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  • × author_ss:"Stvilia, B."
  • × year_i:[2010 TO 2020}
  1. Lee, D.J.L.; Stvilia, B.: Developing a data identifier taxonomy (2014) 0.01 
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    Abstract
    As the amount of research data management is growing, the use of identity metadata for discovering, linking, and citing research data is growing too. To support the awareness of different identifier systems and the comparison and selection of an identifier for a particular data management environment, there is need for a knowledge base. This article contributes to that goal and analyzes the data management and related literatures to develop a data identifier taxonomy. The taxonomy includes four categories (domain, entity types, activities, and quality dimensions). In addition, the article describes 14 identifiers referenced in the literature and analyzes them along the taxonomy.
  2. Stvilia, B.; Hinnant, C.C.; Schindler, K.; Worrall, A.; Burnett, G.; Burnett, K.; Kazmer, M.M.; Marty, P.F.: Composition of scientific teams and publication productivity at a national science lab (2011) 0.00 
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    Date
    22. 1.2011 13:19:42
  3. Huang, H.; Stvilia, B.; Jörgensen, C.; Bass, H.W.: Prioritization of data quality dimensions and skills requirements in genome annotation work (2012) 0.00 
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    Abstract
    The rapid accumulation of genome annotations, as well as their widespread reuse in clinical and scientific practice, poses new challenges to management of the quality of scientific data. This study contributes towards better understanding of scientists' perceptions of and priorities for data quality and data quality assurance skills needed in genome annotation. This study was guided by a previously developed general framework for assessment of data quality and by a taxonomy of data-quality (DQ) skills, and intended to define context-sensitive models of criteria for data quality and skills for genome annotation. Analysis of the results revealed that genomics scientists recognize specific sets of criteria for quality in the genome-annotation context. Seventeen data quality dimensions were reduced to 5-factor constructs, and 17 relevant skills were grouped into 4-factor constructs. The constructs defined by this study advance the understanding of data quality relationships and are an important contribution to data and information quality research. In addition, the resulting models can serve as valuable resources to genome data curators and administrators for developing data-curation policies and designing DQ-assurance strategies, processes, procedures, and infrastructure. The study's findings may also inform educators in developing data quality assurance curricula and training courses.
  4. Stvilia, B.; Wu, S.; Lee, D.J.: Researchers' uses of and disincentives for sharing their research identity information in research information management systems (2018) 0.00 
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