cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6411336
title: Crowdsourced air traffic data from The OpenSky Network 2020

description: <strong>Motivation</strong> The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data will be periodically included in the dataset until the end of the COVID-19 pandemic. <strong>License</strong> See LICENSE.txt <strong>Disclaimer</strong> The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous. Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted.<br> Fields <strong>origin</strong> or <strong>destination</strong> are empty when no airport could be found. Aircraft information come from the OpenSky aircraft database. Fields <strong>typecode</strong> and <strong>registration</strong> are empty when the aircraft is not present in the database. <strong>Description of the dataset</strong> One file per month is provided as a csv file with the following features: <strong>callsign</strong>: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.) <strong>number</strong>: the commercial number of the flight, when available (the matching with the callsign comes from public open API) <strong>icao24</strong>: the transponder unique identification number; <strong>registration</strong>: the aircraft tail number (when available); <strong>typecode</strong>: the aircraft model type (when available); <strong>origin</strong>: a four letter code for the origin airport of the flight (when available); <strong>destination</strong>: a four letter code for the destination airport of the flight (when available); <strong>firstseen</strong>: the UTC timestamp of the first message received by the OpenSky Network; <strong>lastseen</strong>: the UTC timestamp of the last message received by the OpenSky Network; <strong>day</strong>: the UTC day of the last message received by the OpenSky Network; <strong>latitude_1</strong>, <strong>longitude_1</strong>, <strong>altitude_1</strong>: the first detected position of the aircraft; <strong>latitude_2</strong>, <strong>longitude_2</strong>, <strong>altitude_2</strong>: the last detected position of the aircraft. <strong>Examples</strong> Possible visualisations and a more detailed description of the data are available at the following page:<br> &lt;https://traffic-viz.github.io/gallery/covid19.html&gt; <strong>Credit</strong> If you use this dataset, please cite: Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders<br> <strong>"</strong>Crowdsourced air traffic data from the OpenSky Network 2019–2020<strong>"</strong><br> <em>Earth System Science Data</em> 13(2), 2021<br> https://doi.org/10.5194/essd-13-357-2021
DOI: 10.5281/zenodo.6603766
title: Crowdsourced air traffic data from The OpenSky Network 2020

description: <strong>Motivation</strong> The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data will be periodically included in the dataset until the end of the COVID-19 pandemic. <strong>License</strong> See LICENSE.txt <strong>Disclaimer</strong> The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous. Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted.<br> Fields <strong>origin</strong> or <strong>destination</strong> are empty when no airport could be found. Aircraft information come from the OpenSky aircraft database. Fields <strong>typecode</strong> and <strong>registration</strong> are empty when the aircraft is not present in the database. <strong>Description of the dataset</strong> One file per month is provided as a csv file with the following features: <strong>callsign</strong>: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.) <strong>number</strong>: the commercial number of the flight, when available (the matching with the callsign comes from public open API) <strong>icao24</strong>: the transponder unique identification number; <strong>registration</strong>: the aircraft tail number (when available); <strong>typecode</strong>: the aircraft model type (when available); <strong>origin</strong>: a four letter code for the origin airport of the flight (when available); <strong>destination</strong>: a four letter code for the destination airport of the flight (when available); <strong>firstseen</strong>: the UTC timestamp of the first message received by the OpenSky Network; <strong>lastseen</strong>: the UTC timestamp of the last message received by the OpenSky Network; <strong>day</strong>: the UTC day of the last message received by the OpenSky Network; <strong>latitude_1</strong>, <strong>longitude_1</strong>, <strong>altitude_1</strong>: the first detected position of the aircraft; <strong>latitude_2</strong>, <strong>longitude_2</strong>, <strong>altitude_2</strong>: the last detected position of the aircraft. <strong>Examples</strong> Possible visualisations and a more detailed description of the data are available at the following page:<br> &lt;https://traffic-viz.github.io/gallery/covid19.html&gt; <strong>Credit</strong> If you use this dataset, please cite: Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders<br> <strong>"</strong>Crowdsourced air traffic data from the OpenSky Network 2019–2020<strong>"</strong><br> <em>Earth System Science Data</em> 13(2), 2021<br> https://doi.org/10.5194/essd-13-357-2021
DOI: 10.5281/zenodo.6797232
title: Crowdsourced air traffic data from The OpenSky Network 2020

description: <strong>Motivation</strong> The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data will be periodically included in the dataset until the end of the COVID-19 pandemic. <strong>License</strong> See LICENSE.txt <strong>Disclaimer</strong> The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous. Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted.<br> Fields <strong>origin</strong> or <strong>destination</strong> are empty when no airport could be found. Aircraft information come from the OpenSky aircraft database. Fields <strong>typecode</strong> and <strong>registration</strong> are empty when the aircraft is not present in the database. <strong>Description of the dataset</strong> One file per month is provided as a csv file with the following features: <strong>callsign</strong>: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.) <strong>number</strong>: the commercial number of the flight, when available (the matching with the callsign comes from public open API) <strong>icao24</strong>: the transponder unique identification number; <strong>registration</strong>: the aircraft tail number (when available); <strong>typecode</strong>: the aircraft model type (when available); <strong>origin</strong>: a four letter code for the origin airport of the flight (when available); <strong>destination</strong>: a four letter code for the destination airport of the flight (when available); <strong>firstseen</strong>: the UTC timestamp of the first message received by the OpenSky Network; <strong>lastseen</strong>: the UTC timestamp of the last message received by the OpenSky Network; <strong>day</strong>: the UTC day of the last message received by the OpenSky Network; <strong>latitude_1</strong>, <strong>longitude_1</strong>, <strong>altitude_1</strong>: the first detected position of the aircraft; <strong>latitude_2</strong>, <strong>longitude_2</strong>, <strong>altitude_2</strong>: the last detected position of the aircraft. <strong>Examples</strong> Possible visualisations and a more detailed description of the data are available at the following page:<br> &lt;https://traffic-viz.github.io/gallery/covid19.html&gt; <strong>Credit</strong> If you use this dataset, please cite: Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders<br> <strong>"</strong>Crowdsourced air traffic data from the OpenSky Network 2019–2020<strong>"</strong><br> <em>Earth System Science Data</em> 13(2), 2021<br> https://doi.org/10.5194/essd-13-357-2021
DOI: 10.5281/zenodo.7065179
title: Crowdsourced air traffic data from The OpenSky Network 2020

description: <strong>Motivation</strong> The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data will be periodically included in the dataset until the end of the COVID-19 pandemic. <strong>License</strong> See LICENSE.txt <strong>Disclaimer</strong> The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous. Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted.<br> Fields <strong>origin</strong> or <strong>destination</strong> are empty when no airport could be found. Aircraft information come from the OpenSky aircraft database. Fields <strong>typecode</strong> and <strong>registration</strong> are empty when the aircraft is not present in the database. <strong>Description of the dataset</strong> One file per month is provided as a csv file with the following features: <strong>callsign</strong>: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.) <strong>number</strong>: the commercial number of the flight, when available (the matching with the callsign comes from public open API) <strong>icao24</strong>: the transponder unique identification number; <strong>registration</strong>: the aircraft tail number (when available); <strong>typecode</strong>: the aircraft model type (when available); <strong>origin</strong>: a four letter code for the origin airport of the flight (when available); <strong>destination</strong>: a four letter code for the destination airport of the flight (when available); <strong>firstseen</strong>: the UTC timestamp of the first message received by the OpenSky Network; <strong>lastseen</strong>: the UTC timestamp of the last message received by the OpenSky Network; <strong>day</strong>: the UTC day of the last message received by the OpenSky Network; <strong>latitude_1</strong>, <strong>longitude_1</strong>, <strong>altitude_1</strong>: the first detected position of the aircraft; <strong>latitude_2</strong>, <strong>longitude_2</strong>, <strong>altitude_2</strong>: the last detected position of the aircraft. <strong>Examples</strong> Possible visualisations and a more detailed description of the data are available at the following page:<br> &lt;https://traffic-viz.github.io/gallery/covid19.html&gt; <strong>Credit</strong> If you use this dataset, please cite: Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders<br> <strong>"</strong>Crowdsourced air traffic data from the OpenSky Network 2019–2020<strong>"</strong><br> <em>Earth System Science Data</em> 13(2), 2021<br> https://doi.org/10.5194/essd-13-357-2021
DOI: 10.5281/zenodo.7323875
title: Crowdsourced air traffic data from The OpenSky Network 2020

description: <strong>Motivation</strong> The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data will be periodically included in the dataset until the end of the COVID-19 pandemic. <strong>License</strong> See LICENSE.txt <strong>Disclaimer</strong> The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous. Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted.<br> Fields <strong>origin</strong> or <strong>destination</strong> are empty when no airport could be found. Aircraft information come from the OpenSky aircraft database. Fields <strong>typecode</strong> and <strong>registration</strong> are empty when the aircraft is not present in the database. <strong>Description of the dataset</strong> One file per month is provided as a csv file with the following features: <strong>callsign</strong>: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.) <strong>number</strong>: the commercial number of the flight, when available (the matching with the callsign comes from public open API) <strong>icao24</strong>: the transponder unique identification number; <strong>registration</strong>: the aircraft tail number (when available); <strong>typecode</strong>: the aircraft model type (when available); <strong>origin</strong>: a four letter code for the origin airport of the flight (when available); <strong>destination</strong>: a four letter code for the destination airport of the flight (when available); <strong>firstseen</strong>: the UTC timestamp of the first message received by the OpenSky Network; <strong>lastseen</strong>: the UTC timestamp of the last message received by the OpenSky Network; <strong>day</strong>: the UTC day of the last message received by the OpenSky Network; <strong>latitude_1</strong>, <strong>longitude_1</strong>, <strong>altitude_1</strong>: the first detected position of the aircraft; <strong>latitude_2</strong>, <strong>longitude_2</strong>, <strong>altitude_2</strong>: the last detected position of the aircraft. <strong>Examples</strong> Possible visualisations and a more detailed description of the data are available at the following page:<br> &lt;https://traffic-viz.github.io/gallery/covid19.html&gt; <strong>Credit</strong> If you use this dataset, please cite: Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders<br> <strong>"</strong>Crowdsourced air traffic data from the OpenSky Network 2019–2020<strong>"</strong><br> <em>Earth System Science Data</em> 13(2), 2021<br> https://doi.org/10.5194/essd-13-357-2021
DOI: 10.6084/m9.figshare.22736696
title: Live body mass of solitary bees in the Sierra de Cazorla region (Jaén province, Spain) over the period 1990-2023

description: This dataset contains live body mass data for 1,704 individuals, 137 species, 27 genera and 6 families of wild solitary bees (Hymenoptera, Apoidea) sampled in the Sierra de Cazorla región (Jaén province, southeastern Spain) over the period 1990-2023. Sampling site, and the geographical coordinates and elevation for each sampled bee, are also included.
DOI: 10.34740/kaggle/dsv/5661426
title: AutoXGB
DOI: 10.20387/bonares-2r4s-qe93
title: Soil_microbial_nutrient_limitation_sugar_beet_yield_fertilization_experiment_SPEYER_2016

description: The study site in SPEYER was the International Mineral and Organic Nitrogen Fertilization Trial (IOSDV) located in Rinkenbergerhof (49°21’N 8°25′E, 99 m a.s.l.) and maintained by the Landwirtschaftliche Untersuchungs und Forschungsanstalt (LUFA) Speyer, Germany. Soil type was Cambisol with pH 6-6.5

Sugar beet was grown on 12 plots with 4 levels of mineral fertilisation (N0 – control 0 kg N/ha, N2 – 120 kg N/ha, N3 – 180 kg N/ha and N4 – 240 kg N/ha) and 3 levels of organic fertilisation (Z0 – control, HM – horse manure, and RC – harvest crop residues) resulting in 12 fertiliser treatments.  

Throughout the growing period (T3 and T4) soil samples were taken and analysed for microbial biomass carbon (Cmic) and microbial nutrient limitation using the substrate induced respiration method. At the end of the growing period (T4), the sugar beet crop was harvested and dry weight of leaves and roots were determined.

Soil sampling took place in 2016, in June (timepoint T3) and November (timepoint T4). Twelve soil cores of 2 cm Ø and 0-10cm depth were taken per plot and homogenised to 2 mm.

Research domain: Soil Sciences

Research question: Detailed information on the method can be found in the publication: Lemanski K, Armbruster M, Bonkowski M (2019) Linking soil microbial nutrient limitation to fertilizer regime and sugar beet yield. Plant and Soil 441: 253-259. doi: 10.1007/s11104-019-04114-w.
DOI: 10.5281/zenodo.7015082
title: The first Chinese 10-m-resolution building height (CNBH-10m)

description: We developed an approach to estimate the building height of entire China on a 10 m spatial resolution in 2020 combined with methine learning and all-weather earth observations (radar, optical and night light image et al.). Results indicate that the building height simulation performed well on a national scale (RMSE of 6.1m, R<sup>2</sup>=0.6)
DOI: 10.5281/zenodo.7064268
title: A first Chinese building height at 10 m resolution (CNBH-10 m)

description: Building height is a crucial variable in the study of urban environments, regional climates, and human-environment interactions. However, high-resolution data on building height, especially at the national scale, are limited. Fortunately, high spatial-temporal resolution earth observations, harnessed using a cloud-based platform, offer an opportunity to fill this gap. We describe an approach to estimate 2020 building height for China at 10 m spatial resolution based on all-weather earth observations (radar, optical, and night light images) using the Random Forest (RF) model. Results show that our building height simulation has a strong correlation with real observations at the national scale (RMSE of 6.1 m, MAE = 5.2 m, <em>R</em> = 0.77). The Combinational Shadow Index (CSI) is the most important contributor (15.1%) to building height simulation. Analysis of the distribution of building morphology reveals significant differences in building volume and average building height at the city scale across China. Macau has the tallest buildings (22.3 m) among Chinese cities, while Shanghai has the largest building volume (298.4 10<sup>8</sup> m<sup>3</sup>). The strong correlation between modelled building volume and socio-economic parameters indicates the potential application of building height products. The building height map developed in this study with a resolution of 10 m is open access, provides insights into the 3D morphological characteristics of cities and serves as an important contribution to future urban studies in China.
DOI: 10.5281/zenodo.7824061
title: A first Chinese building height at 10 m resolution (CNBH-10 m)

description: Building height is a crucial variable in the study of urban environments, regional climates, and human-environment interactions. However, high-resolution data on building height, especially at the national scale, are limited. Fortunately, high spatial-temporal resolution earth observations, harnessed using a cloud-based platform, offer an opportunity to fill this gap. We describe an approach to estimate 2020 building height for China at 10 m spatial resolution based on all-weather earth observations (radar, optical, and night light images) using the Random Forest (RF) model. Results show that our building height simulation has a strong correlation with real observations at the national scale (RMSE of 6.1 m, MAE = 5.2 m, <em>R</em> = 0.77). The Combinational Shadow Index (CSI) is the most important contributor (15.1%) to building height simulation. Analysis of the distribution of building morphology reveals significant differences in building volume and average building height at the city scale across China. Macau has the tallest buildings (22.3 m) among Chinese cities, while Shanghai has the largest building volume (298.4 10<sup>8</sup> m<sup>3</sup>). The strong correlation between modelled building volume and socio-economic parameters indicates the potential application of building height products. The building height map developed in this study with a resolution of 10 m is open access, provides insights into the 3D morphological characteristics of cities and serves as an important contribution to future urban studies in China.
description: {"references": ["Wu W B, Ma J, Banzhaf E, et al. A first Chinese building height estimate at 10 m resolution (CNBH-10 m) using multi-source earth observations and machine learning[J]. Remote Sensing of Environment, 2023, 291: 113578."]}
DOI: 10.5281/zenodo.7827315
title: CNBH-10 m: A first Chinese building height at 10 m resolution

description: Building height is a crucial variable in the study of urban environments, regional climates, and human-environment interactions. However, high-resolution data on building height, especially at the national scale, are limited. Fortunately, high spatial-temporal resolution earth observations, harnessed using a cloud-based platform, offer an opportunity to fill this gap. We describe an approach to estimate 2020 building height for China at 10 m spatial resolution based on all-weather earth observations (radar, optical, and night light images) using the Random Forest (RF) model. Results show that our building height simulation has a strong correlation with real observations at the national scale (RMSE of 6.1 m, MAE = 5.2 m, <em>R</em> = 0.77). The Combinational Shadow Index (CSI) is the most important contributor (15.1%) to building height simulation. Analysis of the distribution of building morphology reveals significant differences in building volume and average building height at the city scale across China. Macau has the tallest buildings (22.3 m) among Chinese cities, while Shanghai has the largest building volume (298.4 10<sup>8</sup> m<sup>3</sup>). The strong correlation between modelled building volume and socio-economic parameters indicates the potential application of building height products. The building height map developed in this study with a resolution of 10 m is open access, provides insights into the 3D morphological characteristics of cities and serves as an important contribution to future urban studies in China.
description: {"references": ["Wu W B, Ma J, Banzhaf E, et al. A first Chinese building height estimate at 10 m resolution (CNBH-10 m) using multi-source earth observations and machine learning[J]. Remote Sensing of Environment, 2023, 291: 113578."]}