A Capability Maturity Model for Research Data Management
CMM for RDM » 1. Data Management in General » 1.4 Process Assessment

Changes for document 1.4 Process Assessment

Last modified by Arden Kirkland on 2014/06/06 12:53
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edited by Jian Qin
on 2014/03/19 21:02
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edited by Arden Kirkland
on 2014/05/11 15:16
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13 13 The second step in process assessment focuses on continuous process improvement. The effectiveness and quality measures established through the first step will be used to identify weaknesses and strengthen the process proactively, with the goal of preventing the occurrence of defects. Data on the effectiveness of the RDM process is used to perform cost benefit analyses of RDM.
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15 -There is very little available in literature to generalize the characteristics of level 4 and level 5 of capability maturity in RDM. The measurement and quality management for RDM is therefore defined in terms of analogy to the original CMM ([[Paulk et al., 1993>>||anchor="Paulk"]]).
15 +There is very little available in the literature to generalize the characteristics of level 4 and level 5 of capability maturity in RDM. The measurement and quality management for RDM is therefore defined in terms of analogy to the original CMM ([[Paulk et al., 1993>>||anchor="Paulk"]]).
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17 17 == 1.4.1 Measurement and Analysis ==
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19 -The goal of RDM varies because the nature and characteristics of research types and data differ from discipline to discipline. Data flows and stages in field observations and lab experiments will be different from those in computer simulations or computational intensive type of research. The involvement of scientists and data professionals in data flows and stages is also different, e.g, data collection during a field visit will be usually conducted by researchers while datasets ready for curation are handled by data mangers or librarians. The measurements for process assessment should maintain its focus on effectiveness and quality while recognizing these differences and complexities. The following therefore is targeted to establishing the measurements regardless who (researchers, data staff, or librarians) perform it:
19 +The goal of RDM varies because the nature and characteristics of research types and data differ from discipline to discipline. Data flows and stages in field observations and lab experiments will be different from those in computer simulations or computational intensive types of research, for example. The involvement of researchers and data professionals in data flows and stages is also different, e.g, data collection during a field visit will be usually conducted by researchers while datasets ready for curation are handled by data mangers or librarians. The measurements for process assessment should maintain a focus on effectiveness and quality while recognizing these differences and complexities. The following therefore is targeted to establishing the measurements regardless who (researchers, data staff, or librarians) perform it:
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21 -* //The amount of effort went into the process//, e.g., how many redundant runs were performed to complete the processing.
22 -* //Time spent on a task//, e.g., how long it was taken to verify/check data, code data, or transform data.
21 +* //The amount of effort that went into the process//, e.g., how many redundant runs were performed to complete the processing.
22 +* //Time spent on a task//, e.g., how long it took to verify/check data, code data, or transform data.
23 23 * //Presence (or absence) of process data collection//: when data about process effectiveness is collected on the spot, it is easier to do than after the fact. It is tedious to do it afterwards and the data can easily become inaccurate.
24 24 * //Data points produced//: e.g., number of survey responses generated, number of data frames segmented.
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26 26 (((
27 -Measurements can be constructed from the perspective of input, output, and throughput, or from the perspective of workflows. The amount of effort, for example, can be considered as an input measurement, while data points produced would be an output measurement. Effectiveness is getting things right. Process measurements can help us identify problems, especially the causes of the problems. if you observe the missing data is high, then it makes sense to look for what caused the missing data.
27 +Measurements can be constructed from the perspective of input, output, and throughput, or from the perspective of workflows. The amount of effort, for example, can be considered as an input measurement, while data points produced would be an output measurement. Effectiveness is getting things right. Process measurements can help to identify problems, especially the causes of the problems. If you observe the missing data is high, then it makes sense to look for what caused the missing data.
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29 29 == 1.4.2 Verifying Implementation ==
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