Taxonomy Structure Research Articles

Integration of the DIAMONDS and CAPTION situation taxonomies based on the South Korean culture.

In this study, we developed an integrated situational taxonomy by merging the second-generation situational taxonomies of the DIAMONDS and CAPTION models. The study participants included 363 adults aged 25-39 years, residing in South Korea, with at least a college degree, and currently employed. To integrate the factors of both models, we conducted an exploratory factor analysis and further examined the hierarchical structure of these factors using bass-ackwards analysis. Our analysis revealed that the integrated situational taxonomy comprises seven domains with a confirmed hierarchical structure. Building on these findings, we further conducted a comparative analysis of the results with prior situational taxonomy research. It was found that the factors constituting integrated domains derived from previous studies that employed correlation analysis or factor analysis differed from those of our study. However, the taxonomy of the three domains in the third level (negativity, positivity, and tasks) aligned with that of previous relevant research, suggesting that these domains are universally applicable to situational taxonomy. Hence, although the taxonomy of the three domains does not encapsulate specific situational characteristics, like the seven domains, if one seeks a culturally universal, statistically clear, and concise structure of situational taxonomy, the three-domain one is a promising alternative to the seven domains. Moreover, this study is the first situational taxonomic research outside the United States and European cultural spheres that confirms that an integrated situational taxonomy is similarly applicable in East Asian cultures.

Maintenance Operations on Cloud, Edge, and IoT Environments: Taxonomy, Survey, and Research Challenges

The emergence of the Internet of Things (IoT) introduced new classes of applications whose latency and bandwidth requirements could not be satisfied by the traditional Cloud Computing model. Consequently, the Internet Technology community promoted the cooperation of two paradigms, Cloud Computing and Edge Computing, combining large-scale computing power and real-time processing capabilities. A significant management challenge in such complex infrastructure concerns the development of efficient maintenance strategies to preserve the environment’s performance and security. While the abundant resources from the academic literature could support the design of novel maintenance solutions, extracting actionable insights from the existing approaches is challenging, given the massive number of published papers. Furthermore, existing review papers, which could help summarize the state-of-the-art, scope their investigations to the maintenance of certain components in particular scenarios. This work fills this gap with a broader literature analysis that covers maintenance strategies targeting physical and logical components in cloud, edge, and IoT environments. First, we introduce a taxonomy that organizes existing solutions according to several characteristics. Then, we review the literature following the taxonomy structure to facilitate the understanding of the research landscape and the comparison between existing works. Finally, we shed light on open challenges that represent promising research directions.

Advancing the discourse: A next-generation value chain-based taxonomy for circular economy key performance indicators

The growth of interest in circular economy (CE) has been accompanied by different approaches to measurement of CE outcomes and impacts, leading to a wide portfolio of indicators with varying degrees of overlap, inconsistency, and convergence. The aim of this paper is to propose a unifying framework for CE indicators, as the next generation of CE taxonomies. We first undertake a scoping review of 59 review papers on CE indicators using manual and computational methods (i.e., topic modelling) to inform the taxonomy structure and content. As a result, we report on 11 clusters of approaches that have been attentive to different dimensions of CE (e.g. horizontal value chain, vertical scale of operation (macro, meso, micro), impact category (economic, biophysical, social), material vs product focus, etc.). Highlighting the strengths and weakness of these approaches, we identify gaps in dimensions related to horizontal and vertical scales of measurement, and propose an agnostic, integrative framework that builds on the scientific foundations of previous research, within a more systemic and comprehensive taxonomy. This taxonomy could be used as a guiding framework or heuristic for regulators, both nationally and internationally, and for practitioners to undertake a comprehensive measurement and assessment of CE related interventions and initiatives at scale.

HiTaxon: a hierarchical ensemble framework for taxonomic classification of short reads.

Whole microbiome DNA and RNA sequencing (metagenomics and metatranscriptomics) are pivotal to determining the functional roles of microbial communities. A key challenge in analyzing these complex datasets, typically composed of tens of millions of short reads, is accurately classifying reads to their taxa of origin. While still performing worse relative to reference-based short-read tools in species classification, ML algorithms have shown promising results in taxonomic classification at higher ranks. A recent approach exploited to enhance the performance of ML tools, which can be translated to reference-dependent classifiers, has been to integrate the hierarchical structure of taxonomy within the tool's predictive algorithm. Here, we introduce HiTaxon, an end-to-end hierarchical ensemble framework for taxonomic classification. HiTaxon facilitates data collection and processing, reference database construction and optional training of ML models to streamline ensemble creation. We show that databases created by HiTaxon improve the species-level performance of reference-dependent classifiers, while reducing their computational overhead. In addition, through exploring hierarchical methods for HiTaxon, we highlight that our custom approach to hierarchical ensembling improves species-level classification relative to traditional strategies. Finally, we demonstrate the improved performance of our hierarchical ensembles over current state-of-the-art classifiers in species classification using datasets comprised of either simulated or experimentally derived reads. HiTaxon is available at: https://github.com/ParkinsonLab/HiTaxon.

Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions

The emergence of Industry 4.0 marks a transformative era for businesses and industries, characterized by advanced technologies like automation, Internet of Things (IoT), artificial intelligence (AI), smart factories, and cyber-physical systems. This revolution promises significant advantages, including enhanced productivity, sustainable progress, and heightened resilience. However, the integration of Industry 4.0 is challenged by the need for a skilled workforce with expertise in areas such as information technology and data analytics. Higher education institutions (HEIs) play a vital role in equipping future professionals with these skills, necessitating curriculum updates and infrastructure enhancements. Simultaneously, the importance of education for sustainable development (ESD) has been underscored by global initiatives like the Sustainable Development Goals (SDGs). ESD instills a sense of responsibility for economic, ecological, and equitable well-being. As digital technologies blur the lines between industries, education faces the challenge of adapting to evolving demands. The integration of AI tools in education has emerged as a catalyst for reshaping learning experiences, fostering innovation, and preparing individuals for the digital age. AI chatbots such as ChatGPT have garnered widespread attention and possess the potential to revolutionize various aspects of education. However, their integration raises ethical concerns, necessitates curriculum redesign, requires strategies for continuous learning, and demands alignment with industry standards. While the potential of AI integration in education is promising, there is a notable gap in the existing literature when it comes to exploring the ethical implications, the influence of AI on ESD, the impact on the structure of Blooms Taxonomy, collaboration between academia and industry, strategies for continuous learning, and the effective integration of AI tools for personalized learning. This paper aims to critically examine the integration of AI tools, with a specific emphasis on ChatGPT, in education within the context of ESD. It delves into the transformative potential, ethical considerations, imperatives for continuous learning, and the role of industry partnerships. By providing insights and strategies, this paper contributes to the ongoing discussion about the evolving nature of education in a technologically driven world, equipping academic institutions to navigate the complexities and opportunities associated with AI integration in education more effectively.

Behaviour change techniques taxonomy v1: Feedback to inform the development of an ontology.

Background: To build cumulative evidence about what works in behaviour change interventions, efforts have been made to develop classification systems for specifying the content of interventions. The Behaviour Change Techniques (BCT) Taxonomy v1 (BCTTv1) is one of the most widely used classifications of behaviour change techniques across a variety of behaviours. The BCTTv1 was intentionally named version 1 to allow for further revisions to the taxonomy. This study aimed to gather data to improve the BCTTv1 and provide recommendations for developing it into a more elaborated knowledge structure, an ontology. Methods: Feedback from users of BCTTv1 about limitations and proposed improvements was collected through the BCT website, user survey, researchers and experts involved in the Human Behaviour-Change Project, and a consultation. In addition, relevant published research reports and other classification systems of BCTs were analysed. These data were synthesised to produce recommendations to inform the development of an ontology of BCTs. Results: A total of 282 comments from six sources were reviewed and synthesised into four categories of suggestions: additional BCTs, amendments to labels and definitions of specific BCTs, amendments to the groupings, and general improvements. Feedback suggested some lack of clarity regarding understanding and identifying techniques from labels, definitions, and examples; distinctions and relations between BCTs; and knowing what they would look like in practice. Three recommendations to improve the BCTTv1 resulted from this analysis: to review the label and definition of each BCT, the 16 groupings of BCTs, and the examples illustrating BCTs. Conclusions : This review of feedback about BCTTv1 identified the need to improve the precision and knowledge structure of the current taxonomy. A BCT ontology would enable the specification of relationships between BCTs, more precise definitions, and allow better interoperability with other ontologies. This ontology will be developed as part of the Human Behaviour-Change Project.

Ecological landscape explains aquifers microbial structure

Aquifers have significant social, economic, and ecological importance. They supply 30 % of the freshwater for human consumption worldwide, including agricultural and industrial use. Despite aquifers' importance, the relationships between aquifer categories and their inhabiting microbial communities are still unknown. Characterizing variations within microbial communities' function and taxonomy structure at different aquifers could give a panoramic view of patterns that may enable the detection and prediction of environmental impact caused by multiple sources. Using publicly available shotgun metagenomic datasets, we examined whether soil properties, land use, and climate variables would have a more significant influence on the taxonomy and functional structure of the microbial communities than the ecological landscapes of the aquifer (i.e., Karst, Porous, Saline, Geyser, and Porous Contaminated). We found that these categories are stronger predictors of microbial communities' structure than geographical localization. In addition, our results show that microbial richness and dominance patterns are the opposite of those found in multicellular life, where extreme habitats harbour richer functional and taxonomic microbial communities. We found that low-abundant and recently described candidate taxa, such as the chemolithoautotrophic genus Candidatus Altiarcheum and the Candidate phylum Parcubacteria, are the main contributors to aquifer microbial communities' dissimilarities. Genes related to gram-negative bacteria proteins, cell wall structures, and phage activity were the primary contributors to aquifer microbial communities' dissimilarities among the aquifers' ecological landscapes. The results reported in the present study highlight the utility of using ecological landscapes for investigating aquifer microbial communities. In addition, we suggest that functions played by recently described and low abundant bacterial groups need further investigation once they might affect water quality, geochemical cycles, and the effects of anthropogenic disturbances such as pollution and climatic events on aquifers.

Getting the GIST: Testing an integrative data structure for linking taxonomy, biodiversity and conservation

Assessing and addressing biodiversity needs are of critical and time-sensitive importance, with the post-2020 Global Biodiversity Framework’s Global Taxonomy Initiative underscoring the need to build capacity in how we conceptualize biodiversity (Abrahamse et al. 2021). Species—as biological units—and their names are the backbone for the data integration and synthesis needed for biodiversity research and conservation decision-making (Grace et al. 2021). In integrating name sources for a single taxonomic group, barriers frequently limit linking species names across regional and global authoritative sources. In response to such challenges, we present a case study testing a Globally Integrated Structure of Taxonomy (GIST) to ensure the integration of taxonomy in biodiversity and conservation sciences. This framework characterizes the components enabling transparent association of species names through synonymy (alternate names or spellings), authorship, specimens, versions and timestamps, and taxonomic relationships in space and time. Taxonomy provides a critical link between biodiversity data types and databases. Efforts towards global taxonomic integration are confounded by insufficient connectivity between taxonomic assemblages, with implications for research, monitoring, and conservation practice (Christie et al. 2021, Jetz et al. 2019, McClure et al. 2020). In attempting to match multiple taxonomic groups across databases, we highlight current progress and remaining challenges to produce and use a GIST. We evaluate the standardized, comprehensive taxonomies of mammals, birds, reptiles, amphibians, dragonflies and damselflies, butterflies, ants, plants, and crabs produced for the Map of Life project (Jetz et al. 2012), identifying which missing components impede their utility. We show that for terrestrial mammals, GIST standards are almost fulfilled, but for invertebrate taxa, such as butterflies, GIST standards are unmet, resulting in broken taxonomic links between aggregators of genetic, spatial, functional, and physical data. We find that even the comprehensive taxonomies we examine do not harmonize well with taxonomies of global genetics, phylogenetics, macroecology, and conservation databases. This is because current taxonomic data infrastructures on biodiversity respositories lack the necessary structural components, searchability, and name source transparency to fully integrate taxonomies, as different independently advancing data sources lack standard metadata practices and operable interfaces. The GIST components enable data linkage and provide clear sourcing and metadata, enabling taxonomic data accessibility, reuse, and interoperability. This structure can act as a step toward open and FAIR (Findable, Accessible, Interoperable, and Reusable) data practice as it relates to taxon names (Wilkinson et al. 2016). Without transparent, integrated, accessible, and updated taxonomic information, macroecological inferences and conservation decisions for even charismatic groups are impeded.

Authorship in Collective Scientific Publications: Taxonomy of Roles, Contribution Politics and Responsibility

The study found that every year there is a growing trend towards an increase in collective publications not only in technical, but also in social and humanitarian sciences. In this article, author describes the situation regarding the essential characteristics and problems of authorship regulation in collective scientific publications. The research used methods of analysis of recommendations, regulatory instructions, requirements of scientific journals regarding the distribution of roles based on differentiation and identification of contributions in collective publications. The generalization of the obtained data made it possible to find out the motives and problems of the distribution of the roles of the lead author, co-authors and participants of the publication. The article attempts to provide answers to questions about identifying their contribution to research and publication, establishing the order of authors in the list of authors of collective publications, responsibility of the lead author, corresponding author and co-authors for research and collective publication. The author substantiates the need for culture formation and implementation the policy of contributions and responsibility into the practice of research activity of Ukrainian scientists, national specialized and other scientific publications. A methodological basis for the formation and implementation of the contribution policy has been developed, based on the differentiation of the taxonomy of roles in a collective publication. This methodology involves a combination of horizontal and vertical approaches to assessing the contribution and distribution of roles in a collective science work based on the analysis of functional and hierarchical structure of roles taxonomy. Performing a functional analysis of the horizontal level allows identifying a group of persons who correspond to the status of "author" and establish two groups of roles — “authors” and “non-authors”/”participants” of collective science publication. The next one, hierarchical analysis is carried out among all those who acquired the status of “author” as a result of horizontal structuring. It ensures the formation of a vertical substructure of the taxonomy of roles, which includes the roles of “lead author”, “corresponding author” and “co-authors” of a collective publication. The author emphasizes that an important issue of contribution policy and an integral condition for the quality and reliability of a collective scientific publication is the responsibility that rests on the lead author, corresponding author and co-authors for the collective research and content of the publication.

Behaviour change techniques taxonomy v1: Feedback to inform the development of an ontology

Background: To build cumulative evidence about what works in behaviour change interventions, efforts have been made to develop classification systems for specifying the content of interventions. The Behaviour Change Techniques (BCT) Taxonomy v1 (BCTTv1) is one of the most widely used classifications of behaviour change techniques across a variety of behaviours. The BCTTv1 was intentionally named version 1 to allow for further revisions to the taxonomy. This study aimed to gather data to inform the development of BCTTv1 into a more elaborated knowledge structure, an ontology. Methods: Feedback from users of BCTTv1 about limitations and proposed improvements was collected through the BCT website, a user survey, researchers and experts involved in the Human Behaviour Change Project, and a consultation. In addition, relevant published research reports and other classification systems of BCTs were analysed. These data were synthesised to produce recommendations to inform the development of an ontology of BCTs. Results: A total of 282 comments from six sources were reviewed and synthesised into four categories of suggestions: additional BCTs, amendments to labels and definitions of specific BCTs, amendments to the groupings, and general improvements. Feedback suggested some lack of clarity regarding understanding and identifying techniques from labels, definitions, and examples; distinctions and relations between BCTs; and knowing what they would look like in practice. Three recommendations resulted from this analysis: to review the label and definition of each BCT, the 16 groupings of BCTs, and the examples illustrating BCTs. Conclusions: This review of feedback about BCTTv1 identified the need to improve the precision and knowledge structure of the current taxonomy. A BCT ontology would enable the specification of relationships between BCTs, more precise definitions, and allow better interoperability with other ontologies. This ontology will be developed as part of the Human Behaviour Change Project.

The Competitive Position of Small Business Furniture Industry Enterprises in Poland in the Context of Sustainable Management: Relationships, Interdependencies, and Effects of Activities

This publication discusses the issue of small industrial business companies’ competitiveness in the context of managing competitiveness for the purpose of achieving a specific market position and gaining competitive advantage. The objective of this work is to analyse and present the specificity of competitive activities in small business entities and competitiveness management and to identify changes in their competitive position in the different long-term periods analysed. The theoretical part of the work presents a literature review of small business companies’ competitiveness and sustainable management of competitiveness. In this part of the article, the authors present the definition of competitiveness, competitiveness management, and sustainable competitiveness management and present their own model approach based on mind mapping. The study used statistical methods such as taxonomy structure, the Kruskal–Wallis test for independent analyses, and Friedman’s one-way analysis of variance for dependent data. The empirical part presents the authors’ research into the competitive position achieved and declared by companies classified as manufacturers of furniture. The research was divided into two parts: part one refers to qualitative research based on 10 micro businesses; part two presents quantitative research among SMEs. In total, the study covers 304 enterprises, divided into groups by size: 117 micro entities, 100 small entities, and 87 medium companies. The analysed period takes into account the long term, within the years 2008–2020. The main findings of the empirical research are as follows: the competitiveness of small business companies is determined by their qualitative features as well as by the adopted management system, and the sources of competitive advantage in the particular groups of SMEs are not highly diversified by size, with a larger number of medium entities attributing their competitive advantage to qualitative rather than quantitative measures. The achievement of this research objective has allowed for the authors to develop a model of sustainable development of competitiveness in small business companies and present the results of empirical studies. The conducted research is a pioneering contribution to the assessment of the competitive position of the surveyed enterprises and factors influencing their competitiveness in Poland in periods with different external conditions, including the COVID-19 pandemic. Taking into account the nature of empirical research undertaken in the article, it is necessary to point out their pioneering character and the related contribution to the development of science in the field of sustainable management and competitiveness of enterprises from the SME sector, and it is also necessary to fill the existing research gap in this field.

Diversity of butterflies in the tropical wetland of Kayan-Sembakung Delta, North Kalimantan, Indonesia

Abstract. Harmonis, Rahim A, Hidayat HA, Saud OR, Wilujeng M, Sampe R, Kartika KF, Aminudin, Butar TB. 2022. Diversity of butterflies in the tropical wetland of Kayan-Sembakung Delta, North Kalimantan, Indonesia. Biodiversitas 23: 3303-3312. Butterflies, which belong to the order Lepidoptera, are considered superior biological indicators to evaluate environmental conditions in various types of habitats due to their high richness in biodiversity, short generation time, good movement, and high sensitivity. Since butterflies produce various benefits for the ecosystem, their conservation currently receives significant attention in order to improve human well-being. However, there is still limited research about butterfly diversity in tropical wetlands, especially in Borneo (Kalimantan) Island. Herein, we identify the diversity of butterflies found in the Kayan-Sembakung Delta, North Kalimantan, which is still unexplored so far. The area was dominated by mangroves, peat swamps, and brackish swamps. The research locations inside the Kayan-Sembakung Delta were divided into 20 groups. The specimen was collected by an aerial insect net and bait traps. The diversity index, taxonomy structure, and domination index were further investigated. The results showed that the highest individuals (69) and species (36) had been found in Pembeliangan. Altogether, we found 608 individuals of 92 species in the areas studied. Among others, the family of Nymphalidae was noted as the most dominant. We stated that the diversity of butterflies was also influenced by the type of habitat, as it could be found at the peat swamp forest where the highest species were found. In the meantime, some unique species have been found in the Kayan-Sembakung Delta, including Arhopala overdijkinki, Hypolycaena thecloides, Drupadia johorensis, Idea leuconoe, and Polyura jalysus. The results of this study are expected to give more information for future management and conservation of the wetland areas, especially in the Kayan-Sembakung Delta.

Stochastic block models reveal a robust nested pattern in healthy human gut microbiomes.

A key question in human gut microbiome research is what are the robust structural patterns underlying its taxonomic composition. Herein, we use whole metagenomic datasets from healthy human guts to show that such robust patterns do exist, albeit not in the conventional enterotype sense. We first introduce the concept of mixed-membership enterotypes using a network inference approach based on stochastic block models. We find that gut microbiomes across a group of people (hosts) display a nested structure, which has been observed in a number of ecological systems. This finding led us to designate distinct ecological roles to both microbes and hosts: generalists and specialists. Specifically, generalist hosts have microbiomes with most microbial species, while specialist hosts only have generalist microbes. Moreover, specialist microbes are only present in generalist hosts. From the nested structure of microbial taxonomies, we show that these ecological roles of microbes are generally conserved across datasets. Our results show that the taxonomic composition of healthy human gut microbiomes is associated with robustly structured combinations of generalist and specialist species.

Enhancing Recommendation with Automated Tag Taxonomy Construction in Hyperbolic Space.

The sparse interactions between users and items on the web have aggravated the difficulty of their representations in recommender systems. Existing approaches leverage tags to alleviate the data sparsity problem, so as to enhance the performance and interpretability of recommendation. However, directly using flat item tags fails to fully exploit the hierarchical relations in data, but tag taxonomies are not always available. To this end, we propose TaxoRec to jointly construct a tag taxonomy automatically and perform recommendation accurately in hyperbolic space. Specifically, we first leverage hyperbolic space and enable the optimization of a discrete taxonomy structure via a representation-aware scoring function and an adaptive clustering algorithm, and preserve the hierarchical structure for interpretability. Then, we propose to capture the complex relations among users, items, and tags in a unified hyperbolic metric space, where a novel tag-enhanced aggregation mechanism and tag-enhanced metric learning algorithm for users and items are defined. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed TaxoRec framework, which constantly achieves an average of 7.76% improvement over the state-of-the-art baselines regarding both Recall and NDCG metrics. Insightful case studies also show that our automatically constructed tag taxonomies are highly accurate and interpretable.

The airway microbiota of non-small cell lung cancer patients and its relationship to tumor stage and EGFR gene mutation.

BackgroundAccumulating studies have suggested the airway microbiota in lung cancer patients is significantly different from that of healthy controls. However, little is known about the relationship between airway microbiota and important clinical parameters of lung cancer. In this study, we aimed to explore the association between sputum microbiota and lung cancer stage, lymph node metastasis, intrathoracic metastasis, and epidermal growth factor receptor (EGFR) gene mutation.MethodsThe microbiota of sputum samples from 85 newly‐diagnosed NSCLC patients were sequenced via 16S rRNA sequencing of the V3–V4 region. Sequencing reads were filtered using QIIME2 and clustered against UPARSE.ResultsAlpha‐ and β‐diversity was significantly different between patients in stages I to II (early stage, ES) and patients in stages III to IV (advanced stage, AS). Linear discriminant analysis Effect Size (LEfSe) identified that genera Granulicatella and Actinobacillus were significantly enriched in ES, and the genus Actinomyces was significantly enriched in AS. PICRUSt2 identified that the NAD salvage pathway was significantly enriched in AS, which was positively associated with Granulicatella. Patients with intrathoracic metastasis were associated with increased genus Peptostreptococcus and incomplete reductive TCA cycle, which was associated with increased Peptostreptococcus. Genera Parvimonas, Pseudomona and L‐valine biosynthesis were positively associated with lymph node metastasis. L‐valine biosynthesis was related with increased Pseudomona. Finally, the genus Parvimonas was significantly enriched in adenocarcinoma patients with EGFR mutation.ConclusionThe taxonomy structure differed between different lung cancer stages. The tumor stage, intrathoracic metastasis, lymph node metastasis, and EGFR mutation were associated with alteration of specific airway genera and metabolic function of sputum microbiota.

Delimitation of the tick-borne flaviviruses. Resolving the tick-borne encephalitis virus and louping-ill virus paraphyletic taxa

The tick-borne flavivirus (TBFV) group contains at least 12 members where five of them are important pathogens of humans inducing diseases with varying severity (from mild fever forms to acute encephalitis). The taxonomy structure of TBFV is not fully clarified at present. In particular, there is a number of paraphyletic issues of tick-borne encephalitis virus (TBEV) and louping-ill virus (LIV). In this study, we aimed to apply different bioinformatic approaches to analyze all available complete genome amino acid sequences to delineate TBFV members at the species level. Results showed that the European subtype of TBEV (TBEV-E) is a distinct species unit. LIV, in turn, should be separated into two species. Additional analysis of TBEV and LIV antigenic determinant diversity also demonstrate that TBEV-E and LIV are significantly different both from each other and from the other TBEV subtypes. The analysis of available literature provided data on other virus phenotypic particularities that supported our hypothesis. So, within the TBEV + LIV paraphyletic group, we offer to assign four species to get a more accurate understanding of the TBFV interspecies structure according to the modern monophyletic conception.

Struktural trasformations of tasocene Collembola under the influence of red oak invasion in the forest ecosystem of the Yavorivsky NNP

A comparative analysis of the structure of the Collembola taxocene in the root phytocenosis of hornbeam beech (Carici pilosae-Fagetum) and invasive red oak (Quercus rubra) on the territory of Yavoriv NNP was carried out. It is established that due to the replacement of the edificator of the stand in the studied areas there were changes in the taxonomic composition and ecological structure of the taxonomy of Collembola, which is a sign of degradation of indigenous forest ecosystems under the influence of phytoinvasions. Analysis of the results of studies of the impact of red oak invasion on the taxonomy of Collembola beech showed relatively insignificant structural changes in the Collembola taxonomy, which may be due to similar edificative properties of both stands, resistance of soil environment to changes in terrestrial vegetation and insufficient time to change edaphic conditions. Changes in the synecological structure of the Collembola taxonomy have been noted, indicating the relevance of the research and the importance of using these pedobionts as zooindicators of phytoinvasions in natural forest ecosystems.

РОЛЬ ФОРМАТА XBRL ДЛЯ КОНТРОЛЯ КАЧЕСТВА ОТЧЕТНЫХ ДАННЫХ НЕКРЕДИТНЫХ ФИНАНСОВЫХ ОРГАНИЗАЦИЙ

Quality control of reporting data is the most important task of each organization individually and the supervisory authority as a whole. Since 2015, the Bank of Russia has been implementing the transition of non-credit financial organizations to submit reporting data in XBRL format by supervised organizations. It is aimed at improving the quality of reporting data. This is provided by the architecture of the taxonomy structure of the XBRL format of the Bank of Russia and control ratios of indicators implemented both inside and outside the taxonomy.

Connecting Taxonomic Backbones using Global Names Tools

Biodiversity taxonomy provides a means to organize information about living organisms into maintainable tree- or graph-like structures (taxonomic backbones). Taxonomy is tightly bound to biodiversity nomenclature—a collection of recommendations, rules and conventions for naming living organisms. Species are often considered to be the most important unit of taxonomy structures. Keeping scientific names of species and other taxa accurate and up to date are major challenges during creation and maintenance of large taxonomic backbones. Global Names Architecture (Global Names) is an initiative that developed tools and databases for detecting, parsing, and verifying scientific names. Verification tools also provide information about which taxonomic and nomenclatural resources contain information for a given scientific name. Taxonomic intelligence provided by resources aggregated by Global Names allows resolving of taxon names from different backbones, even if their "current" scientific names vary. Parsing of scientific names with GNparser allows for normalization of names, making them comparable. Fast name matching (reconciliation) and discovery of a taxonomic meaning (resolution) by GNverifier connects information from various resources. The most recently developed tools by Global Names provide name verification and taxon matching on an unprecedented scale. During this presentation we are going to describe Global Names tools and show how they can be used for reconciliation of lexical variants of scientific names, for extracting the authorship metadata, how names can be verified and resolved, and how data can be connected to a variety of biodiversity resources.

The Healthy States of America: Creating a Health Taxonomy with Social Media

Since the uptake of social media, researchers have mined online discussions to track the outbreak and evolution of specific diseases or chronic conditions such as influenza or depression. To broaden the set of diseases under study, we developed a Deep Learning tool for Natural Language Processing that extracts mentions of virtually any medical condition or disease from unstructured social media text. With that tool at hand, we processed Reddit and Twitter posts, analyzed the clusters of the two resulting co-occurrence networks of conditions, and discovered that they correspond to well-defined categories of medical conditions. This resulted in the creation of the first comprehensive taxonomy of medical conditions automatically derived from online discussions. We validated the structure of our taxonomy against the official International Statistical Classification of Diseases and Related Health Problems (ICD-11), finding matches of our clusters with 20 official categories, out of 22. Based on the mentions of our taxonomy's sub-categories on Reddit posts geo-referenced in the U.S., we were then able to compute disease-specific health scores. As opposed to counts of disease mentions or counts with no knowledge of our taxonomy's structure, we found that our disease-specific health scores are causally linked with the officially reported prevalences of 18 conditions.