Answers to the questions we hear most often about the B-Cubed – what it does, who it is for, and how to get started with its open tools, data cubes and workflows.
The B-Cubed project aimed to transform biodiversity monitoring from a disconnected, labour-intensive activity into a rapid, agile, and responsive global process. It achieved this by standardising access to biodiversity data and providing tools that can generate on-demand models and indicators of biodiversity status and change.
B-Cubed is designed to be useful for a wide variety of stakeholders, including data collectors, academic researchers, data scientists, conservation NGOs, and policymakers. The project aimed to democratise access to biodiversity data products globally and lower the technical thresholds and costs required for running automated biodiversity assessments.
Yes, the tools and algorithms developed by B-Cubed are provided as free and open-source software, typically under an MIT License or equivalent. The project strictly adheres to open science and FAIR (Findable, Accessible, Interoperable, and Reusable) principles, ensuring that datasets, software, and training materials are openly available to both the scientific community and the general public.
For technical support, B-Cubed uses the GitHub Issues system where users can ask questions, request clarifications, and report problems directly to the software developers in real time. We encourage questions being asked on the respective repos of the software packages and on the cube service repo (github.com/gbif/occurrence-cube/issues). Additionally, users can access a comprehensive documentation website (docs.b-cubed.eu) that hosts a wide collection of tutorials, step-by-step guides, and training materials.
Yes, users are not restricted solely to global databases. The B-Cubed suite includes software packages, such as gcube, that allow users to create custom spatial grids and integrate their own local, independent, or non-GBIF occurrences for analysis alongside standard B-Cubed functions.
Biodiversity data comes from a highly diverse range of sources, including citizen scientists, museums, automatic sensors, and satellite tracking, which makes integrating the information a major challenge. Data cubes solve this by standardising primary species occurrence data into uniform units across spatial, temporal, and taxonomic dimensions. This standardisation makes it possible to seamlessly integrate biodiversity data with other environmental data (such as climate and land-use scenarios) to create reliable predictive models and indicators for policymakers.
While the algorithms, software packages, and workflows developed by the B-Cubed project are provided as free and open-source tools to lower technical thresholds and overall expenses, users looking to deploy these automated workflows independently on commercial cloud computing platforms (such as Microsoft Azure, AWS, or Google Cloud) will need to consider the associated infrastructure costs. Cloud computing operates on metered charges, meaning that costs are based entirely on the amount of computational resources (processors) and disk space you consume. Consequently, the more computationally intensive your analysis is, and the larger the data file sizes you are processing, the higher the resource implications and financial costs will be.
It is also worth noting that while independent cloud deployments require careful resource planning, core B-Cubed services, like the Species Occurrence Cube download service, are already integrated into GBIF's infrastructure and are maintained as a free service as part of their core work programme.
Still have a question? Reach the developers on the cube service repository or browse the full documentation at docs.b-cubed.eu.