Data

BILBI - example dataset and code

Commonwealth Scientific and Industrial Research Organisation
Hoskins, Andrew ; Harwood, Tom ; Ware, Chris ; Ferrier, Simon
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/5f0c0beb3aad3&rft.title=BILBI - example dataset and code&rft.identifier=10.25919/5f0c0beb3aad3&rft.publisher=Commonwealth Scientific and Industrial Research Organisation (CSIRO)&rft.description=BILBI (the Biogeographic Infrastructure for Large-scaled Biodiversity Indicators) is a CSIRO capability for global biodiversity assessment. BILBI uses best available biological and environmental data, modelling and high performance computing to assess biodiversity change at fine spatial resolution across the global land surface. The example dataset and code are designed to be used together, to provide a demonstration of the BILBI implementation, stepping through an example of model fitting, through to indicator calculation and mapping.The example dataset is a subset of the datasets developed for, and used within, BILBI, while the code provides an example implementation of the BILBI capability, stepping through model fitting, to indicator calculation and mapping. A full description of the BILBI framework, including the underlying modelling, indicator algorithms, and datasets are fulling described in https://www.biorxiv.org/content/10.1101/309377v3.full. Because BILBI is designed to run highly optimized C++ code and custom data structures on CSIRO's high performance computing clusters, and the underpinning datasets are > 2.5TB in size, for reference, we provide the example dataset, and example code implementation in R.&rft.creator=Hoskins, Andrew &rft.creator=Harwood, Tom &rft.creator=Ware, Chris &rft.creator=Ferrier, Simon &rft.date=2020&rft.edition=v1&rft.relation=https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.14663&rft.relation=https://www.nature.com/articles/s41586-019-1567-7&rft.coverage=northlimit=90.0; southlimit=-60.0; westlimit=-180.0; eastLimit=180.0; projection=WGS84&rft_rights=All Rights (including copyright) CSIRO 2020.&rft_rights=Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/&rft_subject=Biodiversity, BILBI, biodiversity indicators, biodiversity targets, biodiversity patterns, bending the curve, global, macroecology, conservation, reporting, compositional-modelling, generalised dissimilarity modelling, beta diversity&rft_subject=Conservation and Biodiversity&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=ENVIRONMENTAL SCIENCE AND MANAGEMENT&rft.type=dataset&rft.language=English Access the data

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Creative Commons Attribution
https://creativecommons.org/licenses/by/4.0/

All Rights (including copyright) CSIRO 2020.

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Data is accessible online and may be reused in accordance with licence conditions

Brief description

BILBI (the Biogeographic Infrastructure for Large-scaled Biodiversity Indicators) is a CSIRO capability for global biodiversity assessment. BILBI uses best available biological and environmental data, modelling and high performance computing to assess biodiversity change at fine spatial resolution across the global land surface. The example dataset and code are designed to be used together, to provide a demonstration of the BILBI implementation, stepping through an example of model fitting, through to indicator calculation and mapping.

Lineage

The example dataset is a subset of the datasets developed for, and used within, BILBI, while the code provides an example implementation of the BILBI capability, stepping through model fitting, to indicator calculation and mapping. A full description of the BILBI framework, including the underlying modelling, indicator algorithms, and datasets are fulling described in https://www.biorxiv.org/content/10.1101/309377v3.full. Because BILBI is designed to run highly optimized C++ code and custom data structures on CSIRO's high performance computing clusters, and the underpinning datasets are > 2.5TB in size, for reference, we provide the example dataset, and example code implementation in R.

Data time period: 2015-01-01 to 2015-01-01

This dataset is part of a larger collection

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