recipe bioconductor-bioimagedbs

Bio- and biomedical imaging dataset for machine learning and deep learning (for ExperimentHub)






The package provides a bioimage dataset for the image analysis using machine learning and deep learning. The dataset includes microscopy imaging data with supervised labels. The data is provided as R list data that can be loaded to Keras/tensorflow in R.

package bioconductor-bioimagedbs

(downloads) docker_bioconductor-bioimagedbs



depends bioconductor-annotationhub:


depends bioconductor-data-packages:


depends bioconductor-ebimage:


depends bioconductor-experimenthub:


depends curl:

depends r-animation:

depends r-base:


depends r-einsum:

depends r-filesstrings:

depends r-magick:

depends r-magrittr:

depends r-markdown:

depends r-rmarkdown:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-bioimagedbs

and update with::

   mamba update bioconductor-bioimagedbs

To create a new environment, run:

mamba create --name myenvname bioconductor-bioimagedbs

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-bioimagedbs/tags`_ for valid values for ``<tag>``)

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