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      To increase trust, change the social design behind aggregated biodiversity data

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      Database: The Journal of Biological Databases and Curation

      Oxford University Press

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          Abstract

          Growing concerns about the quality of aggregated biodiversity data are lowering trust in large-scale data networks. Aggregators frequently respond to quality concerns by recommending that biologists work with original data providers to correct errors ‘at the source.’ We show that this strategy falls systematically short of a full diagnosis of the underlying causes of distrust. In particular, trust in an aggregator is not just a feature of the data signal quality provided by the sources to the aggregator, but also a consequence of the social design of the aggregation process and the resulting power balance between individual data contributors and aggregators. The latter have created an accountability gap by downplaying the authorship and significance of the taxonomic hierarchies—frequently called ‘backbones’—they generate, and which are in effect novel classification theories that operate at the core of data-structuring process. The Darwin Core standard for sharing occurrence records plays an under-appreciated role in maintaining the accountability gap, because this standard lacks the syntactic structure needed to preserve the taxonomic coherence of data packages submitted for aggregation, potentially leading to inferences that no individual source would support. Since high-quality data packages can mirror competing and conflicting classifications, i.e. unsettled systematic research, this plurality must be accommodated in the design of biodiversity data integration. Looking forward, a key directive is to develop new technical pathways and social incentives for experts to contribute directly to the validation of taxonomically coherent data packages as part of a greater, trustworthy aggregation process.

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          Most cited references 67

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          New developments in museum-based informatics and applications in biodiversity analysis.

          Information from natural history collections (NHCs) about the diversity, taxonomy and historical distributions of species worldwide is becoming increasingly available over the Internet. In light of this relatively new and rapidly increasing resource, we critically review its utility and limitations for addressing a diverse array of applications. When integrated with spatial environmental data, NHC data can be used to study a broad range of topics, from aspects of ecological and evolutionary theory, to applications in conservation, agriculture and human health. There are challenges inherent to using NHC data, such as taxonomic inaccuracies and biases in the spatial coverage of data, which require consideration. Promising research frontiers include the integration of NHC data with information from comparative genomics and phylogenetics, and stronger connections between the environmental analysis of NHC data and experimental and field-based tests of hypotheses.
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            Error cascades in the biological sciences: the unwanted consequences of using bad taxonomy in ecology.

            Why do ecologists seem to underestimate the consequences of using bad taxonomy? Is it because the consequences of doing so have not been yet scrutinized well enough? Is it because these consequences are irrelevant? In this paper I examine and discuss these questions, focusing on the fact that because ecological works provide baseline information for many other biological disciplines, they play a key role in spreading and magnifying the abundance of a variety of conceptual and methodological errors. Although overlooked and underestimated, this cascade-like process originates from trivial taxonomical problems that affect hypotheses and ideas, but it soon shifts into a profound practical problem affecting our knowledge about nature, as well as the ecosystem structure and functioning and the efficiency of human health care programs. In order to improve the intercommunication among disciplines, I propose a set of specific requirements that peer reviewed journals should request from all authors, and I also advocate for urgent institutional and financial support directed at reinvigorating the formation of scientific collections that integrate taxonomy and ecology.
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              Epistemic Dependence

               John Hardwig (1985)
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                Author and article information

                Journal
                Database (Oxford)
                Database (Oxford)
                databa
                Database: The Journal of Biological Databases and Curation
                Oxford University Press
                1758-0463
                2018
                04 January 2018
                04 January 2018
                : 2018
                Affiliations
                School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
                Author notes
                Corresponding author: Tel: (480) 965-2036; Fax: (480) 965-6899; Email: nico.franz@ 123456asu.edu

                Citation details: Franz,N.M. and Sterner,B.W. To increase trust, change the social design behind aggregated biodiversity data. Database (2017) Vol. 2017: article ID bax100; doi:10.1093/database/bax100.

                Article
                bax100
                10.1093/database/bax100
                7206650
                29315357
                8b9047a8-dbda-4065-bb31-e8fdfb34b8c7
                © The Author(s) 2017. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Pages: 12
                Product
                Funding
                Funded by: Division of Environmental Biology 10.13039/100000155
                Award ID: 1155984
                Funded by: Division of Social and Economic Sciences 10.13039/100000077
                Award ID: 1153114
                Funded by: Division of Biological Infrastructure 10.13039/100000153
                Award ID: 1342595
                Categories
                Perspective/Opinion

                Bioinformatics & Computational biology

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