Update README.md

README.md

@@ -6,11 +6,43 @@
 
 # Dataset Card for carps
 
-This dataset contains several optimizer runs on a subselection of blackbox tasks (test subset).
-The entries already are normalized.
+This dataset contains several optimizer runs on a subselection of blackbox tasks.
 
-Repo: https://github.com/automl/CARP-S/tree/main/carps
-Paper: https://arxiv.org/abs/2506.06143
+## Dataset Details
+
+### Dataset Description
+
+This dataset contains several optimizer runs on a subselection of blackbox tasks (test subset). The entries already are normalized.
+
+
+- **Curated by:** C. Benjamins, H. Graf, S. Segel, D. Deng, T. Ruhkopf, L. Hennig, S. Basu, N. Mallik, E. Bergman,
+ D. Chen, F. Clément, A. Tornede, M. Feurer, K. Eggensperger, F. Hutter, C. Doerr, M. Lindauer
+- **License:** BSD License
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** https://github.com/automl/CARP-S
+- **Paper:** https://arxiv.org/abs/2506.06143
+- **Tutorial:** [[More Information Needed]](https://colab.research.google.com/drive/1WPqyk-RLd-xLeG68BkoUSQ03k-CAh6tk?usp=sharing&authuser=1#scrollTo=C5OTUSthJIt8)
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+### Direct Use
+
+- **Meta learning for sequential optimization:** Learning patterns across optimization trajectories to build better models
+- **Algorithm Selection:**  Using previous runs to learn which optimizer works best on which type of task
+- **Performance Prediction (Surrogate Modeling):** Training surrogate models that approximate the objective function of a task, the performance curves of different optimizers, or the distribution of costs/time across trials.
+- **Benchmarking and comparing optimizers**
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+
+N/A
 
 ## Dataset Structure
 
@@ -64,6 +96,4 @@ benchmark_id: The identifier of the benchmark collection the task belongs to.
 task_type: The task type, either blackbox, multi-fidelity, multi-objective, or multi-fidelity-objective / momf.
 subset_id: The subset id, mostly `None`, `dev` or `test`.
 set: Another name for `subset_id`.
-task.optimization_resources.n_trials: The optimization resources for the tasks in terms of number of trials.
-```
-
+task.optimization_resources.n_trials: The optimization resources for the tasks in terms of number of trials.