Update ReadME

ReadME

@@ -1,151 +1,80 @@
----
-license: cc-by-4.0
-task_categories:
-- image-classification
-language:
-- en
-pretty_name: bacterial_morphology_classification
----
-# Dataset Card for Dataset Name
-
-<!-- Provide a quick summary of the dataset. -->
-The dataset includes images of bacteria that correspond to the following morphology: cocci, bacilli, and spirilla. 
+# Dataset Card for Bacterial Morphology Classification Dataset
 
-## Dataset Details
-
-### Dataset Description
-
-<!-- Provide a longer summary of what this dataset is. -->
-The dataset contains 600 images split into three sections: training, validation, and test sets. All the classes are balanced and each class consists of 200 images. Both the training (360 images) and validation (120 images) sets are grouped by class, with each class in a separate subfolder. The test set consists of 120 images without labels or sorted folders. 
+## Dataset Summary
 
+This dataset contains images of bacteria categorized into three morphological classes: cocci, bacilli, and spirilla. It is designed for training, validating, and testing machine learning models for bacterial morphology classification.
 
-- **Curated by:** [Yola Charara, University of Michigan-Flint, M.S Data Science-Student]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [English]
-- **License:** [CC BY 4.0]
+## Dataset Details
 
-### Dataset Sources [optional]
+### Dataset Description
 
-<!-- Provide the basic links for the dataset. -->
+This dataset was created to support research in microbiology and machine learning, focusing on bacterial morphology classification. Images in the dataset are labeled into three classes: cocci, bacilli, and spirilla. The dataset was processed to ensure compatibility and uniformity, including re-saving images in the RGB JPEG format to address encoding issues.  
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+* Dataset Title: Bacterial Morphology Classification
+* Dataset Curator: Yola Charara, University of Michigan-Flint, M.S Data Science-Student
+* Dataset Version: The data was collected from September 28, 2024 till October 1, 2024.
+* Data Card Author: Yola Charara, University of Michigan-Flint, M.S Data Science-Student
+* Data Card Version: December 7, 2024
+* License: CC BY 4.0
 
 ## Uses
 
-<!-- Address questions around how the dataset is intended to be used. -->
-The dataset will be used to classify bacteria images into three classes: cocci (round-shaped), bacilli (rod-shaped), and spirilla (spiral-shaped). 
-
 ### Direct Use
 
-<!-- This section describes suitable use cases for the dataset. -->
-The dataset will be used to train a model that accurately identifies the different bacterial shapes.
-
-[More Information Needed]
+The dataset is intended for training and evaluating machine learning models to classify bacteria by morphology, supporting tasks in microbiology and biomedical research.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-Do not remove or modify any image in the dataset.
-
-[More Information Needed]
+The dataset is not suitable for applications outside bacterial morphology classification. Misuse could include attempts to infer specific bacterial species or other unrelated properties.
 
 ## Dataset Structure
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-
-[More Information Needed]
+The dataset is divided into training, validation, and test sets. Images in the training and validation sets are grouped by class, while the test set contains images without class-based folders.
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-<!-- Motivation for the creation of this dataset. -->
-The intention behind this dataset is to build a model that accurately classifies three bacterial morphology (cocci, bacilli, spirilla).
-Identifying the morphology of bacteria is a crucial first step because it aids in the categorization of bacteria into species and the investigation of their biological functions in order to develop potentially successful treatments. 
-The approach seeks to speed up diagnosis and improve accuracy in laboratories.
-
-[More Information Needed]
+The dataset was created to facilitate automated bacterial classification, reducing reliance on manual microscopy analysis and accelerating research workflows.
 
 ### Source Data
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-The data is extracted from downloaded images of bacteria seen under different types of microscope using the python library "bing-image-downloader" which downloads a bulk of images from Bing. 
-
 #### Data Collection and Processing
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-[More Information Needed]
+Images were curated from the python library bing-downloader which downloads a bulk of images from bing images. Preprocessing included resizing, normalizing, and re-saving all `.jpg` images in RGB mode to ensure compatibility and resolve encoding issues.
 
 #### Who are the source data producers?
 
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-
-[More Information Needed]
+The images were sourced from microbiological research or publicly available datasets. Specific data producers are not disclosed.
 
-### Annotations [optional]
-
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+### Annotations 
 
 #### Annotation process
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
+Each image was labeled based on its morphological class by graduate students.
 
 #### Personal and Sensitive Information
 
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-[More Information Needed]
+The dataset does not contain any personal or sensitive information.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The dataset is limited to three morphological classes and may not generalize well to other bacterial shapes or conditions. There may be inherent biases in the dataset depending on the sources of the images.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
+Users should test models on other datasets to ensure generalizability and reduce bias, as this dataset may not cover all variations. Additional data from diverse sources is recommended to improve model robustness.
 
-## More Information [optional]
+## Glossary 
 
-[More Information Needed]
+**Cocci:** Round-shaped bacteria.  
+**Bacilli:** Rod-shaped bacteria.  
+**Spirilla:** Spiral-shaped bacteria.
 
-## Dataset Card Authors [optional]
+## More Information 
 
-[More Information Needed]
+Codalab link for this dataset: https://codalab.lisn.upsaclay.fr/competitions/20680?secret_key=b04da988-294a-4491-831c-3f4b55529a0b 
 
 ## Dataset Card Contact
 
-[More Information Needed]
+email: yolac@umich.edu