All processes fit into a broad S-shaped envelope extending from the briefest to the most enduring biological events. For the first time, we have the first simple model that depicts the scope and scale of biology.
Arctic tern by Mark Stock, Schleswig-Holstein Wadden Sea National Park. License: CC BY-SA.
As biology is progressing into a digital age, it is creating new opportunities for discovery.
Increasingly, information from investigations into aspects of biology from ecology to molecular biology is available in a digital form. Older ‘legacy’ information is being digitized. Together, the digital information is accumulated in databases from which it can be harvested and examined with an increasing array of algorithmic and visualization tools.
From this trend has emerged a vision that, one day, we should be able to analyze any and all aspects of biology in this digital world.
However, before this can happen, there will need to be an infrastructure that gathers information from ALL sources, reshapes it as standardized data using universal metadata and ontologies, and made freely available for analysis.
That information also must make its way to trustworthy repositories to guarantee the permanent access to the data in a polished and fully suited for re-use state.
The first layer in the infrastructure is the one that gathers all old and new information, whether it be about the migrations of ocean mammals, the sequence of bases in ribosomal RNA, or the known locations of particular species of ciliated protozoa.
How many of these subdomains will be there?
To answer this, we need to have a sense of the scope and scale of biology.
With the Nature’s Envelope we have, for the first time, a simple model that depicts the scope and scale of biology. Presented as a rhetorical device by its author Dr David J. Patterson (University of Sydney, Australia), the Nature’s Envelope is described in a Forum Paper, published in the open-science journal Research Ideas and Outcomes(RIO).
This is achieved by compiling information about the processes conducted by all living organisms. The processes occur at all levels of organization, from sub-molecular transactions, such as those that underpin nervous impulses, to those within and among plants, animals, fungi, protists and prokaryotes. Further, they are also the actions and reactions of individuals and communities; but also the sum of the interactions that make up an ecosystem; and finally, the consequences of the biosphere as a whole system.
Nature’s Envelope, in green, includes all processes carried out by, involving, or the result of the activities of any and all organisms. The axes depict the duration of events and the sizes of participants using a log10 scale. Image by David J. Patterson. License: CC BY.
In the Nature’s Envelope, information on sizes of participants and durations of processes from all levels of organization are plotted on a grid. The grid uses a logarithmic (base 10) scale, which has about 21 orders of magnitude of size and 35 orders of magnitude of time. Information on processes ranging from the subatomic, through molecular, cellular, tissue, organismic, species, communities to ecosystems is assigned to the appropriate decadal blocks.
Examples include movements from the stepping motion of molecules like kinesin that move forward 8 nanometres in about 10 milliseconds; or the migrations of Arctic terns which follow routes of 30,000 km or more from Europe to Antarctica over 3 to 4 months.
The extremes of life processes are determined by the smallest and largest entities to participate, and the briefest and most enduring processes.
The briefest event to be included is the transfer of energy from a photon to a photosynthetic pigment as the photon passes through a chlorophyll molecule several nanometres in width at a speed of 300,000 km per second. That transaction is conducted in about 10-17 seconds. As it involves the smallest subatomic particles, it defines the lower left corner of the grid.
The most enduring is the process of evolution that has been progressing for almost 4 billion years. The influence of the latter has created the biosphere (the largest living object) and affects the gas content of the atmosphere. This process established the upper right extreme of the grid.
All biological processes fit into a broad S-shaped envelope that includes about half of the decadal blocks in the grid. The envelope drawn round the initial examples is Nature’s Envelope.
“Nature’s envelope will be a useful addition to many discussions, whether they deal with the infrastructure that will manage the digital age of biology, or provide the context for education on the diversity and range of processes that living systems engage in.
The version of Nature’s Envelopepublished in the RIO journal is seen as a first version, to be refined and enhanced through community participation,”
comments Patterson.
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Original source:
Patterson DJ (2022) The scope and scale of the life sciences (‘Nature’s envelope’). Research Ideas and Outcomes 8: e96132. https://doi.org/10.3897/rio.8.e96132
Key figures from Naturalis Biodiversity Center, Plazi and Pensoft were amongst the first to sign the Declaration at the closing session of the First International Conference on FAIR Digital Objects (FDO2022)
Several of the BiCIKL partners signed the Leiden Declaration on FAIR Digital Objects, thereby committing to “a new environment that works as a truly meaningful data space,” as framed by the organisers of the conference, whose first instalment turned out to be the perfect occasion for the formal publication of the pact.
The conference brought together key international technical, scientific, industry and science-policy stakeholders with the aim to boost the development and implementation of FAIR Digital Objects (FDOs) worldwide. It was organised by the FDO Forum, an initiative supported by major global initiatives and by a variety of regional and national initiatives with the shared goal to achieve a better coherence amongst the increasing number of initiatives working on FDO-based designs and implementations.
By joining the Declaration’s signees, the BiCIKL partners formally committed to:
Support the FAIR guiding principles to be applied (ultimately) to each digital object in a web of FAIR data and services;
Support open standards and protocols;
Support data and services to be as open as possible, and only as restricted as necessary;
Support distributed solutions where useful to achieve robustness and scalability, but recognise the need for centralised approaches where necessary;
Support the restriction of standards and protocols to the absolute minimum;
Support freedom to operate wherever possible;
Help to avoid monopolies and provider lock-in wherever possible.
During the event, Plazi and Pensoft held a presentation demonstrating how their Biodiversity Literature Repository turns taxonomic treatments ‘locked’ in legacy scientific literature into FAIR Digital Objects. As a result of the collaboration between Plazi and Pensoft – a partnership long-preceding their involvement in BiCIKL – this workflow has also been adapted to modern-day publishing, in order to FAIRify data as soon as it is published.
A slide from the Plazi presentation at the FDO2022, Leiden, the Netherlands. Credit: Plazi.
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Ahead of FDO2022, all submitted conference abstracts – including the one associated with Plazi’s presentation – were made publicly available in a collection of their own in Pensoft’s open-science journal Research Ideas and Outcomes (RIO). Thus, not only did the organisers make the conference outputs available to the participants early on, so that they can familiarise themselves with the upcoming talks and topics in advance, but they also ensure that the contributions are permanently preserved and FAIR in their own turn.
The conference collection, guest edited by Tina Loo (Naturalis Biodiversity Center), contains a total of 51 conference abstracts, where each is published in HTML, XML and PDF formats, and assigned with its own persistent identifier (DOI) just like the collection in its entirety (10.3897/rio.coll.190).
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Read more about the declaration and sign it yourself from this link. You can also follow the FDO Forum on Twitter (@FAIRDOForum).
The FAIR Data Place – the key and final product of the partnership – is meant to provide scientists with all types of biodiversity data “at their fingertips”
The Horizon 2020 – funded project BiCIKL has reached its halfway stage and the partners gathered in Plovdiv (Bulgaria) from the 22nd to the 25th of October for the Second General Assembly, organised by Pensoft.
The BiCIKL project will launch a new European community of key research infrastructures, researchers, citizen scientists and other stakeholders in the biodiversity and life sciences based on open science practices through access to data, tools and services.
BiCIKL’s goal is to create a centralised place to connect all key biodiversity data by interlinking 15 research infrastructures and their databases. The 3-year European Commission-supported initiative kicked off in 2021 and involves 14 key natural history institutions from 10 European countries.
BiCIKL is keeping pace as expected with 16 out of the 48 final deliverables already submitted, another 9 currently in progress/under review and due in a few days. Meanwhile, 21 out of the 48 milestones have been successfully achieved.
Prof. Lyubomir Penev (BiCIKL’s project coordinator Prof. Lyubomir Penev and CEO and founder of Pensoft) opens the 2nd General Assembly of BiCIKL in Plovdiv, Bulgaria.
The hybrid format of the meeting enabled a wider range of participants, which resulted in robust discussions on the next steps of the project, such as the implementation of additional technical features of the FAIR Data Place (FAIR being an abbreviation for Findable, Accessible, Interoperable and Reusable).
This FAIR Data Place online platform – the key and final product of the partnership and the BiCIKL initiative – is meant to provide scientists with all types of biodiversity data “at their fingertips”.
This data includes biodiversity information, such as detailed images, DNA, physiology and past studies concerning a specific species and its ‘relatives’, to name a few. Currently, the issue is that all those types of biodiversity data have so far been scattered across various databases, which in turn have been missing meaningful and efficient interconnectedness.
Additionally, the FAIR Data Place, developed within the BiCIKL project, is to give researchers access to plenty of training modules to guide them through the different services.
Halfway through the duration of BiCIKL, the project is at a turning point, where crucial discussions between the partners are playing a central role in the refinement of the FAIR Data Place design. Most importantly, they are tasked with ensuring that their technologies work efficiently with each other, in order to seamlessly exchange, update and share the biodiversity data every one of them is collecting and taking care of.
By Year 3 of the BiCIKL project, the partners agree, when those infrastructures and databases become efficiently interconnected to each other, scientists studying the Earth’s biodiversity across the world will be in a much better position to build on existing research and improve the way and the pace at which nature is being explored and understood. At the end of the day, knowledge is the stepping stone for the preservation of biodiversity and humankind itself.
The Biodiversity Knowledge Hub is no longer existing on paper only! We are opening the 2nd day of the #BiCIKL_H2020 General Assembly looking at the prototype of the BKH created by the @LifeWatchERIC team. It's so exciting to see projects taking shape! 🤩 #Plovdivpic.twitter.com/RidzwBpxZx
“Needless to say, it’s an honour and a pleasure to be the coordinator of such an amazing team spanning as many as 14 partnering natural history and biodiversity research institutions from across Europe, but also involving many global long-year collaborators and their infrastructures, such as Wikidata, GBIF, TDWG, Catalogue of Life to name a few,”
said BiCIKL’s project coordinator Prof. Lyubomir Penev, CEO and founder of Pensoft.
“I see our meeting in Plovdiv as a practical demonstration of our eagerness and commitment to tackle the long-standing and technically complex challenge of breaking down the silos in the biodiversity data domain. It is time to start building freeways between all biodiversity data, across (digital) space, time and data types. After the last three days that we spent together in inspirational and productive discussions, I am as confident as ever that we are close to providing scientists with much more straightforward routes to not only generate more biodiversity data, but also build on the already existing knowledge to form new hypotheses and information ready to use by decision- and policy-makers. One cannot stress enough how important the role of biodiversity data is in preserving life on Earth. These data are indeed the groundwork for all that we know about the natural world”
Prof. Lyubomir Penev added.
Christos Arvanitidis (CEO of LifeWatch ERIC) at the 2nd General Assembly of the BiCIKL project.
“The point is: do we want an integrated structure or do we prefer federated structures? What are the pros and cons of the two options? It’s essential to keep the community united and allied because we can’t afford any information loss and the stakeholders should feel at home with the Project and the Biodiversity Knowledge Hub.”
Joe Miller, Executive Secretary and Director at GBIF, commented:
“We are a brand new community, and we are in the middle of the growth process. We would like to already have answers, but it’s good to have this kind of robust discussion to build on a good basis. We must find the best solution to have linkages between infrastructures and be able to maintain them in the future because the Biodiversity Knowledge Hub is the location to gather the community around best practices, data and guidelines on how to use the BiCIKL services… In order to engage even more partners to fill the eventual gaps in our knowledge.”
Joana Pauperio (biodiversity curator at EMBL-EBI) atthe 2nd General Assembly of the BiCIKL project.
“BiCIKL is leading data infrastructure communities through some exciting and important developments”
said Dr Guy Cochrane, Team Leader for Data Coordination and Archiving and Head of the European Nucleotide Archive at EMBL’s European Bioinformatics Institute (EMBL-EBI).
“In an era of biodiversity change and loss, leveraging scientific data fully will allow the world to catalogue what we have now, to track and understand how things are changing and to build the tools that we will use to conserve or remediate. The challenge is that the data come from many streams – molecular biology, taxonomy, natural history collections, biodiversity observation – that need to be connected and intersected to allow scientists and others to ask real questions about the data. In its first year, BiCIKL has made some key advances to rise to this challenge,”
“As a partner, we, at the Biodiversity Information Standards – TDWG, are very enthusiastic that our standards are implemented in BiCIKL and serve to link biodiversity data. We know that joining forces and working together is crucial to building efficient infrastructures and sharing knowledge.”
The project will go on with the first Round Table of experts in December and the publications of the projects who participated in the Open Call and will be founded at the beginning of the next year.
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Learn more about BiCIKL on the project’s website at: bicikl-project.eu
Within Biodiversity Community Integrated Knowledge Library (BiCIKL), 14 key research and natural history institutions commit to link infrastructures and technologies to provide flawless access to biodiversity data.
In a recently started Horizon 2020-funded project, 14 European institutions from 10 countries, representing both the continent’s and global key players in biodiversity research and natural history, deploy and improve their own and partnering infrastructures to bridge gaps between each other’s biodiversity data types and classes. By linking their technologies, they are set to provide flawless access to data across all stages of the research cycle.
Three years in, BiCIKL (abbreviation for Biodiversity Community Integrated Knowledge Library) will have created the first-of-its-kind Biodiversity Knowledge Hub, where a researcher will be able to retrieve a full set of linked and open biodiversity data, thereby accessing the complete story behind an organism of interest: its name, genetics, occurrences, natural history, as well as authors and publications mentioning any of those.
Ultimately, the project’s products will solidify Open Science and FAIR (Findable, Accessible, Interoperable and Reusable) data practices by empowering and streamlining biodiversity research.
Together, the project partners will redesign the way biodiversity data is found, linked, integrated and re-used across the research cycle. By the end of the project, BiCIKL will provide the community with a more transparent, trustworthy and efficient highly automated research ecosystem, allowing for scientists to access, explore and put into further use a wide range of data with only a few clicks.
“In recent years, we’ve made huge progress on how biodiversity data is located, accessed, shared, extracted and preserved, thanks to a vast array of digital platforms, tools and projects looking after the different types of data, such as natural history specimens, species descriptions, images, occurrence records and genomics data, to name a few. However, we’re still missing an interconnected and user-friendly environment to pull all those pieces of knowledge together. Within BiCIKL, we all agree that it’s only after we puzzle out how to best bridge our existing infrastructures and the information they are continuously sourcing that future researchers will be able to realise their full potential,”
explains BiCIKL’s project coordinator Prof. Lyubomir Penev, CEO and founder of Pensoft, a scholarly publisher and technology provider company.
Continuously fed with data sourced by the partnering institutions and their infrastructures, BiCIKL’s key final output: the Biodiversity Knowledge Hub, is set to persist with time long after the project has concluded. On the contrary, by accelerating biodiversity research that builds on – rather than duplicates – existing knowledge, it will in fact be providing access to exponentially growing contextualised biodiversity data.
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Learn more about BiCIKL on the project’s website at: bicikl-project.eu
By Mariya Dimitrova, Georgi Zhelezov, Teodor Georgiev and Lyubomir Penev
The use of written language to record new knowledge is one of the advancements of civilisation that has helped us achieve progress. However, in the era of Big Data, the amount of published writing greatly exceeds the physical ability of humans to read and understand all written information.
More than ever, we need computers to help us process and manage written knowledge. Unlike humans, computers are “naturally fluent” in many languages, such as the formats of the Semantic Web. These standards were developed by the World Wide Web Consortium (W3C) to enable computers to understand data published on the Internet. As a result, computers can index web content and gather data and metadata about web resources.
To help manage knowledge in different domains, humans have started to develop ontologies: shared conceptualisations of real-world objects, phenomena and abstract concepts, expressed in machine-readable formats. Such ontologies can provide computers with the necessary basic knowledge, or axioms, to help them understand the definitions and relations between resources on the Web. Ontologies outline data concepts, each with its own unique identifier, definition and human-legible label.
Matching data to its underlying ontological model is called ontology population and involves data handling and parsing that gives it additional context and semantics (meaning). Over the past couple of years, Pensoft has been working on an ontology population tool, the Pensoft Annotator, which matches free text to ontological terms.
The Pensoft Annotator is a web application, which allows annotation of text input by the user, with any of the available ontologies. Currently, they are the Environment Ontology (ENVO) and the Relation Ontology (RO), but we plan to upload many more. The Annotator can be run with multiple ontologies, and will return a table of matched ontological term identifiers, their labels, as well as the ontology from which they originate (Fig. 1). The results can also be downloaded as a Tab-Separated Value (TSV) file and certain records can be removed from the table of results, if desired. In addition, the Pensoft Annotator allows to exclude certain words (“stopwords”) from the free text matching algorithm. There is a list of default stopwords, common for the English language, such as prepositions and pronouns, but anyone can add new stopwords.
Figure 1. Interface of the Pensoft Annotator application
In Figure 1, we have annotated a sentence with the Pensoft Annotator, which yields a single matched term, labeled ‘host of’, from the Relation Ontology (RO). The ontology term identifier is linked to a webpage in Ontobee, which points to additional metadata about the ontology term (Fig. 2).
Figure 2. Web page about ontology term
Such annotation requests can be run to perform text analyses for topic modelling to discover texts which contain host-pathogen interactions. Topic modelling is used to build algorithms for content recommendation (recommender systems) which can be implemented in online news platforms, streaming services, shopping websites and others.
At Pensoft, we use the Pensoft Annotator to enrich biodiversity publications with semantics. We are currently annotating taxonomic treatments with a custom-made ontology based on the Relation Ontology (RO) to discover treatments potentially describing species interactions. You can read more about using the Annotator to detect biotic interactions in this abstract.
The Pensoft Annotator can also be used programmatically through an API, allowing you to integrate the Annotator into your own script. For more information about using the Pensoft Annotator, please check out the documentation.
Proofreading the text of scientific papers isn’t hard, although it can be tedious. Are all the words spelled correctly? Is all the punctuation correct and in the right place? Is the writing clear and concise, with correct grammar? Are all the cited references listed in the References section, and vice-versa? Are the figure and table citations correct?
Proofreading of text is usually done first by the reviewers, and then finished by the editors and copy editors employed by scientific publishers. A similar kind of proofreading is also done with the small tables of data found in scientific papers, mainly by reviewers familiar with the management and analysis of the data concerned.
But what about proofreading the big volumes of data that are common in biodiversity informatics? Tables with tens or hundreds of thousands of rows and dozens of columns? Who does the proofreading?
Sadly, the answer is usually “No one”. Proofreading large amounts of data isn’t easy and requires special skills and digital tools. The people who compile biodiversity data often lack the skills, the software or the time to properly check what they’ve compiled.
The result is that a great deal of the data made available through biodiversity projects like GBIF is — to be charitable — “messy”. Biodiversity data often needs a lot of patient cleaning by end-users before it’s ready for analysis. To assist end-users, GBIF and other aggregators attach “flags” to each record in the database where an automated check has found a problem. These checks find the most obvious problems amongst the many possible data compilation errors. End-users often have much more work to do after the flags have been dealt with.
In 2017, Pensoft employed a data specialist to proofread the online datasets that are referenced in manuscripts submitted to Pensoft’s journals as data papers. The results of the data-checking are sent to the data paper’s authors, who then edit the datasets. This process has substantially improved many datasets (including those already made available through GBIF) and made them more suitable for digital re-use. At blog publication time, more than 200 datasets have been checked in this way.
Note that a Pensoft data audit does not check the accuracy of the data, for example, whether the authority for a species name is correct, or whether the latitude/longitude for a collecting locality agrees with the verbal description of that locality. For a more or less complete list of what does get checked, see the Data checklist at the bottom of this blog post. These checks are aimed at ensuring that datasets are correctly organised, consistently formatted and easy to move from one digital application to another. The next reader of a digital dataset is likely to be a computer program, not a human. It is essential that the data are structured and formatted, so that they are easily processed by that program and by other programs in the pipeline between the data compiler and the next human user of the data.
Pensoft’s data-checking workflow was previously offered only to authors of data paper manuscripts. It is now available to data compilers generally, with three levels of service:
Basic: the compiler gets a detailed report on what needs fixing
Standard: minor problems are fixed in the dataset and reported
Premium: all detected problems are fixed in collaboration with the data compiler and a report is provided
Because datasets vary so much in size and content, it is not possible to set a price in advance for basic, standard and premium data-checking. To get a quote for a dataset, send an email with a small sample of the data topublishing@pensoft.net.
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Data checklist
Minor problems:
dataset not UTF-8 encoded
blank or broken records
characters other than letters, numbers, punctuation and plain whitespace
more than one version (the simplest or most correct one) for each character
unnecessary whitespace
Windows carriage returns (retained if required)
encoding errors (e.g. “Dum?ril” instead of “Duméril”)
missing data with a variety of representations (blank, “-“, “NA”, “?” etc)
Major problems:
unintended shifts of data items between fields
incorrect or inconsistent formatting of data items (e.g. dates)
different representations of the same data item (pseudo-duplication)
for Darwin Core datasets, incorrect use of Darwin Core fields
data items that are invalid or inappropriate for a field
data items that should be split between fields
data items referring to unexplained entities (e.g. “habitat is type A”)
truncated data items
disagreements between fields within a record
missing, but expected, data items
incorrectly associated data items (e.g. two country codes for the same country)
duplicate records, or partial duplicate records where not needed
For details of the methods used, see the author’s online resources:
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.
Pensoft creates a specialised data paper article type for the omics community within Biodiversity Data Journal to reflect the specific nature of omics data. The scholarly publisher and technology provider established a manuscript template to help standardise the description of such datasets and their most important features.
By Mariya Dimitrova, Raïssa Meyer, Pier Luigi Buttigieg, Lyubomir Penev
Data papers are scientific papers which describe a dataset rather than present and discuss research results. The concept was introduced to the biodiversity community by Chavan and Penev in 2011 as the result of a joint project of GBIF and Pensoft.
Since then, Pensoft has implemented the data paper in several of its journals (Fig. 1). The recognition gained through data papers is an important incentive for researchers and data managers to author better quality metadata and to make it Findable, Accessible, Interoperable and Re-usable (FAIR). High quality and FAIRness of (meta)data are promoted through providing peer review, data audit, permanent scientific record and citation credit as for any other scholarly publication. One can read more on the different types of data papers and how they help to achieve these goals in the Strategies and guidelines for scholarly publishing of biodiversity data (https://doi.org/10.3897/rio.3.e12431).
Fig. 1 Number of data papers published in Pensoft’s journals since 2011.
The data paper concept was initially based on the standard metadata descriptions, using the Ecological Metadata Language (EML). Apart from distinguishing a specialised place for dataset descriptions by creating a data paper article type, Pensoft has developed multiple workflows for streamlined import of metadata from various repositories and their conversion into data paper a manuscripts in Pensoft’s ARPHA Writing Tool (AWT). You can read more about the EML workflow in this blog post.
Similarly, we decided to create a specialised data paper article type for the omics community within Pensoft’s Biodiversity Data Journal to reflect the specific nature of omics data. We established a manuscript template to help standardise the description of such datasets and their most important features. This initiative was supported in part by the IGNITE project.
How can authors publish omics data papers?
There are two ways to do publish omics data papers – (1) to write a data paper manuscript following the respective template in the ARPHA Writing Tool (AWT) or (2) to convert metadata describing a project or study deposited in EMBL-EBI’s European Nucleotide Archive (ENA) into a manuscript within the AWT.
The first method is straightforward but the second one deserves more attention. We focused on metadata published in ENA, which is part of the International Nucleotide Sequence Database Collaboration (INSDC) and synchronises its records with these of the other two members (DDBJ and NCBI). ENA is linked to the ArrayExpress and BioSamples databases, which describe sequencing experiments and samples, and follow the community-accepted metadata standards MINSEQE and MIxS. To auto populate a manuscript with a click of a button, authors can provide the accession number of the relevant ENA Study of Project and our workflow will automatically retrieve all metadata from ENA, as well as any available ArrayExpress or BioSamples records linked to it (Fig. 2). After that, authors can edit any of the article sections in the manuscript by filling in the relevant template fields or creating new sections, adding text, figures, citations and so on.
An important component of the OMICS data paper manuscript is a supplementary table containing MIxS-compliant metadata imported from BioSamples. When available, BioSamples metadata is automatically converted to a long table format and attached to the manuscript. The authors are not permitted to edit or delete it inside the ARPHA Writing Tool. Instead, if desired, they should correct the associated records in the sourced BioSamples database. We have implemented a feature allowing the automatic re-import of corrected BioSamples records inside the supplementary table. In this way, we ensure data integrity and provide a reliable and trusted source for accessing these metadata.
Fig. 2 Automated generation of omics data paper manuscripts through import and conversion of metadata associated with the Project ID or Study ID at ENA
Here is a step-by-step guide for conversion of ENA metadata into a data paper manuscript:
The author has published a dataset to any of the INSDC databases. They copy its ENA Study or Project accession number.
The author goes to the Biodiversity Data Journal (BDJ) webpage, clicks the “Start a manuscript” buttоn and selects OMICS Data Paper template in the ARPHA Writing Tool (AWT). Alternatively, the author can also start from the AWT website, click “Create a manuscript”, and select “OMICS Data Paper” as the article type, the Biodiversity Data Journal will be automatically marked by the system. The author clicks the “Import a manuscript” button at the bottom of the webpage.
The author pastes the ENA Study or Project accession number inside the relevant text box (“Import an European Nucleotide Archive (ENA) Study ID or Project ID”) and clicks “Import”.
The Project or Study metadata is converted into an OMICS data paper manuscript along with the metadata from ArrayExpress and BioSamples if available. The author can start making changes to the manuscript, invite co-authors and then submit it for technical evaluation, peer review and publication.
Our innovative workflow makes authoring omics data papers much easier and saves authors time and efforts when inserting metadata into the manuscript. It takes advantage of existing links between data repositories to unify biodiversity and omics knowledge into a single narrative. This workflow demonstrates the importance of standardisation and interoperability to integrate data and metadata from different scientific fields.
We have established a special collection for OMICS data papers in the Biodiversity Data Journal. Authors are invited to describe their omics datasets by using the novel streamlined workflow for creating a manuscript at a click of a button from metadata deposited in ENA or by following the template to create their manuscript via the non-automated route.
To stimulate omics data paper publishing, the first 10 papers will be published free of charge. Upon submission of an omics data paper manuscript, do not forget to assign it to the collection Next-generation publishing of omics 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.
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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
Pensoft’s flagship journal ZooKeys invites free-to-publish research on key biological traits of SARS-like viruses potential hosts and vectors; Plazi harvests and brings together all relevant data from legacy literature to a reliable FAIR-data repository
To bridge the huge knowledge gaps in the understanding of how and which animal species successfully transmit life-threatening diseases to humans, thereby paving the way for global health emergencies, scholarly publisher Pensoft and literature digitisation provider Plazi join efforts, expertise and high-tech infrastructure.
By using the advanced text- and data-mining tools and semantic publishing workflows they have developed, the long-standing partners are to rapidly publish easy-to-access and reusable biodiversity research findings and data, related to hosts or vectors of the SARS-CoV-2 or other coronaviruses, in order to provide the stepping stones needed to manage and prevent similar crises in the future.
Already, there’s plenty of evidence pointing to certain animals, including pangolins, bats, snakes and civets, to be the hosts of viruses like SARS-CoV-2 (coronaviruses), hence, potential triggers of global health crises, such as the currently ravaging Coronavirus pandemic. However, scientific research on what biological and behavioural specifics of those species make them particularly successful vectors of zoonotic diseases is surprisingly scarce. Even worse, the little that science ‘knows’ today is often locked behind paywalls and copyright laws, or simply ‘trapped’ in formats inaccessible to text- and data-mining performed by search algorithms.
This is why Pensoft’s flagship zoological open-access, peer-reviewed scientific journal ZooKeysrecently announced its upcoming, special issue, titled “Biology of pangolins and bats”, to invite research papers on relevant biological traits and behavioural features of bats and pangolins, which are or could be making them efficient vectors of zoonotic diseases. Another open-science innovation champion in the Pensoft’s portfolio, Research Ideas and Outcomes (RIO Journal) launched another free-to-publish collection of early and/or brief outcomes of research devoted to SARS-like viruses.
Due to the expedited peer review and publication processes at ZooKeys, the articles will rapidly be made public and accessible to scientists, decision-makers and other experts, who could then build on the findings and eventually come up with effective measures for the prevention and mitigation of future zoonotic epidemics. To further facilitate the availability of such critical research, ZooKeys is waiving the publication charges for accepted papers.
Meanwhile, the literature digitisation provider Plazi is deploying its text- and data-mining expertise and tools, to locate and acquire publications related to hosts of coronaviruses – such as those expected in the upcoming “Biology of pangolins and bats” special issue in ZooKeys – and deposit them in a newly formed Coronavirus-Host Community, a repository hosted on the Zenodo platform. There, all publications will be granted persistent open access and enhanced with taxonomy-specific data derived from their sources. Contributions to Plazi can be made at various levels: from sending suggestions of articles to be added to the Zotero bibliographic public libraries on virus-hosts associations and hosts’ taxonomy, to helping the conversion of those articles into findable, accessible, interoperable and reusable (FAIR) knowledge.
Pensoft’s and Plazi’s collaboration once again aligns with the efforts of the biodiversity community, after the natural science collections consortium DiSSCo (Distributed System of Scientific Collections) and the Consortium of European Taxonomic Facilities (CETAF), recently announced the COVID-19 Task Force with the aim to create a network of taxonomists, collection curators and other experts from around the globe.
