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103 results for “Seamless”
ChinaHighNO₂: Daily Seamless 1 km Ground-Level NO₂ Dataset for China (2019–Present)
<p>ChinaHighNO<sub>2</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level NO<sub>2</sub> dataset for China <strong>from 2019 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.93, a root-mean-square error (RMSE) of 4.89 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 3.48 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighNO<sub>2</sub> dataset in your scientific research, please cite the following references (Wei et al., EST, 2022; Wei et al., ACP, 2023):</p> <ul> <li> <p>Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R., Dickerson, R., Lin, J., Boersma, K., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., and Wang, J. <a href="https://weijing-rs.github.io/publications/Wei_et_al-EST-2022.pdf">Ground-level NO<sub>2</sub> surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence</a>. <em>Environmental Science & Technology</em>, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834</p> </li> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> </ul> <p><strong>Note that the ChinaHighNO<sub>2 </sub>dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km:</strong></p> <p> all (including <strong>daily</strong>) data for the years <strong>2008–2018 </strong>is accessible at: <strong><a href="https://doi.org/10.5281/zenodo.4641542">https://doi.org/10.5281/zenodo.4641542</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighNO₂: Daily Seamless 10 km Ground-Level NO₂ Dataset for China (2008–2018)
<p>ChinaHighNO<sub>2</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level NO<sub>2</sub> dataset for China <strong>from 2008 to 2018</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.84, a root-mean-square error (RMSE) of 7.99 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 5.34 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighNO<sub>2</sub> dataset in your scientific research, please cite the following references (Wei et al., ACP, 2023; Wei et al., EST, 2022):</p> <ul> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> <li> <p>Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R., Dickerson, R., Lin, J., Boersma, K., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., and Wang, J. <a href="https://weijing-rs.github.io/publications/Wei_et_al-EST-2022.pdf">Ground-level NO<sub>2</sub> surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence</a>. <em>Environmental Science & Technology</em>, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834</p> </li> </ul> <p><strong>Note that the ChinaHighNO<sub>2</sub> dataset was improved to a 1 km resolution after 2019:</strong></p> <p> all (including <strong>daily</strong>) data for the years after <strong>2019</strong><strong> </strong>are accessible at: <strong><a href="https://doi.org/10.5281/zenodo.4571660">https://doi.org/10.5281/zenodo.4571660</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
GlobalHighPM₂.₅: Global Daily Seamless 1 km Ground-Level PM₂.₅ Dataset over Land (2017–Present)
<p>GlobalHighPM<sub>2.5</sub> is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>This dataset contains input data, analysis codes, and generated dataset used for the following article. If you use the GlobalHighPM<sub>2.5</sub> dataset in your scientific research, please cite the following reference (Wei et al., NC, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. <a href="https://weijing-rs.github.io/publications/Wei_et_al-NC-2023.pdf" target="_blank" rel="noopener">First close insight into global daily gapless 1 km PM<sub>2.5</sub> pollution, variability, and health impact</a>. <em>Nature Communications</em>, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3</p> </li> </ul> <p><strong>Input Data</strong></p> <p>Relevant raw data for each figure (compiled into a single sheet within an Excel document) in the manuscript.</p> <p><strong>Code</strong></p> <p>Relevant Python scripts for replicating and ploting the analysis results in the manuscript, as well as codes for converting data formats.</p> <p><strong>Generated Dataset</strong></p> <p>Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level PM<sub>2.5</sub> dataset over land from 2017 to the present. This dataset exhibits high quality, with cross-validation coefficients of determination (CV-R<sup>2</sup>) of 0.91, 0.97, and 0.98, and root-mean-square errors (RMSEs) of 9.20, 4.15, and 2.77 µg m<sup>-3</sup> on the daily, monthly, and annual bases, respectively.</p> <p><strong>Due to data volume limitations, </strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2022 </strong>is accessible at: <strong><a href="../records/10795661">GlobalHighPM2.5 (2022)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2021 </strong>is accessible at: <strong><a href="../records/10398385">GlobalHighPM2.5 (2021)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2020 </strong>is accessible at: <strong><a href="../records/10402639">GlobalHighPM2.5 (2020)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2019 </strong>is accessible at: <strong><a href="../records/10402723">GlobalHighPM2.5 (2019)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2018 </strong>is accessible at: <strong><a href="../records/10402824">GlobalHighPM2.5 (2018)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2017 </strong>is accessible at: <strong><a href="../records/10403497">GlobalHighPM2.5 (2017)</a></strong></p> <p> continuously updated...</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighCO: Daily Seamless 1 km Ground-Level CO Dataset for China (2019–Present)
<p>ChinaHighCO is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level CO dataset for China <strong>from 2019 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.80, a root-mean-square error (RMSE) of 0.29 mg m<sup>-3</sup>, and a mean absolute error (MAE) of 0.16 mg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighCO dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> </ul> <p><strong>Note that the ChinaHighCO<sub> </sub>dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km:</strong></p> <p> all (including <strong>daily</strong>) data for the years <strong>2013–2018 </strong>are accessible at: <strong><a href="https://doi.org/10.5281/zenodo.4641530">https://doi.org/10.5281/zenodo.4641530</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighSO₂: Daily Seamless 1 km Ground-Level SO₂ Dataset for China (2019–Present)
<p>ChinaHighSO<sub>2</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level SO<sub>2</sub> dataset for China <strong>from 2019 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.84, a root-mean-square error (RMSE) of 10.07 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 4.68 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighSO<sub>2</sub> dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> </ul> <p><strong>Note that the ChinaHighSO<sub>2 </sub>dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km:</strong></p> <p> all (including <strong>daily</strong>) data for the years <strong>2013–2018 </strong>are accessible at: <strong><a href="https://doi.org/10.5281/zenodo.4641538">https://doi.org/10.5281/zenodo.4641538</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighO₃: Daily Seamless 1 km Ground-Level O₃ Dataset for China (2000–Present)
<p>ChinaHighO<sub>3</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level maximum daily 8-hour average (MDA8) O<sub>3</sub> dataset for China <strong>from 2000 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.89, a root-mean-square error (RMSE) of 15.77 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 10.48 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighO<sub>3</sub> dataset in your scientific research, please cite the following references (Yang et al., RSE, 2025; Wei et al., RSE, 2022):</p> <ul> <li>Yang, Z., Li, Z., Cheng, F., Lv, Q., Li, K., Zhang, T., Zhou, Y., Zhao, B., Xue, W., and Wei, J. <a href="https://weijing-rs.github.io/publications/Yang_et_al-RSE-2025.pdf" target="_blank" rel="noopener">Two-decade surface ozone (O<sub>3</sub>) pollution in China: enhanced fine-scale estimations and environmental health implications</a>. <em>Remote Sensing of Environment</em>, 2025, 317, 114459. https://doi.org/10.1016/j.rse.2024.114459</li> </ul> <ul> <li> <p>Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-RSE-2022.pdf">Full-coverage mapping and spatiotemporal variations of ground-level ozone (O<sub>3</sub>) pollution from 2013 to 2020 across China</a>. <em>Remote Sensing of Environment</em>, 2022, 270, 112775. https://doi.org/10.1016/j.rse.2021.112775</p> </li> </ul> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighCO: Daily Seamless 10 km Ground-Level CO Dataset for China (2013–2018)
<p>ChinaHighCO is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level CO dataset for China <strong>from 2013 to 2018</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.80, a root-mean-square error (RMSE) of 0.29 mg m<sup>-3</sup>, and a mean absolute error (MAE) of 0.16 mg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighCO dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> </ul> <p><strong>Note that the ChinaHighCO dataset was improved to a 1 km resolution after 2019:</strong></p> <p> all (including <strong>daily</strong>) data for the years after <strong>2019 </strong>are accessible at: <strong><a href="https://doi.org/10.5281/zenodo.10477022">https://doi.org/10.5281/zenodo.10477022</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighSO₂: Daily Seamless 10 km Ground-Level SO₂ Dataset for China (2013–2018)
<p>ChinaHighSO<sub>2</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level SO<sub>2</sub> dataset for China <strong>from 2013 to 2018</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.84, a root-mean-square error (RMSE) of 10.07 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 4.68 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighSO<sub>2</sub> dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> </ul> <p><strong>Note that the ChinaHighSO<sub>2</sub> dataset was improved to a 1 km resolution after 2019:</strong></p> <p> all (including <strong>daily</strong>) data for the years after <strong>2019 </strong>are accessible at: <strong><a href="https://doi.org/10.5281/zenodo.10476944">https://doi.org/10.5281/zenodo.10476944</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighPM₂.₅: Daily Seamless 1 km Ground-Level PM₂.₅ Dataset for China (2000–Present)
<p>ChinaHighPM<sub>2.5</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM<sub>2.5</sub> dataset for China <strong>from 2000 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.92, a root-mean-square error (RMSE) of 10.76 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 6.32 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighPM<sub>2.5</sub> dataset in your scientific research, please cite the following references (Wei et al., RSE, 2021; Wei et al., ACP, 2020):</p> <ul> <li> <p>Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-RSE-2021.pdf">Reconstructing 1-km-resolution high-quality PM<sub>2.5</sub> data records from 2000 to 2018 in China: spatiotemporal variations and policy implications</a>. <em>Remote Sensing of Environment</em>, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136</p> </li> <li> <p>Wei, J., Li, Z., Cribb, M., Huang, W., Xue, W., Sun, L., Guo, J., Peng, Y., Li, J., Lyapustin, A., Liu, L., Wu, H., and Song, Y. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2020.pdf">Improved 1 km resolution PM<sub>2.5</sub> estimates across China using enhanced space-time extremely randomized trees</a>. <em>Atmospheric Chemistry and Physics</em>, 2020, 20(6), 3273–3289. https://doi.org/10.5194/acp-20-3273-2020</p> </li> </ul> <p><strong>The data is continuously updated, and</strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2022 </strong>is accessible at: <strong><a href="https://doi.org/10.5281/zenodo.10472665">ChinaHighPM2.5 (2022)</a></strong></p> <p><strong> </strong> all (including <strong>daily</strong>) data for the year <strong>2023</strong> is accessible at: <strong><a href="https://doi.org/10.5281/zenodo.10472665">ChinaHighPM2.5 (2023)</a></strong></p> <p><strong> </strong>all (including <strong>daily</strong>) data for the year <strong>2024 </strong>is accessible at: <strong><a href="https://doi.org/10.5281/zenodo.10472665">ChinaHighPM2.5 (2024)</a></strong></p> <p> more data is coming soon...</p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighPM₁₀: Daily Seamless 1 km Ground-Level PM₁₀ Dataset for China (2000–Present)
<p>ChinaHighPM<sub>10</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM<sub>10</sub> dataset for China <strong>from 2000 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.90, a root-mean-square error (RMSE) of 21.12 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 11.22 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighPM<sub>10</sub> dataset in your scientific research, please cite the following reference (Wei et al., EI, 2021):</p> <ul> <li> <p>Wei, J., Li, Z., Xue, W., Sun, L., Fan, T., Liu, L., Su, T., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-EI-2021.pdf">The ChinaHighPM<sub>10</sub> dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China</a>. <em>Environment International</em>, 2021, 146, 106290. https://doi.org/10.1016/j.envint.2020.106290</p> </li> </ul> <p><strong>The data is continuously updated, and</strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2022 </strong>is accessible at: <strong><a href="https://doi.org/10.5281/zenodo.10475457">ChinaHighPM10 (2022)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2023 </strong>is accessible at: <strong><a href="https://doi.org/10.5281/zenodo.10475457">ChinaHighPM10 (2023)</a></strong></p> <p><strong> </strong>all (including <strong>daily</strong>) data for the year <strong>2024 </strong>is accessible at: <strong><a href="https://doi.org/10.5281/zenodo.10475457">ChinaHighPM10 (2024)</a></strong></p> <p> more data is coming soon...</p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
SGD-SM: Generating Seamless Global Daily AMSR2 Soil Moisture Long-term Products (2013-2019)
<p><strong>If you used our dataset, please cite our reference:</strong></p> <p><strong>Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019, Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, 2021.</strong></p> <p><strong>Description:</strong></p> <ul> <li>A <strong>seamless global daily</strong> (<strong>SGD</strong>) AMSR2 soil moisture long-term (2013-2019) dataset is generated through the proposed model. This daily products include <strong>2553</strong> global soil moisture NetCDF4 files, starting from Jan 01, 2013 to Dec 31, 2019 (about <strong>20GB</strong> memory after uncompressing this zip file).</li> <li>To further validate the effectiveness of these products, three verification ways are employed as follow: 1) In-situ validation; 2) Time-series validation; And 3) simulated missing regions validation. More validation results can be viewed at <strong><a href="https://qzhang95.github.io/Projects/Global-Daily-Seamless-AMSR2">SGD-SM</a></strong>.</li> <li>An example Python code of extracting this dataset is also available at <strong><a href="https://github.com/qzhang95/SGD-SM">https://github.com/qzhang95/SGD-SM</a></strong>.</li> <li>Official LPRM AMSR2 Descending L3 soil moisture products indeed only have 28 daily files in May 2013 (missing data files in date May 11, May 12, and May 13).</li> <li>This soil moisture dataset is comprised of netCDF4 (*.nc) files. Therefore, users need to install <strong>netCDF4</strong> toolkit before reading the data: <pre><code class="language-python">pip install netCDF4 pip install numpy</code></pre> <p> </p> </li> <li>It should be noted that the original and reconstructed soil moisture data are both recorded in one NC file. User can read the original data, reconstructed data, and mask data as follows:</li> <li> <pre><code class="language-python">Data = nc.Dataset(NC_file_position) Ori_data = Data.variables['original_sm_c1'] Rec_data = Data.variables['reconstructed_sm_c1'] Ori = Ori_data[0:720, 0:1440] Rec = Rec_data[0:720, 0:1440] Mask_ori = np.ma.getmask(Ori)</code></pre> <p> </p> </li> </ul>
USHighPM₂.₅: Daily Seamless 1 km Ground-Level PM₂.₅ Dataset for the United States (2000–Present)
<p>USHighPM<sub>2.5</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for the United States (i.e., USHighAirPollutants, USHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM<sub>2.5</sub> dataset for the United States from 2000 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.92 and a normalized root-mean-square error (NRMSE) of 0.4 on a daily basis.</p> <p>If you use the USHighPM<sub>2.5</sub> dataset in your scientific research, please cite the following reference (Wei et al., LPH, 2023):</p> <ul> <li>Wei, J., Wang, J., Li, Z., Kondragunta, S., Anenberg, S., Wang, Y., Zhang, H., Diner, D., Hand, J., Lyapustin, A., Kahn, R., Colarco, P., da Silva, A., and Ichoku, C. <a href="https://weijing-rs.github.io/publications/Wei_et_al-LPH-2023.pdf">Long-term mortality burden trends attributed to black carbon and PM2.5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study</a>. <em>The Lancet Planetary Health</em>, 2023, 7, e963–e975. https://doi.org/10.1016/S2542-5196(23)00235-8</li> </ul> <p><strong>More USHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighTEMmax: Daily Seamless 1 km Maximum Air Temperature Dataset for China (2003–Present)
<p>ChinaHighTEM is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) <strong>maximum air temperature </strong>(TEMmax) dataset for China <strong>from 2003 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.98 and a root-mean-square error (RMSE) of 1.49 ℃ on a daily basis.</p> <p>If you use the ChinaHighTEMmax dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):</p> <ul> <li>Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. <a href="https://weijing-rs.github.io/publications/Wang_et_al-SD-2024.pdf" target="_blank" rel="noopener">Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022</a>. <em>Scientific Data</em>, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z</li> </ul> <p><strong>More CHAP datasets for different air pollutants are available at: </strong><a href="https://weijing-rs.github.io/product.html"><strong>https://weijing-rs.github.io/product.html</strong></a></p>
A global spatiotemporally seamless daily mean land surface temperature from 2003 to 2019
<p>The global daily mean land surface temperature product (GADTC product) was generated based on the improved ADTC-based framework (termed IADTC framework) which basically combines the annual temperature cycle and diurnal temperature cycle model. </p> <p>The GADTC product is organized by year and each .tif image contains the global spatiotemporally seamless daily mean land surface temperature (LST) for each day with the unit of Kelvin. </p> <p>The demo code of the IADTC framework is available at https://github.com/faluhong/IADTC-framework. </p>
ChinaHighO₃: Hourly Seamless 1 km Ground-Level O₃ Dataset for China (2019)
<p>ChinaHighO<sub>3</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) hourly 1 km (i.e., H1K) O<sub>3</sub> dataset for China for the year<strong> 2019</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.89, a root-mean-square error (RMSE) of 16.35 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 11.53 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighO<sub>3</sub> dataset in your scientific research, please cite the following reference (Cheng et al., RSE, 2025):</p> <ul> <li> <p>Cheng, F., Li, Z., Yang, Z., Li, R., Wang, D., Jia, A., Li, K., Zhao, B., Wang, S., Yin, D., Li, S., Xue, W., Cribb, M., and Wei, J. <a href="https://weijing-rs.github.io/publications/Cheng_et_al-RSE-2025.pdf" target="_blank" rel="noopener">First retrieval of 24-hourly 1-km-resolution gapless surface ozone (O<sub>3</sub>) from space in China using artificial intelligence: diurnal variations and implications for air quality and phytotoxicity</a>. <em>Remote Sensing of Environment</em>, 2025, 316, 114482. https://doi.org/10.1016/j.rse.2024.114482</p> </li> </ul> <p><strong>More CHAP datasets for different air pollutants are available at: </strong><a href="https://weijing-rs.github.io/product.html"><strong>https://weijing-rs.github.io/product.html</strong></a></p>
Seamless 30 meter Sentinel-2 L2A Pan-European seasonal cloudless mosaics from winter 2018 to spring 2020
<p>Seasonal composites of <a href="https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html">Sentinel-2 L2A</a> imagery created as part of the <a href="https://opendatascience.eu/geo-harmonizer/">Geo-harmonizer project</a>, containing median of the blue, green, red, NIR, SWIR1 and SWIR2 bands, as well as pixel counts per season, produced in the ETRS89-extended / LAEA Europe (<a href="https://epsg.io/3035">EPSG:3035</a>) spatial reference system. Mosaics were produced from winter 2017 to spring 2020, with the imaging intervals per season being:</p> <ul> <li>winter: 02/12 of previous year to 20/03</li> <li>spring: 21/03 to 24/06</li> <li>summer: 25/06 to 12/09</li> <li>fall: 13/09 to 01/12</li> </ul> <p>Seamlessness of the composites was achieved through overlapping pixel averaging weighted by distance from the suborbital track.</p> <p>The data are provided as UINT8 values and were scaled with a common threshold (13712) chosen to minimize compression loss across the dataset. Data at the original (UINT16) scale can be obtained as follows:</p> <p><span>\(x_{\text{uint16}} = 13712 {x_{\text{uint8}} \over 254}\)</span></p> <p>For any additional questions regarding the data please contact the authors at <a href="mailto:multione@multione.hr?subject=S2L2A%20Europe%20mosaics">multione[at]multione.hr</a>.</p>
Experiment output data from a seamless sea ice prediction system based on AWI-CM 1.1
<p>The data provide experiment output of the seamless sea ice prediction system based on AWI-CM 1.1.</p> <p>Exp_C_T_SIT_2007-2012.nc is the monthly sea ice thickness analysis averaged over from 2007 to 2012 for Exp_C_T.</p> <p>Exp_twin_SIC_SIT.tar.bz is the sea ice concentration and sea ice thickness analysis for Exp_twin.</p> <p>Exp_CTD_T_atm_T2m_n63grid.nc is the averaged 2m atmosphere temperature from 2007-2018 on N63 grid for Exp_CTD_T.<br> Exp_CTD_T_atm_u10_n63grid.nc is the averaged 10m wind velocity (u-component) from 2007-2018 on N63 grid for Exp_CTD_T.<br> Exp_CTD_T_atm_v10_n63grid.nc is the averaged 10m wind velocity (v-component) from 2007-2018 on N63 grid for Exp_CTD_T.<br> Exp_CTD_T_SALT_2007-2018_monmean.nc is the averaged ocean salinity from 2007-2018 for Exp_CTD_T. </p> <p>Exp_CTD_T_TEMP_2007-2018_monmean.nc is the averaged ocean temperature from 2007-2018 for Exp_CTD_T.<br> Exp_CTD_T_SIC_SIT_SIV.tar.bz is the analysis of sea ice concentration, sea ice thickness, and sea ice drift from 2007-2018 for Exp_CTD_T.<br> Exp_CTD_T_SIV_INC.nc is the sea ice drift increment from 2007-2018 for Exp_CTD_T.<br> Exp_CTD_T_TEMP_FCST_2007-2018_monmean.nc is the monthly mean ocean temperature forecast averaged over 2007-2018 for Exp_CTD_T.<br> Exp_CTD_T_UEL_2014-2018_mean.nc is the mean oceanic velocity (u-component) averaged over 2014-2018 for Exp_CTD_T.<br> Exp_CTD_T_VEL_2014-2018_mean.nc is the mean oceanic velocity (v-component) averaged over 2014-2018 for Exp_CTD_T.</p> <p><br> Exp_CTRL_atm_T2m_n63grid.nc is the averaged 2m atmosphere temperature from 2007-2018 on N63 grid for Exp_CTRL.<br> Exp_CTRL_atm_u10_n63grid.nc is the averaged 10m wind velocity (u-component) from 2007-2018 on N63 grid for Exp_CTRL.<br> Exp_CTRL_atm_v10_n63grid.nc is the averaged 10m wind velocity (v-component) from 2007-2018 on N63 grid for Exp_CTRL.<br> Exp_CTRL_SALT_2007-2018_monmean.nc is the averaged ocean salinity from 2007-2018 for Exp_CTRL. <br> Exp_CTRL_SIC_SIT.tar.bz is the sea ice concentration and sea ice thickness simulation from 1997-2018 for Exp_CTRL.<br> Exp_CTRL_SIV.tar.bz is sea ice drift simulation from 1997-2018 for Exp_CTRL.<br> Exp_CTRL_TEMP_2007-2018_monmean.nc is the averaged ocean temperature from 2007-2018 for Exp_CTRL. <br> Exp_CTRL_UEL_2014-2018_mean.nc is the averaged oceanic velocity (u-component) from 2014-2018 for Exp_CTRL. <br> Exp_CTRL_VEL_2014-2018_mean.nc is the averaged oceanic velocity (v-component) from 2014-2018 for Exp_CTRL. </p> <p>fesom.initial.mesh.diag.nc contains the area and volume of the CORE-II mesh.<br> mesh_core2.tar.bz contains the detailed CORE-II mesh information.<br> </p>
SMAP Daily Seamless Soil Moisture Products from 2015 to 2022 (Physics-constrained Gap-filling Method,PhyFill)
<p>The launch of Soil Moisture Active Passive (SMAP) satellite in 2015 has resulted in significant achievements in global soil moisture mapping. Nonetheless, spatiotemporal discontinuities in the soil moisture products have arisen due to the limitations of its orbit scanning gap and retrieval algorithms. To address this issue, this dataset presents a physics-constrained gap-filling method, shortly named PhyFill. The PhyFill method employs a partial convolutional neural network to explore spatial domain features of the original SMAP soil moisture data. Then, it incorporates variations in soil moisture induced by precipitation events and dry-down events as penalty terms in the loss function, thereby accounting for monotonicity and boundary constraints in the physical processes governing the dynamic fluctuations of soil moisture. The PhyFill model was applied to SMAP soil moisture data, resulting in continuous daily soil moisture data on a global scale. The core validation sites demonstrated that the reconstructed soil moisture data has a consistent ubRMSE compared with the original SMAP soil moisture data. The PhyFill method can generate globally continuous, high-accuracy soil moisture estimates, providing remarkable support for advanced hydrological applications, e.g., global soil moisture dry-down events and patterns.</p>
Data for fitting a statistical global burned area model for seamless integration into Dynamic Global Vegetation Models
<p>The dataset is a large R data.table object saved in RDS format. It contains global, monthly data spanning the period from 2002 to 2018, with a 0.5 degrees spatial resolution. The dataset is utilized to develop and validate statistical models for predicting global burnt areas resulting from wildfires.</p>
GlobalHighCO: Global Daily Seamless 1 km Ground-Level CO Dataset over Land (2018–Present)
<p>GlobalHighCO is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived gapless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level CO dataset over land <strong>from 2019 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.93 and a root-mean-square error (RMSE) of 0.21 mg m<sup>-3</sup> on a daily basis.</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.