Update README.md

README.md

@@ -1,22 +1,34 @@
 ---
-license: cc-by-4.0
+license: mit
 tags:
 - print-duration
 - 3D-printing
 - G-code
 - supervised-learning
 - regression
+- time-series
 size_categories:
 - 1M<n<10M
+language:
+- en
+pretty_name: 3DTime
 ---
 
 # 3DTime dataset: (sample of) A Large Dataset of Multivariate Time-Series for 3D-printing Duration
 
-This dataset is a small sample of the 3DTime dataset, for which the paper is currently under review for ICML 2026.
-
-We could not find a way to anonymously publish the full 1.3 TB dataset, hence this smaller version.
+This dataset is a small sample of the 3DTime dataset, for which the paper is currently under review for NeurIPS Datasets and Benchmarks 2026.
 
 This smaller version contains:
 
 - 82 3D models (~0.08% of the full dataset), their corresponding sliced G-code, compressed annotated G-code, and binary vectorized files
-- A total of 5,855,369 G-code instructions (~0.07% of the full dataset)
+- A total of 5,855,369 G-code instructions (~0.07% of the full dataset)
+
+## Other links
+
+Full dataset access link (NOTE FOR REVIEWERS: this DOI link will be made accessible upon acceptance, in the meantime, the OpenReview submission contains an equivalent private link):
+
+> Masked for review
+
+Code repository (highly recommended in order to use the dataset):
+
+> https://github.com/3DTimeDataset/3DTime_pytorch_dataloader