SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Momentum128 3D SR is created by sampling  $128^{3}$  sub-volumes of density and velocity from the uniform regions of all BLASTNet 2.0 datasets. This results in 12750 sub-volume samples, out of which 2000 sub-volumes are sampled to create Momentum128 3D SR. To ensure statistical representativeness of the original dataset, these 2000 samples are collected in these steps:
1. Extracting four statistical moments of the three velocity-components of each of the 12750 sub-volumes.
2. Applying k-means clustering with the elbow method to partition the sub-volumes in 18 clusters.
3. Sampling 2000 sub-volumes with balanced proportions from each cluster to generate the labels.
4. Favre-filtering the labels to generate the features.
Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted? If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation. N/A.
Yes. BLASTNet 2.0 is publicly available via https://blastnet.github.io.
Python is used to sample from BLASTNet 2.0. Code for Favre-filtering is attached to https://github.com/blastnet/blastnet2_SR_benchmark.
The Momentum128 3D SR dataset was specifically sub-sampled for the purpose of super-resolution and turbulence closure modeling. This dataset can be used for closure modeling via direct regression of the subgrid-scale terms. This high-fidelity data can also be employed for generative and deterministic reconstruction.
This dataset is specifically generated for SR and turbulent closure modeling purpose from BLASTNet 2.0. Thus, it should not be used for other tasks.
Yes, the dataset is publicly available at https://www.kaggle.com/datasets/waitongchung/blastnet-momentum-3d-sr-dataset.
The data is available through Kaggle at https://www.kaggle.com/datasets/waitongchung/blastnet-momentum-3d-sr-dataset. This dataset shares a common DOI with BLASTNet 2.0, at https://doi.org/10.5281/zenodo.7242864.
We are planning to release a fully-tested version of Momentum128 3D SR prior to NeurIPS 2023, depending on the outcome of the review process. However, a publicly-available version that can be peer-reviewed is released on June 14 2023.
Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license and/or ToU, and
provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions. Licensed via CC BY-SA NC 4.0.
Momentum128 3D SR is hosted on Kaggle. Stanford Laboratory of Fluids in Complex Systems (https://web.stanford.edu/group/ihmegroup/cgi-bin/MatthiasIhme) will maintain this dataset, along with the support of contributors from Sandia National Laboratory, University of Melbourne, Polytechnique Montreal, and University of Connecticut.
The BLASTNet team can be reached at blast.net.data@gmail.com. The users are encouraged to submit any inquiries to GitHub page https://github.com/blastnet/blastnet.github.io/issues. Lastly, Wai Tong Chung (wtchung@stanford.edu) and Matthias Ihme (mihme@stanford.edu) can be reached for further inquiries and collaborations.
Yes, the team will release minor updates to the dataset in case of any identified errors. Theses updates will be announced in the landing page https://blastnet.github.io and GitHub https://github.com/blastnet/blastnet.github.io/issues.
Yes. Version histories and logs from the Kaggle repository will be used to inform users of changes.
Yes. Kaggle hosts a comments section that enables user feedback and suggestions. In addition, contact information of the data curators are also provided in https://blastnet.github.io.