Readme updated

README.md

@@ -35,15 +35,15 @@
 
 The aim of the project is to provide comprehensive overview of datasets for wildlife individual re-identification and an easy-to-use package for developers of machine learning methods. The core functionality includes:
 
-- overview of 41 publicly available wildlife re-identification datasets.
+- overview of 42 publicly available wildlife re-identification datasets.
 - utilities to mass download and convert them into a unified format and fix some wrong labels.
 - default splits for several machine learning tasks including the ability create additional splits.
 
 An introductory example is provided in a [Jupyter notebook](notebooks/introduction.ipynb). The package provides a natural synergy with [Wildlife tools](https://github.com/WildlifeDatasets/wildlife-tools), which provides our [MegaDescriptor](https://huggingface.co/BVRA/MegaDescriptor-L-384) model and tools for training neural networks. 
 
 ## Changelog
 
-[08/10/2024] Added AmvrakikosTurtles, ReunionTurtles, ZakynthosTurtles (sea turtles), ELPephants (elephants) and Chicks4FreeID (chickens).  
+[08/10/2024] Added AmvrakikosTurtles, ReunionTurtles, SouthernProvinceTurtles, ZakynthosTurtles (sea turtles), ELPephants (elephants) and Chicks4FreeID (chickens).  
 [13/06/2024] Added WildlifeReID-10k (unification of multiple datasets).  
 [09/05/2024] Added CatIndividualImages (cats), CowDataset (cows) and DogFaceNet (dogs).  
 [28/02/2024] Added MPDD (dogs), PolarBearVidID (polar bears) and SeaStarReID2023 (sea stars).  

docs/index.md

@@ -2,6 +2,6 @@ This site contains the documentation for the [Wildlife datasets](https://github.
 
 The aim of the project is to provide comprehensive overview of datasets for wildlife individual re-identification and an easy-to-use package for developers of machine learning methods. The core functionality includes:
 
-- overview of 41 publicly available wildlife re-identification datasets.
+- overview of 42 publicly available wildlife re-identification datasets.
 - utilities to mass download and convert them into a unified format and fix some wrong labels.
 - default splits for several machine learning tasks including the ability create additional splits.