The Automated Taxonomic Concept Reasoner

We present a visual and interactive taxonomic Artificial Intelligence (AI) tool, the Automated Taxonomic Concept Reasoner (ATCR), whose graphical web interface is under development and will also become available via an Application Programming Interface (API). The tool employs automated reasoning (Beeson 2014) to align multiple taxonomies visually, in a web browser, using user or expert-provided taxonomic articulations, i.e. "Region Connection Calculus (RCC-5) relationships between taxonomic concepts, provided in a specific logical language (Fig. 1). It does this by representing the problem of taxonomic alignment under these constraints in terms of logical inference, while performing these inferences computationally and leveraging the powerful Microsoft Z3 Satisfiability Modulo Theory (SMT) solver (de Moura and Bjørner 2008). This tool represents further development of utilities for the taxonomic concept approach, which fundamentally addresses the challenge of robust biodiversity data aggregation in light of multiple conflicting sources (and source classifications) from which primary biodiversity data almost invariably originate. The approach has proven superior to aggregation, based just on the syntax and semantics provided by the Darwin Core standard Franz and Sterner 2018). Fig. 1 provides an artificial example of such an alignment. Two taxonomies, A and B, are shown. There are five taxonomic concepts, A.One, A.Two, A.Three, B.One and B.Two. A.Two and A.Three are sub-concepts (children) of A.One, and B.Two is a sub-concept (child) of B.One. These are represented by the direction of the grey arrows. The undirected mustard-coloured lines represent relationships, i.e., the articulations referred to in the previous paragraph. These may be of five kinds: congruent (==), includes (<) and included in (>), overlap (><), and disjointness. These five relationships are known in the AI literature as the Region Connection Calculus-5 (RCC-5) (Randell et al. 1992, Bennett 1994, Bennett 1994), and taken exclusively and in conjunction with each other, have certain desirable properties with respect to the representation of spatial relationships. The provided relationship (i.e. the articulation) may also be an arbitrary disjunction of these five fundamental kinds, thus allowing for representation of some degree of logical uncertainty. Then, and under three assumptions that: "sibling" concepts are disjoint in their instances, all instances of a parent concept are instances of at least one of its child concepts, and every concept has at least one instance - the SMT-based automated reasoner is able to deduce the relationships represented by the undirected green lines. It is also able to deduce disjunctive relationships where these are logically implied. "sibling" concepts are disjoint in their instances, all instances of a parent concept are instances of at least one of its child concepts, and every concept has at least one instance - the SMT-based automated reasoner is able to deduce the relationships represented by the undirected green lines. It is also able to deduce disjunctive relationships where these are logically implied. ATCR is related to Euler/X (Franz et al. 2015), an existing tool for the same kinds of taxonomic alignment problems, which was used, for example, to obtain an alignment of two influential primate classifications (Franz et al. 2016). It differs from Euler/X in that it employs a different logical encoding that enables more efficient and more informative computational reasoning, and also in that it provides a graphical web interface, which Euler/X does not.

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Biodiversity databases in Russia: towards a national portal

Arctic Science ◽

10.1139/as-2016-0050 ◽

2017 ◽

Vol 3 (3) ◽

pp. 560-576 ◽

Cited By ~ 4

Author(s):

Natalya V. Ivanova ◽

Maxim P. Shashkov

Keyword(s):

Annual Reports ◽

Data Publishing ◽

Web Interface ◽

File Server ◽

Biodiversity Data ◽

Web Interfaces ◽

Conceptual Structures ◽

Darwin Core ◽

Many Sources ◽

Biodiversity Databases

Russia holds massive biodiversity data accumulated in botanical and zoological collections, literature publications, annual reports of natural reserves, nature conservation, and monitoring study project reports. While some data have been digitized and organized in databases or spreadsheets, most of the biodiversity data in Russia remain dormant and digitally inaccessible. Concepts of open access to research data is spreading, and the lack of data publishing tradition and of use of data standards remain prominent. A national biodiversity information system is lacking and most of the biodiversity data are not available or the available data are not consolidated. As a result, Russian biodiversity data remain fragmented and inaccessible for researchers. The majority of Russian biodiversity databases do not have web interfaces and are accessible only to a limited numbers of researchers. The main reason for lack of access to these resources relates to the fact that the databases have previously been developed only as a local resource. In addition, many sources have previously been developed in the desktop database environments mainly using MS Access and, in some cases, earlier DBMS for DOS, i.e., file-server system, which does not have the functionality to create access to records through a web interface. Among the databases with a web interface, a few information systems have interactive maps with the species occurrence data and systems allowing registered users to upload data. It is important to note that the conceptual structures of these databases were created without taking into account modern standards of the Darwin Core; furthermore, some data sources were developed prior to the first work version of the Darwin Core release in 2001. Despite the complexity and size of the biodiversity data landscape in Russia, the interest in publishing data through international biodiversity portals is increasing among Russian researchers. Since 2014, institutional data publishers in Russia have published about 140 000 species occurrences through gbif.org. The increase in data publishing activity calls for the creation of a GBIF node in Russia, aiming to support Russian biodiversity experts in international data work.

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Transforming Biodiversity Data into Knowledge for Decision-making

Biodiversity Information Science and Standards ◽

10.3897/biss.3.37171 ◽

2019 ◽

Vol 3 ◽

Author(s):

Lauren Weatherdon

Keyword(s):

Decision Making ◽

Biodiversity Data ◽

Biodiversity Indicators ◽

Technological Advances ◽

Darwin Core ◽

Metadata Standards ◽

Essential Biodiversity Variables ◽

Development Goals ◽

Multiple Levels ◽

Communication Needs

Ensuring that we have the data and information necessary to make informed decisions is a core requirement in an era of increasing complexity and anthropogenic impact. With cumulative challenges such as the decline in biodiversity and accelerating climate change, the need for spatially-explicit and methodologically-consistent data that can be compiled to produce useful and reliable indicators of biological change and ecosystem health is growing. Technological advances—including satellite imagery—are beginning to make this a reality, yet uptake of biodiversity information standards and scaling of data to ensure its applicability at multiple levels of decision-making are still in progress. The complementary Essential Biodiversity Variables (EBVs) and Essential Ocean Variables (EOVs), combined with Darwin Core and other data and metadata standards, provide the underpinnings necessary to produce data that can inform indicators. However, perhaps the largest challenge in developing global, biological change indicators is achieving consistent and holistic coverage over time, with recognition of biodiversity data as global assets that are critical to tracking progress toward the UN Sustainable Development Goals and Targets set by the international community (see Jensen and Campbell (2019) for discussion). Through this talk, I will describe some of the efforts towards producing and collating effective biodiversity indicators, such as those based on authoritative datasets like the World Database on Protected Areas (https://www.protectedplanet.net/), and work achieved through the Biodiversity Indicators Partnership (https://www.bipindicators.net/). I will also highlight some of the characteristics of effective indicators, and global biodiversity reporting and communication needs as we approach 2020 and beyond.

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DiSSCo, iDigBio and the Future of Global Collaboration

Biodiversity Information Science and Standards ◽

10.3897/biss.3.37896 ◽

2019 ◽

Vol 3 ◽

Cited By ~ 1

Author(s):

Gil Nelson ◽

Deborah L Paul

Keyword(s):

Working Group ◽

Application Programming Interface ◽

The United States ◽

Common Source ◽

Data Generation ◽

Biodiversity Data ◽

Global Biodiversity Information Facility ◽

Data Accessibility ◽

The Us ◽

Biodiversity Information

Integrated Digitized Biocollections (iDigBio) is the United States’ (US) national resource and coordinating center for biodiversity specimen digitization and mobilization. It was established in 2011 through the US National Science Foundation’s (NSF) Advancing Digitization of Biodiversity Collections (ADBC) program, an initiative that grew from a working group of museum-based and other biocollections professionals working in concert with NSF to make collections' specimen data accessible for science, education, and public consumption. The working group, Network Integrated Biocollections Alliance (NIBA), released two reports (Beach et al. 2010, American Institute of Biological Sciences 2013) that provided the foundation for iDigBio and ADBC. iDigBio is restricted in focus to the ingestion of data generated by public, non-federal museum and academic collections. Its focus is on specimen-based (as opposed to observational) occurrence records. iDigBio currently serves about 118 million transcribed specimen-based records and 29 million specimen-based media records from approximately 1600 datasets. These digital objects have been contributed by about 700 collections representing nearly 400 institutions and is the most comprehensive biodiversity data aggregator in the US. Currently, iDigBio, DiSSCo (Distributed System of Scientific Collections), GBIF (Global Biodiversity Information Facility), and the Atlas of Living Australia (ALA) are collaborating on a global framework to harmonize technologies towards standardizing and synchronizing ingestion strategies, data models and standards, cyberinfrastructure, APIs (application programming interface), specimen record identifiers, etc. in service to a developing consolidated global data product that can provide a common source for the world’s digital biodiversity data. The collaboration strives to harness and combine the unique strengths of its partners in ways that ensure the individual needs of each partner’s constituencies are met, design pathways for accommodating existing and emerging aggregators, simultaneously strengthen and enhance access to the world’s biodiversity data, and underscore the scope and importance of worldwide biodiversity informatics activities. Collaborators will share technology strategies and outputs, align conceptual understandings, and establish and draw from an international knowledge base. These collaborators, along with Biodiversity Information Standards (TDWG), will join iDigBio and the Smithsonian National Museum of Natural History as they host Biodiversity 2020 in Washington, DC. Biodiversity 2020 will combine an international celebration of the worldwide progress made in biodiversity data accessibility in the 21st century with a biodiversity data conference that extends the life of Biodiversity Next. It will provide a venue for the GBIF governing board meeting, TDWG annual meeting, and the annual iDigBio Summit as well as three days of plenary and concurrent sessions focused on the present and future of biodiversity data generation, mobilization, and use.

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The Physiology Constant Database of Teen-Agers in Beijing

The Scientific World JOURNAL ◽

10.1100/tsw.2004.98 ◽

2004 ◽

Vol 4 ◽

pp. 442-448

Author(s):

Wei Wei-Qi ◽

Zhu Guang-Jin ◽

Xu Cheng-Li ◽

Han Shao-Mei ◽

Qi Bao-Shen ◽

...

Keyword(s):

Relational Database ◽

Database System ◽

Living Systems ◽

Scatter Diagram ◽

Web Interface ◽

Web Browser ◽

Relational Database System ◽

Advanced Search ◽

Statistical Table ◽

The Web

Physiology constants of adolescents are important to understand growing living systems and are a useful reference in clinical and epidemiological research. Until recently, physiology constants were not available in China and therefore most physiologists, physicians, and nutritionists had to use data from abroad for reference. However, the very difference between the Eastern and Western races casts doubt on the usefulness of overseas data. We have therefore created a database system to provide a repository for the storage of physiology constants of teen-agers in Beijing. The several thousands of pieces of data are now divided into hematological biochemistry, lung function, and cardiac function with all data manually checked before being transferred into the database. The database was accomplished through the development of a web interface, scripts, and a relational database. The physiology data were integrated into the relational database system to provide flexible facilities by using combinations of various terms and parameters. A web browser interface was designed for the users to facilitate their searching. The database is available on the web. The statistical table, scatter diagram, and histogram of the data are available for both anonym and user according to queries, while only the user can achieve detail, including download data and advanced search.

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Knowledge sharing on deep learning in physics research using VISPA

EPJ Web of Conferences ◽

10.1051/epjconf/202024505040 ◽

2020 ◽

Vol 245 ◽

pp. 05040

Author(s):

Max Beer ◽

Niclas Eich ◽

Martin Erdmann ◽

Peter Fackeldey ◽

Benjamin Fischer ◽

...

Keyword(s):

Deep Learning ◽

Knowledge Sharing ◽

Work Environment ◽

Central Place ◽

Current Status ◽

Web Interface ◽

Software Environment ◽

Web Browser ◽

Learning Platform ◽

Hands On

The VISPA (VISual Physics Analysis) project provides a streamlined work environment for physics analyses and hands-on teaching experiences with a focus on deep learning. VISPA has already been successfully used in HEP analyses and teaching and is now being further developed into an interactive deep learning platform. One specific example is to meet knowledge sharing needs in deep learning by combining paper, code and data at a central place. Additionally the possibility to run it directly from the web browser is a key feature of this development. Any SSH reachable resource can be accessed via the VISPA web interface. This enables a flexible and experiment agnostic computing experience. The user interface is based on JupyterLab and is extended with analysis specific tools, such as a parametric file browser and TensorBoard. Our VISPA instance is backed by extensive GPU resources and a rich software environment. We present the current status of the VISPA project and its upcoming new features.

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Wanted: Standards for FAIR taxonomic concept representations and relationships

Biodiversity Information Science and Standards ◽

10.3897/biss.5.75587 ◽

2021 ◽

Vol 5 ◽

Author(s):

Beckett Sterner ◽

Nathan Upham ◽

Prashant Gupta ◽

Caleb Powell ◽

Nico Franz

Keyword(s):

North American ◽

Multiple Sources ◽

Biodiversity Data ◽

Taxonomic Concept ◽

Reference Sequences ◽

Global Biodiversity ◽

Concept Representations ◽

Biodiversity Information ◽

Diagnostic Traits ◽

Occurrence Records

Making the most of biodiversity data requires linking observations of biological species from multiple sources both efficiently and accurately (Bisby 2000, Franz et al. 2016). Aggregating occurrence records using taxonomic names and synonyms is computationally efficient but known to experience significant limitations on accuracy when the assumption of one-to-one relationships between names and biological entities breaks down (Remsen 2016, Franz and Sterner 2018). Taxonomic treatments and checklists provide authoritative information about the correct usage of names for species, including operational representations of the meanings of those names in the form of range maps, reference genetic sequences, or diagnostic traits. They increasingly provide taxonomic intelligence in the form of precise description of the semantic relationships between different published names in the literature. Making this authoritative information Findable, Accessible, Interoperable, and Reusable (FAIR; Wilkinson et al. 2016) would be a transformative advance for biodiversity data sharing and help drive adoption and novel extensions of existing standards such as the Taxonomic Concept Schema and the OpenBiodiv Ontology (Kennedy et al. 2006, Senderov et al. 2018). We call for the greater, global Biodiversity Information Standards (TDWG) and taxonomy community to commit to extending and expanding on how FAIR applies to biodiversity data and include practical targets and criteria for the publication and digitization of taxonomic concept representations and alignments in taxonomic treatments, checklists, and backbones. As a motivating case, consider the abundantly sampled North American deer mouse—Peromyscus maniculatus (Wagner 1845)—which was recently split from one continental species into five more narrowly defined forms, so that the name P. maniculatus is now only applied east of the Mississippi River (Bradley et al. 2019, Greenbaum et al. 2019). That single change instantly rendered ambiguous ~7% of North American mammal records in the Global Biodiversity Information Facility (n=242,663, downloaded 2021-06-04; GBIF.org 2021) and ⅓ of all National Ecological Observatory Network (NEON) small mammal samples (n=10,256, downloaded 2021-06-27). While this type of ambiguity is common in name-based databases when species are split, the example of P. maniculatus is particularly striking for its impact upon biological questions ranging from hantavirus surveillance in North America to studies of climate change impacts upon rodent life-history traits. Of special relevance to NEON sampling is recent evidence suggesting deer mice potentially transmit SARS-CoV-2 (Griffin et al. 2021). Automating the updating of occurrence records in such cases and others will require operational representations of taxonomic concepts—e.g., range maps, reference sequences, and diagnostic traits—that are FAIR in addition to taxonomic concept alignment information (Franz and Peet 2009). Despite steady progress, it remains difficult to find, access, and reuse authoritative information about how to apply taxonomic names even when it is already digitized. It can also be difficult to tell without manual inspection whether similar types of concept representations derived from multiple sources, such as range maps or reference sequences selected from different research articles or checklists, are in fact interoperable for a particular application. The issue is therefore different from important ongoing efforts to digitize trait information in species circumscriptions, for example, and focuses on how already digitized knowledge can best be packaged to inform human experts and artifical intelligence applications (Sterner and Franz 2017). We therefore propose developing community guidelines and criteria for FAIR taxonomic concept representations as "semantic artefacts" of general relevance to linked open data and life sciences research (Le Franc et al. 2020).

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Unlocking Inventory Data Capture, Sharing and Reuse: The Humboldt Extension to Darwin Core

Biodiversity Information Science and Standards ◽

10.3897/biss.5.74275 ◽

2021 ◽

Vol 5 ◽

Author(s):

Yanina Sica ◽

Paula Zermoglio

Keyword(s):

Relevant Information ◽

Task Group ◽

Quality Data ◽

Systematic Sampling ◽

Sampling Effort ◽

Inventory Data ◽

Biodiversity Data ◽

Darwin Core ◽

Rich Data ◽

Key Aspects

Biodiversity inventories, i.e., recording multiple species at a specific place and time, are routinely performed and offer high-quality data for characterizing biodiversity and its change. Digitization, sharing and reuse of incidental point records (i.e., records that are not readily associated with systematic sampling or monitoring, typically museum specimens and many observations from citizen science projects) has been the focus for many years in the biodiversity data community. Only more recently, attention has been directed towards mobilizing data from both new and longstanding inventories and monitoring efforts. These kinds of studies provide very rich data that can enable inferences about species absence, but their reliability depends on the methodology implemented, the survey effort and completeness. The information about these elements has often been regarded as metadata and captured in an unstructured manner, thus making their full use very challenging. Unlocking and integrating inventory data requires data standards that can facilitate capture and sharing of data with the appropriate depth. The Darwin Core standard (Wieczorek et al. 2012) currently enables reporting some of the information contained in inventories, particularly using Darwin Core Event terms such as samplingProtocol, sampleSizeValue, sampleSizeUnit, samplingEffort. However, it is limited in its ability to accommodate spatial, temporal, and taxonomic scopes, and other key aspects of the inventory sampling process, such as direct or inferred measures of sampling effort and completeness. The lack of a standardized way to share inventory data has hindered their mobilization, integration, and broad reuse. In an effort to overcome these limitations, a framework was developed to standardize inventory data reporting: Humboldt Core (Guralnick et al. 2018). Humboldt Core identified three types of inventories (single, elementary, and summary inventories) and proposed a series of terms to report their content. These terms were organized in six categories: dataset and identification; geospatial and habitat scope; temporal scope; taxonomic scope; methodology description; and completeness and effort. While originally planned as a new TDWG standard and being currently implemented in Map of Life (https://mol.org/humboldtcore/), ratification was not pursued at the time, thus limiting broader community adoption. In 2021 the TDWG Humboldt Core Task Group was established to review how to best integrate the terms proposed in the original publication with existing standards and implementation schemas. The first goal of the task group was to determine whether a new, separate standard was needed or if an extension to Darwin Core could accommodate the terms necessary to describe the relevant information elements. Since the different types of inventories can be thought of as Events with different nesting levels (events within events, e.g., plots within sites), and after an initial mapping to existing Darwin Core terms, it was deemed appropriate to start from a Darwin Core Event Core and build an extension to include Humboldt Core terms. The task group members are currently revising all original Humboldt Core terms, reformulating definitions, comments, and examples, and discarding or adding new terms where needed. We are also gathering real datasets to test the use of the extension once an initial list of revised terms is ready, before undergoing a public review period as established by the TDWG process. Through the ratification of Humboldt Core as a TDWG extension, we expect to provide the community with a solution to share and use inventory data, which improves biodiversity data discoverability, interoperability and reuse while lowering the reporting burden at different levels (data collection, integration and sharing).

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A Google Sheet Add-on for Biodiversity Data Standardization and Sharing

Biodiversity Information Science and Standards ◽

10.3897/biss.4.59228 ◽

2020 ◽

Vol 4 ◽

Author(s):

José Augusto Salim ◽

Antonio Saraiva

Keyword(s):

Information Retrieval ◽

Data Sharing ◽

Information Science ◽

Data Sets ◽

Biodiversity Informatics ◽

Biodiversity Data ◽

Data Standardization ◽

Darwin Core ◽

Rest Api ◽

Biodiversity Information

For those biologists and biodiversity data managers who are unfamiliar with information science data practices of data standardization, the use of complex software to assist in the creation of standardized datasets can be a barrier to sharing data. Since the ratification of the Darwin Core Standard (DwC) (Darwin Core Task Group 2009) by the Biodiversity Information Standards (TDWG) in 2009, many datasets have been published and shared through a variety of data portals. In the early stages of biodiversity data sharing, the protocol Distributed Generic Information Retrieval (DiGIR), progenitor of DwC, and later the protocols BioCASe and TDWG Access Protocol for Information Retrieval (TAPIR) (De Giovanni et al. 2010) were introduced for discovery, search and retrieval of distributed data, simplifying data exchange between information systems. Although these protocols are still in use, they are known to be inefficient for transferring large amounts of data (GBIF 2017). Because of that, in 2011 the Global Biodiversity Information Facility (GBIF) introduced the Darwin Core Archive (DwC-A), which allows more efficient data transfer, and has become the preferred format for publishing data in the GBIF network. DwC-A is a structured collection of text files, which makes use of the DwC terms to produce a single, self-contained dataset. Many tools for assisting data sharing using DwC-A have been introduced, such as the Integrated Publishing Toolkit (IPT) (Robertson et al. 2014), the Darwin Core Archive Assistant (GBIF 2010) and the Darwin Core Archive Validator. Despite promoting and facilitating data sharing, many users have difficulties using such tools, mainly because of the lack of training in information science in the biodiversity curriculum (Convention on Biological Diversiity 2012, Enke et al. 2012). However, most users are very familiar with spreadsheets to store and organize their data, but the adoption of the available solutions requires data transformation and training in information science and more specifically, biodiversity informatics. For an example of how spreadsheets can simplify data sharing see Stoev et al. (2016). In order to provide a more "familiar" approach to data sharing using DwC-A, we introduce a new tool as a Google Sheet Add-on. The Add-on, called Darwin Core Archive Assistant Add-on can be installed in the user's Google Account from the G Suite MarketPlace and used in conjunction with the Google Sheets application. The Add-on assists the mapping of spreadsheet columns/fields to DwC terms (Fig. 1), similar to IPT, but with the advantage that it does not require the user to export the spreadsheet and import it into another software. Additionally, the Add-on facilitates the creation of a star schema in accordance with DwC-A, by the definition of a "CORE_ID" (e.g. occurrenceID, eventID, taxonID) field between sheets of a document (Fig. 2). The Add-on also provides an Ecological Metadata Language (EML) (Jones et al. 2019) editor (Fig. 3) with minimal fields to be filled in (i.e., mandatory fields required by IPT), and helps users to generate and share DwC-Archives stored in the user's Google Drive, which can be downloaded as a DwC-A or automatically uploaded to another public storage resource like a user's Zenodo Account (Fig. 4). We expect that the Google Sheet Add-on introduced here, in conjunction with IPT, will promote biodiversity data sharing in a standardized format, as it requires minimal training and simplifies the process of data sharing from the user's perspective, mainly for those users not familiar with IPT, but that historically have worked with spreadsheets. Although the DwC-A generated by the add-on still needs to be published using IPT, it does provide a simpler interface (i.e., spreadsheet) for mapping data sets to DwC than IPT. Even though the IPT includes many more features than the Darwin Core Assistant Add-on, we expect that the Add-on can be a "starting point" for users unfamiliar with biodiversity informatics before they move on to more advanced data publishing tools. On the other hand, Zenodo integration allows users to share and cite their standardized data sets without publishing them via IPT, which can be useful for users without access to an IPT installation. Additionally, we are working on new features and future releases will include the automatic generation of Global Unique Identifiers for shared records, the possibility of adding additional data standards and DwC extensions, integration with GBIF REST API and with IPT REST API.

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All the Clades in the World: Building a Semantically-Rich and Testable Ontology of Phylogenetic Clade Definitions

Biodiversity Information Science and Standards ◽

10.3897/biss.2.25776 ◽

2018 ◽

Vol 2 ◽

pp. e25776

Author(s):

Gaurav Vaidya ◽

Guanyang Zhang ◽

Hilmar Lapp ◽

Nico Cellinese

Keyword(s):

Markup Language ◽

Biodiversity Data ◽

Phylogenetic Information ◽

Original Intent ◽

Ontology Language ◽

Darwin Core ◽

Formal Definitions ◽

The World ◽

Online Catalog ◽

Application Specific

Taxonomic names are ambiguous as identifiers of biodiversity data, as they refer to a particular concept of a taxon in an expert’s mind (Kennedy et al. 2005). This ambiguity is particularly problematic when attempting to reconcile taxonomic names from disparate sources with clades on a phylogeny. Currently, such reconciliation requires expert interpretation, which is necessarily subjective, difficult to reproduce, and refractory to scaling. In contrast, phylogenetic clade definitions are a well-developed method for unambiguously defining the semantics of a clade concept in terms of shared evolutionary ancestry (Queiroz and Gauthier 1990, Queiroz and Gauthier 1994), and these semantics allow locating clades on any phylogeny. Although a few software tools have been created for resolving clade definitions, including for definitions expressed in the Mathematical Markup Language (e.g. Names on Nodes in Keesey 2007) and as lists of GenBank accession numbers (e.g. mor in Hibbett et al. 2005), these are application-specific representations that do not provide formal definitions with well-defined semantics for every component of a clade definition. Being able to create such machine-interpretable definitions would allow computers to store, compare, distribute and resolve semantically-rich clade definitions. To this end, the Phyloreferencing project (http://phyloref.org, Cellinese and Lapp 2015) is working on a specification for encoding phylogenetic clade definitions as ontologies using the Web Ontology Language (OWL in W3C OWL Working Group 2012). Our specification allows the semantics of these definitions, which we call phyloreferences, to be described in terms of shared ancestor and excluded lineage properties. The aim of this effort is to allow any OWL-DL reasoner to resolve phyloreferences on a phylogeny that has itself been translated into a compatible OWL representation. We have developed a workflow that allows us to curate phyloreferences from phylogenetic clade definitions published in natural language, and to resolve the curated phyloreference against the phylogeny upon which the definition was originally created, allowing us to validate that the phyloreference reflects the authors’ original intent. We have started work on curating dozens of phyloreferences from publications and the clade definition database RegNum (http://phyloregnum.org), which will provide an online catalog of all clade definitions that are part of the Phylonym Volume, to be published together with the PhyloCode (https://www.ohio.edu/phylocode/). We will comprehensively curate these definitions into a reusable and fully computable ontology of phyloreferences. In our presentation, we will provide an overview of phyloreferencing and will describe the model and workflow we use to encode clade definitions in OWL, based on concepts and terms taken from the Comparative Data Analysis Ontology (Prosdocimi et al. 2009), Darwin-SW (Baskauf and Webb 2016) and Darwin Core (Wieczorek et al. 2012). We will demonstrate how phyloreferences can be visualized, resolved and tested on the phylogeny that they were originally described on, and how they resolve on one of the largest synthetic phylogenies available, the Open Tree of Life (Hinchliff et al. 2015). We will conclude with a discussion of the problems we faced in referring to taxonomic units in phylogenies, which is one of the key challenges in enabling better integration of phylogenetic information into biodiversity analyses.

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How about Habitats and Darwin Core?

Biodiversity Information Science and Standards ◽

10.3897/biss.3.39271 ◽

2019 ◽

Vol 3 ◽

Author(s):

Remy Jomier ◽

Remy Poncet ◽

Noemie Michez

Keyword(s):

European Commission ◽

Information Transfer ◽

Data Exchange ◽

Spatial Information ◽

Global Scale ◽

National Standard ◽

Habitats Directive ◽

Biodiversity Data ◽

Biodiversity Knowledge ◽

Darwin Core

As part of the Biodiversity Information System on Nature and Landscapes (SINP), the French National Museum of Natural History was appointed to develop biodiversity data exchange standards, with the goal of sharing French marine and terrestrial data nationally, meeting domestic and European requirements, e.g., the Infrastructure for spatial information in Europe Directive (INSPIRE Directive, European Commission 2007). Data standards are now recognised as useful to improve and share biodiversity knowledge (e.g., species distribution) and play a key role in data valorisation (e.g., vulnerability assessment, conservation policy). For example, in order to fulfill report obligations within the Fauna and Flora Habitats Directive (European Commission 1992), and the Marine Strategy Framework Directive (European Commission 2008), information about taxa and habitat occurrences are required periodically, involving data exchange and compilation at a national scale. National and international data exchange standards are focused on species, and only a few solutions exist when there is a need to deal with habitat data. Darwin Core has been built to fit with species data exchange needs and only contains one habitat attribute that allows for a bit of leeway to have such an information transfer, but is deemed to be one of the least standardized fields. However, Darwin Core does not allow for a transit of only habitat data, as the scientific name of the taxon is mandatory. The SINP standard for habitats was developed by a dedicated working group, representative of biodiversity European Commission 2008 stakeholders in France. This standard focuses on core attributes that characterize habitat observation and monitoring. Interoperability remains to be achieved with the Darwin Core standard, or something similar on a world scale (e.g., Humboldt Core), as habitat data are regularly gathered irrespective of whether taxon occurrences are associated with it. The results of the French initiative proved useful to compile and share data nationally, bringing together data providers that otherwise would have been excluded. However, at a global scale, it faces some challenges that still need to be fully addressed, interoperability being the main one. Regardless of the problems that remain to be solved, some lessons can be learnt from this effort. With the ultimate goal of making biodiversity data readily available, these lessons should be kept in mind for future initiatives. The presentation deals with how this work was undertaken and how the required elements could be integrated into a French national standard to allow for comprehensive habitat data reporting. It will show hypothesis as to what could be added to the Darwin Core to allow for a better understanding of habitats with at least one taxon attached (or not) to them.

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