The Open Biodiversity Knowledge Management System in Scholarly Publishing

Senderov, Viktor; Penev, Lyubomir

doi:10.3897/rio.2.e7757

Cited by 20 publications

(12 citation statements)

References 32 publications

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“…Another seemingly pragmatic argument is that many data packages to be aggregated are themselves syntactically underspecified. Again, while true on the surface, this does not preclude the design of pilot systems that enforce TCS-over DwC-based syntax -at a scale commensurate with individual expert publications (99). Failures to propagate such innovations at greater scales, and continued preferences for Darwin Core for purposes beyond its design scope, send the wrong message to experts.…”

Section: Resultsmentioning

confidence: 99%

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

Franz

Sterner

2017

Preprint

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Growing concerns about the quality of aggregated biodiversity data are lowering trust in largescale 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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Section: Resultsmentioning

confidence: 99%

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

Franz

Sterner

2017

Preprint

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“…The present paper describes the rationale, concept, infrastructure and underlying data of OpenBiodiv-the first LOD-based OBKMS, which integrates knowledge extracted from biodiversity publications and a taxonomic backbone tree used by GBIF. The development of OpenBiodiv began in 2015 [7], a proof of concept being launched and presented during the annual conference of the Biodiversity Information Standards (TDWG) in 2016 [28].…”

Section: Working Examples Of Biodiversity Data Platforms and Knowledgmentioning

confidence: 99%

OpenBiodiv: A Knowledge Graph for Literature-Extracted Linked Open Data in Biodiversity Science

et al. 2019

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Hundreds of years of biodiversity research have resulted in the accumulation of a substantial pool of communal knowledge; however, most of it is stored in silos isolated from each other, such as published articles or monographs. The need for a system to store and manage collective biodiversity knowledge in a community-agreed and interoperable open format has evolved into the concept of the Open Biodiversity Knowledge Management System (OBKMS). This paper presents OpenBiodiv: An OBKMS that utilizes semantic publishing workflows, text and data mining, common standards, ontology modelling and graph database technologies to establish a robust infrastructure for managing biodiversity knowledge. It is presented as a Linked Open Dataset generated from scientific literature. OpenBiodiv encompasses data extracted from more than 5000 scholarly articles published by Pensoft and many more taxonomic treatments extracted by Plazi from journals of other publishers. The data from both sources are converted to Resource Description Framework (RDF) and integrated in a graph database using the OpenBiodiv-O ontology and an RDF version of the Global Biodiversity Information Facility (GBIF) taxonomic backbone. Through the application of semantic technologies, the project showcases the value of open publishing of Findable, Accessible, Interoperable, Reusable (FAIR) data towards the establishment of open science practices in the biodiversity domain.

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“…This last stage of publication of machine-readable, integrated structured biodiversity data and narrative was piloted by the Biodiversity Data Journal (BDJ) and its associated authoring tool, the ARPHA Writing Tool (AWT), launched within the ViBRANT EU Framework Seven (FP7) project (Smith et al 2013). The Biodiversity Data Journal realised in practice the first ever journal publishing workflow that supported the full life cycle of a manuscript, from writing through community peer-review, publication and dissemination within a single, entirely Web-and XML-based, online collaborative platform.…”

Section: Presentationmentioning

confidence: 99%

From Open Access to Open Science from the viewpoint of a scholarly publisher

Penev¹

2017

RIO

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BackgroundThe open access publishing model led to dramatic changes in the way scientists communicate their results. Open access also challenged the traditional business models of academic publishers that have been maintained for hundreds of years. Open access to article content, however, soon appeared insufficient as far as access to underlying data was concerned. Opening research data came as the logical second stage of this challenge which was soon put on the agenda of scientific communities, funding organisations and governments. Open data, by itself, raised the question how we can re-use data and reproduce research results, how transparent is the peer-review and, more generally, how scientific evaluation is being performed. Over time, these and other similar developments morphed into what we now call "open science" or, in more general terms, transforming research into a primarily collaborative rather than a primarily competitive endeavour.

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The Open Biodiversity Knowledge Management System in Scholarly Publishing

Cited by 20 publications

References 32 publications

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

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

OpenBiodiv: A Knowledge Graph for Literature-Extracted Linked Open Data in Biodiversity Science

From Open Access to Open Science from the viewpoint of a scholarly publisher

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