Background: Animal pollination is an important ecosystem function and service, ensuring both the integrity of natural systems and human well-being. Although many knowledge shortfalls remain, some high-quality data sets on biological interactions are now available. The development and adoption of standards for biodiversity data and metadata has promoted great advances in biological data sharing and aggregation, supporting large-scale studies and science-based public policies. However, these standards are currently not suitable to fully support interaction data sharing. Results: Here we present a vocabulary of terms and a data model for sharing plant–pollinator interactions data based on the Darwin Core standard. The vocabulary introduces 48 new terms targeting several aspects of plant–pollinator interactions and can be used to capture information from different approaches and scales. Additionally, we provide solutions for data serialization using RDF, XML, and DwC-Archives and recommendations of existing controlled vocabularies for some of the terms. Our contribution supports open access to standardized data on plant–pollinator interactions. Conclusions: The adoption of the vocabulary would facilitate data sharing to support studies ranging from the spatial and temporal distribution of interactions to the taxonomic, phenological, functional, and phylogenetic aspects of plant–pollinator interactions. We expect to fill data and knowledge gaps, thus further enabling scientific research on the ecology and evolution of plant–pollinator communities, biodiversity conservation, ecosystem services, and the development of public policies. The proposed data model is flexible and can be adapted for sharing other types of interactions data by developing discipline-specific vocabularies of terms.

RESUMO O combate à má alimentação em todas as suas formas, que também se expressa na epidemia da obesidade, é uma das prioridades da Agenda 2030 e dos Objetivos de Desenvolvimento Sustentável. Este artigo traz elementos sobre insegurança alimentar no Brasil, a partir de temas relativos ao crescimento populacional, mudanças climáticas, biodiversidade e sistemas alimentares tradicionais e regenerativos; trata também do papel dos movimentos sociais para a garantia do Direito Humano à Alimentação Adequada. O Grupo de Pesquisa em Nutrição e Pobreza, o Saúde Planetária Brasil, e o Centro de Inteligência Artificial (C4AI) - Eixo AgriBio, entendem que a retomada organizada e sistêmica das frentes apontadas neste artigo de posicionamento, pode constituir um caminho para a articulação entre pesquisa, serviço e políticas públicas na direção do combate à fome e às demais manifestações da má alimentação, enquanto problema complexo prioritário para a garantia do bem-estar social.

This paper presents two key data sets derived from the Pomar Urbano project. The first data set is a comprehensive catalog of edible fruit-bearing plant species, native or introduced to Brazil. The second data set, sourced from the iNaturalist platform, tracks the distribution and monitoring of these plants within urban landscapes across Brazil. The study includes data from the capitals of all 27 federative units of Brazil, focusing on the ten cities that contributed the most observations as of August 2023. The research emphasizes the significance of citizen science in urban biodiversity monitoring and its potential to contribute to various fields, including food and nutrition, creative industry, study of plant phenology, and machine learning applications. We expect the data sets presented in this paper to serve as resources for further studies in urban foraging, food security, cultural ecosystem services, and environmental sustainability.

Urbanization brings forth social challenges in emerging countries such as Brazil, encompassing food scarcity, health deterioration, air pollution, and biodiversity loss. Despite this, urban areas like the city of São Paulo still boast ample green spaces, offering opportunities for nature appreciation and conservation, enhancing city resilience and livability. Citizen science is a collaborative endeavor between professional scientists and nonprofessional scientists in scientific research that may help to understand the dynamics of urban ecosystems. We believe citizen science has the potential to promote human and nature connection in urban areas and provide useful data on urban biodiversity.

A agricultura é um dos setores mais importantes da economia, fundamental para prover alimentos, fibras e energia para a humanidade. No Brasil é importante fonte de empregos e para uma balança comercial positiva. É também um dos setores que mais impactam o ambiente, pela mudança do uso do solo e pelas práticas de produção que podem resultar em poluição de solo, água e ar. Para fazer frente à complexidade dos seus processos de produção, a inteligência artificial tem desempenhado um papel cada vez mais importante na pesquisa agrícola, inovação e transformação social. Este texto aborda esse papel em algumas áreas importantes: gestão da água, produção agrícola e cadeias de valor, e a questão dos dados para a pesquisa.

Managing scientific names in ontologies that represent species taxonomies is challenging due to the ever-evolving nature of these taxonomies. Manually maintaining these names becomes increasingly difficult when dealing with thousands of scientific names. To address this issue, this paper investigates the use of ChatGPT-4 to automate the development of the Organism module in the Agricultural Product Types Ontology (APTO) for species classification. Our methodology involved leveraging ChatGPT-4 to extract data from the GBIF Backbone API and generate OWL files for further integration in APTO. Two alternative approaches were explored: (1) issuing a series of prompts for ChatGPT-4 to execute tasks via the BrowserOP plugin and (2) directing ChatGPT-4 to design a Python algorithm to perform analogous tasks. Both approaches rely on a prompting method where we provide instructions, context, input data, and an output indicator. The first approach showed scalability limitations, while the second approach used the Python algorithm to overcome these challenges, but it struggled with typographical errors in data handling. This study highlights the potential of Large language models like ChatGPT-4 to streamline the management of species names in ontologies. Despite certain limitations, these tools offer promising advancements in automating taxonomy-related tasks and improving the efficiency of ontology development.