Extending the Environment Ontology with Text-mined Habitat Mentions

Ontologies, i.e., formal specifications of concepts and relations relevant to a specialised domain of interest, are information resources which play a crucial role in the tasks of knowledge representation, management and discovery. Knowledge acquisition, the process of curating and updating them, is typically carried out manually, requiring human efforts that are tedious, time-consuming and expensive. This holds true especially in the case of ontologies which are continuously being expanded with new terms, in their aim to support a growing number of use cases. An example of such is the Environment Ontology (ENVO). Initially developed to support the annotation of metagenomic data, ENVO has more recently realigned its goals in support of the Sustainable Development Agenda for 2030 and thus is currently much broader in scope, covering the domains of biodiversity and ecology. As a result, there has been a dramatic increase with respect to ENVO’s number of classes; hence the process of curating and updating the ontology can benefit from automated support. In this work, we aim to help in expanding ENVO in a more efficient manner by automatically discovering new habitat mentions. To this end, we developed a text mining-based approach underpinned by the following pipeline: (1) automatic extraction of habitat mentions from text using named entity recognition methods; (2) normalisation of every extracted mention, i.e., identification of the most relevant ENVO term based on the calculation of lexical similarity between them; (3) application of a filter to retain only habitat mentions that appear to not yet exist in ENVO; and (4) construction of clusters over the remaining mentions. The pipeline results in clusters consisting of potential synonyms and lexical variations of existing terms, as well as semantically related expressions, which can then be evaluated for integration into an existing ENVO class, or, on occasion, be indicative of a new class that could be added to the ontology. Applying our approach to a corpus pertaining to the Dipterocarpaceae family of forest trees (based on documents from the Biological Heritage Library and grey literature), we generated more than 1,000 new habitat terms for potential incorporation into ENVO

OOPS: The Ontology of Plant Stress: A semi-automated standardization methodology

Plant stress traits are important breeding targets for all crop species. Massive amounts of research dollars are spent generating data to combat plant diseases and environmental stress. Often this data is used to achieve a single goal, and then left in a repository to never be used again. As a scientific community, we should be striving to make all publicly funded data reusable, and interoperable. This goal is achievable only through careful annotation using universal data and metadata standards. One such standard is the use of a standardized vocabulary, or ontology. This paper presents a semi-automated method to define and label plant stresses using a combination of web scraping and ontology design patterns. Standardizing the definitions and linking plant stress with established hierarchies leverages previous work of developed knowledge bases such as taxonomic classifications and other ontologies

Agrobiodiversity Index scores show agrobiodiversity is underutilized in national food systems

The diversity of plants, animals and microorganisms that directly or indirectly support food and agriculture is critical to achiev ing healthy diets and agroecosystems. Here we present the Agrobiodiversity Index (based on 22 indicators), which provides a monitoring framework and informs food systems policy. Agrobiodiversity Index calculations for 80 countries reveal a moderate mean agrobiodiversity status score (56.0 out of 100), a moderate mean agrobiodiversity action score (47.8 out of 100) and a low mean agrobiodiversity commitment score (21.4 out of 100), indicating that much stronger commitments and concrete actions are needed to enhance agrobiodiversity across the food system. Mean agrobiodiversity status scores in consumption and conservation are 14–82% higher in developed countries than in developing countries, while scores in production are consis tently low across least developed, developing and developed countries. We also found an absence of globally consistent data for several important components of agrobiodiversity, including varietal, functional and underutilized species diversity

AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture

The future of agricultural research depends on data. The sheer volume of agricultural biological data being produced today makes excellent data management essential. Governmental agencies, publishers and science funders require datamanagement plans for publicly funded research. Furthermore, the value of data increases exponentially when they are properly stored, described, integrated and shared, so that they can be easily utilized in future analyses. AgBioData (https://www.agbiodata.org) is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable. We strive to promote authentic, detailed, accurate and explicit communication between all parties involved in scientific data. As a step toward this goal, we present the current state of biocuration, ontologies, metadata and persistence, database platforms, programmatic (machine) access to data, communication and sustainability with regard to data curation. Each section describes challenges and opportunities for these topics, along with recommendations and best practices

Small-Scale Fisheries and Aquaculture Ontology (SSFO): Labeling fish science data

Heterogeneous and multidisciplinary data are generated by research on sustainable global agriculture and agri-food systems. This data is analysed and often integrated into predictive models for climate change or decision-making tools for fisheries management and aquaculture production. WorldFish (CGIAR) research aims to improve the sustainability, productivity and resilience of aquatic food systems. Harmonising the labelling of aquatic foods data with controlled vocabularies will enable easier data aggregation, interpretation, and analysis. The Fisheries and Aquaculture Ontology Working Group was formed in 2019 to compile, update and contribute fishery related terms to existing controlled vocabularies. The objective is to improve the WorldFish data interoperability into the various projects, databases and repositories by (a) addressing inconsistent use of fisheries and aquaculture related terms across the datasets, (b) highlighting the missing terms in the main semantic resources, and (c) connecting and collaborating with the CGIAR Community of Practice for Ontology.  An ontology is a standardised representation of the definitions and relationships of data from a specific discipline. Ontologies provide a common language for different kinds of data to be easily interpretable and interoperable allowing easier aggregation and analysis

Agrobiodiversity Index scores for 80+ countries

This dataset provides Agrobiodiversity Index scores for 80+ globally dispersed countries computed using global datasets

Agrobiodiversity Index Report 2019: risk and resilience

The first Agrobiodiversity Index Report assesses dimensions of agrobiodiversity in ten countries to measure food system sustainability and resilience. Countries receive an overall Agrobiodiversity Index score that indicates their progress in using and safeguarding agrobiodiversity to create sustainable food systems. They receive also individual scores for their progress for healthy diets, sustainable production and genetic resource conservation. The focus of this report is agrobiodiversity, risk and resilience. Eight thought pieces, authored by experts from around the world in diverse fields from nutrition and agricultural sustainability to seed systems and genetic resources, stimulate thinking on aspects of agrobiodiversity and risk and/or resilience