cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.bqxe5f
title: Occurrence Download

description: A dataset containing 1408535255 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year 1820-2020"
 ]
}

 The dataset includes 1408535255 records from 23289 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032671-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10472665
title: ChinaHighPM₂.₅ (2022–Present)

description: 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 2022 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 for the period 2000–2021 are accessible at: https://doi.org/10.5281/zenodo.3539349

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.10472666
title: ChinaHighPM2.5 (2022)

description: Here is the big data-derived gapless (spatial coverage = 100%) daily, monthy, and annual 1 km (i.e., D1K, M1K, and Y1K) PM2.5 dataset in China for the year 2022. This dataset yields a 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 for related scientific research, please cite the below-listed corresponding references first (Wei et al., RSE, 2021; Wei et al., ACP, 2020), and the reference will be updated once our new paper is accepted.





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 for the period 2000-2021 is accessible at: https://doi.org/10.5281/zenodo.3539349

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). 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.13340222
title: ChinaHighPM2.5 (2022-2023)

description: Here is the big data-derived gapless (spatial coverage = 100%) daily, monthy, and annual 1 km (i.e., D1K, M1K, and Y1K) PM2.5 dataset in China for the years 2022 and 2023. This dataset yields a 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 for related scientific research, please cite the below-listed corresponding references first (Wei et al., RSE, 2021; Wei et al., ACP, 2020), and the reference will be updated once our new paper is accepted.





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 for the period 2000-2021 is accessible at: https://doi.org/10.5281/zenodo.3539349

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). 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.15208529
title: ChinaHighPM₂.₅ (2022–Present)

description: 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 2022 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 for the period 2000–2021 are accessible at: https://doi.org/10.5281/zenodo.3539349

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: 10.15468/dl.7hhhpa
title: Occurrence Download

description: A dataset containing 1265 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-101.59045 10.51211,-91.45745 14.45858,-84.84071 20.86637,-103.55982 31.249,-119.61025 32.91943,-114.09038 15.57129,-101.59045 10.51211))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Limosa haemastica (Linnaeus, 1758)"
 ]
}

 The dataset includes 1265 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015091-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10476570
title: ChinaHighNO2: Big Data Seamless 1 km Ground-level NO2 Dataset for China

description: ChinaHighNO2 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 NO2 dataset in China from 2019 to present. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R2) of 0.93, a root-mean-square error (RMSE) of 4.89 µg m-3, and a mean absolute error (MAE) of 3.48 µg m-3 on a daily basis.

If you use the ChinaHighNO2 dataset for related scientific research, please cite the corresponding reference (Wei et al., ES&T, 2023; Wei et al., ACP, 2022):





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. Ground-level NO2 surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence. Environmental Science & Technology, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834




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 ChinaHighNO2 dataset is also available for periods before 2019, but with a spatial resolution of 10 km:

        all (including daily) data for the years 2008-2018 is accessible at: https://doi.org/10.5281/zenodo.4641542

        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.13340370
title: ChinaHighNO2: Big Data Seamless 1 km Ground-level NO2 Dataset for China (2019-Present)

description: ChinaHighNO2 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 NO2 dataset in China from 2019 to the present. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R2) of 0.93, a root-mean-square error (RMSE) of 4.89 µg m-3, and a mean absolute error (MAE) of 3.48 µg m-3 on a daily basis.

If you use the ChinaHighNO2 dataset for related scientific research, please cite the corresponding reference (Wei et al., ES&T, 2023; Wei et al., ACP, 2022):





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. Ground-level NO2 surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence. Environmental Science & Technology, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834




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 ChinaHighNO2 dataset is also available for periods before 2019, but with a spatial resolution of 10 km:

        all (including daily) data for the years 2008-2018 is accessible at: https://doi.org/10.5281/zenodo.4641542

        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.15218546
title: ChinaHighNO₂: Daily Seamless 1 km Ground-Level NO₂ Dataset for China (2019–Present)

description: ChinaHighNO2 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 NO2 dataset for China from 2019 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.93, a root-mean-square error (RMSE) of 4.89 µg m-3, and a mean absolute error (MAE) of 3.48 µg m-3 on a daily basis.

If you use the ChinaHighNO2 dataset in your scientific research, please cite the following references (Wei et al., EST, 2022; Wei et al., ACP, 2023):





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. Ground-level NO2 surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence. Environmental Science & Technology, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834




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 ChinaHighNO2 dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km:

        all (including daily) data for the years 2008–2018 is accessible at: https://doi.org/10.5281/zenodo.4641542

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.4571660
title: ChinaHighNO₂: Daily Seamless 1 km Ground-Level NO₂ Dataset for China (2019–Present)

description: ChinaHighNO2 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 NO2 dataset for China from 2019 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.93, a root-mean-square error (RMSE) of 4.89 µg m-3, and a mean absolute error (MAE) of 3.48 µg m-3 on a daily basis.

If you use the ChinaHighNO2 dataset in your scientific research, please cite the following references (Wei et al., EST, 2022; Wei et al., ACP, 2023):





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. Ground-level NO2 surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence. Environmental Science & Technology, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834




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 ChinaHighNO2 dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km:

        all (including daily) data for the years 2008–2018 is accessible at: https://doi.org/10.5281/zenodo.4641542

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.4571661
title: ChinaHighNO2: TROPOMI 5 km Ground-level NO2 Dataset for China

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 TROPOMI-derived 5 km (≈ 0.05°) monthly and yearly ground-level NO<sub>2</sub> products in China in 2019. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.87 and a root-mean-square error (RMSE) of 6.36 µg m<sup>-3</sup> on a daily basis. <strong>Note that this dataset is closed access since a longer-term, seamless, high-resolution (1 km), and higher quality ChinaHighNO<sub>2</sub> dataset (Version 1) is available at: https://doi.org/10.5281/zenodo.6622056</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.6622056
title: ChinaHighNO2: Big Data Seamless 1 km Ground-level NO2 Dataset for China

description: ChinaHighNO2 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 NO2 dataset in China from 2019 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R2) of 0.93, a root-mean-square error (RMSE) of 4.89 µg m-3, and a mean absolute error (MAE) of 3.48 µg m-3 on a daily basis.


Note that the ChinaHighNO2 dataset is 1 km after 2019, but 10 km before 2019, which is available at https://doi.org/10.5281/zenodo.4641542. If you use the ChinaHighNO2 dataset for related scientific research, please cite the corresponding reference (Wei et al., ES&T, 2023; Wei et al., ACP, 2022):



	

	
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. Ground-level NO2 surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence. Environmental Science & Technology, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834
	
	

	
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
	



More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact us (Email: weijing_rs@163.com; weijing@umd.edu).
DOI: 10.57841/casdc.0005549
title: 东北地区自然资源综合调查相关信息产品
DOI: 10.15468/dl.wcm6sk
title: Occurrence Download

description: A dataset containing 1029 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "OccurrenceStatus is Present",
 "TaxonKey is Tremarctos ornatus (F.G.Cuvier, 1825)"
 ]
}

 The dataset includes 1029 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015100-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gquxqy
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is North America",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Magnolia grandiflora L.",
 "Year 1991-2025"
 ]
}

 The dataset includes 64 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015098-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aekyu7
title: Occurrence Download

description: A dataset containing 553659 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ajuga reptans L."
 ]
}

 The dataset includes 553659 records from 2611 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015099-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.