Envisaging a global infrastructure to exploit the potential of digitised collections.

Tens of millions of images from biological collections have become available online over the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. While image analysis has become mainstream in consumer applications, it is still used only on an artisanal basis in the biological collections community, largely because the image corpora are dispersed. Yet, there is massive untapped potential for novel applications and research if images of collection objects could be made accessible in a single corpus. In this paper, we make the case for infrastructure that could support image analysis of collection objects. We show that such infrastructure is entirely feasible and well worth investing in.

BiodivNERE: Gold standard corpora for named entity recognition and relation extraction in the biodiversity domain.

Background: Biodiversity is the assortment of life on earth covering evolutionary, ecological, biological, and social forms. To preserve life in all its variety and richness, it is imperative to monitor the current state of biodiversity and its change over time and to understand the forces driving it. This need has resulted in numerous works being published in this field. With this, a large amount of textual data (publications) and metadata (e.g. dataset description) has been generated. To support the management and analysis of these data, two techniques from computer science are of interest, namely Named Entity Recognition (NER) and Relation Extraction (RE). While the former enables better content discovery and understanding, the latter fosters the analysis by detecting connections between entities and, thus, allows us to draw conclusions and answer relevant domain-specific questions. To automatically predict entities and their relations, machine/deep learning techniques could be used. The training and evaluation of those techniques require labelled corpora. New information: In this paper, we present two gold-standard corpora for Named Entity Recognition (NER) and Relation Extraction (RE) generated from biodiversity datasets metadata and abstracts that can be used as evaluation benchmarks for the development of new computer-supported tools that require machine learning or deep learning techniques. These corpora are manually labelled and verified by biodiversity experts. In addition, we explain the detailed steps of constructing these datasets. Moreover, we demonstrate the underlying ontology for the classes and relations used to annotate such corpora.

BEXIS2: A FAIR-aligned data management system for biodiversity, ecology and environmental data.

BACKGROUND: Obtaining fit-to-use data associated with diverse aspects of biodiversity, ecology and environment is challenging since often it is fragmented, sub-optimally managed and available in heterogeneous formats. Recently, with the universal acceptance of the FAIR data principles, the requirements and standards of data publications have changed substantially. Researchers are encouraged to manage the data as per the FAIR data principles and ensure that the raw data, metadata, processed data, software, codes and associated material are securely stored and the data be made available with the completion of the research. NEW INFORMATION: We have developed BEXIS2 as an open-source community-driven web-based research data management system to support research data management needs of mid to large-scale research projects with multiple sub-projects and up to several hundred researchers. BEXIS2 is a modular and extensible system providing a range of functions to realise the complete data lifecycle from data structure design to data collection, data discovery, dissemination, integration, quality assurance and research planning. It is an extensible and customisable system that allows for the development of new functions and customisation of its various components from database schemas to the user interface layout, elements and look and feel.During the development of BEXIS2, we aimed to incorporate key aspects of what is encoded in FAIR data principles. To investigate the extent to which BEXIS2 conforms to these principles, we conducted the self-assessment using the FAIR indicators, definitions and criteria provided in the FAIR Data Maturity Model. Even though the FAIR data maturity model is developed initially to judge the conformance of datasets, the self-assessment results indicated that BEXIS2 remarkably conforms and supports FAIR indicators. BEXIS2 strongly conforms to the indicators Findability and Accessibility. The indicator Interoperability is moderately supported as of now; however, for many of the lesssupported facets, we have concrete plans for improvement. Reusability (as defined by the FAIR data principles) is partially achieved.This paper also illustrates community deployment examples of the BEXIS2 instances as success stories to exemplify its capacity to meet the biodiversity and ecological data management needs of differently sized projects and serve as an organisational research data management system.

Comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries.

BACKGROUND: Morphological leaf traits are frequently used to quantify, understand and predict plant and vegetation functional diversity and ecology, including environmental and climate change responses. Although morphological leaf traits are easy to measure, their coverage for characterising variation within species and across temporal scales is limited. At the same time, there are about 3100 herbaria worldwide, containing approximately 390 million plant specimens dating from the 16th to 21st century, which can potentially be used to extract morphological leaf traits. Globally, plant specimens are rapidly being digitised and images are made openly available via various biodiversity data platforms, such as iDigBio and GBIF. Based on a pilot study to identify the availability and appropriateness of herbarium specimen images for comprehensive trait data extraction, we developed a spatio-temporal dataset on intraspecific trait variability containing 128,036 morphological leaf trait measurements for seven selected species. NEW INFORMATION: After scrutinising the metadata of digitised herbarium specimen images available from iDigBio and GBIF (21.9 million and 31.6 million images for Tracheophyta; accessed date December 2020), we identified approximately 10 million images potentially appropriate for our study. From the 10 million images, we selected seven species (Salix bebbiana Sarg., Alnus incana (L.) Moench, Viola canina L., Salix glauca L., Chenopodium album L., Impatiens capensis Meerb. and Solanum dulcamara L.) , which have a simple leaf shape, are well represented in space and time and have high availability of specimens per species. We downloaded 17,383 images. Out of these, we discarded 5779 images due to quality issues. We used the remaining 11,604 images to measure the area, length, width and perimeter on 32,009 individual leaf blades using the semi-automated tool TraitEx. The resulting dataset contains 128,036 trait records.We demonstrate its comparability to trait data measured in natural environments following standard protocols by comparing trait values from the TRY database. We conclude that the herbarium specimens provide valuable information on leaf sizes. The dataset created in our study, by extracting leaf traits from the digitised herbarium specimen images of seven selected species, is a promising opportunity to improve ecological knowledge about the adaptation of size-related leaf traits to environmental changes in space and time.

Evolution of interdisciplinarity in biodiversity science.

The study of biodiversity has grown exponentially in the last thirty years in response to demands for greater understanding of the function and importance of Earth's biodiversity and finding solutions to conserve it. Here, we test the hypothesis that biodiversity science has become more interdisciplinary over time. To do so, we analyze 97,945 peer-reviewed articles over a twenty-two-year time period (1990-2012) with a continuous time dynamic model, which classifies articles into concepts (i.e., topics and ideas) based on word co-occurrences. Using the model output, we then quantify different aspects of interdisciplinarity: concept diversity, that is, the diversity of topics and ideas across subdisciplines in biodiversity science, subdiscipline diversity, that is, the diversity of subdisciplines across concepts, and network structure, which captures interactions between concepts and subdisciplines. We found that, on average, concept and subdiscipline diversity in biodiversity science were either stable or declining, patterns which were driven by the persistence of rare concepts and subdisciplines and a decline in the diversity of common concepts and subdisciplines, respectively. Moreover, our results provide evidence that conceptual homogenization, that is, decreases in temporal ß concept diversity, underlies the observed trends in interdisciplinarity. Together, our results reveal that biodiversity science is undergoing a dynamic phase as a scientific discipline that is consolidating around a core set of concepts. Our results suggest that progress toward addressing the biodiversity crisis via greater interdisciplinarity during the study period may have been slowed by extrinsic factors, such as the failure to invest in research spanning across concepts and disciplines. However, recent initiatives such as the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) may attract broader support for biodiversity-related issues and hence interdisciplinary approaches to address scientific, political, and societal challenges in the coming years.

CMKb: a web-based prototype for integrating Australian Aboriginal customary medicinal plant knowledge.

BACKGROUND: The customary medicinal plant knowledge possessed by the Australian Aboriginal people is a significant resource. Published information on it is scattered throughout the literature, in heterogeneous data formats, and is scattered among various Aboriginal communities across Australia, due to a multiplicity of languages. This ancient knowledge is at risk due to loss of biodiversity, cultural impact and the demise of many of its custodians. We have developed the Customary Medicinal Knowledgebase (CMKb), an integrated multidisciplinary resource, to document, conserve and disseminate this knowledge. DESCRIPTION: CMKb is an online relational database for collating, disseminating, visualising and analysing initially public domain data on customary medicinal plants. The database stores information related to taxonomy, phytochemistry, biogeography, biological activities of customary medicinal plant species as well as images of individual species. The database can be accessed at http://biolinfo.org/cmkb. Known bioactive molecules are characterized within the chemoinformatics module of CMKb, with functions available for molecular editing and visualization. CONCLUSION: CMKb has been developed as a prototype data resource for documenting, integrating, disseminating, analysing multidisciplinary customary medicinal plant data from Australia and to facilitate user-defined complex querying. Each species in CMKb is linked to online resources such as the Integrated Taxonomic Information System (ITIS), NCBI Taxonomy, Australia's SpeciesLinks-Integrated Botanical Information System (IBIS) and Google images. The bioactive compounds are linked to the PubChem database. Overall, CMKb serves as a single knowledgebase for holistic plant-derived therapeutics and can be used as an information resource for biodiversity conservation, to lead discovery and conservation of customary medicinal knowledge.

An ethnobotanical study of medicinal plants used by the Yaegl Aboriginal community in northern New South Wales, Australia.

ETHNOPHARMACOLOGICAL RELEVANCE: Documentation of Australian bush medicines is of utmost importance to the preservation of this disappearing and invaluable knowledge. This collaboration between the Yaegl Aboriginal community in northern New South Wales (NSW), Australia and an academic institution, demonstrates an effective means of preserving and adding value to this information. MATERIALS AND METHODS: Questionnaire-guided interviews were performed with community Elders under a framework of participatory action research. Medicinal plant knowledge was collated in a handbook to aid interviews and to be used as an ongoing resource by the community. Specimens for all non-cultivar plants that were discussed were collected and deposited in herbaria with unique voucher numbers. This medicinal knowledge was checked against the literature for reports of related use and studies of biological activity. RESULTS: Nineteen Elders were interviewed, leading to discussions on fifty four plant preparations used for medicinal purposes. These plant preparations involved thirty two plants coming from twenty one families, reflecting the botanical diversity of the area. The plants retained in the Yaegl pharmacopoeia correspond to their accessibility and ease of preparation, reflected in their ongoing utilisation. Several plant uses did not appear elsewhere in the literature. CONCLUSIONS: This study is the first comprehensive documentation of the medicinal knowledge of the Yaegl Aboriginal community. It has been conducted using participatory action research methods and adds to the recorded customary knowledge of the region. The customary medicinal knowledge retained by the Yaegl Aboriginal community is related to the evolving needs of the community and accessibility of plants.