linked data

BiCIKL Project supports article collection in Biodiversity Data Journal about use of linked data

Welcomed are taxonomic and other biodiversity-related research articles, which demonstrate the advantages and novel approaches in accessing and (re-)using linked biodiversity data

The EU-funded project BiCIKL (Biodiversity Community Integrated Knowledge Library) will support free of charge publications* submitted to the dedicated topical collection: “Linking FAIR biodiversity data through publications: The BiCIKL approach” in the Biodiversity Data Journal, demonstrating advanced publishing methods of linked biodiversity data, so that they can be easily harvested, distributed and re-used to generate new knowledge.

BiCIKL is dedicated to building a new community of key research infrastructures, researchers and citizen scientists by using linked FAIR biodiversity data at all stages of the research lifecycle, from specimens through sequencing, imaging, identification of taxa, etc. to final publication in novel, re-usable, human-readable and machine-interpretable scholarly articles.

Achieving a culture change in how biodiversity data are being identified, linked, integrated and re-used is the mission of the BiCIKL consortium. By doing so, BiCIKL is to help increase the transparency, trustworthiness and efficiency of the entire research ecosystem.

The new article collection welcomes taxonomic and other biodiversity-related research articles, data papers, software descriptions, and methodological/theoretical papers. These should demonstrate the advantages and novel approaches in accessing and (re-)using linked biodiversity data.

To be eligible for the collection, a manuscript must comply with at least two of the conditions listed below. In the submission form, the author needs to specify the condition(s) applicable to the manuscript. The author should provide the explanation in a cover letter, using the Notes to the editor field.

All submissions must abide by the community-agreed standards for terms, ontologies and vocabularies used in biodiversity informatics.

The data used in the articles must comply with the Data Quality Checklist and Fair Data Checklist available in the Authors’ instructions of the journal.

Conditions for publication in the article collection:

The authors are expected to use explicit Globally Unique Persistent and Resolvable Identifiers (GUPRI) or other persistent identifiers (PIDs), where such are available, for the different types of data they use and/or cite in the manuscripts (specimens IDs, sequence accession numbers, taxon name and taxon treatment IDs, image IDs, etc.)

Manuscripts must use data linked between at least three of the BiCIKL’s partnering research infrastructures (e.g. GBIF, BOLD, DiSSCo, Zenodo, INDSC (ENA), PlutoF, OpenBiodiv, TreatmentBank, Biodiversity Literature Repository, Biodiversity Heritage Library, SiBILS, Catalogue of Life). Highly welcome are also submissions that include additional data from research infrastructures which are not part of BiCIKL (e.g. iDigBio, OBIS, Atlas of Living Australia, Encyclopedia of Life, GLOBI). In their cover letter, the author needs to specify which infrastructures are used and how the data are linked between these.

Global taxon reviews in the form of “cyber-catalogues” are welcome if they contain links of the key data elements (specimens, sequences, taxon treatments, images, literature references, etc.) to their respective records in external repositories. Taxon names in the text should not be hyperlinked. Instead, under each taxon name in the catalogue, the authors should add external links to, for example, Catalogue of Life, nomenclators (e.g. IPNI, MycoBank, Index Fungorum, ZooBank), taxon treatments in Plazi’s TreatmentBank or other relevant trusted resources.

Taxonomic papers (e.g. descriptions of new species or revisions) must contain persistent identifiers for the holotype, paratypes and at least most of the specimens used in the study.

Specimen records that are used for new taxon descriptions or taxonomic revisions and are associated with a particular Barcode Identification Number (BIN) or Species Hypothesis (SH) should be imported directly from BOLD or PlutoF, respectively, via the ARPHA Writing Tool data-import plugin.

More generally, individual specimen records used for various purposes in taxonomic descriptions and inventories should be imported directly into the manuscript from GBIF, iDigBio, or BOLD via the ARPHA Writing Tool data-import plugin.

In-text citations of taxon treatments from Plazi’s TreatmentBank are highly welcome in any taxonomic revision or catalogue. The in-text citations should be hyperlinked to the original treatment data at TreatmentBank.

Hyperlinking other terms of importance in the article text to their original external data sources or external vocabularies is encouraged.

Tables that list gene accession numbers, specimens and taxon names, should conform to the Biodiversity Data Journal’s linked data tables guidelines.

Theoretical or methodological papers on linking FAIR biodiversity data are eligible for the BiCIKL collection if they provide real examples and use cases.

Data papers or software descriptions are eligible if they use linked data from the BiCIKL’s partnering research infrastructures, or describe tools and services that facilitate access to and linking between FAIR biodiversity data.

Data papers describing omics data should use the omics data paper workflow (see also https://doi.org/10.1093/gigascience/giab034).

Articles that contain nanopublications created or added during the authoring process in Biodiversity Data Journal. A nanopublication is a scientifically meaningful assertion about anything that can be uniquely identified and attributed to its author and serve to communicate a single statement, for example biotic relationship between taxa, or habitat preference of a taxon. The in-built workflow ensures the linkage and its persistence, while the information is simultaneously human-readable and machine-interpretable.

Manuscripts that contain or describe any other novel idea or feature related to linked or semantically enhanced biodiversity data will be considered too.

We recommend authors to get acquainted with these two papers before they decide to submit a manuscript to the collection:

Strategies and guidelines for scholarly publishing of biodiversity data

ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform.

Here are several examples of research questions that might be explored using semantically enriched and linked biodiversity data:

(1) How does linking taxon names or Operational Taxonomic Units (OTUs) to related external data (e.g. specimen records, sequences, distributions, ecological & bionomic traits, images) contribute to a better understanding of the functions and regional/local processes within faunas/floras/mycotas or biotic communities?

(2) How could the production and publication of taxon descriptions and inventories – including those based mostly on genomic and barcoding data – be streamlined?

(3) How could general conclusions, assertions and citations in biodiversity articles be expressed in formal, machine-actionable language, either to update prior work or express new facts (e.g. via nanopublications)?

(4) How could research data and narratives be re-used to support more extensive and data-rich studies?

(5) Are there other taxon- or topic-specific research questions that would benefit from richer, semantically enhanced FAIR biodiversity data?

All manuscripts submitted to the Biodiversity Data Journal have their data audited by data scientists prior to the peer review stage.

Once published, specimen records data are being exported in Darwin Core Archive to GBIF.

The data and taxon treatments are also exported to several additional data aggregators, such as TreatmentBank, the Biodiversity Literature Repository, and SiBILS amongst others. The full-text articles are also converted to Linked Open Data indexed in the OpenBiodiv Knowledge Graph.

All articles will need to acknowledge the BiCIKL project, Grant No 101007492 in the Acknowledgements section.

* The publication fee (APC) is waived for standard-sized manuscripts (up to 40,000 characters, including spaces) normally charged by BDJ at € 650. Authors of larger manuscripts will need to cover the surplus charge (€10 for each 1,000 characters above 40,000). See more about the APC policy at Biodiversity Data Journal, or contact the journal editorial team at: bdj@pensoft.net.

Follow the BiCIKL Project on Twitter and Facebook. Join the conservation on via #BiCIKL_H2020.

You can also follow Biodiversity Data Journal on Twitter and Facebook.

Pensoft – GloBI workflow for FAIR data exchange and indexing of biotic interactions locked within scholarly articles

Pensoft – GloBI workflow for FAIR data exchange and indexing of biotic interactions locked within scholarly articles.

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.

Together with the rest of the article narrative, Pensoft publishes all tables in the semi-structured eXtensible Markup Language (XML) format. Tables are semantically enhanced with annotated taxonomic names, coordinates, localities and other fields from the Darwin Core Standard.

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.

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.

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.

Novel research on African bats pilots new ways in sharing and linking published data

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.

###

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).

###

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