Update README.md

README.md

@@ -42,4 +42,95 @@ configs:
     path: data/validation-*
   - split: test
     path: data/test-*
+task_categories:
+- object-detection
+language:
+- en
+tags:
+- biology
+pretty_name: BF Microscopy SCC
+size_categories:
+- 10K<n<100K
 ---
+# Brightfield Microscopy SCC Dataset
+
+## Dataset Description
+
+The Brightfield Microscopy SCC Dataset contains brightfield microscopy images that have been sliced from 4K resolution images. The cells in the images are labeled with bounding boxes. This dataset is used for training unconditional diffusion models and cell detection models.
+
+## Dataset Structure
+
+The dataset is organized into the following splits:
+
+- `train`: 27,161 examples
+- `validation`: 3,302 examples 
+- `test`: 1,580 examples
+
+The total size of the dataset is 7.7 GB, with a download size of 7.7 GB.
+
+### Data Fields
+
+- `image`: The brightfield microscopy image (image)
+- `label`: The split the image belongs to (class label)
+  - `0`: test
+  - `1`: train 
+  - `2`: val
+- `width`: The width of the image (int64)
+- `height`: The height of the image (int64) 
+- `objects`: A struct containing:
+  - `bbox`: A sequence of sequences representing the bounding box coordinates (float64)
+  - `categories`: A sequence of integers representing the object categories (int64)
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset was created to provide a large-scale collection of labeled brightfield microscopy images for training unconditional diffusion models and cell detection models. The images were sliced from high-resolution 4K images to create a diverse set of examples.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The brightfield microscopy images were collected from Synentec GmbH and preprocessed by slicing them from the original 4K resolution images.
+
+#### Who are the source language producers?
+
+The source language producers are the researchers who captured the brightfield microscopy images used in this dataset.
+
+### Annotations
+
+#### Annotation process
+
+The cell bounding boxes were manually annotated by Ben Werdelmann and Sebastian Kollenda using AI-Studio+. The annotators followed a set of guidelines to ensure consistency in the labeling process.
+
+#### Who are the annotators?
+
+The annotators are Ben Werdelmann and Sebastian Kollenda, a team of biologists and imaging experts.
+
+### Personal and Sensitive Information
+
+The dataset does not contain any personal or sensitive information.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The Brightfield Microscopy SCC Dataset has the potential to advance research in cell biology and improve the performance of cell detection models. However, it is important to consider the ethical implications of using machine learning models in biomedical applications.
+
+### Discussion of Biases
+
+The dataset may contain biases due to the limited diversity of the cell types and imaging conditions represented in the data. Users should be aware of these limitations when using the dataset for training models.
+
+### Other Known Limitations
+
+The dataset only includes brightfield microscopy images and may not generalize well to other imaging modalities or cell types.
+
+## Additional Information
+
+### Dataset Curators
+
+The Brightfield Microscopy SCC Dataset was curated by Ben Werdelmann, Sebastian Kollenda and Mario da Graca from Synentec GmbH.
+
+### Licensing Information
+
+The dataset is private.