SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 10: Comparison of NRMSE of five models at 16 and  $32 \times$  SR ratios, evaluated with the baseline test set. Mean and standard deviation from three seeds are reported here. Bold term represents best mean.
Models	↓NRMSEρ,u(x10-2)	↓NRMSEsgs(x10-1)	↓NRMSEk(x10-4)	↓NRMSE(ε(x10-1)
RRDB 16×	5.6±0.0	4.2±0.1	5.5±0.1	4.5±0.0
(+ Grad. Loss)	5.3±0.0	3.9±0.1	7.7±1.0	3.7±0.0
EDSR 16×	6.0±0.2	4.5±0.2	6.1±0.1	4.7±0.1
RCAN 16×	6.7±0.6	5.3±0.6	6.7±0.6	4.9±0.1
Conv-FNO 16×	7.4±0.3	5.9±0.3	9.0±0.8	5.1±0.0
Tricubic 32×	28.3	9.5	156.2	9.6
RRDB 32×	20.2±0.1	7.7±0.1	39.3±1.9	8.3±0.0
(+ Grad. Loss)	19.8±0.0	7.5±0.1	48.1±2.2	8.3±0.0
EDSR 32×	20.1±0.0	7.7±0.1	45.4±1.3	8.4±0.0
RCAN 32×	20.3±0.1	7.9±0.1	43.7±2.8	8.1±0.1
Conv-FNO 32×	20.5±0.1	8.0±0.1	48.2±3.5	8.4±0.1
Table 11: Comparison of NRMSE of five models at 16 and  $32 \times$  SR ratios, evaluated with the forced HIT set. Mean and standard deviation from three seeds are reported here. Bold term represents best mean.
Models	↓NRMSEρ,u(x10-3)	↓NRMSEsgs(x10-2)	↓NRMSEEk(x10-6)	↓NRMSEε(x10-4)
Tricubic 16×	36.2	61.2	7637.2	2517.8
RRDB 16×	3.5±0.1	9.7±0.1	40.8±15.1	48.9±3.6
(+ Grad. Loss)	3.3±0.0	9.4±0.2	242.4±89.6	45.6±12.5
EDSR 16×	4.0±0.3	13.0±1.3	23.3±7.1	83.6±21.9
RCAN 16×	5.1±1.1	18.8±4.9	46.6±22.3	97.9±30.2
Conv-FNO 16×	6.6±0.8	24.8±2.8	124.2±86.1	90.0±10.1
Tricubic 32×	118.4	81.0	68869.9	6689.3
RRDB 32×	47.6±0.7	45.5±1.7	1985.7±554.5	1753.9±57.2
(+ Grad. Loss)	45.6±0.3	41.3±0.5	2753.6±1260.0	1758.2±84.3
EDSR 32×	48.4±1.0	47.7±1.8	5709.1±1100.1	2100.7±86.9
RCAN 32×	49.6±1.9	47.5±2.0	6288.2±809.4	1481.6±274.2
Conv-FNO 32×	48.8±0.5	49.1±1.7	5991.4±105.7	1906.3±177.4
Table 12: Comparison of NRMSE of five models at 8, 16, and  $32 \times$  SR ratios, evaluated with the parametric variation set. Mean and standard deviation from three seeds are reported here. Bold term represents best mean.
Models	↓NRMSEρ,u(x10-2)	↓NRMSEsgs(x10-1)	↓NRMSEEk(x10-4)	↓NRMSE(ε(x10-1))
Tricubic 8×	37.5	70.6	301.1	3329.1
RRDB 8×	4.7±0.1	18.1±0.4	7.0±3.5	551.5±7.0
(+ Grad. Loss)	2.7±0.0	8.7±0.1	15.9±5.2	232.7±11.1
EDSR 8×	4.2±0.3	16.2±1.5	1.4±0.4	487.7±39.7
RCAN 8×	4.3±0.0	16.3±0.2	1.3±0.3	493.3±5.1
Conv-FNO 8×	8.9±0.8	37.2±2.2	6.3±3.1	830.3±38.5
Tricubic 16×	114.7	83.7	1416.9	7139.8
RRDB 16×	39.6±0.3	36.0±0.4	83.9±22.2	3409.4±37.1
(+ Grad. Loss)	36.0±0.3	30.9±0.3	140.9±27.9	2923.1±57.6
EDSR 16×	42.2±1.7	40.0±2.6	52.6±5.7	3556.5±108.8
RCAN 16×	48.7±5.2	48.3±6.7	47.0±6.4	3538.0±108.9
Conv-FNO 16×	54.8±2.9	55.8±2.8	104.5±29.5	3617.7±80.0
Tricubic 32×	242.6	93.0	4743.3	9374.6
RRDB 32×	162.0±0.2	72.2±0.3	615.0±110.0	7546.0±19.1
(+ Grad. Loss)	161.5±0.3	70.5±0.6	820.2±151.2	7694.2±3.9
EDSR 32×	162.4±0.1	74.0±1.1	491.3±2.5	7623.6±83.0
RCAN 32×	164.6±0.8	73.3±1.3	427.5±73.5	6961.8±261.0
Conv-FNO 32×	166.7±1.7	74.2±0.3	590.9±91.9	7450.4±278.4
G.2 Effects of Different Normalization during Evaluation
For the results in Section 5, all evaluation sets are normalized with their own mean and standard deviation, as shown in Table 14, prior to testing. Note that we evaluate the same mean and standard deviation for all three velocity components. This is to account for the random rotation applied to the features and labels, as detailed in Appendix F.4, which results in the velocity channels swapping axes with each other during training.
When comparing the two normalization approaches in Table 15, we observe that poor performance is seen during evaluation on the Forced HIT set when normalizing via the train set, with  $\mathrm{SSIM}_{sgs} = 0$ ,
Table 14: Mean and standard deviation of channel quantities. Values rounded to 2 significant figures.
Split Dataset	ρ [kgm-3]	ui [ms-1]
Mean	Std. Dev.	Mean	Std. Dev.
Train Set	0.23	0.068	28	48.0
Baseline Test Set	0.24	0.068	29	48.0
Parametric Variation Set	0.23	0.059	34	55.0
Forced HIT	11.00	4.600	0	1.4
and low  $\mathrm{SSIM}_{\rho, \mathbf{u}}$  when compared to tricubic interpolation. This is due to the highly different conditions (much higher density and lower velocity) in the Forced HIT set. However, when comparing the two normalization approaches in Table 16, we observe that slightly better performance is seen across the two metrics during evaluation on the Parametric Variation set when normalizing via
Table 15: Evaluating models on the Forced HIT set, with normalization involving means and standard deviations from train and Forced HIT sets.
Model	Np	↑SSIMρ,u	↑SSIMsgs
Normalization with Mean and Std. Dev. from:
Forced HIT Set	Train Set	Forced HIT Set	Train Set
Tricubic 8×	-	0.951	0.951	0.711	0.711
RRDB 8×	50.2M	0.997±0.000	0.258±0.003	0.891±0.003	0.000±0.000
(+ Grad. Loss)	0.998±0.000	0.255±0.005	0.944±0.005	0.000±0.000
EDSR 8×	34.6M	0.999±0.000	0.259±0.001	0.937±0.005	0.000±0.000
RCAN 8×	16.4M	0.999±0.000	0.261±0.001	0.941±0.003	0.000±0.000
Conv-FNO 8×	33.0M	0.849±0.009	0.619±0.014	0.249±0.001	-0.000±0.000
Tricubic 16×	-	0.876	0.876	0.432	0.432
RRDB 16×	50.3M	0.971±0.000	0.248±0.000	0.805±0.003	0.000±0.000
(+ Grad. Loss)	0.973±0.000	0.248±0.001	0.816±0.001	0.000±0.000
EDSR 16×	37.8M	0.969±0.001	0.249±0.000	0.783±0.008	0.000±0.000
RCAN 16×	17.3M	0.961±0.009	0.244±0.005	0.737±0.050	0.000±0.000
Conv-FNO 16×	34.6M	0.644±0.012	0.357±0.022	0.245±0.001	0.000±0.000
Tricubic 32×	-	0.758	0.758	0.156	0.156
RRDB 32×	50.4M	0.845±0.001	0.242±0.001	0.494±0.011	0.000±0.000
(+ Grad. Loss)	0.850±0.000	0.245±0.001	0.516±0.012	0.000±0.000
EDSR 32×	40.9M	0.845±0.001	0.244±0.002	0.463±0.005	0.000±0.000
RCAN 32×	18.2M	0.837±0.003	0.239±0.003	0.448±0.012	0.000±0.000
Conv-FNO 32×	36.2M	0.472±0.001	0.177±0.002	0.238±0.002	0.000±0.000
Table 16: Evaluating models on the Parametric Variation set, with normalization involving means and standard deviations from train and Parametric Variation sets.
Model	Np	↑SSIMρ,u	↑SSIMsgs
Normalization with Mean and Std. Dev. from:
Param. Var. Set	Train Set	Param. Var. Set	Train Set
Tricubic 8×	-	0.800	0.800	0.418	0.418
RRDB 8×	50.2M	0.898±0.003	0.901±0.003	0.755±0.002	0.760±0.003
(+ Grad. Loss)	0.929±0.001	0.932±0.002	0.825±0.001	0.830±0.001
EDSR 8×	34.6M	0.916±0.005	0.917±0.005	0.775±0.010	0.779±0.010
RCAN 8×	16.4M	0.916±0.001	0.918±0.000	0.778±0.001	0.784±0.000
Tricubic 16×	-	0.620	0.620	0.173	0.173
RRDB 16×	50.3M	0.700±0.001	0.703±0.001	0.512±0.002	0.518±0.001
(+ Grad. Loss)	0.719±0.004	0.721±0.003	0.556±0.002	0.559±0.001
EDSR 16×	37.8M	0.693±0.005	0.695±0.005	0.481±0.019	0.484±0.019
RCAN 16×	17.3M	0.665±0.024	0.668±0.023	0.415±0.058	0.417±0.059
Tricubic 32×	-	0.476	0.476	0.087	0.087
RRDB 32×	50.4M	0.482±0.000	0.484±0.000	0.186±0.006	0.187±0.004
(+ Grad. Loss)	0.483±0.001	0.485±0.000	0.188±0.002	0.192±0.002
EDSR 32×	40.9M	0.481±0.002	0.482±0.001	0.187±0.004	0.184±0.004
RCAN 32×	18.2M	0.469±0.002	0.470±0.002	0.185±0.005	0.181±0.005
the train set compared to normalizing via the Parametric Variation set ( $\sim 15\%$  higher mean and standard deviation). Nevertheless, in this work, we choose to normalize via the evaluation sets during inferencing in order to employ a consistent approach that enables sufficiently good performance across all evaluation sets. Note that results on normalization via the test set is not shown, since the mean and standard deviation of the test set is similar to those from the train set.
H Datasheets for Datasets
The following datasheets for BLASTNet 2.0 and Momentum128 3D SR Datasets are based on Datasheets for Datasets [102].
H.1 BLASTNet 2.0
BLASTNet 2.0 was developed to provide researchers in reacting and non-reacting flow physics communities with high-fidelity publicly accessible simulation data for ML applications. With 2.2 TB, 744 full-domain samples, and 34 configurations, BLASTNet can effectively address gaps in data availability and aid in fostering open/fair ML development within reacting and non-reacting flow physics communities. This data is useful for fluid flows in a wide range of ML applications tied to propulsion, energy, and the environment. Specifically, scientific tasks related to these domains may include turbulent closure modeling [37], spatio-temporal modeling [49], and inverse modeling [18].
BLASTNet was initiated by Wai Tong Chung, Ki Sung Jung, Jacqueline H. Chen, and Matthias Ihme [8]. The datasets in BLASTNet were generated by the following researchers:
1. Wai Tong Chung, Jack Guo, Davy Brouzet and Matthias Ihme at Stanford University.
2. Jacqueline H. Chen and Ki Sung Jung at Sandia National Laboratory.
3. Mohsen Talei and Bin Jiang at University of Melbourne.
4. Bruno Savard at Polytechnique Montréal.
5. Alexei Poludnenko at University of Connecticut.
Bassem Akoush, Pushan Sharma and Alex Tamkin at Stanford University contributed in administering, improving, maintaining, and documenting the dataset curation process.
What support was needed to make this dataset? (e.g. who funded the creation of the dataset? If there is an associated grant, provide the name of the grantor and the grant name and number, or if it was supported by a company or government agency, give those details.)
This work is funded by:
1. The U.S. Department of Energy National Nuclear Security Administration, under award No. DE-NA0003968.
2. The NASA Early Stage Innovation Program with award No. 80NSSC22K0257.
3. The Department of Energy Office of Energy Efficiency Renewable Energy (EERE) with award No. DE-EE0008875.
4. Wai Tong Chung received partial financial support from the Stanford Institute for Human-centered Artificial Intelligence Graduate Fellowship.
This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231 using NERSC award ERCAP0021046.
Each instance consists of 3D domain of velocity, temperature, pressure, density and mass fractions of chemical species. These variables are saved as flat arrays in separate .dat binary files, which can be loaded and reshaped to construct the 3D volumes. To enable high I/O speed in loading arrays, the data has consistent little-endian single-precision binaries that can be read with np.fromfile/np.memmap.
BLASTNet contains a total of 744 full-domain samples from a diverse collection of 34 DNS configuration.
BLASTNet data covers the full physical three-dimensional spatial domain defined during simulation. However, the data contains different numbers of timesteps, e.g., inert HIT [20] has uniform samples for 99 timesteps, whereas some of the configurations [27, 29, 67] contain single snapshots.
Each instance consists of 3D flowfields of velocity, temperature, pressure, density and mass fractions of chemical species. These are raw simulation data that have been pre-processed to a consistent format for ML applications.
Yes. All the thermodynamic and chemical quantities are labeled and can be used directly for certain regression problems. On the other hand, the flow-field data can also be used to derive additional labels for particular applications.
Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text. No.
Yes. All instances are related by the same governing equations. Some configurations share multiple time instances. These timesteps are made explicit in a descriptive info.json file.
Are there recommended data splits (e.g., training, development/Validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them. No.
Yes. Well-established high-order numerical solvers [29, 64-67] have been employed, with spatial discretization schemes ranging from 2nd- to 8th-order accuracy and time advancement accuracy ranging from 2nd- to 4th-order. Thus, this data is still subject to small numerical errors. While the
compressible reacting flow equations (Equation (18)), solved to generate this data, are valid for a wide range of conditions, errors can originate from certain assumptions in chemical modeling. Specifically, skeletal finite-rate mechanisms and mixture-averaged transport used in these reacting flow DNS have been validated for their configurations, but are not as fully-representative of real-world reactions. However, rectifying this would require detailed mechanisms (introducing an order of magnitude more PDEs) and multi-component transport that can result in intractable calculations [86].
$$
\partial_ {t} \rho + \nabla \cdot (\rho \mathbf {u}) = 0, \tag {18a}
$$
$$
\partial_ {t} (\rho \mathbf {u}) + \nabla \cdot (\rho \mathbf {u} \otimes \mathbf {u}) = - \nabla p + \nabla \cdot \boldsymbol {\tau}, \tag {18b}
$$
$$
\partial_ {t} \left(\rho e ^ {t}\right) + \nabla \cdot \left[ \mathbf {u} \left(\rho e ^ {t} + p\right) \right] = - \nabla \cdot \mathbf {q} + \nabla \cdot \left[ (\boldsymbol {\tau}) \cdot \mathbf {u} \right], \tag {18c}
$$
$$
\partial_ {t} \left(\rho Y _ {k}\right) + \nabla \cdot \left(\rho \mathbf {u} Y _ {k}\right) = - \nabla \cdot \mathbf {j} _ {k} + \dot {\omega} _ {k}, \tag {18d}
$$
BLASTNet is self-contained.
Does the dataset relate to people? If not, you may skip the remaining questions in this section. No.
Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how. No.
BLASTNet represents a collection of multiple DNS datasets, with analysis previously published in [27-29, 67, 20, 69, 70]. However, the data was not publicly available until the release of this work.
This dataset was collected from published work between years 2019 and 2022.
The DNS cases are performed using well-established numerical solvers [29, 64-67] in this research community, with analysis of the flowfields typically validated via peer-review.
What was the resource cost of collecting the data? (e.g. what were the required computational resources, and the associated financial costs, and energy consumption - estimate the carbon footprint. See Strubell et al.[103] for approaches in this area.)
Compute cost of all DNS is summarized in Table 17. However, we note that BLASTNet curates previously-unreleased already-generated data. Thus, the additional compute cost in developing this dataset is insignificant.
Table 17: Computing cost for dataset collection.
Dataset	Cost [CPU-hr]
Non-reacting HIT [20]	0.029 × 106
Reacting forced HIT [67]	~ 106
Reacting jet flows (BFER case) [29]	0.54 × 106
Reacting jet flows (COFFEE case) [29]	0.64 × 106
Non-reacting transcritical Channel Flow [69]	0.384 × 106
Reacting channel flow [28]	0.5 × 106
Partially premixed slot burner [27]	2.5 × 106
Freely Propagating Flame [70]	12 × 106
There is no sampling involved in BLASTNet 2.0, as it contains all potential instances within three-dimensional spatial domains.
Neither crowdworkers nor contractors were used in data collection. The BLASTNet datasets were created and processed by the authors of this work for no additional payment outside of typical salary and stipend.
Does the dataset relate to people? If not, you may skip the remainder of the questions in this section.
PREPROCESSING / CLEANING / LABELING
Was any preprocessing/cleaning/labeling of the data done(e.g.,discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? If so, please provide a description. If not, you may skip the remainder of the questions in this section.
Yes. The data was generated from different numerical solvers, initially exists in a range of formats (.vtk, .vtu, .tec, and .dat) that are not readily formatted for training ML models. Thus, all data are processed into a consistent format - little-endian single-precision binaries that can be read with np.fromfile/np.memmap. The choice of this data format enables high I/O speed in loading arrays. We provide .json files that store additional information on configurations, chemical mechanisms and transport properties.
No, raw data are not available for public. This is because flow physics datasets are typically stored in a variety of double-precision formats that cannot fit into open repositories, introducing challenges, along with auxiliary derived quantities. However, the shared data are single-precision versions of the original data, with additional files shared in metadata for obtaining the auxiliary quantities.
Python is used to preprocess the raw data in a convenient format for ML applications.
USES
Yes, part of the dataset was introduced previously as BLASTNET 1.0 in [8, 9], and was used for semantic segmentation and reaction closure modeling.
BLASTNet can be used for multiple tasks such as subgrid-scale modeling [37], spatial and temporal prediction [49]. Specifically, this dataset is useful for developing closure models [38] in computational fluid dynamics (in both reacting and non-reacting flows) to capture the interactions between resolved and unresolved turbulence scales and generate closure terms or correction factors to add to the existing turbulence models. Lastly, BLASTNet can also be employed for solving inverse problems [18] via ML methods.
BLASTNet 2.0 data contains data from reacting or non-reacting DNS configurations, which covers a specific range of flow physics conditions. Consequently, this dataset cannot be for unrepresented conditions such as rarefied, multi-phase, micro-fluid, Non-Newtonian, or magneto-hydrodynamic flows.
DISTRIBUTION
Yes, the dataset is publicly available. To circumvent Kaggle storage constraints, we partition our data into a network of  $< 100$  GB subsets, with each subset containing a separate simulation configuration. This partitioned data can then be uploaded as separate datasets on Kaggle, with links consolidated in https://blastnet.github.io.
The data is available through multiple Kaggle repositories, with links consolidated at https://blastnet.github.io/datasets. Each dataset shares a common DOI at https://doi.org/10.5281/zenodo.7242864.
The BLASTNet 1.0 dataset is available online and can be accessed and downloaded via Kaggle. We are planning to release a fully-tested version of BLASTNet 2.0 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.
Licensed via CC BY-SA NC 4.0.
MAINTENANCE
BLASTNet 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 Montr/'eal, and University of Connecticut.
The BLASTNet team can be reached at blast.net.data@gmail.com. Users are encouraged to submit any issues/inquiries to our 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. A detailed issue tracker is provided in https://github.com/blastnet/blastnet.github.io/issues.
If so, please describe how often, by whom, and how updates will be communicated to users (e.g., mailing list, GitHub)?
Yes, the team will release minor updates to the dataset in case of any identified errors. These updates will be communicated via the landing page https://blastnet.github.io and GitHub
https://github.com/blastnet/blastnet.github.io/issues.
Yes. BLASTNet 2.0 builds on data from BLASTNet 1.0. Thus, all previous data will be preserved with each version update. In addition, version histories and logs from individual Kaggle repositories will be used to inform users of changes. Changes will also be communicated to users via the landing page https://blastnet.github.io.
Yes. We plan to accept contributions via https://blastnet.github.io/contribute. Any contributions will be checked for accuracy, robustness, reliability, and formats prior to new release. New contributions will be tracked via our version history.
H.2 Momentum 128 3D SR
Momentum128 3D SR consists of BLASTNet 2.0 data - processed for 3D super-resolution (SR) of density and velocity at 8, 16 and  $32 \times$  SR, while mitigating constraints in memory and grid properties. The single largest sample in BLASTNet is 92 GB (with 1.3B voxels and 15 channels), which is too large to fit in a typical GPU memory. In Momentum128 3D SR, the BLASTNet 2.0 data is downsampled to  $128^{3}$  sub-volumes of density and velocity fields to maintain a low memory footprint. This dataset is useful for training/evaluating 3D SR models, as well as developing closure models [38] in computational fluid dynamics (in both reacting and non-reacting flows) to capture the interactions between resolved and unresolved turbulence scales.
Momentum128 3D SR is created by Wai Tong Chung. The raw datasets used to develop Momentum128 3D SR are contributed by the following researchers:
1. Wai Tong Chung, Davy Brouzet and Matthias Ihme at Stanford University.
2. Jacqueline H. Chen and Ki Sung Jung at Sandia National Laboratory.
3. Mohsen Talei at University of Melbourne.
4. Bruno Savard at Polytechnique Montréal.
5. Alexei Poludnenko at University of Connecticut.
Momentum128 3D SR contains a total of 2000 sub-volumes of density and velocity samples from a diverse collection of 27 DNS configuration.
Momentum128 3D SR is a subset of BLASTNet 2.0. (https://blastnet.github.io), downsampled to preserve statistical characteristics of the larger dataset. This is done via maintaining a good proportion of similar clusters (obtained via k-means with four statistical moments of three velocity components used as inputs).
Labels consists of 3D flowfields of DNS-fidelity velocity and density (of size  $128^{3}$ ). Features consist of corresponding 8,16, and  $32 \times$  Favre-filtered flowfields from the labels.
Yes. The data is fully labeled for 8,16, and  $32 \times \mathrm{SR}$  in turbulent flow applications.
Yes. Features and labels from the same DNS configuration and spatial location share a unique hash identifier.
Yes. We provide train, validation, and test splits in an 80:10:10 ratio. Two additional split sets from unseen DNS configurations are recommended for evaluating out-of-distribution behavior and data normalization.
Yes. Well-established high-order numerical solvers [29, 64-67] have been employed, with spatial discretization schemes ranging from 2nd- to 8th-order accuracy and time advancement accuracy ranging from 2nd- to 4th-order. Thus, this data is still subject to small numerical errors.
Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset
was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a future user? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.
Momentum128 3D SR is self-contained.
Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. No.
Momentum128 3D SR is sampled from BLASTNet 2.0, which was in turn validated through analysis in previous publications [27, 29, 67, 20, 70].
This dataset was collected from work done between years 2019 and 2022.
The DNS cases are performed using well-established numerical solvers [29, 64-67] in this research community.
Compute cost of all employed DNS is summarized in Table 17. However, we note that this data curates already-generated data. Thus, the additional compute cost in developing this dataset is insignificant.
Table 18: Computing cost for dataset collection.
Dataset	Cost [CPU-hr]
Non-reacting HIT [20]	0.029 × 106
Reacting forced HIT [67]	~ 106
Reacting jet flows (BFER case) [29]	0.54 × 106
Reacting jet flows (COFFEE case) [29]	0.64 × 106
Partially premixed slot burner [27]	2.5 × 106
Freely Propagating Flame [70]	12 × 106