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PLoS One. 2017 Jun 28;12(6):e0178731. doi: 10.1371/journal.pone.0178731. eCollection 2017.

A conceptual framework for quality assessment and management of biodiversity data.

Author information

1
University of São Paulo, Research Center on Biodiversity and Computing, São Paulo, São Paulo, Brazil.
2
Australian Biodiversity Information Services, Ballan, Victoria, Australia.
3
Harvard University, Museum of Comparative Zoology, Cambridge, Massachusetts, United States of America.
4
Université de Montréal, Institut de Recherche en Biologie Végétale, Montréal, Québec, Canada.
5
Global Biodiversity Information Facility, Secretariat, Copenhagen, Denmark.

Abstract

The increasing availability of digitized biodiversity data worldwide, provided by an increasing number of institutions and researchers, and the growing use of those data for a variety of purposes have raised concerns related to the "fitness for use" of such data and the impact of data quality (DQ) on the outcomes of analyses, reports, and decisions. A consistent approach to assess and manage data quality is currently critical for biodiversity data users. However, achieving this goal has been particularly challenging because of idiosyncrasies inherent in the concept of quality. DQ assessment and management cannot be performed if we have not clearly established the quality needs from a data user's standpoint. This paper defines a formal conceptual framework to support the biodiversity informatics community allowing for the description of the meaning of "fitness for use" from a data user's perspective in a common and standardized manner. This proposed framework defines nine concepts organized into three classes: DQ Needs, DQ Solutions and DQ Report. The framework is intended to formalize human thinking into well-defined components to make it possible to share and reuse concepts of DQ needs, solutions and reports in a common way among user communities. With this framework, we establish a common ground for the collaborative development of solutions for DQ assessment and management based on data fitness for use principles. To validate the framework, we present a proof of concept based on a case study at the Museum of Comparative Zoology of Harvard University. In future work, we will use the framework to engage the biodiversity informatics community to formalize and share DQ profiles related to DQ needs across the community.

PMID:
28658288
PMCID:
PMC5489162
DOI:
10.1371/journal.pone.0178731
[Indexed for MEDLINE]
Free PMC Article

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