Berkeley Single-Cell Computational Microscopy (BSCCM) dataset

Computational microscopy, in which hardware and algorithms of an imaging system are jointly designed, shows promise for making imaging systems that cost less, perform more robustly, and collect new types of information. Often, the performance of computational imaging systems, especially those that incorporate machine learning, is sample-dependent. Thus, standardized datasets are an essential tool for comparing the performance of different approaches. Here, we introduce the Berkeley Single Cell Computational Microscopy (BSCCM) dataset, which contains over 400,000 images of individual white blood cells. The dataset contains images captured with multiple illumination patterns on an LED array microscope and fluorescent measurements of the abundance of surface proteins that mark different cell types. We hope this dataset will provide a valuable resource for the development and testing of new algorithms in computational microscopy and computer vision with practical biomedical applications., , , # Berkeley Single-Cell Computational Microscopy (BSCCM) Dataset

This dataset contains the raw data for the Berkeley Single Cell Computational Microscopy Dataset. The data is compressed and chunked to facilitate downloading. The easiest way to download and use it is through the `bsccm` python package. The code for this package can be found at and is archived with a DOI at

## Loading data&

The [Getting Started jupyter notebook](https://github.com/Waller-Lab/BSCCM/blob/main/Getting_started.ipynb) shows the full documentation for how to use this dataset, including installation, downloading, image/metadata querying, and more. Here we reproduce the first few steps of the notebook:

* First, install the `bsccm` python package with `pip install bsccm`

* Then download the data:

```
from bsccm import download_dataset
dataset_path = download_dataset('/path/to/download', tiny=True)
print('Downloaded dataset to ' + dataset_path)
```

* Then open an image

```
dataset = BSCCM
(dat...