Datasets:
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t
float64 1
54.1
| P
float64 1
101
| dP_dt
float64 -0.07
26.6
|
|---|---|---|
30.907181 | 101.350029 | 0.014807 |
10.323865 | 101.064964 | 0.306723 |
20.839767 | 101.274414 | 0.092396 |
54.028008 | 101.398209 | -0.03469 |
38.366272 | 101.416855 | -0.053855 |
17.322664 | 101.358025 | 0.006597 |
40.337269 | 101.273148 | 0.093697 |
47.85537 | 101.395142 | -0.031539 |
29.880377 | 101.330627 | 0.034725 |
10.359272 | 101.076759 | 0.294676 |
38.767555 | 101.420876 | -0.057993 |
13.958992 | 101.346436 | 0.018493 |
9.308862 | 100.505058 | 0.875399 |
18.302261 | 101.423561 | -0.060756 |
35.014404 | 101.413116 | -0.050012 |
26.540308 | 101.343178 | 0.02184 |
46.828568 | 101.329285 | 0.036102 |
6.570714 | 88.564133 | 11.526158 |
1.507501 | 2.321638 | 3.424992 |
34.601318 | 101.382057 | -0.018089 |
47.831768 | 101.394669 | -0.03105 |
48.280254 | 101.391449 | -0.027739 |
2.546109 | 8.599655 | 9.430529 |
9.521304 | 100.666801 | 0.711757 |
13.545909 | 101.344521 | 0.020464 |
24.817163 | 101.359337 | 0.005245 |
50.357471 | 101.362999 | 0.001488 |
38.130226 | 101.399414 | -0.035927 |
24.828966 | 101.359856 | 0.004718 |
3.384077 | 19.755344 | 17.583445 |
32.181835 | 101.401329 | -0.037901 |
3.679136 | 25.419212 | 20.693991 |
51.207241 | 101.414017 | -0.050937 |
20.863373 | 101.273956 | 0.092864 |
50.94759 | 101.407784 | -0.044529 |
11.468694 | 101.258354 | 0.108858 |
46.427284 | 101.310204 | 0.055682 |
42.815762 | 101.332054 | 0.033261 |
47.937988 | 101.396248 | -0.032671 |
9.167233 | 100.373749 | 1.007869 |
20.686337 | 101.280449 | 0.086206 |
13.4987 | 101.34494 | 0.020032 |
4.139428 | 35.942024 | 24.719545 |
30.564913 | 101.325417 | 0.040077 |
45.15263 | 101.388382 | -0.024591 |
49.802761 | 101.315956 | 0.049782 |
49.330666 | 101.320404 | 0.045218 |
5.296059 | 65.800224 | 24.001348 |
26.858973 | 101.339157 | 0.025972 |
11.822764 | 101.290565 | 0.075835 |
44.692337 | 101.3881 | -0.024304 |
20.662733 | 101.281799 | 0.08482 |
44.680534 | 101.387688 | -0.023878 |
19.329065 | 101.411316 | -0.048165 |
49.023804 | 101.342087 | 0.022963 |
48.126827 | 101.395439 | -0.031843 |
11.964393 | 101.302971 | 0.063108 |
43.512104 | 101.293701 | 0.072618 |
43.594719 | 101.299271 | 0.0669 |
15.729346 | 101.370705 | -0.006427 |
52.658932 | 101.304962 | 0.06106 |
3.5021 | 21.909836 | 18.84565 |
52.894978 | 101.292297 | 0.074052 |
28.617523 | 101.396477 | -0.032911 |
44.786758 | 101.390694 | -0.026967 |
41.10442 | 101.356125 | 0.00855 |
4.788558 | 52.877426 | 26.500267 |
37.410282 | 101.310509 | 0.055369 |
20.768953 | 101.276573 | 0.090183 |
42.44989 | 101.372002 | -0.007762 |
44.055012 | 101.342331 | 0.02271 |
46.710541 | 101.320808 | 0.044804 |
47.064613 | 101.348488 | 0.016392 |
35.75795 | 101.399918 | -0.036442 |
13.309862 | 101.34684 | 0.018078 |
14.643529 | 101.365334 | -0.000912 |
6.464493 | 87.288506 | 12.497036 |
51.077415 | 101.41217 | -0.049045 |
50.4991 | 101.37635 | -0.012227 |
5.744549 | 75.690453 | 19.844521 |
12.601721 | 101.341316 | 0.023755 |
43.453091 | 101.291451 | 0.074924 |
34.672134 | 101.388824 | -0.025049 |
21.842968 | 101.348343 | 0.016535 |
13.876375 | 101.344864 | 0.020113 |
6.25205 | 84.416718 | 14.564745 |
20.391277 | 101.303429 | 0.062637 |
34.624924 | 101.384361 | -0.020461 |
24.380476 | 101.336586 | 0.028612 |
53.060211 | 101.292305 | 0.074047 |
31.733347 | 101.403 | -0.039613 |
51.05381 | 101.41156 | -0.048415 |
50.912182 | 101.406158 | -0.042865 |
10.890378 | 101.200241 | 0.168393 |
14.112423 | 101.350204 | 0.014626 |
33.303062 | 101.317513 | 0.048182 |
35.321262 | 101.419289 | -0.056356 |
51.407883 | 101.411652 | -0.04851 |
22.338667 | 101.378677 | -0.014618 |
18.137028 | 101.414757 | -0.051701 |
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SIM-Datasets: A Unified Symbolic Regression Benchmark
A standardized benchmark collection designed for the Scientific Intelligent Modelling (SIM) toolkit, providing comprehensive datasets for symbolic regression research and applications.
Overview
SIM-Datasets serves as a unified benchmark for symbolic regression tasks, offering standardized datasets with consistent formatting and evaluation protocols. This collection is specifically curated to support the Scientific Intelligent Modelling ecosystem, enabling researchers and practitioners to develop, test, and compare symbolic regression algorithms effectively.
Installation
Method 1: Git Clone
Clone the repository from Hugging Face:
git lfs install
git clone https://huggingface.co/datasets/scientific-intelligent-modelling/sim-datasets
Or from ModelScope (for users in China):
git lfs install
git clone https://www.modelscope.cn/datasets/scientific-intelligent-modelling/sim-datasets.git
Method 2: Python Package
Install via pip for seamless integration:
pip install sim-datasets
License
This project is licensed under the GPL-3.0 License. See the LICENSE file for details.
Contributing
We welcome contributions! Please feel free to submit issues or pull requests to help improve this benchmark collection.
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