Supported by the EU-funded Biodiversity Community Integrated Knowledge Library (BiCIKL) project, the collection at Biodiversity Data Journal will provide APC waivers for up to 100 publications
The BiCIKL project is dedicated to building new communities of key research infrastructures, researchers, citizen scientists and other stakeholders by using linked and FAIR biodiversity data at all stages of the research lifecycle, from specimens through sequencing and identification of taxa, to final publication in advanced, human- and machine-readable, reusable scholarly articles.
Supported by BiCIKL, the upcoming collection at BDJ will provide an exciting opportunity for biodiversity researchers to enjoy free and technologically advanced publication for up to 100 scholarly articles.
The collection will welcome research articles, data papers, software descriptions, and methodological/theoretical papers that demonstrate the advantages and novel approaches in accessing and (re-)using linked biodiversity data.
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The journal is still looking for guest editors to join the core team. If you are interested, please let us know at bdj@pensoft.net.
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In this collection, the authors will need to ensure that their narratives comply with the community-agreed standards for terms, ontologies and vocabularies. Additionally, they will be required to use explicit persistent identifiers, where such are available.
Here are several examples of research questions concerning semantically enriched biodiversity data:
How linking taxa or OTUs to external data in my study will contribute to a better understanding of the functions and regional/local processes within faunas/floras/mycotas or biotic communities?
How mine and other researchers’ data and narratives (e.g. specimen records, sequences, traits, biotic interactions etc.) can be re-used to support more extensive and data-rich studies?
How to streamline taxon descriptions and inventories, including such based on genomic and barcoding data?
How general conclusions, assertions and citations in my article can be expressed in a formal, machine-actionable language?
Other taxon- or topic-specific research questions that would benefit from richer, semantically enhanced FAIR data.
Conditions for publication and types of articles:
Manuscripts must use data from at least two of the BiCIKL’s partnering research infrastructures. Highly welcome are also submissions that include data from research infrastructures that are not part of BiCIKL.
Taxonomic papers (e.g. descriptions of new species) must contain persistent identifiers for the holotype, paratypes and the majority of the specimens used in the study.
New species descriptions using data associated with a particular Barcode Identification Number (BIN) imported directly from BOLD via the ARPHA Writing Tool are encouraged.
Individual specimen records imported directly from BOLD, GBIF or iDigBio into the manuscript are strongly encouraged.
Hyperlinked in-text citations of taxon treatments from Plazi’s TreatmentBank are highly welcome.
Other terms of value hyperlinked to external resources are encouraged.
Tables that list gene accession numbers, specimens and taxon names, should conform to the Biodiversity Data Journal’s guidelines.
Theoretical or methodological papers on linking of FAIR biodiversity data are eligible for the BiCIKL collection if they provide examples and use cases.
Data papers or software descriptions are eligible if they use data from the BiCIKL’s partnering research infrastructures, or describe tools and services that facilitate access to and linking between FAIR biodiversity data.
You can find full information about the eligibility criteria in the Open Call published on the BiCIKL’s website, or can contact us at bdj@pensloft.net.
by Mariya Dimitrova, Jorrit Poelen, Georgi Zhelezov, Teodor Georgiev, Lyubomir Penev
Fig. 1. Pensoft-GloBI workflow for indexing biotic interactions from scholarly literature
Tables published in scholarly literature are a rich source of primary biodiversity data. They are often used for communicating species occurrence data, morphological characteristics of specimens, links of species or specimens to particular genes, ecology data and biotic interactions between species, etc. Tables provide a structured format for sharing numerous facts about biodiversity in a concise and clear way.
Inspired by the potential use of semantically-enhanced tables for text and data mining, Pensoft and Global Biotic Interactions (GloBI) developed a workflow for extracting and indexing biotic interactions from tables published in scholarly literature. GloBI is an open infrastructure enabling the discovery and sharing of species interaction data. GloBI ingests and accumulates individual datasets containing biotic interactions and standardises them by mapping them to community-accepted ontologies, vocabularies and taxonomies. Data integrated by GloBI is accessible through an application programming interface (API) and as archives in different formats (e.g. n-quads). GloBI has indexed millions of species interactions from hundreds of existing datasets spanning over a hundred thousand taxa.
The workflow
First, all tables extracted from Pensoft publications and stored in the OpenBiodiv triple store were automatically retrieved (Step 1 in Fig. 1). There were 6993 tables from 21 different journals. To identify only the tables containing biotic interactions, we used an ontology annotator, currently developed by Pensoft using terms from the OBO Relation Ontology (RO). The Pensoft Annotator analyses free text and finds words and phrases matching ontology term labels.
We used the RO to create a custom ontology, or list of terms, describing different biotic interactions (e.g. ‘host of’, ‘parasite of’, ‘pollinates’) (Step 2 in Fig. 1).. We used all subproperties of the RO term labeled ‘biotically interacts with’ and expanded the list of terms with additional word spellings and variations (e.g. ‘hostof’, ‘host’) which were added to the custom ontology as synonyms of already existing terms using the property oboInOwl:hasExactSynonym.
This custom ontology was used to perform annotation of all tables via the Pensoft Annotator (Step 3 in Fig. 1). Tables were split into rows and columns and accompanying table metadata (captions). Each of these elements was then processed through the Pensoft Annotator and if a match from the custom ontology was found, the resulting annotation was written to a MongoDB database, together with the article metadata. The original table in XML format, containing marked-up taxa, was also stored in the records.
Thus, we detected 233 tables which contain biotic interactions, constituting about 3.4% of all examined tables. The scripts used for parsing the tables and annotating them, together with the custom ontology, are open source and available on GitHub. The database records were exported as json to a GitHub repository, from where they could be accessed by GloBI.
GloBI processed the tables further, involving the generation of a table citation from the article metadata and the extraction of interactions between species from the table rows (Step 4 in Fig. 1). Table citations were generated by querying the OpenBiodiv database with the DOI of the article containing each table to obtain the author list, article title, journal name and publication year. The extraction of table contents was not a straightforward process because tables do not follow a single schema and can contain both merged rows and columns (signified using the ‘rowspan’ and ‘colspan’ attributes in the XML). GloBI were able to index such tables by duplicating rows and columns where needed to be able to extract the biotic interactions within them. Taxonomic name markup allowed GloBI to identify the taxonomic names of species participating in the interactions. However, the underlying interaction could not be established for each table without introducing false positives due to the complicated table structures which do not specify the directionality of the interaction. Hence, for now, interactions are only of the type ‘biotically interacts with’ (Fig. 2) because it is a bi-directional one (e.g. ‘Species A interacts with Species B’ is equivalent to ‘Species B interacts with Species A’).
Fig. 2. Example of a biotic interaction indexed by GloBI.
Examples of species interactions provided by OpenBiodiv and indexed by GloBI are available on GloBI’s website.
In the future we plan to expand the capacity of the workflow to recognise interaction types in more detail. This could be implemented by applying part of speech tagging to establish the subject and object of an interaction.
In addition to being accessible via an API and as archives, biotic interactions indexed by GloBI are available as Linked Open Data and can be accessed via a SPARQL endpoint. Hence, we plan on creating a user-friendly service for federated querying of GloBI and OpenBiodiv biodiversity data.
This collaborative project is an example of the benefits of open and FAIR data, enabling the enhancement of biodiversity data through the integration between Pensoft and GloBI. Transformation of knowledge contained in existing scholarly works into giant, searchable knowledge graphs increases the visibility and attributed re-use of scientific publications.
Tables published in scholarly literature are a rich source of primary biodiversity data. They are often used for communicating species occurrence data, morphological characteristics of specimens, links of species or specimens to particular genes, ecology data and biotic interactions between species etc. Tables provide a structured format for sharing numerous facts about biodiversity in a concise and clear way.
Inspired by the potential use of semantically-enhanced tables for text and data mining, Pensoft and Global Biotic Interactions (GloBI) developed a workflow for extracting and indexing biotic interactions from tables published in scholarly literature. GloBI is an open infrastructure enabling the discovery and sharing of species interaction data. GloBI ingests and accumulates individual datasets containing biotic interactions and standardises them by mapping them to community-accepted ontologies, vocabularies and taxonomies. Data integrated by GloBI is accessible through an application programming interface (API) and as archives in different formats (e.g. n-quads). GloBI has indexed millions of species interactions from hundreds of existing datasets spanning over a hundred thousand taxa.
The workflow
First, all tables extracted from Pensoft publications and stored in the OpenBiodiv triple store were automatically retrieved (Step 1 in Fig. 1). There were 6,993 tables from 21 different journals. To identify only the tables containing biotic interactions, we used an ontology annotator, currently developed by Pensoft using terms from the OBO Relation Ontology (RO). The Pensoft Annotator analyses free text and finds words and phrases matching ontology term labels.
We used the RO to create a custom ontology, or list of terms, describing different biotic interactions (e.g. ‘host of’, ‘parasite of’, ‘pollinates’) (Step 1 in Fig. 1). We used all subproperties of the RO term labeled ‘biotically interacts with’ and expanded the list of terms with additional word spellings and variations (e.g. ‘hostof’, ‘host’) which were added to the custom ontology as synonyms of already existing terms using the property oboInOwl:hasExactSynonym.
This custom ontology was used to perform annotation of all tables via the Pensoft Annotator (Step 3 in Fig. 1). Tables were split into rows and columns and accompanying table metadata (captions). Each of these elements was then processed through the Pensoft Annotator and if a match from the custom ontology was found, the resulting annotation was written to a MongoDB database, together with the article metadata. The original table in XML format, containing marked-up taxa, was also stored in the records.
Thus, we detected 233 tables which contain biotic interactions, constituting about 3.4% of all examined tables. The scripts used for parsing the tables and annotating them, together with the custom ontology, are open source and available on GitHub. The database records were exported as JSON to a GitHub repository, from where they could be accessed by GloBI.
GloBI processed the tables further, involving the generation of a table citation from the article metadata and the extraction of interactions between species from the table rows (Step 4 in Fig. 1). Table citations were generated by querying the OpenBiodiv database with the DOI of the article containing each table to obtain the author list, article title, journal name and publication year. The extraction of table contents was not a straightforward process because tables do not follow a single schema and can contain both merged rows and columns (signified using the ‘rowspan’ and ‘colspan’ attributes in the XML). GloBI were able to index such tables by duplicating rows and columns where needed to be able to extract the biotic interactions within them. Taxonomic name markup allowed GloBI to identify the taxonomic names of species participating in the interactions. However, the underlying interaction could not be established for each table without introducing false positives due to the complicated table structures which do not specify the directionality of the interaction. Hence, for now, interactions are only of the type ‘biotically interacts with’ because it is a bi-directional one (e.g. ‘Species A interacts with Species B’ is equivalent to ‘Species B interacts with Species A’).
In the future, we plan to expand the capacity of the workflow to recognise interaction types in more detail. This could be implemented by applying part of speech tagging to establish the subject and object of an interaction.
In addition to being accessible via an API and as archives, biotic interactions indexed by GloBI are available as Linked Open Data and can be accessed via a SPARQL endpoint. Hence, we plan on creating a user-friendly service for federated querying of GloBI and OpenBiodiv biodiversity data.
This collaborative project is an example of the benefits of open and FAIR data, enabling the enhancement of biodiversity data through the integration between Pensoft and GloBI. Transformation of knowledge contained in existing scholarly works into giant, searchable knowledge graphs increases the visibility and attributed re-use of scientific publications.
References
Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.
Additional Information
The work has been partially supported by the International Training Network (ITN) IGNITE funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 764840.
A colony of what is apparently a new species of the genus Hipposideros found in an abandoned gold mine in Western Kenya Photo by B. D. Patterson / Field Museum
Newly published findings about the phylogenetics and systematics of some previously known, but also other yet to be identified species of Old World Leaf-nosed bats, provide the first contribution to a recently launched collection of research articles, whose task is to help scientists from across disciplines to better understand potential hosts and vectors of zoonotic diseases, such as the Coronavirus. Bats and pangolins are among the animals already identified to be particularly potent vehicles of life-threatening viruses, including the infamous SARS-CoV-2.
The article, publicly available in the peer-reviewed scholarly journal ZooKeys, also pilots a new generation of Linked Open Data (LOD) publishing practices, invented and implemented to facilitate ongoing scientific collaborations in times of urgency like those we experience today with the COVID-19 pandemic currently ravaging across over 230 countries around the globe.
In their study, an international team of scientists, led by Dr Bruce Patterson, Field Museum‘s MacArthur curator of mammals, point to the existence of numerous, yet to be described species of leaf-nosed bats inhabiting the biodiversity hotspots of East Africa and Southeast Asia. In order to expedite future discoveries about the identity, biology and ecology of those bats, they provide key insights into the genetics and relations within their higher groupings, as well as further information about their geographic distribution.
“Leaf-nosed bats carry coronaviruses–not the strain that’s affecting humans right now, but this is certainly not the last time a virus will be transmitted from a wild mammal to humans. If we have better knowledge of what these bats are, we’ll be better prepared if that happens,”
says Dr Terrence Demos, a post-doctoral researcher in Patterson’s lab and a principal author of the paper.
One of the possibly three new to science bat species, previously referred to as Hipposideros caffer or Sundevall’s leaf-nosed bat Photo by B. D. Patterson / Field Museum
“With COVID-19, we have a virus that’s running amok in the human population. It originated in a horseshoe bat in China. There are 25 or 30 species of horseshoe bats in China, and no one can determine which one was involved. We owe it to ourselves to learn more about them and their relatives,”
comments Patterson.
In order to ensure that scientists from across disciplines, including biologists, but also virologists and epidemiologists, in addition to health and policy officials and decision-makers have the scientific data and evidence at hand, Patterson and his team supplemented their research publication with a particularly valuable appendix table. There, in a conveniently organized table format, everyone can access fundamental raw genetic data about each studied specimen, as well as its precise identification, origin and the natural history collection it is preserved. However, what makes those data particularly useful for researchers looking to make ground-breaking and potentially life-saving discoveries is that all that information is linked to other types of data stored at various databases and repositories contributed by scientists from anywhere in the world.
Furthermore, in this case, those linked and publicly available data or Linked Open Data (LOD) are published in specific code languages, so that they are “understandable” for computers. Thus, when a researcher seeks to access data associated with a particular specimen he/she finds in the table, he/she can immediately access additional data stored at external data repositories by means of a single algorithm. Alternatively, another researcher might want to retrieve all pathogens extracted from tissues from specimens of a specific animal species or from particular populations inhabiting a certain geographical range and so on.
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The data publication and dissemination approach piloted in this new study was elaborated by the science publisher and technology provider Pensoft and the digitisation company Plazi for the purposes of a special collection of research papers reporting on novel findings concerning the biology of bats and pangolins in the scholarly journal ZooKeys. By targeting the two most likely ‘culprits’ at the roots of the Coronavirus outbreak in 2020: bats and pangolins, the article collection aligns with the agenda of the COVID-19 Joint Task Force, a recent call for contributions made by the Consortium of European Taxonomic Facilities (CETAF), the Distributed System for Scientific Collections (DiSSCo) and the Integrated Digitized Biocollections (iDigBio).
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Original source:
Patterson BD, Webala PW, Lavery TH, Agwanda BR, Goodman SM, Kerbis Peterhans JC, Demos TC (2020) Evolutionary relationships and population genetics of the Afrotropical leaf-nosed bats (Chiroptera, Hipposideridae). ZooKeys 929: 117-161. https://doi.org/10.3897/zookeys.929.50240
