cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3988349
title: ChinaHighNO2: OMI/Aura 25 km Ground-level NO2 Dataset for China (Closed)

description: ChinaHighNO<sub>2</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the OMI/Aura-derived yearly 25 km ground-level O<sub>3</sub> products in China from 2005 to 2019, and this dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.72 and a root-mean-square error (RMSE) of 9.97 µg m<sup>-3</sup> on a daily basis. <strong>Note that this dataset is closed access since a longer-term, seamless, high-resolution (10 km), and higher quality ChinaHighNO<sub>2</sub> dataset is available at: https://doi.org/10.5281/zenodo.4641542</strong> <strong>More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html</strong>
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).
description: {"references": ["Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136"]}
DOI: 10.5281/zenodo.3988348
title: ChinaHighNO2: OMI/Aura 25 km Ground-level NO2 Dataset for China (Closed)

description: ChinaHighNO<sub>2</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the OMI/Aura-derived yearly 25 km ground-level O<sub>3</sub> products in China from 2005 to 2019, and this dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.72 and a root-mean-square error (RMSE) of 9.97 µg m<sup>-3</sup> on a daily basis. <strong>Note that this dataset is closed access since a longer-term, seamless, high-resolution (10 km), and higher quality ChinaHighNO<sub>2</sub> dataset is available at: https://doi.org/10.5281/zenodo.4641542</strong> <strong>More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html</strong>
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).
description: {"references": ["Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136"]}
DOI: 10.5281/zenodo.5765105
title: ChinaHighPM2.5: Big Data Seamless 10 km Ground-level PM2.5 Dataset for China (Closed)

description: ChinaHighPM<sub>2.5</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level PM<sub>2.5</sub> dataset in China from 2013 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.91 and a root-mean-square error (RMSE) of 12.67 µg m<sup>-3</sup> on a daily basis. <strong>Note that this dataset is closed access since a longer-term, high-resolution (1 km), and higher quality ChinaHighPM<sub>2.5</sub> dataset is available at: http://doi.org/10.5281/zenodo.6398971</strong> <strong>More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html</strong>
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).
description: {"references": ["Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136", "Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China. Remote Sensing of Environment, 2022, 269, 112775. https://doi.org/10.1016/j.rse.2021.112775"]}
DOI: 10.5281/zenodo.4641554
title: ChinaHighPM2.5: Big Data Seamless 10 km Ground-level PM2.5 Dataset for China (Closed)

description: ChinaHighPM<sub>2.5</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level PM<sub>2.5</sub> dataset in China from 2013 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.91 and a root-mean-square error (RMSE) of 12.67 µg m<sup>-3</sup> on a daily basis. <strong>Note that this dataset is closed access since a longer-term, high-resolution (1 km), and higher quality ChinaHighPM<sub>2.5</sub> dataset is available at: http://doi.org/10.5281/zenodo.6398971</strong> <strong>More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html</strong>
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).
description: {"references": ["Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136", "Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China. Remote Sensing of Environment, 2022, 269, 112775. https://doi.org/10.1016/j.rse.2021.112775"]}
DOI: 10.6073/pasta/e2e7a3ad5334b09e22ef3c6472f1d545
title: Otsego Lake (NY, USA) high-frequency buoy data 2017-2021

description: Yokota Lab, SUNY Oneonta BIological Field Station Volunteer Dive Team and Otsego Lake Association has been operating an NSF-funded (award #1624527) automated lake data buoy since 2017. This data set contains Ice-free season data from 2017 to 2021. Additional support for this project has been provided by New York State Water Research Institute, SUNY Oneonta Faculty Research Grant, and NSF awards to the Global Ecological Lake Observatory Network (GLEON.org). Yokota Lab, SUNY Oneonta BIological Field Station Volunteer Dive Team and Otsego Lake Association has been operating an NSF-funded (award #1624527) automated lake data buoy since 2017. This data set contains Ice-free season data from 2017 to 2021. Additional support for this project has been provided by New York State Water Research Institute, SUNY Oneonta Faculty Research Grant, and NSF awards to the Global Ecological Lake Observatory Network (GLEON.org).
DOI: 10.5281/zenodo.3988420
title: ChinaHighO3: OMI/Aura 25 km Ground-level O3 Dataset for China (Closed)

description: ChinaHighO<sub>3</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the OMI/Aura-derived yearly 25 km ground-level O<sub>3</sub> products in China from 2005 to 2019, and this dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.84 and a root-mean-square error (RMSE) of 20.11 µg m<sup>-3</sup> on a daily basis. <strong>Note that this dataset is closed access since a longer-term, seamless, high-resolution (10 km), and higher quality ChinaHighO<sub>3</sub> dataset is available at: https://doi.org/10.5281/zenodo.4400042</strong> <strong>More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html</strong>
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).
description: {"references": ["Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136"]}
DOI: 10.5281/zenodo.3988419
title: ChinaHighO3: OMI/Aura 25 km Ground-level O3 Dataset for China (Closed)

description: ChinaHighO<sub>3</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the OMI/Aura-derived yearly 25 km ground-level O<sub>3</sub> products in China from 2005 to 2019, and this dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.84 and a root-mean-square error (RMSE) of 20.11 µg m<sup>-3</sup> on a daily basis. <strong>Note that this dataset is closed access since a longer-term, seamless, high-resolution (10 km), and higher quality ChinaHighO<sub>3</sub> dataset is available at: https://doi.org/10.5281/zenodo.4400042</strong> <strong>More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html</strong>
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).
description: {"references": ["Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136"]}
DOI: 10.5281/zenodo.6450020
title: Descarga CMC Mejores ganancias

description: Descarga inicial de CMC para la posterior creación de un dataset mayor.
DOI: 10.5281/zenodo.6450019
title: Descarga CMC Mejores ganancias

description: Descarga inicial de CMC para la posterior creación de un dataset mayor.
DOI: 10.5281/zenodo.6449895
title: Dynamic deformation calculation of articular cartilage and cells using resonance-driven laser scanning microscopy - Osmotic Challenge and Validation Testing Dataset

description: This archive contains 3-D image stacks obtained over time of articular cartilage undergoing osmotic swelling. These were acquired with a resonance scanning protocol, which allows for fast scanning but with decreased image quality. The Python package, resonant_lsm, was developed to segment and analyze the deformation of cells in such images. The archive also contains validation testing data of this software. Included README files document the archive contents and how to reproduce the analyses.
DOI: 10.5281/zenodo.6449894
title: Dynamic deformation calculation of articular cartilage and cells using resonance-driven laser scanning microscopy - Osmotic Challenge and Validation Testing Dataset

description: This archive contains 3-D image stacks obtained over time of articular cartilage undergoing osmotic swelling. These were acquired with a resonance scanning protocol, which allows for fast scanning but with decreased image quality. The Python package, resonant_lsm, was developed to segment and analyze the deformation of cells in such images. The archive also contains validation testing data of this software. Included README files document the archive contents and how to reproduce the analyses.
DOI: 10.5281/zenodo.3987359
title: ChinaHighPM2.5: MODIS/Terra+Aqua 1 km Ground-level PM2.5 Dataset for the Beijing-Tianjin-Hebei Region

description: ChinaHighPM<sub>2.5</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). This dataset is generated from MODIS/Terra+Aqua MAIAC AOD products together with other auxiliary data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using the linear mixed effect (LME) model. This is the MODIS/Terra+Aqua monthly 1 km ground-level PM<sub>2.5</sub> dataset in the Beijing-Tianjin-Hebei region from 2000 to 2018, and this dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) reaching 0.85 and a root-mean-square error (RMSE) of 21.49 µg m<sup>-3</sup> on a daily basis. If you use this dataset for related scientific research, please cite the corresponding reference (Xue et al., 2021, JCP): Xue, W., Zhang, J., Zhong, C., Li, X., and Wei, J. Spatiotemporal PM<sub>2.5</sub> variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region, <em>Journal of Cleaner Production</em>, 2021, 279, 123742. https://doi.org/10.1016/j.jclepro.2020.123742 <strong>More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html</strong>
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).
description: {"references": ["Xue, W., Zhang, J., Zhong, C., Li, X., and Wei, J. Spatiotemporal PM2.5 variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region, Journal of Cleaner Production, 2021, 279, 123742. https://doi.org/10.1016/j.jclepro.2020.123742"]}
DOI: 10.5281/zenodo.3987358
title: ChinaHighPM2.5: MODIS/Terra+Aqua 1 km Ground-level PM2.5 Dataset for the Beijing-Tianjin-Hebei Region

description: ChinaHighPM<sub>2.5</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). This dataset is generated from MODIS/Terra+Aqua MAIAC AOD products together with other auxiliary data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using the linear mixed effect (LME) model. This is the MODIS/Terra+Aqua monthly 1 km ground-level PM<sub>2.5</sub> dataset in the Beijing-Tianjin-Hebei region from 2000 to 2018, and this dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) reaching 0.85 and a root-mean-square error (RMSE) of 21.49 µg m<sup>-3</sup> on a daily basis. If you use this dataset for related scientific research, please cite the corresponding reference (Xue et al., 2021, JCP): Xue, W., Zhang, J., Zhong, C., Li, X., and Wei, J. Spatiotemporal PM<sub>2.5</sub> variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region, <em>Journal of Cleaner Production</em>, 2021, 279, 123742. https://doi.org/10.1016/j.jclepro.2020.123742 <strong>More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html</strong>
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).
description: {"references": ["Xue, W., Zhang, J., Zhong, C., Li, X., and Wei, J. Spatiotemporal PM2.5 variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region, Journal of Cleaner Production, 2021, 279, 123742. https://doi.org/10.1016/j.jclepro.2020.123742"]}
DOI: 10.5281/zenodo.6426889
title: Sèries històriques d'indicadors socio-econòmics per països.

description: A partir de les dades dels diferents indicadors de la web datosmacro.com, es proposa crear un dataset que reculli, de manera unificada, tots els indicadors de tots els països desglossats per sèries temporals. D'aquesta manera cada registre del dataset contindrà: dos camps, que posen en context temporal i geogràfic (país i mes-any), i la resta de camps del dataset, on figurarien els respectius valors dels indicadors. Amb el dataset generat es podrien realitzar anàlisis, comparatives i estudis de correlació entre els diferents indicadors, que no seria possible realitzar a través de la web.
description: Dades extretes de la web https://datosmacro.expansion.com/demografia.
DOI: 10.5281/zenodo.6426888
title: Sèries històriques d'indicadors socio-econòmics per països.

description: A partir de les dades dels diferents indicadors de la web datosmacro.com, es proposa crear un dataset que reculli, de manera unificada, tots els indicadors de tots els països desglossats per sèries temporals. D'aquesta manera cada registre del dataset contindrà: dos camps, que posen en context temporal i geogràfic (país i mes-any), i la resta de camps del dataset, on figurarien els respectius valors dels indicadors. Amb el dataset generat es podrien realitzar anàlisis, comparatives i estudis de correlació entre els diferents indicadors, que no seria possible realitzar a través de la web.
description: Dades extretes de la web https://datosmacro.expansion.com/demografia.
DOI: 10.6084/m9.figshare.19414279
title: Appraisal of dispersion damping functions for the effective fragment potential method

description: The effective fragment potential (EFP) is a polarizable force field whose physically-motivated functional form is parameterized in an automated way from <i>ab initio</i> calculations, and whose dispersion potential has been suggested as a correction for Hartree-Fock or density functional theory calculations. However, the parameter-free dispersion damping potentials that are currently used in EFP do not follow from a rigorous derivation and do not satisfy simple limits for the dispersion energy. We introduce several new damping expressions that correct these deficiencies, then evaluate their performance alongside existing damping functions using a new database of ionic liquid constituents. This data set, which we call IL195×8, consists of complete-basis coupled-cluster interaction energies for 195 ion pairs at each of 8 different intermolecular separations. Ultimately, we recommend a new parameter-free dispersion damping function as a replacement for the one that is currently used in EFP.
DOI: 10.6084/m9.figshare.19414279.v2
title: Appraisal of dispersion damping functions for the effective fragment potential method

description: The effective fragment potential (EFP) is a polarizable force field whose physically-motivated functional form is parameterized in an automated way from <i>ab initio</i> calculations, and whose dispersion potential has been suggested as a correction for Hartree-Fock or density functional theory calculations. However, the parameter-free dispersion damping potentials that are currently used in EFP do not follow from a rigorous derivation and do not satisfy simple limits for the dispersion energy. We introduce several new damping expressions that correct these deficiencies, then evaluate their performance alongside existing damping functions using a new database of ionic liquid constituents. This data set, which we call IL195×8, consists of complete-basis coupled-cluster interaction energies for 195 ion pairs at each of 8 different intermolecular separations. Ultimately, we recommend a new parameter-free dispersion damping function as a replacement for the one that is currently used in EFP.
DOI: 10.6084/m9.figshare.19414279.v1
title: Appraisal of dispersion damping functions for the effective fragment potential method

description: The effective fragment potential (EFP) is a polarizable force field whose physically-motivated functional form is parameterized in an automated way from <i>ab initio</i> calculations, and whose dispersion potential has been suggested as a correction for Hartree-Fock or density functional theory calculations. However, the parameter-free dispersion damping potentials that are currently used in EFP do not follow from a rigorous derivation and do not satisfy simple limits for the dispersion energy. We introduce several new damping expressions that correct these deficiencies, then evaluate their performance alongside existing damping functions using a new database of ionic liquid constituents. This data set, which we call IL195×8, consists of complete-basis coupled-cluster interaction energies for 195 ion pairs at each of 8 different intermolecular separations. Ultimately, we recommend a new parameter-free dispersion damping function as a replacement for the one that is currently used in EFP.