Readme update with DCAI intro

README.md

@@ -58,33 +58,73 @@ tags:
 
 ### Dataset Summary
 
-This dataset ...
+[Data-centric AI](https://datacentricai.org) principles have become increasingly important for real-world use cases. At [Renumics](https://renumics.com/) we believe that classical benchmark datasets and competitions should be extended to reflect this development. 
 
-[//]: # (todo: verantwortlichkeit/attribution)
+This is why we are publishing benchmark datasets with application-specific enrichments (e.g. embeddings, baseline results, uncertainties, label error scores). We hope this helps the ML community in the following ways:
+1. Enable new researchers to quickly develop a profound understanding of the dataset.
+2. Popularize data-centric AI principles and tooling in the ML community.
+3. Encourage the sharing of meaningful qualitative insights in addition to traditional quantitative metrics.
 
-### Explore DCASE23 Task 2 with Spotlight
+This dataset is an enriched version of the [dataset](https://zenodo.org/record/7690148#.ZAXsSdLMLmE) provided in the context of the [anomalous sound detection task](https://dcase.community/challenge2023/task-first-shot-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring) of the [DCASE2023 challenge](https://dcase.community/challenge2023/). The enrichment include an embedding generated by a pre-trained [Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer#transformers.ASTFeatureExtractor) and results of the official challenge [baseline implementation](https://github.com/nttcslab/dase2023_task2_baseline_ae).
+
+### DCASE23 Task2 Dataset
+
+Once a year, the [DCASE community](https://dcase.community/) publishes a [challenge](https://dcase.community/challenge2023/) with several tasks in the context of acoustic event detection and classification. [Task 2 of this challenge](https://dcase.community/challenge2023/task-first-shot-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring) deals with anomalous sound detection for machine condition monitoring. The original dataset is based on the [MIMII DG](https://arxiv.org/abs/2205.13879) and the [ToyADMOS2](https://arxiv.org/abs/2106.02369) datasets. Please cite the papers by [Harada et al.](https://arxiv.org/abs/2106.02369) and [Dohi et al.](https://arxiv.org/abs/2205.13879) if you use this dataset and the paper by [Harada et al.](https://arxiv.org/pdf/2303.00455.pdf) if you use the baseline results.
+
+### Explore Dataset
 
 ![Analyze DCASE23 Task 2 with Spotlight](data/preview_dcase.png "Analyze DCASE23 Task 2 with Spotlight")
 
-Install Spotlight via [pip](https://packaging.python.org/en/latest/key_projects/#pip)
-```bash
-pip install renumics-spotlight
+The enrichments allow you to quickly gain insights into the dataset. The open source data curation tool Renumics Spotlight enables that with just a few lines of code:
+
+Load the dataset from huggingface in your notebook:
+```jupyterpython
+import datasets
+
+dataset = datasets.load_dataset("renumics/dcase23-task2-enriched", "dev", split="all", streaming=False)
 ```
 
-and simply run the following codeblock:
+Install Spotlight via [pip](https://packaging.python.org/en/latest/key_projects/#pip):
+```jupyterpython
+!pip install renumics-spotlight
+```
+
+Start exploring with a simple view that leverages embeddings to identify relevant data segments:
 ```jupyterpython
-from datasets import load_dataset, load_dataset_builder
 from renumics import spotlight
 
-dataset = load_dataset("renumics/dcase23-task2-enriched", "dev", split="all", streaming=False)
-ds_builder = load_dataset_builder("renumics/dcase23-task2-enriched", "dev")
+df = dataset.to_pandas()
+spotlight.show(df, dtype={'audio': spotlight.Audio, "ast-finetuned-audioset-10-10-0.4593-embeddings": spotlight.Embedding}, layout=ds_builder.config.get_layout(config="standard"))
+```
+You can use the UI to interactively configure the view on the data. Depending on the concrete taks (e.g. model comparison, debugging, outlier detection) you might want to leverage different enrichments and metadata.
+
+This example shows a view that is suitable to compare the results of two different ML models:
+
 
-df = ds_builder.config.to_spotlight(dataset)
+```jupyterpython
+from renumics import spotlight
+
+df = dataset.to_pandas()
 spotlight.show(df, dtype={'audio': spotlight.Audio, "ast-finetuned-audioset-10-10-0.4593-embeddings": spotlight.Embedding}, layout=ds_builder.config.get_layout(config="standard"))
 ```
 
-[//]: # (todo: how2 -> dataset -> spotlight)
-[//]: # (todo: spotlight)
+## Using custom model results and enrichments
+
+When developing your custom model you want to use different kinds of information from you model (e.g. embedding, anomaly scores etc.) to gain further insights into the dataset and the model behvior.
+
+Suppose you have your model's embeddings for each datapoint as a 2D-Numpy array and your anomaly score as a 1D-Numpy array. Then you can add this information to the dataset:
+```jupyterpython
+from renumics import spotlight
+#todo
+```
+Depending on your concrete task you might want to use different enrichments. For a good overview on great open source tooling for uncertainty quantification, explainability and outlier detection, you can take a look at our [curated list for open source data-centric AI tooling](https://github.com/Renumics/awesome-open-data-centric-ai) on Github.
+
+You can also save your view configuration in Spotlight in a JSON configuration file by clicking on the respective icon:
+
+![Save a data curation layout in Spotlight](data/spotlight_save_layout.png "Save a data curation layout in Spotlight")
+
+We have also set up a Github repo with interactive notebooks that highlight different enrichment possibilities and curation templates. We encourage you to submit a pull request to share your enrichment code and interaction layouts.
+
 
 ## Dataset Structure