TY - JOUR
T1 - Repositories for Taxonomic Data
T2 - Where We Are and What is Missing
AU - Miralles, Aurélien
AU - Bruy, Teddy
AU - Wolcott, Katherine
AU - Scherz, Mark D.
AU - Begerow, Dominik
AU - Beszteri, Bank
AU - Bonkowski, Michael
AU - Felden, Janine
AU - Gemeinholzer, Birgit
AU - Glaw, Frank
AU - Glöckner, Frank Oliver
AU - Hawlitschek, Oliver
AU - Kostadinov, Ivaylo
AU - Nattkemper, Tim W.
AU - Printzen, Christian
AU - Renz, Jasmin
AU - Rybalka, Nataliya
AU - Stadler, Marc
AU - Weibulat, Tanja
AU - Wilke, Thomas
AU - Renner, Susanne S.
AU - Vences, Miguel
N1 - Funding Information:
This work was supported by the Deutsche Forschungsgemeinschaft (DFG, grant number DFG RE 603/29-1) and benefited from the sharing of expertise within the DFG priority program SPP 1991 Taxon-Omics. We are grateful to William N. Eschmeyer, Jon D. Fong, Ronald Fricke, Darrel R. Frost, Rafa?l Govaerts, Vincent Robert, Peter Uetz, and Richard van der Laan for useful advice and data on rates of species discovery and naming. We thank Christy Hipsley and one anonymous reviewer for constructive feedback on our manuscript. We also thank Steve A. Marshall, Neal Evenhuis, and S?bastien Soubzmaigne for allowing the use of original photographs.
Publisher Copyright:
© The authors 2020. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
PY - 2020
Y1 - 2020
N2 - Natural history collections are leading successful large-scale projects of specimen digitization (images, metadata, DNA barcodes), thereby transforming taxonomy into a big data science. Yet, little effort has been directed towards safeguarding and subsequently mobilizing the considerable amount of original data generated during the process of naming 15,000-20,000 species every year. From the perspective of alpha-taxonomists, we provide a review of the properties and diversity of taxonomic data, assess their volume and use, and establish criteria for optimizing data repositories. We surveyed 4113 alpha-taxonomic studies in representative journals for 2002, 2010, and 2018, and found an increasing yet comparatively limited use of molecular data in species diagnosis and description. In 2018, of the 2661 papers published in specialized taxonomic journals, molecular data were widely used in mycology (94%), regularly in vertebrates (53%), but rarely in botany (15%) and entomology (10%). Images play an important role in taxonomic research on all taxa, with photographs used in >80% and drawings in 58% of the surveyed papers. The use of omics (high-throughput) approaches or 3D documentation is still rare. Improved archiving strategies for metabarcoding consensus reads, genome and transcriptome assemblies, and chemical and metabolomic data could help to mobilize the wealth of high-throughput data for alpha-taxonomy. Because long-term-ideally perpetual-data storage is of particular importance for taxonomy, energy footprint reduction via less storage-demanding formats is a priority if their information content suffices for the purpose of taxonomic studies. Whereas taxonomic assignments are quasifacts for most biological disciplines, they remain hypotheses pertaining to evolutionary relatedness of individuals for alpha-taxonomy. For this reason, an improved reuse of taxonomic data, including machine-learning-based species identification and delimitation pipelines, requires a cyberspecimen approach-linking data via unique specimen identifiers, and thereby making them findable, accessible, interoperable, and reusable for taxonomic research. This poses both qualitative challenges to adapt the existing infrastructure of data centers to a specimen-centered concept and quantitative challenges to host and connect an estimated ≤2 million images produced per year by alpha-taxonomic studies, plus many millions of images from digitization campaigns. Of the 30,000-40,000 taxonomists globally, many are thought to be nonprofessionals, and capturing the data for online storage and reuse therefore requires low-complexity submission workflows and cost-free repository use. Expert taxonomists are the main stakeholders able to identify and formalize the needs of the discipline; their expertise is needed to implement the envisioned virtual collections of cyberspecimens.
AB - Natural history collections are leading successful large-scale projects of specimen digitization (images, metadata, DNA barcodes), thereby transforming taxonomy into a big data science. Yet, little effort has been directed towards safeguarding and subsequently mobilizing the considerable amount of original data generated during the process of naming 15,000-20,000 species every year. From the perspective of alpha-taxonomists, we provide a review of the properties and diversity of taxonomic data, assess their volume and use, and establish criteria for optimizing data repositories. We surveyed 4113 alpha-taxonomic studies in representative journals for 2002, 2010, and 2018, and found an increasing yet comparatively limited use of molecular data in species diagnosis and description. In 2018, of the 2661 papers published in specialized taxonomic journals, molecular data were widely used in mycology (94%), regularly in vertebrates (53%), but rarely in botany (15%) and entomology (10%). Images play an important role in taxonomic research on all taxa, with photographs used in >80% and drawings in 58% of the surveyed papers. The use of omics (high-throughput) approaches or 3D documentation is still rare. Improved archiving strategies for metabarcoding consensus reads, genome and transcriptome assemblies, and chemical and metabolomic data could help to mobilize the wealth of high-throughput data for alpha-taxonomy. Because long-term-ideally perpetual-data storage is of particular importance for taxonomy, energy footprint reduction via less storage-demanding formats is a priority if their information content suffices for the purpose of taxonomic studies. Whereas taxonomic assignments are quasifacts for most biological disciplines, they remain hypotheses pertaining to evolutionary relatedness of individuals for alpha-taxonomy. For this reason, an improved reuse of taxonomic data, including machine-learning-based species identification and delimitation pipelines, requires a cyberspecimen approach-linking data via unique specimen identifiers, and thereby making them findable, accessible, interoperable, and reusable for taxonomic research. This poses both qualitative challenges to adapt the existing infrastructure of data centers to a specimen-centered concept and quantitative challenges to host and connect an estimated ≤2 million images produced per year by alpha-taxonomic studies, plus many millions of images from digitization campaigns. Of the 30,000-40,000 taxonomists globally, many are thought to be nonprofessionals, and capturing the data for online storage and reuse therefore requires low-complexity submission workflows and cost-free repository use. Expert taxonomists are the main stakeholders able to identify and formalize the needs of the discipline; their expertise is needed to implement the envisioned virtual collections of cyberspecimens.
KW - Big data
KW - Cyberspecimen
KW - New Species
KW - Omics
KW - Repositories
KW - Specimen Identifier
KW - Taxonomic Data
KW - Taxonomy
U2 - 10.1093/sysbio/syaa026
DO - 10.1093/sysbio/syaa026
M3 - Review
C2 - 32298457
AN - SCOPUS:85088162011
VL - 69
SP - 1231
EP - 1253
JO - Systematic Biology
JF - Systematic Biology
SN - 1063-5157
IS - 6
ER -