cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/13148231.v2
title: Data for the article: "Root zone soil moisture estimation with Random Forests"

description: The dataset contains information on soil moisture, meteorological conditions, land cover, and soil hydrological groups for the soil moisture stations installed within the Raam soil moisture network. The network has a total of 15 stations within the Raam catchment, located in the southeastern portion of the Netherlands. The datasets covers the periods from April 2016 to December 2018. It was used for predicting root zone soil moisture using a Random Forest model and a 1-dimensional process-based model.<br> For each station, daily in situ measurements of surface soil moisture (SSM) at 5 cm and zone weighted depth-averaged root zone soil moisture (RZSM) are given. The meteorological conditions are obtained from daily datasets available from KNMI. First, the measurements from KNMI meteorological stations are interpolated in order to get spatially distributed values covering the study sites. The values from the interpolated maps were extracted for each point in the Raam network. <br><br>The vegetation characteristics are represented by leaf area index (LAI) obtained from MODIS. The crops at each station for each year are obtained from fieldwork data. The BOdemFysische Eenheden Kaart (BOFEK2012, Wosten et al., 2013), which is a map of soil hydro-physical properties for the Netherlands, was the basis for the information on the soil groups at the study sites. <br> <br>
DOI: 10.4121/13148231
title: Data for the article: "Root zone soil moisture estimation with Random Forests"

description: The dataset contains information on soil moisture, meteorological conditions, land cover, and soil hydrological groups for the soil moisture stations installed within the Raam soil moisture network. The network has a total of 15 stations within the Raam catchment, located in the southeastern portion of the Netherlands. The datasets covers the periods from April 2016 to December 2018. It was used for predicting root zone soil moisture using a Random Forest model and a 1-dimensional process-based model.<br> For each station, daily in situ measurements of surface soil moisture (SSM) at 5 cm and zone weighted depth-averaged root zone soil moisture (RZSM) are given. The meteorological conditions are obtained from daily datasets available from KNMI. First, the measurements from KNMI meteorological stations are interpolated in order to get spatially distributed values covering the study sites. The values from the interpolated maps were extracted for each point in the Raam network. <br><br>The vegetation characteristics are represented by leaf area index (LAI) obtained from MODIS. The crops at each station for each year are obtained from fieldwork data. The BOdemFysische Eenheden Kaart (BOFEK2012, Wosten et al., 2013), which is a map of soil hydro-physical properties for the Netherlands, was the basis for the information on the soil groups at the study sites. <br> <br>
DOI: 10.4121/13376960.v1
title: Spatio-temporal subsurface soil moisture (40 cm depth) within an agricultural field in the Netherlands

description: Spatio - temporal soil moisture measurements at 40 cm depth in an agricultural field in Haps, N. Brabant, the Netherlands. A total of 25 stations are distributed in a cultivates field that covers approximately 2.3 ha. of land area. <br><br>The datasets were collected over three seasons (summer 2017, winter 2017, summer 2018) using 5TM soil moisture sensors. The measurements were logged every 15 minutes. <br><br>In 2017, the crops in the field were potato and corn. After the harvest, a portion of the field was converted to grass for grazing. This was not desired because the sensors could be damaged by cattle. Twelve stations were relocated to another part of the field to avoid the grass field. In 2018, the only corn was planted where the stations were installed. Sandy soils were encountered at the study site.<br><br><br>
DOI: 10.4121/13376960.v2
title: Spatio-temporal subsurface soil moisture (40 cm depth) within an agricultural field in the Netherlands

description: Spatio - temporal soil moisture measurements at 40 cm depth in an agricultural field in Haps, N. Brabant, the Netherlands. A total of 25 stations are distributed in a cultivates field that covers approximately 2.3 ha. of land area. <br><br>The datasets were collected over three seasons (summer 2017, winter 2017, summer 2018) using 5TM soil moisture sensors. The measurements were logged every 15 minutes. <br><br>In 2017, the crops in the field were potato and corn. After the harvest, a portion of the field was converted to grass for grazing. This was not desired because the sensors could be damaged by cattle. Twelve stations were relocated to another part of the field to avoid the grass field. In 2018, the only corn was planted where the stations were installed. Sandy soils were encountered at the study site.<br><br><br>
DOI: 10.4121/13376960.v3
title: Spatio-temporal subsurface soil moisture (40 cm depth) within an agricultural field in the Netherlands

description: Spatio - temporal soil moisture measurements at 40 cm depth in an agricultural field in Haps, N. Brabant, the Netherlands. A total of 25 stations are distributed in a cultivates field that covers approximately 2.3 ha. of land area. <br><br>The datasets were collected over three seasons (summer 2017, winter 2017, summer 2018) using 5TM soil moisture sensors. The measurements were logged every 15 minutes. <br><br>In 2017, the crops in the field were potato and corn. After the harvest, a portion of the field was converted to grass for grazing. This was not desired because the sensors could be damaged by cattle. Twelve stations were relocated to another part of the field to avoid the grass field. In 2018, the only corn was planted where the stations were installed. Sandy soils were encountered at the study site.<br><br><br>
DOI: 10.4121/13376960
title: Spatio-temporal subsurface soil moisture (40 cm depth) within an agricultural field in the Netherlands

description: Spatio - temporal soil moisture measurements at 40 cm depth in an agricultural field in Haps, N. Brabant, the Netherlands. A total of 25 stations are distributed in a cultivates field that covers approximately 2.3 ha. of land area. <br><br>The datasets were collected over three seasons (summer 2017, winter 2017, summer 2018) using 5TM soil moisture sensors. The measurements were logged every 15 minutes. <br><br>In 2017, the crops in the field were potato and corn. After the harvest, a portion of the field was converted to grass for grazing. This was not desired because the sensors could be damaged by cattle. Twelve stations were relocated to another part of the field to avoid the grass field. In 2018, the only corn was planted where the stations were installed. Sandy soils were encountered at the study site.<br><br><br>
DOI: 10.4121/13677322.v1
title: Data underlying the publication: 'Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana'

description: Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates ( R2 = 0.92–0.93) than traditional pantropical models (R2 = 0.85–0.89), and were especially accurate for large trees (diameter &gt; 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested ( R2 = 0.89) and predicted AGB accurately across all size classes—which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees. <br>
DOI: 10.4121/13677322
title: Data underlying the publication: 'Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana'

description: Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates ( R2 = 0.92–0.93) than traditional pantropical models (R2 = 0.85–0.89), and were especially accurate for large trees (diameter &gt; 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested ( R2 = 0.89) and predicted AGB accurately across all size classes—which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees. <br>
DOI: 10.4121/13517384.v1
title: Supporting data for the publication: Rapidly migrating secondary bedforms can persist on the lee of slowly migrating primary river dunes

description: <i>The dataset contains supporting data for the publication Rapidly migrating secondary bedforms can persist in the lee of slowly migrating primary river dunes (2020), Zomer et al.</i><i>The dataset includes ADCP data (including GPS and heading data) and bathymetric data interpolated on an equidistant grid with a horizontal resolution of 0.1m and a vertical resolution of 0.01m. </i><i>The data was acquired on October 11, 2018 in the river Waal. </i>
DOI: 10.4121/12927563.v1
title: Dataset associated to publication: " High-rate biological selenate reduction in a sequencing batch reactor for recovery of hexagonal selenium "

description: Results belonging to paper "High-rate biological selenate reduction in a sequencing batch reactor for recovery of hexagonal selenium".Recovery of selenium (Se) from wastewater provides a solution for both securing Se supply and preventing Se pollution. Here, we developed a high-rate process for biological selenate reduction to elemental selenium. Distinctive from other studies, we aimed for a process with selenate as the main biological electron sink, with minimal formation of methane or sulfide. A sequencing batch reactor, fed with an influent containing 120 mgSe L<sup>-1</sup> selenate and ethanol as electron donor and carbon source, was operated for 495 days. The high rates (419 ± 17 mgSe L<sup>-1</sup> day<sup>-1</sup>) were recorded between day 446 and day 495 for a hydraulic retention time of 6h. The maximum conversion efficiency of selenate amounted to 96% with a volumetric conversion rate of 444 mgSe L<sup>-1</sup> day<sup>-1</sup>, which is 6 times higher than the rates reported in the literature thus far. At the end of the experiment, a highly enriched selenate reducing biomass had developed, with a specific activity of 856±26 mgSe<sup>-1</sup>day<sup>-1</sup>g<sub>biomass</sub><sup>-1</sup>, which was nearly 1000-fold higher than that of the inoculum. No evidence was found for the formation of methane, sulfide, or volatile reduced selenium compounds like dimethyl-selenide or H<sub>2</sub>Se, revealing a high selectivity. Ethanol was incompletely oxidized to acetate. The produced elemental selenium partially accumulated in the reactor as pure (≥80% Se of the total mixture of biomass sludge flocs and flaky aggregates, and ~100% of the specific flaky aggregates) selenium black hexagonal needles, with cluster sizes between 20-200 µm. The new process may serve as the basis for a high-rate technology to remove and recover pure selenium from wastewater or process streams with high selectivity.<br>
DOI: 10.4121/12927563.v2
title: Dataset associated to publication: "High-rate biological selenate reduction in a sequencing batch reactor for recovery of hexagonal selenium"

description: Results belonging to paper "High-rate biological selenate reduction in a sequencing batch reactor for recovery of hexagonal selenium".Recovery of selenium (Se) from wastewater provides a solution for both securing Se supply and preventing Se pollution. Here, we developed a high-rate process for biological selenate reduction to elemental selenium. Distinctive from other studies, we aimed for a process with selenate as the main biological electron sink, with minimal formation of methane or sulfide. A sequencing batch reactor, fed with an influent containing 120 mgSe L<sup>-1</sup> selenate and ethanol as electron donor and carbon source, was operated for 495 days. The high rates (419 ± 17 mgSe L<sup>-1</sup> day<sup>-1</sup>) were recorded between day 446 and day 495 for a hydraulic retention time of 6h. The maximum conversion efficiency of selenate amounted to 96% with a volumetric conversion rate of 444 mgSe L<sup>-1</sup> day<sup>-1</sup>, which is 6 times higher than the rates reported in the literature thus far. At the end of the experiment, a highly enriched selenate reducing biomass had developed, with a specific activity of 856±26 mgSe<sup>-1</sup>day<sup>-1</sup>g<sub>biomass</sub><sup>-1</sup>, which was nearly 1000-fold higher than that of the inoculum. No evidence was found for the formation of methane, sulfide, or volatile reduced selenium compounds like dimethyl-selenide or H<sub>2</sub>Se, revealing a high selectivity. Ethanol was incompletely oxidized to acetate. The produced elemental selenium partially accumulated in the reactor as pure (≥80% Se of the total mixture of biomass sludge flocs and flaky aggregates, and ~100% of the specific flaky aggregates) selenium black hexagonal needles, with cluster sizes between 20-200 µm. The new process may serve as the basis for a high-rate technology to remove and recover pure selenium from wastewater or process streams with high selectivity.<br>
DOI: 10.4121/12927563
title: Dataset associated to publication: "High-rate biological selenate reduction in a sequencing batch reactor for recovery of hexagonal selenium"

description: Results belonging to paper "High-rate biological selenate reduction in a sequencing batch reactor for recovery of hexagonal selenium".Recovery of selenium (Se) from wastewater provides a solution for both securing Se supply and preventing Se pollution. Here, we developed a high-rate process for biological selenate reduction to elemental selenium. Distinctive from other studies, we aimed for a process with selenate as the main biological electron sink, with minimal formation of methane or sulfide. A sequencing batch reactor, fed with an influent containing 120 mgSe L<sup>-1</sup> selenate and ethanol as electron donor and carbon source, was operated for 495 days. The high rates (419 ± 17 mgSe L<sup>-1</sup> day<sup>-1</sup>) were recorded between day 446 and day 495 for a hydraulic retention time of 6h. The maximum conversion efficiency of selenate amounted to 96% with a volumetric conversion rate of 444 mgSe L<sup>-1</sup> day<sup>-1</sup>, which is 6 times higher than the rates reported in the literature thus far. At the end of the experiment, a highly enriched selenate reducing biomass had developed, with a specific activity of 856±26 mgSe<sup>-1</sup>day<sup>-1</sup>g<sub>biomass</sub><sup>-1</sup>, which was nearly 1000-fold higher than that of the inoculum. No evidence was found for the formation of methane, sulfide, or volatile reduced selenium compounds like dimethyl-selenide or H<sub>2</sub>Se, revealing a high selectivity. Ethanol was incompletely oxidized to acetate. The produced elemental selenium partially accumulated in the reactor as pure (≥80% Se of the total mixture of biomass sludge flocs and flaky aggregates, and ~100% of the specific flaky aggregates) selenium black hexagonal needles, with cluster sizes between 20-200 µm. The new process may serve as the basis for a high-rate technology to remove and recover pure selenium from wastewater or process streams with high selectivity.<br>
DOI: 10.4121/13683703.v1
title: Data underlying the publication: Contributions of Arctic sea-ice loss and East Siberian atmospheric blocking to 2020 record-breaking Meiyu-Baiu rainfall - Simulations

description: <b>This is the primary data for a article going to be submitted to AGU GRL, named “contributions of Arctic sea-ice loss and East Siberian atmospheric blocking to 2020 record-breaking Meiyu-Baiu rainfall”. </b>We conducted atmospheric modeling experiments using the Community Atmosphere Model version 6 (CAM6), in the Community Earth System Model, version 2 (CESM2, Danabasoglu et al. 2020) from the National Center for Atmospheric Research (NCAR). The CAM6 was run with 1.25lon*0.9lat horizontal spacing, 32 vertical levels from the surface to 2.26 hPa. In the control experiment (CTL), 1995-2005 monthly climatological SST and SIC were used as the lower boundary condition and run for 75 years, including a 25-year spin-up. To examine the influence of Arctic sea ice anomalies, we further ran two perturbed experiments covering only the period from April 1<sup>st</sup> to July 31<sup>st</sup>. In the first perturbation experiment (ARCTIC), SST and SIC north of 65 were fixed to those of 2020 from May to July and to the climatology for April. The other perturbation experiment (GLOBAL) is the same as ARCTIC except it used the 2020 SST and SIC over the globe from May to July. We performed 20 ensemble simulations with slightly different initial conditions for each of the 4-month perturbation experiments and used their ensemble mean to reduce the internal variabilities. The GLOBAL-minus-CTL differences represent the atmospheric response to the SST and SIC anomalies in late spring-early summer of 2020, and the ARCTIC-minus-CTL differences represent the atmospheric response to the Arctic SIC and SST anomalies in late spring-early summer of 2020. <br><b>“2020mjjsic” denotes the ARCTIC experiment, “globalsst” denotes the GLOBAL experiment, and “ctrl_climatology” denotes the ensemble mean of CTL experiment.</b><br>Danabasoglu, G., Lamarque, J. F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., et al. (2020). The Community Earth System Model Version 2 (CESM2). Journal of Advances in Modeling Earth Systems,12, e2019MS001916.
DOI: 10.4121/13683703
title: Data underlying the publication: Contributions of Arctic sea-ice loss and East Siberian atmospheric blocking to 2020 record-breaking Meiyu-Baiu rainfall - Simulations

description: <b>This is the primary data for a article going to be submitted to AGU GRL, named “contributions of Arctic sea-ice loss and East Siberian atmospheric blocking to 2020 record-breaking Meiyu-Baiu rainfall”. </b>We conducted atmospheric modeling experiments using the Community Atmosphere Model version 6 (CAM6), in the Community Earth System Model, version 2 (CESM2, Danabasoglu et al. 2020) from the National Center for Atmospheric Research (NCAR). The CAM6 was run with 1.25lon*0.9lat horizontal spacing, 32 vertical levels from the surface to 2.26 hPa. In the control experiment (CTL), 1995-2005 monthly climatological SST and SIC were used as the lower boundary condition and run for 75 years, including a 25-year spin-up. To examine the influence of Arctic sea ice anomalies, we further ran two perturbed experiments covering only the period from April 1<sup>st</sup> to July 31<sup>st</sup>. In the first perturbation experiment (ARCTIC), SST and SIC north of 65 were fixed to those of 2020 from May to July and to the climatology for April. The other perturbation experiment (GLOBAL) is the same as ARCTIC except it used the 2020 SST and SIC over the globe from May to July. We performed 20 ensemble simulations with slightly different initial conditions for each of the 4-month perturbation experiments and used their ensemble mean to reduce the internal variabilities. The GLOBAL-minus-CTL differences represent the atmospheric response to the SST and SIC anomalies in late spring-early summer of 2020, and the ARCTIC-minus-CTL differences represent the atmospheric response to the Arctic SIC and SST anomalies in late spring-early summer of 2020. <br><b>“2020mjjsic” denotes the ARCTIC experiment, “globalsst” denotes the GLOBAL experiment, and “ctrl_climatology” denotes the ensemble mean of CTL experiment.</b><br>Danabasoglu, G., Lamarque, J. F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., et al. (2020). The Community Earth System Model Version 2 (CESM2). Journal of Advances in Modeling Earth Systems,12, e2019MS001916.
DOI: 10.4121/13691353.v1
title: Bluesky data: underlying the publication “Velocity Obstacle Based Conflict Avoidance in Urban Environment with Variable Speed Limit”

description: Bluesky scenarios and result files used in the work "Velocity Obstacle Based Conflict Avoidance in Urban Environment with Variable Speed Limit". The scenario files can be used with the Bluesky simulator tool implementation found at https://github.com/TUDelft-CNS-ATM/bluesky. The result files exhibit the results obtained with the previous tool.
DOI: 10.4121/13691353
title: Bluesky data: underlying the publication “Velocity Obstacle Based Conflict Avoidance in Urban Environment with Variable Speed Limit”

description: Bluesky scenarios and result files used in the work "Velocity Obstacle Based Conflict Avoidance in Urban Environment with Variable Speed Limit". The scenario files can be used with the Bluesky simulator tool implementation found at https://github.com/TUDelft-CNS-ATM/bluesky. The result files exhibit the results obtained with the previous tool.