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Source code for constructing bioregions around Australia with Gradient Forest and CASTER

The University of Queensland
Mr Phil Dyer (Aggregated by) Mr Philip Dyer (Aggregated by)
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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:doi10.48610/def22f8&rft.title=Source code for constructing bioregions around Australia with Gradient Forest and CASTER&rft.identifier=RDM ID: 73eb2ac0-5aaf-4441-b5c1-1f79f85d6343&rft.publisher=The University of Queensland&rft.description=The source code used to perform the analysis for the project Novel methods for developing large-scale, data-driven, biologically informed bioregionalisations. The project analyses publicly available datasets, but the datasets are not contained here. The source code assumes specific external datasets are placed in appropriate folders, as described in the readme file. The analysis is performed in R and bash. The code is capable of running in HPC environments to speed up execution. Some stages of the analysis require large amounts of memory.&rft.creator=Mr Phil Dyer&rft.creator=Mr Philip Dyer&rft.date=2024&rft_rights= https://creativecommons.org/licenses/by/4.0/deed.en&rft_subject=eng&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=Conservation and biodiversity&rft_subject=Environmental management&rft_subject=MATHEMATICAL SCIENCES&rft_subject=Statistics&rft_subject=Applied statistics&rft.type=dataset&rft.language=English Access the data
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[email protected]
School of Mathematics and Physics

Full description

The source code used to perform the analysis for the project "Novel methods for developing large-scale, data-driven, biologically informed bioregionalisations". The project analyses publicly available datasets, but the datasets are not contained here. The source code assumes specific external datasets are placed in appropriate folders, as described in the readme file. The analysis is performed in R and bash. The code is capable of running in HPC environments to speed up execution. Some stages of the analysis require large amounts of memory.

Issued: 28 03 2024

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Subjects
Applied Statistics | Conservation and Biodiversity | Environmental Sciences | Environmental Management | Mathematical Sciences | Statistics | eng |

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Identifiers
ACN 633 798 857