cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14195187
title: ChinaHighTEMmin: Daily Seamless 1 km Minimum Air Temperature Dataset for China (2003–Present)

description: 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.

Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) minimum air temperature (TEMmin) dataset for China from 2003 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.98 and a root-mean-square error (RMSE) of 1.53 ℃ on a daily basis.

If you use the ChinaHighTEMmin dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):



Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022. Scientific Data, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z


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 Kelvin (K) and can be converted to Celsius (°C) by subtracting 273.15.

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.14195188
title: ChinaHighTEMmin: Daily Seamless 1 km Minimum Air Temperature Dataset for China (2003–Present)

description: 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.

Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) minimum air temperature (TEMmin) dataset for China from 2003 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.98 and a root-mean-square error (RMSE) of 1.53 ℃ on a daily basis.

If you use the ChinaHighTEMmin dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):



Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022. Scientific Data, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z


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 Kelvin (K) and can be converted to Celsius (°C) by subtracting 273.15.

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.29133353.v5
title: CCK-8.rar

description: CCK-8 result
DOI: 10.5281/zenodo.14231767
title: ChinaHighTEMmax: Daily Seamless 1 km Maximum Air Temperature Dataset for China (2003–Present)

description: 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.

Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) maximum air temperature (TEMmax) dataset for China from 2003 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.98 and a root-mean-square error (RMSE) of 1.49 ℃ on a daily basis.

If you use the ChinaHighTEMmax dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):



Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022. Scientific Data, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z


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 Kelvin (K) and can be converted to Celsius (°C) by subtracting 273.15.

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.14193937
title: ChinaHighTEMmax: Daily Seamless 1 km Maximum Air Temperature Dataset for China (2003–Present)

description: 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.

Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) maximum air temperature (TEMmax) dataset for China from 2003 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.98 and a root-mean-square error (RMSE) of 1.49 ℃ on a daily basis.

If you use the ChinaHighTEMmax dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):



Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022. Scientific Data, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z


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 Kelvin (K) and can be converted to Celsius (°C) by subtracting 273.15.

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: 10.15468/dl.96845v
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aegotheles savesi E.L.Layard & E.L.C.Layard, 1881"
 ]
}

 The dataset includes 5 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015088-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14189257
title: ChinaHighTEMavg: Daily Seamless 1 km Average Air Temperature Dataset for China (2003–Present)

description: 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.

Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) average air temperature (TEMavg) dataset for China from 2003 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.99 and a root-mean-square error (RMSE) of 1.18 ℃ on a daily basis.

If you use the ChinaHighTEMavg dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):



Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022. Scientific Data, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z


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 Kelvin (K) and can be converted to Celsius (°C) by subtracting 273.15.

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.14189258
title: ChinaHighTEMavg: Daily Seamless 1 km Average Air Temperature Dataset for China (2003–Present)

description: 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.

Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) average air temperature (TEMavg) dataset for China from 2003 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.99 and a root-mean-square error (RMSE) of 1.18 ℃ on a daily basis.

If you use the ChinaHighTEMavg dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):



Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022. Scientific Data, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z


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 Kelvin (K) and can be converted to Celsius (°C) by subtracting 273.15.

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.29133353
title: CCK-8.rar

description: CCK-8 result
DOI: 10.6084/m9.figshare.29133353.v7
title: CCK-8.rar

description: CCK-8 result
DOI: 10.6084/m9.figshare.29133353.v6
title: CCK-8.rar

description: CCK-8 result
DOI: 10.57841/casdc.0005538
title: 中国北方绿水青山1：20万区划二级分区（2022年）
DOI: 10.15468/dl.h7v45m
title: Occurrence Download

description: A dataset containing 57190 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Helianthus annuus L.",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Occurrence evidence)"
 ]
}

 The dataset includes 57190 records from 378 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015089-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57841/casdc.0005539
title: 北方区“绿水青山”类型1:20万区划分幅图（2023年）
DOI: 10.57841/casdc.0005540
title: 中国北方绿水青山1:20万区划瓶颈问题与提质增效对策（2022年）
DOI: 10.57841/casdc.0005541
title: 中国北方绿水青山1:20万区划三级分区（2022年）
DOI: 10.57841/casdc.0005542
title: 基本典型区“绿水青山”类型1:10000区划分幅图（2023年）
DOI: 10.15468/dl.bebqg9
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Silene latifolia subsp. alba (Mill.) Greuter & Burdet"
 ]
}

 The dataset includes 21 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015090-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57841/casdc.0005543
title: 中国北方绿水青山1:20万区划一级分区（2022年）
DOI: 10.5281/zenodo.10476944
title: ChinaHighSO₂: Daily Seamless 1 km Ground-Level SO₂ Dataset for China (2019–Present)

description: ChinaHighSO2 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 SO2 dataset for China from 2019 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.84, a root-mean-square error (RMSE) of 10.07 µg m-3, and a mean absolute error (MAE) of 4.68 µg m-3 on a daily basis.

If you use the ChinaHighSO2 dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):





Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023



Note that the ChinaHighSO2 dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km:

        all (including daily) data for the years 2013–2018 are accessible at: https://doi.org/10.5281/zenodo.4641538

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) and measured under room conditions (i.e., 298 K and 1013 hPa).

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.10476945
title: ChinaHighSO2: Big Data Seamless 1 km Ground-level SO2 Dataset for China

description: ChinaHighSO2 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 1 km (i.e., D1K, M1K, and Y1K) ground-level SO2 dataset in China since 2019 to present. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R2) of 0.84, a root-mean-square error (RMSE) of 10.07 µg m-3, and a mean absolute error (MAE) of 4.68 µg m-3 on a daily basis.

If you use the ChinaHighSO2 dataset for related scientific research, please cite the corresponding reference (Wei et al., ACP, 2023), and the reference will be updated once our new paper is accepted:





Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023



Note that the ChinaHighSO2 dataset is also available for periods before 2019, but with a spatial resolution of 10 km:

        all (including daily) data for the years 2013-2018 is accessible at: https://doi.org/10.5281/zenodo.4641538

        Continuously updated...

More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html
description: Note that the data are recorded in local time (i.e., Beijing time: GMT+8), and measured at the standard condition (i.e., 273 K and 1013 hPa). The concentrations can be converted to the room condition (i.e., 298 K and 1013 hPa) by dividing by a factor of 1.09375 (MEE, 2018).

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@umd.edu).
DOI: 10.5281/zenodo.13340506
title: ChinaHighSO2: Big Data Seamless 1 km Ground-level SO2 Dataset for China (2019-Present)

description: ChinaHighSO2 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 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 1 km (i.e., D1K, M1K, and Y1K) ground-level SO2 dataset in China from 2019 to the present. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R2) of 0.84, a root-mean-square error (RMSE) of 10.07 µg m-3, and a mean absolute error (MAE) of 4.68 µg m-3 on a daily basis.

If you use the ChinaHighSO2 dataset for related scientific research, please cite the corresponding reference (Wei et al., ACP, 2023), and the reference will be updated once our new paper is accepted:





Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023



Note that the ChinaHighSO2 dataset is also available for periods before 2019, but with a spatial resolution of 10 km:

        all (including daily) data for the years 2013-2018 is accessible at: https://doi.org/10.5281/zenodo.4641538

        Continuously updated...

More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html
description: Note that the data are recorded in local time (i.e., Beijing time: GMT+8), and measured at the standard condition (i.e., 273 K and 1013 hPa). The concentrations can be converted to the room condition (i.e., 298 K and 1013 hPa) by dividing by a factor of 1.09375 (MEE, 2018).

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@umd.edu).
DOI: 10.5281/zenodo.15226517
title: ChinaHighSO₂: Daily Seamless 1 km Ground-Level SO₂ Dataset for China (2019–Present)

description: ChinaHighSO2 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 SO2 dataset for China from 2019 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.84, a root-mean-square error (RMSE) of 10.07 µg m-3, and a mean absolute error (MAE) of 4.68 µg m-3 on a daily basis.

If you use the ChinaHighSO2 dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):





Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023



Note that the ChinaHighSO2 dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km:

        all (including daily) data for the years 2013–2018 are accessible at: https://doi.org/10.5281/zenodo.4641538

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) and measured under room conditions (i.e., 298 K and 1013 hPa).

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: 10.57841/casdc.0005544
title: 扩展典型区“绿水青山”类型1:50000区划分幅图（2023年）
DOI: 10.57841/casdc.0005545
title: 中国北方绿水青山1:20万区划价值分类（2022年）
DOI: 10.57841/casdc.0005546
title: 中国北方重要“绿水青山”类型区划指标集（2024年）
DOI: 10.6084/m9.figshare.29133350
title: Supplemental material

description: Dairy cows with ketosis often experience liver dysfunction, characterized by hepatocellular injury and increased hepatic inflammation response. However, the underlying mechanism of these pathogenic events remains unclear. Necroptosis, a form of programmed cell death that promotes organ inflammatory injury, plays a critical role in the pathogenesis of liver diseases. In this study, we confirmed the occurrence of necroptosis in the liver of dairy cows with ketosis. We found that tumor necrosis factor-alpha (TNF-α), as a ketosis-associated inflammatory cytokine, induced necroptosis in bovine hepatocytes. Furthermore, inhibiting necroptosis via the RIPK1 kinase inhibitor Nec-1 and RIPK3 kinase inhibitor GSK-872 alleviated TNF-α-induced cellular damage. These findings suggest that targeting necroptosis could be a potential therapeutic strategy to mitigate hepatic damage in dairy cows with ketosis.
DOI: 10.6084/m9.figshare.29133350.v1
title: Supplemental material

description: Dairy cows with ketosis often experience liver dysfunction, characterized by hepatocellular injury and increased hepatic inflammation response. However, the underlying mechanism of these pathogenic events remains unclear. Necroptosis, a form of programmed cell death that promotes organ inflammatory injury, plays a critical role in the pathogenesis of liver diseases. In this study, we confirmed the occurrence of necroptosis in the liver of dairy cows with ketosis. We found that tumor necrosis factor-alpha (TNF-α), as a ketosis-associated inflammatory cytokine, induced necroptosis in bovine hepatocytes. Furthermore, inhibiting necroptosis via the RIPK1 kinase inhibitor Nec-1 and RIPK3 kinase inhibitor GSK-872 alleviated TNF-α-induced cellular damage. These findings suggest that targeting necroptosis could be a potential therapeutic strategy to mitigate hepatic damage in dairy cows with ketosis.
DOI: 10.57841/casdc.0005547
title: 中国北方绿水青山1:20万区划健康分类（2022年）
DOI: 10.15468/dl.wmejaq
title: Occurrence Download

description: A dataset containing 100243052 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "GadmGid is USA.5_1"
 ]
}

 The dataset includes 100243052 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015087-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.