Visible Evidence of Invisible Quality Dimensions and the Role of Data Management

Past research has shown that data reusers are concerned with the issue of data quality and the identified attributes of quality. While data reusers find evidence of the attributes of data quality during their assessment of data for reuse, there may be other dimensions of data quality that reusers are concerned about but that are not always visible to them. This study explores these invisible dimensions of data quality that have been identified by data reusers. The findings of this study indicate that data reusers are concerned with two kinds of invisible characteristics for assessing the data: the efforts put on data, and the ethics behind the data. While these quality dimensions cannot be easily measured at face-level, data reusers find proxy evidence that indicates the presence of these invisibilities. This finding signifies the role of data management that can make these invisible data qualities visible.

[1]  Anany Levitin,et al.  The Notion of Data and Its Quality Dimensions , 1994, Inf. Process. Manag..

[2]  George A. Marcoulides,et al.  Introduction to Psychometric Theory , 2010 .

[3]  Ann Zimmerman,et al.  New Knowledge from Old Data , 2008 .

[4]  Stuart E. Madnick,et al.  Overview and Framework for Data and Information Quality Research , 2009, JDIQ.

[5]  Anne E. Trefethen,et al.  The Data Deluge: An e-Science Perspective , 2003 .

[6]  Andreas V. Hense,et al.  Acquiring High Quality Research Data , 2011, D Lib Mag..

[7]  Ofer Arazy,et al.  On the measurability of information quality , 2011, J. Assoc. Inf. Sci. Technol..

[8]  Diane M. Strong,et al.  Beyond Accuracy: What Data Quality Means to Data Consumers , 1996, J. Manag. Inf. Syst..

[9]  Christine L. Borgman,et al.  The conundrum of sharing research data , 2012, J. Assoc. Inf. Sci. Technol..

[10]  Diane M. Strong,et al.  AIMQ: a methodology for information quality assessment , 2002, Inf. Manag..

[11]  Nancy A. Van House,et al.  Cooperative knowledge work and practices of trust: sharing environmental planning data sets , 1998, CSCW '98.

[12]  Christine L. Borgman,et al.  Research Data: Who Will Share What, with Whom, When, and Why? , 2010 .

[13]  Gary Marchionini,et al.  Curating for Quality: Ensuring Data Quality to Enable New Science , 2012 .

[14]  Tony Hey,et al.  The Fourth Paradigm: Data-Intensive Scientific Discovery , 2009 .

[15]  Melissa H. Cragin,et al.  Scientific Data Collections and Distributed Collective Practice , 2006, Computer Supported Cooperative Work (CSCW).

[16]  Michael J. Giarlo Academic Libraries as Data Quality Hubs , 2013 .

[17]  Ixchel M. Faniel,et al.  Reusing Scientific Data: How Earthquake Engineering Researchers Assess the Reusability of Colleagues’ Data , 2010, Computer Supported Cooperative Work (CSCW).