13 datasets found
  1. f

    Comparison of MAEs of various HR estimation methods for the ISPC dataset.

    • plos.figshare.com
    xls
    Updated May 31, 2023
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    Heewon Chung; Hooseok Lee; Jinseok Lee (2023). Comparison of MAEs of various HR estimation methods for the ISPC dataset. [Dataset]. http://doi.org/10.1371/journal.pone.0215014.t004
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    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Heewon Chung; Hooseok Lee; Jinseok Lee
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Comparison of MAEs of various HR estimation methods for the ISPC dataset.

  2. f

    Out-of-sample traditional and rolling window forecasts accuracy measures...

    • figshare.com
    • plos.figshare.com
    xls
    Updated May 20, 2025
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    Aline Armanini Stefanan; Murilo Sagrillo; Bruna G. Palm; Fábio M. Bayer (2025). Out-of-sample traditional and rolling window forecasts accuracy measures MAE, RMSE, MAPE, and MDA for MKSARMAX, SARMA, SARMAX, additive Holt-Winters, and KARMA models forecast of the Caconde UV dataset. The best value for each measure is shaded light gray. [Dataset]. http://doi.org/10.1371/journal.pone.0324721.t005
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    xlsAvailable download formats
    Dataset updated
    May 20, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Aline Armanini Stefanan; Murilo Sagrillo; Bruna G. Palm; Fábio M. Bayer
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Out-of-sample traditional and rolling window forecasts accuracy measures MAE, RMSE, MAPE, and MDA for MKSARMAX, SARMA, SARMAX, additive Holt-Winters, and KARMA models forecast of the Caconde UV dataset. The best value for each measure is shaded light gray.

  3. f

    Parameter setting situation.

    • plos.figshare.com
    xls
    Updated Jun 8, 2023
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    Xiaoying Yu; Hongsheng Su; Zeyuan Fan; Yu Dong (2023). Parameter setting situation. [Dataset]. http://doi.org/10.1371/journal.pone.0226751.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 8, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Xiaoying Yu; Hongsheng Su; Zeyuan Fan; Yu Dong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Parameter setting situation.

  4. f

    Comparison of four algorithm prediction indicators.

    • plos.figshare.com
    xls
    Updated Jun 3, 2023
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    Xiaoying Yu; Hongsheng Su; Zeyuan Fan; Yu Dong (2023). Comparison of four algorithm prediction indicators. [Dataset]. http://doi.org/10.1371/journal.pone.0226751.t003
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    xlsAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Xiaoying Yu; Hongsheng Su; Zeyuan Fan; Yu Dong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Comparison of four algorithm prediction indicators.

  5. f

    DF11 locomotive wheel diameter measurement report (five times).

    • figshare.com
    xls
    Updated Jun 5, 2023
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    Xiaoying Yu; Hongsheng Su; Zeyuan Fan; Yu Dong (2023). DF11 locomotive wheel diameter measurement report (five times). [Dataset]. http://doi.org/10.1371/journal.pone.0226751.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Xiaoying Yu; Hongsheng Su; Zeyuan Fan; Yu Dong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    DF11 locomotive wheel diameter measurement report (five times).

  6. f

    Comparison summary of the HR estimation results obtained with and without MA...

    • plos.figshare.com
    xls
    Updated Jun 4, 2023
    + more versions
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    Heewon Chung; Hooseok Lee; Jinseok Lee (2023). Comparison summary of the HR estimation results obtained with and without MA cancellation. [Dataset]. http://doi.org/10.1371/journal.pone.0215014.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Heewon Chung; Hooseok Lee; Jinseok Lee
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Each result was obtained from all datasets (n = 47): ISPC (n = 23) and BAMI (n = 24) datasets. The performance was evaluated on the basis of the mean absolute error (MAE).

  7. f

    Comparison of the interpolation accuracies achieved using MSE, MAE and MRE...

    • figshare.com
    xls
    Updated Jun 2, 2023
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    Longxiang Li; Jianhua Gong; Jieping Zhou (2023). Comparison of the interpolation accuracies achieved using MSE, MAE and MRE based on a cross-validation analysis. [Dataset]. http://doi.org/10.1371/journal.pone.0096111.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Longxiang Li; Jianhua Gong; Jieping Zhou
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Comparison of the interpolation accuracies achieved using MSE, MAE and MRE based on a cross-validation analysis.

  8. f

    Cascaded tanks 5-fold cross validated accuracies: Derivatives, mean absolute...

    • plos.figshare.com
    xls
    Updated Sep 20, 2024
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    Kyle Hayes; Michael W. Fouts; Ali Baheri; David S. Mebane (2024). Cascaded tanks 5-fold cross validated accuracies: Derivatives, mean absolute error (MAE). [Dataset]. http://doi.org/10.1371/journal.pone.0309661.t001
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    xlsAvailable download formats
    Dataset updated
    Sep 20, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Kyle Hayes; Michael W. Fouts; Ali Baheri; David S. Mebane
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Cascaded tanks 5-fold cross validated accuracies: Derivatives, mean absolute error (MAE).

  9. f

    Performance comparison of the DFDF, FSM-DFDF, and FSM-SGPS HR estimation...

    • plos.figshare.com
    xls
    Updated Jun 4, 2023
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    Heewon Chung; Hooseok Lee; Jinseok Lee (2023). Performance comparison of the DFDF, FSM-DFDF, and FSM-SGPS HR estimation methods. [Dataset]. http://doi.org/10.1371/journal.pone.0215014.t003
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    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Heewon Chung; Hooseok Lee; Jinseok Lee
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Performance comparison of the DFDF, FSM-DFDF, and FSM-SGPS HR estimation methods.

  10. f

    Performance metrics (R2, RMSE, MSE, MAE) for eight machine learning...

    • plos.figshare.com
    xls
    Updated Jun 21, 2024
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    Sherin Kularathne; Namal Rathnayake; Madhawa Herath; Upaka Rathnayake; Yukinobu Hoshino (2024). Performance metrics (R2, RMSE, MSE, MAE) for eight machine learning algorithms in predicting economic factors related to rice production. [Dataset]. http://doi.org/10.1371/journal.pone.0303883.t008
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    xlsAvailable download formats
    Dataset updated
    Jun 21, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Sherin Kularathne; Namal Rathnayake; Madhawa Herath; Upaka Rathnayake; Yukinobu Hoshino
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Performance metrics (R2, RMSE, MSE, MAE) for eight machine learning algorithms in predicting economic factors related to rice production.

  11. Database statistics for day (07:00–19:00) and night (19:00–07:00) periods.

    • plos.figshare.com
    xls
    Updated Jan 14, 2025
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    Décio Alves; Fábio Mendonça; Sheikh Shanawaz Mostafa; Diogo Freitas; João Pestana; Dinarte Vieira; Marko Radeta; Fernando Morgado-Dias (2025). Database statistics for day (07:00–19:00) and night (19:00–07:00) periods. [Dataset]. http://doi.org/10.1371/journal.pone.0316548.t002
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    xlsAvailable download formats
    Dataset updated
    Jan 14, 2025
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Décio Alves; Fábio Mendonça; Sheikh Shanawaz Mostafa; Diogo Freitas; João Pestana; Dinarte Vieira; Marko Radeta; Fernando Morgado-Dias
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Database statistics for day (07:00–19:00) and night (19:00–07:00) periods.

  12. f

    The optimal MAE values and corresponding hyperparameters (Gaussian noise...

    • plos.figshare.com
    xls
    Updated Dec 27, 2024
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    Limin Ma; Bingxue Wu; Yudong Yao; Yueyang Teng (2024). The optimal MAE values and corresponding hyperparameters (Gaussian noise with σ = 10−3). [Dataset]. http://doi.org/10.1371/journal.pone.0311227.t003
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    xlsAvailable download formats
    Dataset updated
    Dec 27, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Limin Ma; Bingxue Wu; Yudong Yao; Yueyang Teng
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The optimal MAE values and corresponding hyperparameters (Gaussian noise with σ = 10−3).

  13. f

    Data from: Accurate Reproduction of Quantum Mechanical Many-Body...

    • acs.figshare.com
    txt
    Updated Jun 16, 2023
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    Shiji Zhao; Haixin Wei; Piotr Cieplak; Yong Duan; Ray Luo (2023). Accurate Reproduction of Quantum Mechanical Many-Body Interactions in Peptide Main-Chain Hydrogen-Bonding Oligomers by the Polarizable Gaussian Multipole Model [Dataset]. http://doi.org/10.1021/acs.jctc.2c00710.s001
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    txtAvailable download formats
    Dataset updated
    Jun 16, 2023
    Dataset provided by
    ACS Publications
    Authors
    Shiji Zhao; Haixin Wei; Piotr Cieplak; Yong Duan; Ray Luo
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Description

    A key advantage of polarizable force fields is their ability to model the atomic polarization effects that play key roles in the atomic many-body interactions. In this work, we assessed the accuracy of the recently developed polarizable Gaussian Multipole (pGM) models in reproducing quantum mechanical (QM) interaction energies, many-body interaction energies, as well as the nonadditive and additive contributions to the many-body interactions for peptide main-chain hydrogen-bonding conformers, using glycine dipeptide oligomers as the model systems. Two types of pGM models were considered, including that with (pGM-perm) and without (pGM-ind) permanent atomic dipoles. The performances of the pGM models were compared with several widely used force fields, including two polarizable (Amoeba13 and ff12pol) and three additive (ff19SB, ff15ipq, and ff03) force fields. Encouragingly, the pGM models outperform all other force fields in terms of reproducing QM interaction energies, many-body interaction energies, as well as the nonadditive and additive contributions to the many-body interactions, as measured by the root-mean-square errors (RMSEs) and mean absolute errors (MAEs). Furthermore, we tested the robustness of the pGM models against polarizability parameterization errors by employing alternative polarizabilities that are either scaled or obtained from other force fields. The results show that the pGM models with alternative polarizabilities exhibit improved accuracy in reproducing QM many-body interaction energies as well as the nonadditive and additive contributions compared with other polarizable force fields, suggesting that the pGM models are robust against the errors in polarizability parameterizations. This work shows that the pGM models are capable of accurately modeling polarization effects and have the potential to serve as templates for developing next-generation polarizable force fields for modeling various biological systems.

  14. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Heewon Chung; Hooseok Lee; Jinseok Lee (2023). Comparison of MAEs of various HR estimation methods for the ISPC dataset. [Dataset]. http://doi.org/10.1371/journal.pone.0215014.t004

Comparison of MAEs of various HR estimation methods for the ISPC dataset.

Related Article
Explore at:
xlsAvailable download formats
Dataset updated
May 31, 2023
Dataset provided by
PLOS ONE
Authors
Heewon Chung; Hooseok Lee; Jinseok Lee
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Comparison of MAEs of various HR estimation methods for the ISPC dataset.

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