Update README.md

rename data files

README.md

@@ -27,7 +27,7 @@ Data has been created with [`QuantumGrav.jl`](https://github.com/ssciwr/QuantumG
 Used to train models for classifying causal sets, or for representation learning on causal sets. Other use cases have not been explored.
 
 ## Dataset Structure
-## 2048_5k.zip:
+## 2048_5k.tar.gzip2:
 - for each type: 5000 causal sets of cardinality 512 to 2048 uniformly sampled
 - a zarr v2 store for each type of causal sets
 - a .yaml config file for each that defines all parameters used to create the data. This includes data about the git commit and branch used.
@@ -46,15 +46,12 @@ Each group contains the following arrays and attributes:
 - relation numbers
 - relation dimension
 - cardinality histogram (spectrum)
+- maximum forward- and backward path length in the graph represented by the link matrix for each node. These are called `max_pathlen_forward` and `max_pathlen_backward`.
 
 All properties of the dataset have been uniformly sampled. See the `config.yaml` files for details.
 Additionally, parallel coordinates plots for the sampled properties are provided.
 
-## 2048_5k_augmented.zip:
-This is a copy of 2048_5k.zip with additional node-level features:
-- maximum forward- and backward path length in the graph represented by the link matrix for each node. These are called `max_pathlen_forward` and `max_pathlen_backward`.
-
-## 2048_5k_normaldist.zip:
+## 2048_5k_normaldist.tar.gzip2:
 This is largely the same as 2048_5k.zip, but the properties of the causal sets have been drawn from normal distributions instead of uniform distributions.
 **Note:**
 - There is no deterministic way to assure (without additional modifications of the sampling code) that the ambiguous types are always within the same range as the non-ambiguous types when drawing from normal distributions.