Create README.md

Creation of the dataset card.

README.md

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+# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
+{}
+---
+
+# Dataset Card for Dataset Name
+
+<!-- Provide a quick summary of the dataset. -->
+
+We provide here datasets to help in building classification for quality of astronomical images. It is inspired from the publication 
+[Assessment of Astronomical Images Using Combined Machine-learning Models](https://doi.org/10.3847/1538-3881/ab7938).
+Authors of the publication did not provide access to the datasets used.
+We provide 2 different datasets:
+- raw dataset: astronomical images with LDAC files containing features extracted with the tool SExtractor,
+- processed dataset: catalogs of features usable as inputs for modeling.
+
+These datasets are part of the project [astro_iqa](https://github.com/mfournigault/astro_iqa).
+
+## Raw Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+Raw data sources are a compilation of images captured by the MegaCam camera at the Canada-France-Hawaii Telescope, and captured with my personal telescope/camera.
+Features are extracted from images by using the software SExtractor.
+For each FITS file present in the directory "./data/raw", the software will produce a LDAC file in the format "FITS_1.0".
+Each image is associated to one LDAC file.
+
+- **Curated by:** [selfmaker]
+- **Funded by [selfmaker]:** 
+- **Shared by [selfmaker]:** 
+- **License:** [CC-BY-NC-SA-4.0]
+
+### Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** [./raw]
+- **Demo:** [For usage details, see the notebooks SOM_datasets_preparation and dnn_datasets_preparation in the github project repo]
+
+## Processed Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+The dataset is composed of feature catalogs and image annotations.
+
+**The catalogs** are built by combining all the LDAC files produced by the SExtractor software. For each object, SExtractor is used to output the following features:
+- X and Y coordinates of the object in the image,
+- ISO0,
+- ELONGATION,
+- ELLIPTICITY,
+- CLASS_STAR,
+- BACKGROUND.
+The exposure time of the image is also added to the catalog as it can significantly affect the quality and characteristics of the detected sources.
+
+- **Curated by:** [selfmaker]
+- **Funded by [selfmaker]:** 
+- **Shared by [selfmaker]:** 
+- **License:** [CC-BY-NC-SA-4.0]
+
+### Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** [./for_modeling]
+- **Demo:** [For usage details, see the notebooks SOM_datasets_preparation, dnn_datasets_preparation and datasets_verifiation in the github project repo]
+
+
+Annotations follow the COCO format:
+"info": {
+        ...
+    },
+    "images": [
+        filenames, ...
+    ],
+    "categories": [
+        "GOOD",
+        "B_SEEING",
+        "BGP",
+        "BT",
+        "RBT"
+    ],
+    "annotations": { ... }
+Annotations files are located in the fold "data/for_modeling". The ones used to compose the current dataset are:
+- map_images_labels_cadc2.json
+- map_images_labels_ngc0869.json
+- map_images_labels_ngc0896.json
+- 8595 map_images_labels_ngc7000.json
+
+Annotations are already reported in the parquet files.
+
+To built tensorflow datasets from the parquet catalogs see the github project repo: [https://github.com/mfournigault/astro_iqa](https://github.com/mfournigault/astro_iqa).
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+These datasets aim to develop a quality assessment tool for astronomical images. 
+Given an astronomical image, to classify the image between categories: good, bad tracking, very bad tracking, bad seeing, or background issues.
+This classification can then be used:
+- during image capturing with a telescope to warn the user of potential issues,
+- during image stacking to discard bad images of bad quality, and so enable a stacking pipeline completely automated.
+For usage details, see the notebooks SOM_datasets_preparation, dnn_datasets_preparation and datasets_verification in the github project repo
+
+## 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. -->
+See the project documentation for a complete description of dataset structures..
+
+
+## More Information [optional]
+
+For more information, see the documentation of the project [astro_iqa](https://github.com/mfournigault/astro_iqa).