Update README.md

README.md

@@ -9,7 +9,7 @@ This one of several datasets we used for training our scaled-up Panda-72M model
 Details: Each subdirectory is the named as {Base System}-pp{param_idx} where the param_idx is the index of the specific parameter perturbation. The files in each subdirectory correspond to different initial conditions, all with different number of "periods" on Fourier timescale. Specifically, we provide 16 initial conditions (each with different number of periods) for each parameter perturbation.
 
 **_NOTE (on historical changelog):_** 
-The dataset in train.parquet is a result of us combining two data directories, “train” and “train_more” which used to exist separately on our servers. We combined these two splits to provide the community with a more complete dataset for future works. We clarify that “train_more” is generated exactly the same way as “train”; however, “train_more” contains 9 initial conditions (ic8-ic16) per perturbed system whereas “train” contains 7 initial conditions (ic1-ic7) per perturbed system. This discrepancy was due to an *indexing error*. Moreover, there are actually only 109 “ic1” trajectories in the entire “train” dataset; these are all for “pp0” i.e. the unperturbed original base systems.
+The dataset in train.parquet is a result of us combining two data directories, “train” and “train_more” which used to exist separately on our servers. We combined these two splits to provide the community with a more complete dataset for future works. We clarify that “train_more” is generated exactly the same way as “train”; however, “train_more” contains 9 initial conditions (ic8-ic16) per perturbed system whereas “train” contains 7 initial conditions (ic1-ic7) per perturbed system. This discrepancy was due to an **indexing error**. Moreover, there are actually only 109 “ic1” trajectories in the entire “train” dataset; these are all for “pp0” i.e. the unperturbed original base systems.
 
 We emphasize that we trained Panda-72M on only this original “train” split, not on “train_more”. For more details, wandb run log, and book-keeping, see **_GilpinLab/dataset-panda72M-repro_** which contains the parquet files of the exact splits we used in our training.