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

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edited by Arden Kirkland
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To version 14.1
edited by Arden Kirkland
on 2014/05/18 22:59
Change comment: proofreading

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10 10
11 11 == 4.3.1 Identify and manage data products ==
12 12
13 -Along a research lifecycle data come in various forms and with different levels of processing. They can be categorized based on the nature of research as observational, experimental, simulation, and derived (or compiled). ([[DataONE, 2011e>>||anchor="DataONE-e"]]). The nature of research determines what types of data will be produced and what format these data will take ([[DataONE, 2011c>>||anchor="DataONE-c"]]). Before these data become sharable, they must be processed, "packaged", and registered in a repository or catalog of data products. 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 a list of criteria and procedures to identify individual datasets that can be deemed as data products for sharing and any restrictions of access and usage associated with each of them.
13 +Along a research lifecycle data come in various forms and with different levels of processing. They can be categorized based on the nature of research as observational, experimental, derived (or compiled), or simulation ([[DataONE, 2011e>>||anchor="DataONE-e"]]). The nature of research determines what types of data will be produced and what format these data will take ([[DataONE, 2011c>>||anchor="DataONE-c"]]). Before these data become sharable, they must be processed, "packaged," and registered in a repository or catalog of data products. According to the level of processing, data products can range from raw data, calibrated data, or derived/calculated data to visualized and interactable data. While data sharing policies define the classification of data to be shared, this process requires a list of criteria and procedures to identify individual datasets that can be deemed as data products for sharing and any restrictions of access and usage associated with each of them.
14 14
15 15 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 datasets 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 data packages be represented ([[Jones et al., 2001>>||anchor="Jones"]]). The dissemination service interfaces should be based upon Open Standards ([[DataONE, 2011d>>||anchor="DataONE-d"]]).
16 16
17 17 == 4.3.2 Encourage sharing ==
18 18
19 -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"]]). 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"]])
19 +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"]]). However, data sharing policies following the institution's commitment to perform data dissemination do not always function as an incentive to motivate researchers 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 researchers. 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"]])
20 20
21 21 == 4.3.3 Enable data discovery ==
22 22
23 -Data discovery is a key function of all data repository systems. The discovery services should take into consideration of the needs of both domain experts and non-expert users. For data products that might be useful for interdisciplinary research, it is even more important for the discovery service to facilitate and support discovery functions through enabling search and browsing. In other words, make your outputs perceivable ([[DataONE, 2011b>>||anchor="DataONE-b"]]).
23 +Data discovery is a key function of all data repository systems. The discovery services should take into consideration the needs of both domain experts and non-expert users. For data products that might be useful for interdisciplinary research, it is even more important for the discovery service to facilitate and support discovery functions through enabling search and browsing. In other words, make your outputs perceivable ([[DataONE, 2011b>>||anchor="DataONE-b"]]).
24 24
25 -Discovery services should also allow the addition of community tagging, annotation, and comments ([[DataONE, 2011f>>||anchor="DataONE-f"]]). For example, researchers can share and publish data using Web-based datacasting tools and services ([[DataONE, 2011a>>||anchor="DataONE-a"]]).
25 +Discovery services should also allow the addition of community tagging, annotation, and comments ([[DataONE, 2011f>>||anchor="DataONE-f"]]). For example, researchers can share and publish data using web-based datacasting tools and services ([[DataONE, 2011a>>||anchor="DataONE-a"]]).
26 26
27 27 == 4.3.4 Distribute data ==
28 28
29 29 Multiple channels can be established for data distribution to allow the widest possible coverage and timely dissemination. These channels include:
30 30
31 -* //Linking data to publications//: Dryad Digital Repository ([[http:~~/~~/datadryad.org/>>http://datadryad.org/||rel="__blank"]]) and Astrophysics Data Systems (ADS) ([[http:~~/~~/adsabs.harvard.edu/index.html>>http://adsabs.harvard.edu/index.html||rel="__blank"]]) are two examples of this type of services. Linking services enables bi-directional discovery, i.e., finding and obtaining data through publications or vice versus.
31 +* //Linking data to publications//: Dryad Digital Repository ([[http:~~/~~/datadryad.org/>>http://datadryad.org/||rel="__blank"]]) and Astrophysics Data Systems (ADS) ([[http:~~/~~/adsabs.harvard.edu/index.html>>http://adsabs.harvard.edu/index.html||rel="__blank"]]) are two examples of this type of services. Linking services enables bi-directional discovery, i.e., finding and obtaining data through publications or vice versa.
32 32 * //Registering the data repository in a data union catalog//: Examples includes DataBib ([[http:~~/~~/databib.org/>>http://databib.org/||rel="__blank"]]) and the Registry of Research Data Repositories (re3data, [[http:~~/~~/www.re3data.org/>>http://www.re3data.org/||rel="__blank"]]). Joining a union catalog or data registry allows for federated and other broader searches, which affords the data to be distributed to much wider communities.
33 33 * //Distribute information on data products through Web services//: (% style="font-family: sans-serif; font-size: 14px; font-style: normal; line-height: 19.600000381469727px; text-align: start;" %)Open Standards for Web services include (%%)RSS/Atom and Web Services Definition Language ([[DataONE, 2011d>>||anchor="DataONE-d"]]). Users may subscribe these services to receive timely updates on data product information.
34 34
35 35 == 4.3.5 Ensure data citation ==
36 36
37 -Data citation embodies two notions: to credit the data creator and to enable data reuse, verification, and impact tracking ([[DataCite, 2014>>||anchor="datacite"]]). To enable consistent practice of data citation, guidelines should be provided regarding what information should be included (content) and how the information should be presented in a data citation (style). The Socioeconomic Data and Applications Center ([[SEDAC>>url:http://sedac.ciesin.columbia.edu]]) provides an exemplar guidelines for citing the data from this center. This guideline specifies the required information for a data citation as:
37 +Data citation embodies two notions: to credit the data creator and to enable data reuse, verification, and impact tracking ([[DataCite, 2014>>||anchor="datacite"]]). To enable consistent practice of data citation, guidelines should be provided regarding what information should be included (content) and how the information should be presented in a data citation (style). The Socioeconomic Data and Applications Center ([[SEDAC>>url:http://sedac.ciesin.columbia.edu]]) provides examples of guidelines for citing the data from this center. This guideline specifies the required information for a data citation as:
38 38
39 39 * Primary responsibility party
40 40 * Year of publication, issue, release
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66 66 |Level 5: Optimizing
67 67 Focus on process improvement|Processes regarding workflow for data dissemination, including sharing, discovery, and citation, are evaluated on a regular basis, and necessary improvements are implemented
68 68
69 -
70 70 == References ==
71 71
72 72 DataCite. (2014). Why cite data? Retrieved from [[https:~~/~~/www.datacite.org/whycitedata>>url:https://www.datacite.org/whycitedata||rel="__blank"]]

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