A Capability Maturity Model for Research Data Management

2.1 Commitment to Perform

Last modified by Arden Kirkland on 2014/06/06 12:53

2.1 Commitment to Perform

Commitment to Perform describes the actions the organization must take to ensure that the process is established and will endure. Commitment to Perform typically involves establishing organizational policies and senior management sponsorship.

2.1.1 Develop data quality control policies

The goal of developing data quality control policies is to establish a shared understanding of the goals, rules and responsibilities for data quality assurance (Hook et al., 2010). The policies should provide a clear definition of what quality data means in the context of the research given the data to be collected. 

Developing data quality policies is important to ensure that different actors in the data collection process have common understandings of the goals and rules for ensuring data quality and that there are clear responsibilities for these actions. 

Quality might refer to the level or nature of error in the measurements, e.g., whether the error is randomly distributed (noise) or systematic (bias) and the expected magnitudes of the error. Data quality policies should also address the coverage of the data, e.g., how wide a geographic, temporal or conceptual range is covered, how fine the geographic or temporal sampling and how representative the sample. Policies should reflect the desired tradeoffs between these characteristics. For example, it may be that one project determines that it is more valuable to have a broader geographic scope of data collection, trading off the need to sample within that region, while another elects to emphasize repeated measurement at regular time intervals, trading off geographic scope, while a third emphasizes the precision and accuracy of measurements, trading off the volume of data collected. 

2.1.2 Develop data documentation policies

The goal of developing data documentation policies is to establish a shared understanding of the goals, rules and responsibilities for creating data documentation. The policies should provide a clear definition of what data documentation needs to be collected along with the data, what that documentation should include, and who is responsible for collecting the documentation (DataONE, 2011). 

Developing data documentation policies is important to ensure that different actors in the data collection process have common understandings of the goals and rules for collecting data documentation and that there are clear responsibilities for these actions.

For example, when collecting field observations, data documentation might include such details as the observation protocol followed. Lab data documentation might similarly describe the equipment used as well as the protocols followed. Human subjects data documentation should include details about required institutional review board protections, such as informed consent requirements.  

For more discussion about data documentation, please see 3.1.1 Develop metadata policies.

Rubric

 Rubric for  2.1 - Commitment to Perform
Level 0
This process or practice is not being observed 
No steps have been taken to establish organizational policies or senior management sponsorship for data quality or documentation
Level 1: Initial
Data are managed intuitively at project level without clear goals and practices 
Data quality and documentation have been considered minimally by individual team members, but nothing has been codified or included in organizational policies or senior management sponsorship
Level 2: Managed
DM process is characterized for projects and often reactive 
Data quality and documentation have been addressed for this project, but have not taken wider community needs or standards into account and have not resulted in organizational policies or senior management sponsorship
Level 3: Defined
DM is characterized for the organization/community and proactive 
The project follows approaches to data quality and documentation that have been defined for the entire community or institution, as codified in organizational policies with senior management sponsorship
Level 4: Quantitatively Managed
DM is measured and controlled  
Quantitative quality goals have been established regarding data quality and documentation, and are codified in organizational policies with senior management sponsorship; both data and practices are systematically measured for quality
Level 5: Optimizing
Focus on process improvement  
Processes regarding data quality and documentation are evaluated on a regular basis, as codified in organizational policies with senior management sponsorship, and necessary improvements are implemented

References


DataONE. (2011). Develop a quality assurance and quality control plan. Retrieved from https://www.dataone.org/best-practices/develop-quality-assurance-and-quality-control-plan


Hook, L. A., Vannan, S. K. S., Beaty, T. W., Cook, R. B., & Wilson, B. E. (2010). Best Practices for Preparing Environmental Data Sets to Share and Archive. Oak Ridge National Laboratory Distributed Active  Archive Center. Retrieved from http://daac.ornl.gov/PI/BestPractices-2010.pdf

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