B-Cubed: Leveraging analysis-ready biodiversity datasets and cloud computing for timely and actionable biodiversity monitoring

Effective biodiversity management and policy decisions require timely access to accurate and reliable information on biodiversity status, trends, and threats. However, the process of data cleaning, aggregation, and analysis is often time-consuming, convoluted, laborious, and irreproducible. Biodiversity monitoring across large areas faces challenges in evaluating data completeness and quantifying sampling effort. Despite these obstacles, unprecedented amounts of biodiversity data are being accumulated from diverse sources, aided by emerging technologies such as automatic sensors, eDNA, and satellite tracking. To address these challenges, the development of tools and infrastructure is crucial for meaningful interpretations and deeper understanding of biodiversity data (Kissling et al. 2017). Furthermore, a significant delay exists in converting biodiversity data into actionable knowledge. Efforts have been made to reduce this lag through rapid mobilisation of biodiversity observations, digitization of collections (Nelson and Ellis 2018), and streamlined workflows for data publication (Reyserhove et al. 2020). However, delays still occur in the analysis, publication, and dissemination of data. The B-Cubed project (Biodiversity Building Blocks for Policy)*1 proposes solutions to overcome these challenges. It implements the concept of Occurrence Cubes (Oldoni et al. 2020), which aggregate occurrence data along spatial, temporal and taxonomic dimensions. Cube generation will be available as a new service provided by the Global Biodiversity Information Facility (GBIF). By leveraging aggregated occupancy cubes as analysis-ready biodiversity datasets, we aim to enhance comprehension and reduce barriers to accessing and interpreting biodiversity data. Automation of workflows will provide regular and reproducible indicators and models that are open and useful to users. Additionally, the use of cloud computing offers scalability, flexibility, and collaborative opportunities for applying advanced data science techniques anywhere. Finally, close collaboration with stakeholders will inform us of the requirements for tools, increase impact, and facilitate the flow of information from primary data to the decision-making processes.