BACKGROUND: The standardization of biological data using unique identifiers is vital for seamless data integration, comprehensive interpretation, and reproducibility of research findings, contributing to advancements in bioinformatics and systems biology. Despite being widely accepted as a universal identifier, scientific names for biological species have inherent limitations, including lack of stability, uniqueness, and convertibility, hindering their effective use as identifiers in databases, particularly in natural product (NP) occurrence databases, posing a substantial obstacle to utilizing this valuable data for large-scale research applications.
RESULTS: To address these challenges and facilitate high-throughput analysis of biological data involving scientific names, we developed PhyloSophos, a Python package that considers the properties of scientific names and taxonomic systems to accurately map name inputs to entries within a chosen reference database. We illustrate the importance of assessing multiple taxonomic databases and considering taxonomic syntax-based pre-processing using NP occurrence databases as an example, with the ultimate goal of integrating heterogeneous information into a single, unified dataset.
CONCLUSIONS: We anticipate PhyloSophos to significantly aid in the systematic processing of poorly digitized and curated biological data, such as biodiversity information and ethnopharmacological resources, enabling full-scale bioinformatics analysis using these valuable data resources.

The official specifications for food additives from natural sources list the species according to their scientific and Japanese names, thereby providing a unique identifier for the species. This helps to prevent the use of nonprescribed species, which might cause unexpected or unintended health hazards. However, there are cases in which the names of the source species listed in the official specifications differ from the accepted scientific names based on the latest taxonomic research. In this paper, we argue that it is more important to define scientific and Japanese names with an emphasis on traceability in order to control the range of food additive ingredients in a rational and sustainable manner. Therefore, we proposed a method for ensuring traceability as well as a specific notation procedure for scientific and Japanese names. Using this method, we examined the source species for three food additives. In some cases, the range of sources species expanded with the change in scientific names. Ensuring traceability is extremely important, but it is also necessary to confirm whether unexpected species are included when names are changed.

BACKGROUND: Scientific names in biology act as universal links. They allow us to cross-reference information about organisms globally. However variations in spelling of scientific names greatly diminish their ability to interconnect data. Such variations may include abbreviations, annotations, misspellings, etc. Authorship is a part of a scientific name and may also differ significantly. To match all possible variations of a name we need to divide them into their elements and classify each element according to its role. We refer to this as \'parsing\' the name. Parsing categorizes name\'s elements into those that are stable and those that are prone to change. Names are matched first by combining them according to their stable elements. Matches are then refined by examining their varying elements. This two stage process dramatically improves the number and quality of matches. It is especially useful for the automatic data exchange within the context of \"Big Data\" in biology.
RESULTS: We introduce Global Names Parser (gnparser). It is a Java tool written in Scala language (a language for Java Virtual Machine) to parse scientific names. It is based on a Parsing Expression Grammar. The parser can be applied to scientific names of any complexity. It assigns a semantic meaning (such as genus name, species epithet, rank, year of publication, authorship, annotations, etc.) to all elements of a name. It is able to work with nested structures as in the names of hybrids. gnparser performs with ≈99% accuracy and processes 30 million name-strings/hour per CPU thread. The gnparser library is compatible with Scala, Java, R, Jython, and JRuby. The parser can be used as a command line application, as a socket server, a web-app or as a RESTful HTTP-service. It is released under an Open source MIT license.
CONCLUSIONS: Global Names Parser (gnparser) is a fast, high precision tool for biodiversity informaticians and biologists working with large numbers of scientific names. It can replace expensive and error-prone manual parsing and standardization of scientific names in many situations, and can quickly enhance the interoperability of distributed biological information.

In conservation science, assessments of trends and priorities for actions often focus on species as the management unit. Studies on species coverage in online media are commonly conducted by using species vernacular names. However, the use of species vernacular names for web-based data search is problematic due to the high risk of mismatches in results. While the use of Latin names may produce more consistent results, it is uncertain whether a search using Latin names will produce unbiased results as compared to vernacular names. We assessed the potential of Latin names to be used as an alternative to vernacular names for the data mining within the field of conservation science. By using Latin and vernacular names, we searched for species from four species groups: diurnal birds of prey, Carnivora, Primates and marine mammals. We assessed the relationship of the results obtained within different online sources, such as Internet pages, newspapers and social media networks. Results indicated that the search results based on Latin and vernacular names were highly correlated, and confirmed that one may be used as an alternative for the other. We also demonstrated the potential of the number of images posted on the Internet to be used as an indication of the public attention towards different species.

BACKGROUND: The scientific names of plants and animals play a major role in Life Sciences as information is indexed, integrated, and searched using scientific names. The main problem with names is their ambiguous nature, because more than one name may point to the same taxon and multiple taxa may share the same name. In addition, scientific names change over time, which makes them open to various interpretations. Applying machine-understandable semantics to these names enables efficient processing of biological content in information systems. The first step is to use unique persistent identifiers instead of name strings when referring to taxa. The most commonly used identifiers are Life Science Identifiers (LSID), which are traditionally used in relational databases, and more recently HTTP URIs, which are applied on the Semantic Web by Linked Data applications.
RESULTS: We introduce two models for expressing taxonomic information in the form of species checklists. First, we show how species checklists are presented in a relational database system using LSIDs. Then, in order to gain a more detailed representation of taxonomic information, we introduce meta-ontology TaxMeOn to model the same content as Semantic Web ontologies where taxa are identified using HTTP URIs. We also explore how changes in scientific names can be managed over time.
CONCLUSIONS: The use of HTTP URIs is preferable for presenting the taxonomic information of species checklists. An HTTP URI identifies a taxon and operates as a web address from which additional information about the taxon can be located, unlike LSID. This enables the integration of biological data from different sources on the web using Linked Data principles and prevents the formation of information silos. The Linked Data approach allows a user to assemble information and evaluate the complexity of taxonomical data based on conflicting views of taxonomic classifications. Using HTTP URIs and Semantic Web technologies also facilitate the representation of the semantics of biological data, and in this way, the creation of more \"intelligent\" biological applications and services.