Update README.md

README.md

@@ -38,7 +38,6 @@ Pretrained models: https://huggingface.co/mikkoim/aquamonitor-baselines
 
 ## Uses
 
-
 ### Direct Use
 
 The dataset is intended for benchmarking computer vision methods applied to aquatic invertebrate identification. Specifically, it is used to define and evaluate performance on three benchmark tasks:
@@ -82,4 +81,57 @@ Few-shot benchmark: Targeting categories with limited training examples to evalu
 
 ### Columns
 
-See `README-metadata.md` for metadata column descriptions.
+See `README-metadata.md` for metadata column descriptions.
+
+## Usage
+
+Using Huggingface `datasets`:
+```python
+import datasets
+ds = datasets.load_dataset("mikkoim/aquamonitor", data_dir="images", split="train", cache_dir="aquamonitor")
+```
+The full dataset will consume \~100GB of disk space, and it is recommended to cache it to a known location.
+
+For testing, you can use the thumbnail dataset (~10GB):
+```python
+ds_thumbs = datasets.load_dataset("mikkoim/aquamonitor", data_dir="thumbnail", split="train", cache_dir="aquamonitor")
+```
+
+You can also download the raw `.tar` partitions from [here](https://huggingface.co/datasets/mikkoim/aquamonitor/tree/main/images)
+
+The metadata can be accessed straight from Huggingface using pandas:
+
+```python
+import pandas as pd
+df = pd.read_parquet("https://huggingface.co/datasets/mikkoim/aquamonitor/resolve/main/aquamonitor-monitor.parquet.gzip")
+df_train = df.query("fold0 == 'train'")
+df_val = df.query("fold0 == 'val'")
+```
+
+The benchmark splits are in separate files:
+
+```python
+df_classif = pd.read_parquet("https://huggingface.co/datasets/mikkoim/aquamonitor/resolve/main/aquamonitor-classif.parquet.gzip")
+df_fewshot = pd.read_parquet("https://huggingface.co/datasets/mikkoim/aquamonitor/resolve/main/aquamonitor-fewshot.parquet.gzip")
+```
+
+## Dataset Creation
+
+### Source Data
+#### Data Collection and Processing
+
+The samples are from a national monitoring programme for agriculture and forestry diffuse loading impacts on streams and lakes (MaaMet-monitoring) [More information in Finnish](https://www.syke.fi/fi/palvelut/seurannat-ja-inventoinnit/maa-ja-metsatalouden-seurantaohjelma).
+
+The samples imaged are only from lakes and not streams.
+
+Comparing our specimen counts to a national monitoring database, we were able to image 89.58% (out of 25,546) of 2021 specimens and 72.65% (out of 27,952) of 2022 specimens.
+
+Taxonomic coverage is 152 taxa out of 161 taxa encountered during the two monitoring years.
+
+The specimens were imaged using the [BIODISCOVER device](https://github.com/Aarhus-University-MPE/BioDiscover/) and its software.
+
+Detailed code repositories for all phases of dataset processing can be found from https://github.com/mikkoim/aquamonitor-codes
+
+## Bias, Risks, and Limitations
+
+The dataset is limited to Finnish lake invertebrates, collected with kick-sampling methods.