cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3538555
title: ChinaHighPM₁: Daily Seamless 1 km Ground-Level PM₁ Dataset for China (2000–Present)

description: ChinaHighPM1 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.

Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM1 dataset for China from 2000 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.83, a root-mean-square error (RMSE) of 9.50 µg m-3, and a mean absolute error (MAE) of 6.17 µg m-3 on a daily basis.

If you use the ChinaHighPM1 dataset in your scientific research, please cite the following reference (Wei et al., EST, 2019):





Wei, J., Li, Z., Guo, J., Sun, L., Huang, W., Xue, W., Fan, T., and Cribb, M. Satellite-derived 1-km-resolution PM1 concentrations from 2014 to 2018 across China. Environmental Science & Technology, 2019, 53(22), 13265–13274. https://doi.org/10.1021/acs.est.9b03258



More CHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html
description: Note that the data are recorded in local time (i.e., Beijing time: GMT+8). 

This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).
DOI: 10.5281/zenodo.7512820
title: ChinaHighPM₁: Daily Seamless 1 km Ground-Level PM₁ Dataset for China (2000–Present)

description: ChinaHighPM1 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.

Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM1 dataset for China from 2000 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.83, a root-mean-square error (RMSE) of 9.50 µg m-3, and a mean absolute error (MAE) of 6.17 µg m-3 on a daily basis.

If you use the ChinaHighPM1 dataset in your scientific research, please cite the following reference (Wei et al., EST, 2019):





Wei, J., Li, Z., Guo, J., Sun, L., Huang, W., Xue, W., Fan, T., and Cribb, M. Satellite-derived 1-km-resolution PM1 concentrations from 2014 to 2018 across China. Environmental Science & Technology, 2019, 53(22), 13265–13274. https://doi.org/10.1021/acs.est.9b03258



More CHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html
description: Note that the data are recorded in local time (i.e., Beijing time: GMT+8). 

This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).
DOI: 10.6084/m9.figshare.29133476
title: Gender wage gaps in the formal and informal sectors in Ghana

description: <i>This paper examines gender earnings gaps in Ghana using data from the Ghana Living Standards Survey (GLSS7). Focusing on both the formal and informal sectors, we apply Oaxaca–Blinder decompositions and Recentered Influence Function (RIF) regressions to investigate mean and distributional disparities in log earnings between men and women. The evidence points towards a long-term gender pay gap, with females receiving significantly less than males, particularly in the informal economy. RIF regressions along the wage distribution show that the gender wage gap is more substantial in the upper quantiles in the informal sector. In contrast, the formal sector has narrower or even reversed gaps at specific quantiles.</i>
DOI: 10.15468/dl.49y9v3
title: Occurrence Download

description: A dataset containing 3097 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is one of (North America, South America)",
 "Country is one of (United States of America, Argentina, Mexico, Brazil, Venezuela (Bolivarian Republic of), Canada, Peru, Guyana, Puerto Rico, Paraguay)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Peltula Nyl."
 ]
}

 The dataset includes 3097 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015124-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15493354
title: Lists of modelled hot and cold high-electricity demand days in Victoria, Australia

description: These files contain lists of dates that were identified as high-demand electricity days in the state of Victoria, Australia, over the period 1990-2018. The days are split into high-demand cooling-degree days (days with likely air-conditioning load) and high-demand heating-degree days (days with likely heating load). 

The days are further classified into days with good (capacity factor > 0.9) or poor (capacity factor < 0.3) wind energy resource in the Gippsland offshore wind energy develoment area. 

These days are identified based on the BARRA V1.0 reanalysis over Australia (Su et al. 2019). Capacity factors are calculated using wind speed at a height of 100 m, using a power curve with a cut-in wind speed of 3.8 m/s, rated capacity at 11 m/s and a cut-out wind speed of 25m/s.

High demand days are identified using a random forest model (described in Vincent et al. (2025)) that fits observed maximum daily demand to the meteorological conditions over the Greater Melbourne region for the period 2015-2018, and then applies the model to the period 1990-2018. 

Su, C.-H., Eizenberg, N., Steinle, P., Jakob, D., Fox-Hughes, P., White, C. J., Rennie, S., Franklin, C., Dharssi, I., and Zhu, H.: BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia, Geosci. Model Dev., 12, 2049–2068, https://doi.org/10.5194/gmd-12-2049-2019, 2019.

Vincent, C.L., Nahar, A., Say, K., Wind resources of southeast Australia during peak electricity demand days [submitted], 2025.
DOI: 10.5281/zenodo.15493355
title: Lists of modelled hot and cold high-electricity demand days in Victoria, Australia

description: These files contain lists of dates that were identified as high-demand electricity days in the state of Victoria, Australia, over the period 1990-2018. The days are split into high-demand cooling-degree days (days with likely air-conditioning load) and high-demand heating-degree days (days with likely heating load). 

The days are further classified into days with good (capacity factor > 0.9) or poor (capacity factor < 0.3) wind energy resource in the Gippsland offshore wind energy develoment area. 

These days are identified based on the BARRA V1.0 reanalysis over Australia (Su et al. 2019). Capacity factors are calculated using wind speed at a height of 100 m, using a power curve with a cut-in wind speed of 3.8 m/s, rated capacity at 11 m/s and a cut-out wind speed of 25m/s.

High demand days are identified using a random forest model (described in Vincent et al. (2025)) that fits observed maximum daily demand to the meteorological conditions over the Greater Melbourne region for the period 2015-2018, and then applies the model to the period 1990-2018. 

Su, C.-H., Eizenberg, N., Steinle, P., Jakob, D., Fox-Hughes, P., White, C. J., Rennie, S., Franklin, C., Dharssi, I., and Zhu, H.: BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia, Geosci. Model Dev., 12, 2049–2068, https://doi.org/10.5194/gmd-12-2049-2019, 2019.

Vincent, C.L., Nahar, A., Say, K., Wind resources of southeast Australia during peak electricity demand days [submitted], 2025.
DOI: 10.15468/dl.6gmsvc
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eleutharrhena macrocarpa (Diels) Forman"
 ]
}

 The dataset includes 31 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015125-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15493413
title: Pengujian Non-Destruktif untuk Mendapatkan Parameter Velositas Bangunan Eksisting di Kabupaten Tanah Datar, Provinsi Sumatera Barat.

description: Prinsip dasar pengujian non-destruktif adalah menangkap gelombang pada material kayu, untuk selanjutnya diolah menjadi parameter sifat mekanika kayu yaitu modulus elastisitas. Selanjutnya parameter ini dapat dikonversi menjadi modulus elastisitas statik dan kekuatan tekan dengan menggunakan faktor konversi. Penelitian ini dilakukan di Kabupaten Tanah Datar, Provinsi Sumatera Barat, dengan mengambil sampel kolom dan balok pada duabangunan eksisting. Penelitian ini merupakan bagian dari Hibah Fundamental Reguler Kemendiksaintek dengan pendanaan tahun 2024.
DOI: 10.5281/zenodo.15493414
title: Pengujian Non-Destruktif untuk Mendapatkan Parameter Velositas Bangunan Eksisting di Kabupaten Tanah Datar, Provinsi Sumatera Barat.

description: Prinsip dasar pengujian non-destruktif adalah menangkap gelombang pada material kayu, untuk selanjutnya diolah menjadi parameter sifat mekanika kayu yaitu modulus elastisitas. Selanjutnya parameter ini dapat dikonversi menjadi modulus elastisitas statik dan kekuatan tekan dengan menggunakan faktor konversi. Penelitian ini dilakukan di Kabupaten Tanah Datar, Provinsi Sumatera Barat, dengan mengambil sampel kolom dan balok pada duabangunan eksisting. Penelitian ini merupakan bagian dari Hibah Fundamental Reguler Kemendiksaintek dengan pendanaan tahun 2024.
DOI: 10.5281/zenodo.3752465
title: ChinaHighPM₁₀: Daily Seamless 1 km Ground-Level PM₁₀ Dataset for China (2000–Present)

description: ChinaHighPM10 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.

Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM10 dataset for China from 2000 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.90, a root-mean-square error (RMSE) of 21.12 µg m-3, and a mean absolute error (MAE) of 11.22 µg m-3 on a daily basis.

If you use the ChinaHighPM10 dataset in your scientific research, please cite the following reference (Wei et al., EI, 2021):





Wei, J., Li, Z., Xue, W., Sun, L., Fan, T., Liu, L., Su, T., and Cribb, M. The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China. Environment International, 2021, 146, 106290. https://doi.org/10.1016/j.envint.2020.106290



The data is continuously updated, and

        all (including daily) data for the year 2022 is accessible at: ChinaHighPM10 (2022)

        all (including daily) data for the year 2023 is accessible at: ChinaHighPM10 (2023)

        all (including daily) data for the year 2024 is accessible at: ChinaHighPM10 (2024)

        more data is coming soon...

More CHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html
description: Note that the data are recorded in local time (i.e., Beijing time: GMT+8). 

This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).
DOI: 10.5281/zenodo.3752466
title: ChinaHighPM10 (Version 1)

description: The high-resolution (1 km) and high-quality PM<sub>10</sub> dataset in China (i.e., ChinaHighPM<sub>10</sub> dataset) from 2014 to 2019 are generated for the first time. This dataset is generated using a newly developed space-time extremely randomized trees (STET) model based on the newly released MODIS Collection 6 MAIAC 1 km AOD products, meteorological variables, emissions, and auxiliary data. The STET model can derive daily PM<sub>10</sub> concentrations with an average across-validation coefficient of determination of 0.86, a low root-mean-square error of 24.28 μg/m<sup>3</sup>, and a mean absolute error of 14.52 μg/m<sup>3</sup>. The ChinaHighPM<sub>10</sub> dataset can be greatly useful for air pollution studies in medium- or small-scale areas. <strong>Note that this dataset is closed since a new version is published at </strong>10.5281/zenodo.3752465<strong>.</strong>
description: If you have any questions about the data set or need higher temporal-resolution, i.e., daily or monthly PM10 data, please contact me (weijing_rs@163.com; weijing.rs@gmail.com).
description: {"references": ["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. Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees, Atmospheric Chemistry and Physics, 2020, 20(6), 3273-3289. https://doi.org/10.5194/acp-20-3273-2020"]}
DOI: 10.5281/zenodo.4396133
title: ChinaHighPM10 (Version 2)

description: ChinaHighPM<sub>10</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 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 artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the MODIS/Terra+Aqua Level 3 (L3) Yearly 0.01 degree (≈ 1 km) gridded ground-level PM<sub>10</sub> products in Eastern China (ECHAP_PM10_Y1K) from 2013 to 2020, which are averaged from the Level 2 daily products. This dataset has high accuracy with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.86 and a root-mean-square error (RMSE) of 24.28 µg m<sup>-3</sup> on a daily basis. <strong>Note that this dataset is closed since a new version is published at </strong>10.5281/zenodo.3752465<strong>.</strong>
description: Email: weijing_rs@163.com; weijing.rs@gmail.com
description: {"references": ["Wei, J., Li, Z., Xue, W., Sun, L., Fan, T., Liu, L., Su, T., and Cribb, M. The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China. Environment International, 2021, 146, 106290. https://doi.org/10.1016/j.envint.2020.106290"]}
DOI: 10.5281/zenodo.5089979
title: ChinaHighPM10 (Version 3)

description: ChinaHighPM<sub>10</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 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 artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the MODIS/Terra+Aqua Level 3 (L3) Yearly 0.01 degree (≈ 1 km) gridded ground-level PM<sub>10</sub> products in Eastern China (ECHAP_PM10_Y1K) from 2013 to 2020, which are averaged from the Level 2 daily products (doi: 10.5281/zenodo.5090006). This dataset has high accuracy with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.86 and a root-mean-square error (RMSE) of 24.28 µg m<sup>-3</sup> on a daily basis. <strong>Note that this dataset is closed since a new version is published at </strong>10.5281/zenodo.3752465<strong>.</strong>
description: CHAP website: https://weijing-rs.github.io/product.html
description: {"references": ["Wei, J., Li, Z., Xue, W., Sun, L., Fan, T., Liu, L., Su, T., and Cribb, M. The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China. Environment International, 2021, 146, 106290. https://doi.org/10.1016/j.envint.2020.106290"]}
DOI: 10.5281/zenodo.6449937
title: ChinaHighPM₁₀: Daily Seamless 1 km Ground-Level PM₁₀ Dataset for China (2000–Present)

description: ChinaHighPM10 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.

Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM10 dataset for China from 2000 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.90, a root-mean-square error (RMSE) of 21.12 µg m-3, and a mean absolute error (MAE) of 11.22 µg m-3 on a daily basis.

If you use the ChinaHighPM10 dataset in your scientific research, please cite the following reference (Wei et al., EI, 2021):





Wei, J., Li, Z., Xue, W., Sun, L., Fan, T., Liu, L., Su, T., and Cribb, M. The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China. Environment International, 2021, 146, 106290. https://doi.org/10.1016/j.envint.2020.106290



The data is continuously updated, and

        all (including daily) data for the year 2022 is accessible at: ChinaHighPM10 (2022)

        all (including daily) data for the year 2023 is accessible at: ChinaHighPM10 (2023)

        all (including daily) data for the year 2024 is accessible at: ChinaHighPM10 (2024)

        more data is coming soon...

More CHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html
description: Note that the data are recorded in local time (i.e., Beijing time: GMT+8). 

This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).
DOI: 10.5281/zenodo.3539349
title: ChinaHighPM₂.₅: Daily Seamless 1 km Ground-Level PM₂.₅ Dataset for China (2000–Present)

description: ChinaHighPM2.5 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.

Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM2.5 dataset for China from 2000 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.92, a root-mean-square error (RMSE) of 10.76 µg m-3, and a mean absolute error (MAE) of 6.32 µg m-3 on a daily basis.

If you use the ChinaHighPM2.5 dataset in your scientific research, please cite the following references (Wei et al., RSE, 2021; Wei et al., ACP, 2020):





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., Cribb, M., Huang, W., Xue, W., Sun, L., Guo, J., Peng, Y., Li, J., Lyapustin, A., Liu, L., Wu, H., and Song, Y. Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees. Atmospheric Chemistry and Physics, 2020, 20(6), 3273–3289. https://doi.org/10.5194/acp-20-3273-2020



The data is continuously updated, and

        all (including daily) data for the year 2022 is accessible at: ChinaHighPM2.5 (2022)

        all (including daily) data for the year 2023 is accessible at: ChinaHighPM2.5 (2023)

        all (including daily) data for the year 2024 is accessible at: ChinaHighPM2.5 (2024)

        more data is coming soon...

More CHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html
description: Note that the data are recorded in local time (i.e., Beijing time: GMT+8).

This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).
DOI: 10.5281/zenodo.3753614
title: ChinaHighPM2.5 (Version 2)

description: This is the high-resolution (1 km) and high-quality near-surface PM<sub>2.5</sub> dataset for China (i.e., ChinaHighPM<sub>2.5</sub>) from 2014 to 2019, which is generated from the MODIS MAIAC AOD products based on satellite remote sensing and artificial intelligence. <strong>Note that this dataset is closed since a new version is published at </strong>10.5281/zenodo.3539349.
description: 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., Cribb, M., Huang, W., Xue, W., Sun, L., Guo, J., Peng, Y., Li, J., Lyapustin, A., Liu, L., Wu, H., and Song, Y. Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees, Atmospheric Chemistry and Physics, 2020, 20(6), 3273-3289. https://doi.org/10.5194/acp-20-3273-2020"]}