calculated data to visualized and interactable data. While data sharing policies define the classification of data to be shared, this process requires an operational list of criteria and procedures to identify individual datasets that can be shared and whether there is any restrictions associated with each of them.
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13 -The identification and management of data products relies heavily on the metadata descriptions (a key process area described in [[Chapter 3>>doc:3\. Data description and representation]]) and tools. As data products vary in their content and complexity, e.g. both a large collection of data and documentation files or only a single data file may be viewed as a data product, it is essential to have clear guidelines for how data products may be grouped, packaged, or aggregated. It is also necessary that d(% style="font-family: sans-serif; font-size: 14px; font-style: normal; line-height: 19.600000381469727px; text-align: start;" %)ata packages be represented ([[(%%)Jones et al, 2001>>||anchor="Jones" style="font-family: sans-serif; font-size: 14px; font-style: normal; line-height: 19.600000381469727px; text-align: start;"]](% style="font-family: sans-serif; font-size: 14px; font-style: normal; line-height: 19.600000381469727px; text-align: start;" %)).(% style="font-size: 14px;" %) The dissemination service interfaces should be based upon Open Standards ([[(%%)DataONE, 2011d>>||anchor="DataONE-d" style="font-size: 14px;"]](% style="font-size: 14px;" %)).
17 +The identification and management of data products relies heavily on the metadata descriptions (a key process area described in [[Chapter 3>>doc:3\. Data description and representation]]) and tools. As data products vary in their content and complexity, e.g. both a large collection of data and documentation files or only a single data file may be viewed as a data product, it is essential to have clear guidelines for how data products may be grouped, packaged, or aggregated. It is also necessary that d(% style="font-family: sans-serif; font-size: 14px; font-style: normal; line-height: 19.600000381469727px; text-align: start;" %)ata packages be represented ([[Jones et al, 2001>>||anchor="Jones" style="font-family: sans-serif; font-size: 14px; font-style: normal; line-height: 19.600000381469727px; text-align: start;"]]).(% style="font-size: 14px;" %) The dissemination service interfaces should be based upon Open Standards ([[DataONE, 2011d>>||anchor="DataONE-d" style="font-size: 14px;"]]).
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15 15 == 4.3.2 Encourage sharing ==
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17 -(% style="font-family: sans-serif; font-size: 14px; font-style: normal; line-height: 19.600000381469727px; text-align: start;" %)Shared data can improve research by providing greater spatial, temporal, and disciplinary coverage than individual organizations can offer. Data submitted to a data repository are integrated and provide a way for organizations to build repositories of cohesive, high-quality data ([[(%%)Hale et al, 2003>>||anchor="Hale" style="font-family: sans-serif; font-size: 14px; font-style: normal; line-height: 19.600000381469727px; text-align: start; text-decoration: underline;"]](% style="font-family: sans-serif; font-size: 14px; font-style: normal; line-height: 19.600000381469727px; text-align: start;" %)). However, (%%)data sharing policies as the institution's commitment to perform data dissemination do not always function as an incentive to motivate scientists to share data. A variety of venues should be used to convey the benefits of sharing data and the protection of data confidentiality and intellectual property rights to raise the awareness among scientists. Incentives such as impact and usage metrics embedded in the dissemination service system should be implemented as a reward mechanism to encourage sharing. Create shared need for data among partners to encourage better data stewardship ([[Hale et al, 2003>>||anchor="Hale"]])
21 +(% style="font-family: sans-serif; font-size: 14px; font-style: normal; line-height: 19.600000381469727px; text-align: start;" %)Shared data can improve research by providing greater spatial, temporal, and disciplinary coverage than individual organizations can offer. Data submitted to a data repository are integrated and provide a way for organizations to build repositories of cohesive, high-quality data ([[Hale et al, 2003>>||anchor="Hale" style="font-family: sans-serif; font-size: 14px; font-style: normal; line-height: 19.600000381469727px; text-align: start; text-decoration: underline;"]]). However, (%%)data sharing policies as the institution's commitment to perform data dissemination do not always function as an incentive to motivate scientists to share data. A variety of venues should be used to convey the benefits of sharing data and the protection of data confidentiality and intellectual property rights to raise the awareness among scientists. Incentives such as impact and usage metrics embedded in the dissemination service system should be implemented as a reward mechanism to encourage sharing. Create shared need for data among partners to encourage better data stewardship ([[Hale et al, 2003>>||anchor="Hale"]])
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19 19 == 4.3.3 Enable data discovery ==
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30 30 * (% style="font-size: 14px;" %)Linking data to publication, e.g., [[Dryad Digital Repository>>url:http://datadryad.org/]] and [[Astrophysics Data Systems (ADS)>>url:http://adsabs.harvard.edu/index.html]].
31 31 * (% style="font-size: 14px;" %)Registering the data repository in a data union catalog such as [[DataBib>>url:http://databib.org/]], and [[Registry of Research Data Repositories (re3data)>>url:http://www.re3data.org/]].
32 -* (% style="font-size: 14px;" %)Publishing metadata for published services based upon Open Standards ([[(%%)DataONE, 2011d>>||anchor="DataONE-d" style="font-size: 14px;"]](% style="font-size: 14px;" %)). (This approach requires advanced technologies in Linked Data and Semantic Web to publish the metadata as linked open data for other Web services to use)
36 +* (% style="font-size: 14px;" %)Publishing metadata for published services based upon Open Standards ([[DataONE, 2011d>>||anchor="DataONE-d" style="font-size: 14px;"]]). (This approach requires advanced technologies in Linked Data and Semantic Web to publish the metadata as linked open data for other Web services to use)
33 33 *
34 34
35 35 • SDM4.7 Package and deliver data archives

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