A Capability Maturity Model for Research Data Management
CMM for RDM » 3. Data description and representation » 3.3 Activities Performed

Changes for document 3.3 Activities Performed

Last modified by Arden Kirkland on 2014/06/06 12:59
From version 53.3
edited by Arden Kirkland
on 2014/03/12 13:00
To version 54.1
edited by Arden Kirkland
on 2014/03/13 22:41
Change comment: citations

Content changes

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8 -(% style="font-size: 14px; line-height: 1.4em; color: rgb(64, 64, 64);" %)//**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.//
8 +//**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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10 +3.3.1 Generate metadata according to agreed upon procedures
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12 +Follow agreed upon procedures for generating metadata for variables, files, and studies to ensure the ability of future users to find, identify, select, and obtain data. There is not a single set of metadata that applies in all situations, but consider which elements are important for lower levels of granularity and higher-level description of the dataset as a whole.
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13 -== 3.3.1 Generate metadata according to agreed upon procedures ==
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15 -(% style="font-size: 14px; line-height: 1.4em; color: rgb(64, 64, 64);" %)Follow agreed upon procedures for generating metadata for variables, files, and studies to ensure the ability of future users to find, identify, select, and obtain data. There is not a single set of metadata that applies in all situations, but consider which elements are important for lower levels of granularity and higher-level description of the dataset as a whole.
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17 17 === 3.3.1.1 Document variables ===
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19 -Document individual data items such as variables (columns in structured tabular data), with names, labels and descriptions. Examples of elements of variable documentation are data type; units of measurement; formats for date, time, and geography; method of measurement, coverage (e.g. geographic, temporal), and codes and classification schemes (e.g. codes for missing data, flags for quality issues or qualifying values). ICPSR offers extensive guidelines for variable documentation based on the DDI standard for quantitative social science data. DataOne offers guidelines based on best practices in the natural and physical sciences.
16 +Document individual data items such as variables (columns in structured tabular data), with names, labels and descriptions. Examples of elements of variable documentation are data type; units of measurement; formats for date, time, and geography; method of measurement, coverage (e.g. geographic, temporal), and codes and classification schemes (e.g. codes for missing data, flags for quality issues or qualifying values). ICPSR offers extensive guidelines for variable documentation based on the DDI standard for quantitative social science data. DataOne ([[2011>>||anchor="DataONE"]]) offers guidelines based on best practices in the natural and physical sciences.
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21 -Document variables in the data file, and in a separate file. Long (2009) offers guidelines for naming and describing variables and values (p. 143-194). For structured, tabular data a well-documented data dictionary provides a concise guide to understanding and using the data. An example of a data dictionary is available from the Colorado Clinical and Translational Sciences Institute: [[http:~~/~~/cctsi.ucdenver.edu/RIIC/Documents/Data-Management-Figure-3.pdf>>url:http://cctsi.ucdenver.edu/RIIC/Documents/Data-Management-Figure-3.pdf]]
18 +Document variables in the data file, and in a separate file. Long ([[2009>>||anchor="Long"]]) offers guidelines for naming and describing variables and values (p. 143-194). For structured, tabular data a well-documented data dictionary provides a concise guide to understanding and using the data. An example of a data dictionary is available from the Colorado Clinical and Translational Sciences Institute: [[http:~~/~~/cctsi.ucdenver.edu/RIIC/Documents/Data-Management-Figure-3.pdf>>url:http://cctsi.ucdenver.edu/RIIC/Documents/Data-Management-Figure-3.pdf]]
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23 23 For qualitative data, offering structured contextual information in a separate data list provides users with a guide to the data. The UK Data Archive has examples and templates for data lists: [[http:~~/~~/www.data-archive.ac.uk/create-manage/document/data-level?index=2 >>url:http://www.data-archive.ac.uk/create-manage/document/data-level?index=2]]
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53 53 Brase, J., Socha, Y., Callaghan, S., Borgman, C.L., Uhlir, P.F., Carroll, B. (2014). Data citation: Principles and practice. In J. Ray (Ed.), //Research Data Management: Practical Strategies for Information Professionals (Charleston Insights in Library, Information, and Archival Sciences).// Purdue University Press.
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55 -Long, J Scott. (2009). //The workflow of data analysis using Stata//. College Station, Texas: Stata Press Books.
52 +DataONE. (2011). Best Practices. Retrieved from [[https:~~/~~/www.dataone.org/best-practices>>url:https://www.dataone.org/best-practices]]
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58 58 {{id name="Faniel"/}}
55 +Faniel, I. M., & Zimmerman, A. (2011). Beyond the Data Deluge: A Research Agenda for Large-Scale Data Sharing and Reuse. //International Journal of Digital Curation//, //6//(1), 58–69. doi:10.2218/ijdc.v6i1.172. Retrieved from [[http:~~/~~/www.ijdc.net/index.php/ijdc/article/view/163/231>>url:http://www.ijdc.net/index.php/ijdc/article/view/163/231||rel="__blank" style="font-size: 14px;"]]
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60 -(% style="font-size: 14px;" %)Faniel, I. M., & Zimmerman, A. (2011). Beyond the Data Deluge: A Research Agenda for Large-Scale Data Sharing and Reuse. //International Journal of Digital Curation//, //6//(1), 58–69. doi:10.2218/ijdc.v6i1.172. Retrieved from [[http:~~/~~/www.ijdc.net/index.php/ijdc/article/view/163/231>>url:http://www.ijdc.net/index.php/ijdc/article/view/163/231||rel="__blank" style="font-size: 14px;"]]
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62 62 Long, J. S. (2009). //The workflow of data analysis using Stata//. College Station, Tex.: Stata Press.
63 63
64 64 Mayernik, M. S. (2010). Metadata tensions: A case study of library principles vs. everyday scientific data practices. //Proceedings of the American Society for Information Science and Technology//, //47//(1), 1–2. doi:10.1002/meet.14504701337. Retrieved from [[http:~~/~~/www.asis.org/asist2010/proceedings/proceedings/ASIST_AM10/submissions/337_Final_Submission.p>>url:http://www.asis.org/asist2010/proceedings/proceedings/ASIST_AM10/submissions/337_Final_Submission.pdf||rel="__blank"]]

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