Missouri Botanical Garden Open Conference Systems, TDWG 2014 ANNUAL CONFERENCE

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Toward a Conceptual Framework for the Assessment and Management of the Fitness-for-Use of Biodiversity Data
Allan Koch Veiga, Antonio Mauro Saraiva

Building: Elmia Congress Centre, Jönköping
Room: Rum 11
Date: 2014-10-30 11:25 AM – 11:40 AM
Last modified: 2014-10-03

Abstract


Several initiatives have arisen which intended to tackle Data Quality (DQ) issues. However, to foster discussions and actions among the Biodiversity Informatics (BI) community, in order to design and to share DQ solutions in a collaborative way, it is necessary to have a common conceptual framework.

By “conceptual framework” we mean “the way ideas are organized to achieve a research project’s purpose”[1], where the research project’s purpose here is to enable the BI community to collaboratively reuse and design solutions for allowing data users to assess and to manage the fitness-for-use of biodiversity data.

Due to the fact that DQ is an idiosyncratic concept, the assessment of the fitness-for-use of biodiversity data is probably the most important DQ issue in BI. The management of DQ is also an important aspect, because the improvement of DQ will make data be fit-for-use for a wider range of usages.

In this sense, it is very important that the whole community (Biodiversity and Informatics researchers) work together in DQ solutions. A survey about experiences on DQ with GBIF nodes reported [2] that data publishers and data users are the main responsibles to tackle DQ. That makes sense, since the owners of data are usually responsible for quality management and the data users are usually responsible for quality assessment. However, data owners and users are usually different people and DQ particulars are not always agreed. Therefore, appropriate solutions for DQ assessment and management, for a myriad of standpoints, should be built together by the whole community of stakeholders.

This framework proposal comprises concepts concerning valuable data for specific usages, quality aspects of those data, DQ assertions, quality improvement recommendations and specification of mechanisms for assertion validation, dimension measurement and improvement recommendation.

Thus, this work presents a proposal of a conceptual framework that could support be used by BI community to work collaboratively in reusable solutions for DQ assessment and management. This proposal of a conceptual framework will be discussed in the proposed TDWG Biodiversity Data Quality Interest Group (BDQ-IG) and it might be adopted to guide future Task Groups.


[1] Shields, Patricia and Rangarjan, N. 2013. A Playbook for Research Methods: Integrating Conceptual Frameworks and Project Management. Stillwater, OK: New Forums Press. p. 24.

[2] Heughebaert, A. 2013. Nodes experiences on Data Quality. Global Notes meeting, Berlin. http://community.gbif.org/mod/file/download.php?file_guid=37080