diff --git a/README.md b/README.md
index a0357a53..3511211b 100644
--- a/README.md
+++ b/README.md
@@ -31,11 +31,10 @@ The optional dependencies are required for the corresponding models.
 
 ## Usage
 
-To reproduce the results locally or test a custom model, please refer to the `ASEModel.evaluate` method.
+To reproduce the results locally or test a custom model, please refer to the `ASEModel.evaluate` method. The test data can be found [here](https://aissquare.com/datasets/detail?pageType=datasets&name=LAMBench-DatasetCards&id=336).
 
-- For direct prediction tasks, you can use the staticmethod `run_ase_dptest(calc: Calculator, test_data: Path) -> dict`. The test data can be found [here](https://www.aissquare.com/datasets/detail?pageType=datasets&name=LAMBench-TestData-v1&id=295).
+- For direct prediction tasks, you can use the staticmethod `run_ase_dptest(calc: Calculator, test_data: Path) -> dict`.
 - For calculator tasks, you can use the corresponding scripts provided in `lambench.tasks.calculator`.
-  - The phonon test data can be found [here](https://www.aissquare.com/datasets/detail?pageType=datasets&name=LAMBench-Phonon-MDR&id=310).
   - An `ASEModel` object is needed for such tasks; you can create a dummy model as follows:
 
     ```python
diff --git a/lambench/metrics/results/metadata.json b/lambench/metrics/results/metadata.json
index 19978992..53f20a51 100644
--- a/lambench/metrics/results/metadata.json
+++ b/lambench/metrics/results/metadata.json
@@ -643,7 +643,7 @@
     "DISPLAY_NAME":"Generalizability Tests (Property Calculation)",
     "DESCRIPTION": "Evaluation metrics for domain specific property calculation tasks when utilizing the LAM as the force field.",
     "phonon_mdr": {
-      "DISPLAY_NAME": "Phonon Properties",
+      "DISPLAY_NAME": "Phonon MDR",
       "DESCRIPTION": "Evaluation metrics based on phonon calculations.",
       "mae_entropy": {
         "DISPLAY_NAME": "Entropy MAE (J/K/mol)",
@@ -667,7 +667,7 @@
       }
     },
     "torsionnet": {
-      "DISPLAY_NAME": "Torsional Barrier",
+      "DISPLAY_NAME": "TorsionNet500",
       "DESCRIPTION": "Evaluation of torsional barrier related metrics on the TorsionNet-500 dataset at the CCSD(T)/CBS level.",
       "MAE": {
         "DISPLAY_NAME": "MAE (kcal/mol)",
@@ -687,8 +687,8 @@
       }
     },
     "neb": {
-      "DISPLAY_NAME": "NEB",
-      "DESCRIPTION": "Evaluation of NEB related metrics on the OC20NEB-OOD dataset. The dataset contains 460 NEB trajectories over 3 types of reactions: desorption, dissociation, and transfer. For details, please refer to https://arxiv.org/abs/2405.02078. The energy barrier height is calculated with single point energy prediction on DFT relaxed structures using the LAM model without performing NEB optimization.",
+      "DISPLAY_NAME": "OC20NEB-OOD",
+      "DESCRIPTION": "Evaluation of energy barrier related metrics on the OC20NEB-OOD dataset. The dataset contains 460 NEB trajectories over 3 types of reactions: desorption, dissociation, and transfer. For details, please refer to https://arxiv.org/abs/2405.02078. The energy barrier height is calculated with single point energy prediction on DFT relaxed structures using the LAM model without performing NEB optimization.",
       "MAE_Ea": {
         "DISPLAY_NAME": "MAE_Ea (eV)",
         "DESCRIPTION": "The mean absolute error of the energy barrier prediction across all OOD trajectories."