cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8274916
title: ELITE land surface temperature: seamless 1km LST over China (2013)

description: The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn). This dataset is the ELITE seamless 1km LST over China landmass (2002-2020). Firstly, a look-up-table-based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled using the geographically weighted regression to obtain 1km LST. Finally, the multi-scale kalman filter is used to fuse MODIS LST and AMSR-E/AMSR2 LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST. This is the seamless LST dataset in 2013. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2012 and <em><strong>click here</strong></em> to download the ELITE LST product in 2014. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2013 Spatial Resolution: 1 KM Temporal Resolution: 2 times per day Data Format: hdf Scale: 0.02 <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Xu, S., & Cheng, J. (2021). A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254, 112256 Zhang, Q., Wang, N., Cheng, J., & Xu, S. (2020). A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5669-5681 Zhang, Q., & Cheng, J. (2020). An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables. Earth and Space Science, 7, e2019EA001006. https://doi.org/10.1029/2019EA001006 If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).
DOI: 10.5281/zenodo.8274965
title: ELITE land surface temperature: seamless 1km LST over China (2014)

description: The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn). This dataset is the ELITE seamless 1km LST over China landmass (2002-2020). Firstly, a look-up-table-based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled using the geographically weighted regression to obtain 1km LST. Finally, the multi-scale kalman filter is used to fuse MODIS LST and AMSR-E/AMSR2 LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST. This is the seamless LST dataset in 2014. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2013 and <em><strong>click here</strong></em> to download the ELITE LST product in 2015. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2014 Spatial Resolution: 1 KM Temporal Resolution: 2 times per day Data Format: hdf Scale: 0.02 <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Xu, S., & Cheng, J. (2021). A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254, 112256 Zhang, Q., Wang, N., Cheng, J., & Xu, S. (2020). A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5669-5681 Zhang, Q., & Cheng, J. (2020). An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables. Earth and Space Science, 7, e2019EA001006. https://doi.org/10.1029/2019EA001006 If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).
DOI: 10.5281/zenodo.8274964
title: ELITE land surface temperature: seamless 1km LST over China (2014)

description: The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn). This dataset is the ELITE seamless 1km LST over China landmass (2002-2020). Firstly, a look-up-table-based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled using the geographically weighted regression to obtain 1km LST. Finally, the multi-scale kalman filter is used to fuse MODIS LST and AMSR-E/AMSR2 LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST. This is the seamless LST dataset in 2014. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2013 and <em><strong>click here</strong></em> to download the ELITE LST product in 2015. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2014 Spatial Resolution: 1 KM Temporal Resolution: 2 times per day Data Format: hdf Scale: 0.02 <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Xu, S., & Cheng, J. (2021). A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254, 112256 Zhang, Q., Wang, N., Cheng, J., & Xu, S. (2020). A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5669-5681 Zhang, Q., & Cheng, J. (2020). An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables. Earth and Space Science, 7, e2019EA001006. https://doi.org/10.1029/2019EA001006 If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).
DOI: 10.5281/zenodo.8268924
title: Replication Package of Pandemic Pedagogy: Evaluating Remote Education Strategies during COVID-19

description: The COVID-19 pandemic precipitated an abrupt shift in the educational landscape, compelling universities to transition from in-person to online instruction. This sudden shift left many university instructors grappling with the intricacies of remote teaching. Now, with the pandemic behind us, we present a retrospective study aimed at understanding and evaluating the remote teaching practices employed during that period. Drawing from a cross-sectional analysis of 300 computer science students who underwent a full year of online education during the lockdown, our findings indicate that while remote teaching practices moderately influenced students' learning outcomes, they had a pronounced positive impact on student satisfaction. Remarkably, these outcomes were consistent across various demographics, including country, gender, and educational level. As we reflect on the lessons from this global event, this research offers evidence-based recommendations that could inform educational strategies in unwelcoming future scenarios of a similar nature, ensuring both student satisfaction and effective learning outcomes in online settings.
DOI: 10.5281/zenodo.8268923
title: Replication Package of Pandemic Pedagogy: Evaluating Remote Education Strategies during COVID-19

description: The COVID-19 pandemic precipitated an abrupt shift in the educational landscape, compelling universities to transition from in-person to online instruction. This sudden shift left many university instructors grappling with the intricacies of remote teaching. Now, with the pandemic behind us, we present a retrospective study aimed at understanding and evaluating the remote teaching practices employed during that period. Drawing from a cross-sectional analysis of 300 computer science students who underwent a full year of online education during the lockdown, our findings indicate that while remote teaching practices moderately influenced students' learning outcomes, they had a pronounced positive impact on student satisfaction. Remarkably, these outcomes were consistent across various demographics, including country, gender, and educational level. As we reflect on the lessons from this global event, this research offers evidence-based recommendations that could inform educational strategies in unwelcoming future scenarios of a similar nature, ensuring both student satisfaction and effective learning outcomes in online settings.
DOI: 10.57841/casdc.0001984
title: 原位合成M23C6-WC双相碳化物协同增强激光熔覆层摩擦磨损行为的研究
DOI: 10.5281/zenodo.8274959
title: ELITE land surface temperature: seamless 1km LST over China (2015)

description: The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn). This dataset is the ELITE seamless 1km LST over China landmass (2002-2020). Firstly, a look-up-table-based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled using the geographically weighted regression to obtain 1km LST. Finally, the multi-scale kalman filter is used to fuse MODIS LST and AMSR-E/AMSR2 LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST. This is the seamless LST dataset in 2015. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2014 and <em><strong>click here</strong></em> to download the ELITE LST product in 2016. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2015 Spatial Resolution: 1 KM Temporal Resolution: 2 times per day Data Format: hdf Scale: 0.02 <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Xu, S., & Cheng, J. (2021). A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254, 112256 Zhang, Q., Wang, N., Cheng, J., & Xu, S. (2020). A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5669-5681 Zhang, Q., & Cheng, J. (2020). An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables. Earth and Space Science, 7, e2019EA001006. https://doi.org/10.1029/2019EA001006 If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).
DOI: 10.5281/zenodo.8274958
title: ELITE land surface temperature: seamless 1km LST over China (2015)

description: The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn). This dataset is the ELITE seamless 1km LST over China landmass (2002-2020). Firstly, a look-up-table-based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled using the geographically weighted regression to obtain 1km LST. Finally, the multi-scale kalman filter is used to fuse MODIS LST and AMSR-E/AMSR2 LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST. This is the seamless LST dataset in 2015. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2014 and <em><strong>click here</strong></em> to download the ELITE LST product in 2016. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2015 Spatial Resolution: 1 KM Temporal Resolution: 2 times per day Data Format: hdf Scale: 0.02 <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Xu, S., & Cheng, J. (2021). A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254, 112256 Zhang, Q., Wang, N., Cheng, J., & Xu, S. (2020). A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5669-5681 Zhang, Q., & Cheng, J. (2020). An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables. Earth and Space Science, 7, e2019EA001006. https://doi.org/10.1029/2019EA001006 If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).
DOI: 10.12770/26840434-8354-4856-9d5e-e562c8de7252
title: SEARSE project: metabarcoding data (V4-18S, V4V5-16S)

description: Sequences were obtained through metabarcoding of V4-18S and V4V5-16S rDNA genes from surface water samples collected during three campaigns in the New Caledonia lagoon, with one campaign conducted after the passage of a cyclone.
DOI: 10.5281/zenodo.8296746
title: Advancing crop disease early warning in South Asia by complementing expert surveys with internet media scraping - supporting data and analysis

description: Contains data from experiments and analysis that support the associated paper currently under review. Also contains scripts to: - Get the latest scraped media reports from the online dashboard (see doi: 10.5281/zenodo.8024493), and generate proxy surveys from them; - Simulate wheat rust spore production based on available survey datasets. - Analyse results of surveys, and models of spore production and spore dispersal, and reproduce the figures contained in the associated paper and its supplement. Requirements: Docker installed and 6GB of available storage space. Steps to use: 1) Download and unzip the contained file. 2) (Linux) Run setup.sh. (Windows/OSX/other) Look in setup.sh for instructions on where to unzip the files contained in supporting_data/ and build the Docker image and container. 3) Start and enter the Docker container, then run /home/code/sources-media-reports/run_all.sh.
description: This work was funded by a grant from the Bill & Melinda Gates Foundation to the Cambridge group, as well as the United Kingdom Foreign, Commonwealth and Development Office's programme Asia Regional Resilience in a Changing Climate (ARRCC). This work is also mapped to the One CGIAR Regional Integrated initiative Transforming Agrifood Systems in South Asia (TAFSSA). Accordingly, we would like to thank all funders who supported this research through their contributions to the CGIAR Trust Fund. Some of the software contained with this dataset are under an MIT license, see within.
DOI: 10.34740/kaggle/dsv/6382987
title: Egypt Gold Prices | Daily Updated
DOI: 10.5281/zenodo.8274969
title: ELITE land surface temperature: seamless 1km LST over China (2017)

description: The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn). This dataset is the ELITE seamless 1km LST over China landmass (2002-2020). Firstly, a look-up-table-based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled using the geographically weighted regression to obtain 1km LST. Finally, the multi-scale kalman filter is used to fuse MODIS LST and AMSR-E/AMSR2 LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST. This is the seamless LST dataset in 2017. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2016 and <em><strong>click here</strong></em> to download the ELITE LST product in 2018. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2017 Spatial Resolution: 1 KM Temporal Resolution: 2 times per day Data Format: hdf Scale: 0.02 <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Xu, S., & Cheng, J. (2021). A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254, 112256 Zhang, Q., Wang, N., Cheng, J., & Xu, S. (2020). A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5669-5681 Zhang, Q., & Cheng, J. (2020). An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables. Earth and Space Science, 7, e2019EA001006. https://doi.org/10.1029/2019EA001006 If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).