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      Wanted: Standards for FAIR taxonomic concept representations and relationships

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          Abstract

          Making the most of biodiversity data requires linking observations of biological species from multiple sources both efficiently and accurately ( Bisby 2000, Franz et al. 2016). Aggregating occurrence records using taxonomic names and synonyms is computationally efficient but known to experience significant limitations on accuracy when the assumption of one-to-one relationships between names and biological entities breaks down ( Remsen 2016, Franz and Sterner 2018). Taxonomic treatments and checklists provide authoritative information about the correct usage of names for species, including operational representations of the meanings of those names in the form of range maps, reference genetic sequences, or diagnostic traits. They increasingly provide taxonomic intelligence in the form of precise description of the semantic relationships between different published names in the literature. Making this authoritative information Findable, Accessible, Interoperable, and Reusable (FAIR; Wilkinson et al. 2016) would be a transformative advance for biodiversity data sharing and help drive adoption and novel extensions of existing standards such as the Taxonomic Concept Schema and the OpenBiodiv Ontology ( Kennedy et al. 2006, Senderov et al. 2018). We call for the greater, global Biodiversity Information Standards (TDWG) and taxonomy community to commit to extending and expanding on how FAIR applies to biodiversity data and include practical targets and criteria for the publication and digitization of taxonomic concept representations and alignments in taxonomic treatments, checklists, and backbones.

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          Most cited references17

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            SARS-CoV-2 infection and transmission in the North American deer mouse

            Widespread circulation of SARS-CoV-2 in humans raises the theoretical risk of reverse zoonosis events with wildlife, reintroductions of SARS-CoV-2 into permissive nondomesticated animals. Here we report that North American deer mice ( Peromyscus maniculatus ) are susceptible to SARS-CoV-2 infection following intranasal exposure to a human isolate, resulting in viral replication in the upper and lower respiratory tract with little or no signs of disease. Further, shed infectious virus is detectable in nasal washes, oropharyngeal and rectal swabs, and viral RNA is detectable in feces and occasionally urine. We further show that deer mice are capable of transmitting SARS-CoV-2 to naïve deer mice through direct contact. The extent to which these observations may translate to wild deer mouse populations remains unclear, and the risk of reverse zoonosis and/or the potential for the establishment of Peromyscus rodents as a North American reservoir for SARS-CoV-2 remains unknown. Deer mice are natural hosts for a number of human pathogens. Here, Griffin et al. report that intranasal exposure of the North American deer mouse to SARS-CoV-2 results in virus replication and shedding, despite causing only mild or asymptomatic illness. Additionally, infected deer mice can transmit SARS-CoV-2 to naïve deer mice.
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              The quiet revolution: biodiversity informatics and the internet.

              The massive development of biodiversity-related information systems on the Internet has created much that appears exciting but chaotic, a diversity to match biodiversity itself. This richness and the arrays of new sources are counterbalanced by the maddening difficulty in knowing what is where, or of comparing like with like. But quietly, behind the first waves of exuberance, biologists and computer scientists have started to pull together in a rising tide of coherence and organization. The fledgling field of biodiversity informatics looks set to deliver major advances that could turn the Internet into a giant global biodiversity information system.
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                Author and article information

                Journal
                9918383983606676
                51562
                Biodivers Inf Sci Stand
                Biodivers Inf Sci Stand
                Biodiversity Information Science and Standards
                2535-0897
                17 April 2022
                2021
                23 September 2021
                22 April 2022
                : 5
                : e75587
                Affiliations
                []Arizona State University, Tempe, United States of America
                Author notes

                Presenting author

                Beckett Sterner

                Corresponding author: Beckett Sterner ( bsterne1@ 123456asu.edu )
                Article
                NIHMS1797651
                10.3897/biss.5.75587
                9028594
                35462676
                28fdbcae-0c6b-435f-808e-ec57d07da134

                This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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                fair principles,open data,taxonomic intelligence
                fair principles, open data, taxonomic intelligence

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