Publication
B-Cubed: Leveraging Analysis-Ready Biodiversity Datasets and Cloud Computing for Timely and Actionable Biodiversity Monitoring
Publisher:
Pensoft Publishers
Date:
09-08-2023
DOI:
10.3897/BISS.7.110734
Abstract: Effective bio ersity management and policy decisions require timely access to accurate and reliable information on bio ersity status, trends, and threats. However, the process of data cleaning, aggregation, and analysis is often time-consuming, convoluted, laborious, and irreproducible. Bio ersity monitoring across large areas faces challenges in evaluating data completeness and quantifying s ling effort. Despite these obstacles, unprecedented amounts of bio ersity data are being accumulated from erse 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 bio ersity data (Kissling et al. 2017). Furthermore, a significant delay exists in converting bio ersity data into actionable knowledge. Efforts have been made to reduce this lag through rapid mobilisation of bio ersity 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 (Bio ersity 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 Bio ersity Information Facility (GBIF). By leveraging aggregated occupancy cubes as analysis-ready bio ersity datasets, we aim to enhance comprehension and reduce barriers to accessing and interpreting bio ersity 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.