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...

计算显微镜（computational microscopy）是将成像系统的硬件与算法进行联合设计的技术，其展现出显著应用潜力：可降低成像系统的制造成本、提升运行鲁棒性，并能够采集新型信息。通常，计算成像系统——尤其是集成了机器学习的系统——的性能往往依赖于测试样本。因此，标准化数据集是对比不同技术方案性能的核心工具。本文介绍伯克利单细胞计算显微镜（BSCCM）数据集，该数据集包含超过40万张单个白细胞（white blood cells）的图像。数据采集自LED阵列显微镜（LED array microscope）下的多种照明模式，同时包含标记不同细胞类型的表面蛋白丰度的荧光测量数据。我们期望本数据集能够为计算显微镜与计算机视觉领域的新型算法开发与测试提供宝贵资源，并推动其在生物医学领域的实际应用。

# 伯克利单细胞计算显微镜（BSCCM）数据集

本数据集包含伯克利单细胞计算显微镜数据集的原始数据。数据经压缩分块处理，以优化下载效率。最便捷的下载与使用方式为通过`bsccm` Python包实现。该包的代码可在指定位置获取，并已通过数字对象标识符（DOI）完成归档。

## 数据加载与
[入门Jupyter Notebook](https://github.com/Waller-Lab/BSCCM/blob/main/Getting_started.ipynb) 详细说明了本数据集的完整使用方法，包括安装、下载、图像/元数据查询等更多内容。下文将复现该Notebook的前几步操作：
* 首先，使用`pip install bsccm`命令安装`bsccm` Python包。
* 随后下载数据：

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

* 随后加载图像：

dataset = BSCCM
(dat...