Update README.md

README.md

@@ -39,36 +39,32 @@ license: cc-by-nc-4.0
 license: cc-by-nc-4.0
 ---
 
-# BATIS: Benchmarking Bayesian Approaches for Improving Species Distribution Models
+# BATIS: Bayesian Approaches for Targeted Improvement of Species Distribution Models
 
-This repository contains the dataset used in experiments shown in BATIS: Benchmarking Bayesian Approaches for Improving Species Distribution Models (preprint). To download the dataset, you can use the `load_dataset` function from HuggingFace. For example :
+This repository contains the dataset used in experiments shown in BATIS: Bayesian Approaches for Targeted Improvement of Species Distribution Models. To download the dataset, you can use the `load_dataset` function from HuggingFace. For example :
 
 ```python
 from datasets import load_dataset
 
 # Training Split for Kenya
-training_kenya = load_dataset("cathv/batis_benchmark_2025", name="Kenya", split="train")
+training_kenya = load_dataset("cathv/BATIS", name="Kenya", split="train")
 
 # Validation Split for South Africa
-validation_south_africa = load_dataset("cathv/batis_benchmark_2025", name="South_Africa", split="val")
+validation_south_africa = load_dataset("cathv/BATIS", name="South_Africa", split="val")
 
 # Test Split for USA-Summer
-test_usa_summer = load_dataset("cathv/batis_benchmark_2025", name="USA_Summer", split="test")
+test_usa_summer = load_dataset("cathv/BATIS", name="USA_Summer", split="test")
 ```
 
-The code to reproduce the experiments presented in the paper is available in our [GitHub repository](https://github.com/cath34/batis_benchmark_neurips_2025). Please note that the checklists data is **NOT AVAILABLE** in this current repository, in order to comply with the [eBird Data Access Terms of Use
-](https://www.birds.cornell.edu/home/ebird-data-access-terms-of-use/). If you are interested in downloading the checklists data, please formulate a [request for access](https://support.ebird.org/en/support/solutions/articles/48000838205-download-ebird-data) to the eBird team. 
+## Licenses 
 
-## ⚠️ !!! ERRATUM IN THE MAIN PAPER !!! ⚠️
+The **BATIS Benchmark** is released under a [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License](https://creativecommons.org/licenses/by-nc/4.0/).
 
-We would like to apologize to the reviewers for a typo in Table 1 of the main paper. The table incorrectly suggests that hundreds of thousands of species can be observed in the United States during summer, and nearly 50,000 in winter. While many birders would surely dream of such an extraordinary high avian biodiversity, these numbers are clearly far from the reality. The values intended for the `number_of_hotspots` column were unfortunately placed in the `number_of_species` column. The first table of the Appendix reports the appropriate numbers, but we also include it here to avoid any confusion : 
+The use of our dataset should also comply with the following:
 
-| **Region**           | **Date Range**              | **Number of Checklists** | **Number of Hotspots** | **Number of Species** | **Species List** |
-|----------------------|-----------------------------|---------------------------|-------------------------|------------------------|------------------|
-| Kenya (KE)           | 2010-01-01 to 2023-12-31    | 44,852                    | 8,551                   | 1,054                  | Avibase          |
-| South Africa (ZA)    | 2018-01-01 to 2024-06-17    | 498,867                   | 6,643                   | 755                    | BirdLife         |
-| USA-Winter (US-W)    | 2022-12-01 to 2023-01-31    | 3,673,742                 | 45,882                  | 670                    | ABA 1-2          |
-| USA-Summer (US-S)    | 2022-06-01 to 2022-07-31    | 3,920,846                 | 98,443                  | 670                    | ABA 1-2          |
+- [eBird Terms of Use](https://www.birds.cornell.edu/home/terms-of-use/)
+- [eBird API Terms of Use](https://www.birds.cornell.edu/home/ebird-api-terms-of-use/)
+- [eBird Data Access Terms of Use](https://www.birds.cornell.edu/home/ebird-data-access-terms-of-use/) 
 
 ## Dataset Configurations and Splits
 
@@ -79,12 +75,12 @@ The dataset contains the following four configurations :
 - **USA-Winter :** Containing the data used to train our models for predicting bird species distribution in the United States of America during the winter season.
 - **USA-Summer :** Containing the data used to train our models for predicting bird species distribution in the United States of America during the summer season.
 
-Each subset can be further divided into `train`, `test` and `split`. These splits are the same as the one we used in our paper, and were generated by following the pre-processing pipeline described in our paper, which can be easily reproduced by re-using our [code](https://github.com/cath34/batis_benchmark_neurips_2025/blob/main/Splits/combined_script.py). 
+Each subset can be further divided into `train`, `test` and `split`. These splits are the same as the one we used in our paper, and were generated by following the pre-processing pipeline described in our paper, which can be easily reproduced by re-using our code.
 
 ## Dataset Structure
 
 ```
-/batis_benchmark_2025/
+/BATIS/
     Kenya/
         images.tar.gz
         environmental.tar.gz
@@ -128,7 +124,7 @@ Each subset can be further divided into `train`, `test` and `split`. These split
 
 ```
 
-The files `train_filtered.csv`, `test_filtered.csv` and `valid_filtered.csv` are containing the informations one can see from the Dataset Viewer. The archives `targets`, `images`, `environmental` are respectively containing the target vectors (i.e., the estimated ground truth encounter rate probability). The `Species_ID/` folder contains the species list files for each subset. 
+The files `train_filtered.csv`, `test_filtered.csv` and `valid_filtered.csv` are containing the informations one can see from the Dataset Viewer. The archives `targets`, `images`, `environmental` are respectively containing the target vectors (i.e., the estimated ground truth encounter rate probability), the satellite images (in .tif format) and the environmental rasters from WorldClim (in .npy format) associated with each hotspot. The `Species_ID/` folder contains the species list files for each subset. 
 
 
 ## Data Fields
@@ -136,40 +132,36 @@ The files `train_filtered.csv`, `test_filtered.csv` and `valid_filtered.csv` are
 - `hotspot_id` : The unique ID associated with a given hotspot. The `hotspot_id`value can be used to upload date coming from either `targets`, `environmental` or variance, as they are all formulated as
 
 ```
-/batis_benchmark_2025/
+/BATIS/
     images/
-        {hotspot_id_1}.tar.gz
+        {hotspot_id_1}.tif
         ...
-        {hotspot_id_n}.tar.gz
+        {hotspot_id_n}.tif
     environmental/
-        {hotspot_id_1}.tar.gz
+        {hotspot_id_1}.npy
         ...
-        {hotspot_id_1}.tar.gz
+        {hotspot_id_1}.npy
     targets/
-        {hotspot_id_1}.tar.gz
+        {hotspot_id_1}.json
         ...
-        {hotspot_id_1}.tar.gz
+        {hotspot_id_1}.json
 ```
+
 - `lon` : Longitude coordinate of the hotspot
 - `latitude` : Latitude coordinate of the hotspot
 - `num_complete_checklists` : Number of complete checklists collected in that hotspot
-- `bio_1` to `bio_19`: Environmental covariates values associated with that hotspot, extracted from the WorldClim model. For more details on each of these variables, please refer to Table 5 of the appendix.
+- `bio_1` to `bio_19`: Environmental covariates values associated with that hotspot, extracted from the WorldClim model. For more details on each of these variables, please refer to the appendix.
 - `split` : The split associated with that hotspot (either `train`, `valid` or `test`)
 
-## Authors 
-
-- **Curated by:** Catherine Villeneuve, Mélisande Teng, Benjamin Akera, David Rolnick
-- **Language:** English
-- **Repository:** https://huggingface.co/datasets/cathv/batis_benchmark_2025
-- **Paper:** IN REVIEW
+## Reconstructing Satellite Image Archive Files for the USA Subsets 
+The satellite images archive files for the USA Summer and USA Winter subsets are very large. To facilitate download through Hugging Face, we decided to split these archives into multiple binary files. You can reconstruct the original archive using the `cat` command in a terminal, which will join the binary files in chronological order and reconstruct the original `.tar.gz` archive.
 
-## Licenses 
-
-The **BATIS Benchmark** is released under a [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License](https://creativecommons.org/licenses/by-nc/4.0/).
-
-The use of our dataset should also comply with the following:
-
-- [eBird Terms of Use](https://www.birds.cornell.edu/home/terms-of-use/)
-- [eBird API Terms of Use](https://www.birds.cornell.edu/home/ebird-api-terms-of-use/)
-- [eBird Data Access Terms of Use](https://www.birds.cornell.edu/home/ebird-data-access-terms-of-use/) 
+To reconstruct the archive for the USA-Winter subset, run:
+```
+cat images_chunk_aa images_chunk_ab images_chunk_ac images_chunk_ad > images.tar.gz
+```
 
+To reconstruct the archive for the USA-Summer subset, run:
+```
+cat images_chunk_aa images_chunk_ab images_chunk_ac images_chunk_ad images_chunk_ae images_chunk_af > images.tar.gz
+```