cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/22148099
title: Data underlying the publication: Influence of Charge Regulation on the Performance of Shock Electrodialysis

description: The goal of the research was to determine the effect of charge regulation on a shock electrodialysis process by performing numerical Comsol simulations. All the data in this dataset was obtained by calculating theoretical models either in Matlab or Comsol. The Excel files summarize the quantitative data obtained from the attached Comsol simulations. The Matlab file "Shock ED plotting" is used for plotting the graphs from the Excel sheet data. The Matlab file "charge regulation model" is used to calculate the charge (of silica) for different salt concentrations and temperatures. All the relevant Comsol model files (version 6.0) used to obtain the results are included in the dataset. <br> <br>
DOI: 10.4121/21656672
title: Delft Parametric Flutter Margin (PFM) Test Data Archive

description: The dataset corresponds to the supplementary data for the paper titled 'Safe Flutter Determination for Wings Undergoing Large Deflections' (link: https://arc.aiaa.org/eprint/HDIFICQ9BW3BMFT2ZAAM/full/10.2514/6.2023-0379) and the MSc thesis entitled 'Safe Flutter Determination for Wings Undergoing Large Deflections' (link: http://resolver.tudelft.nl/uuid:b0e12214-ca2a-4dde-8499-955d2da08937). <br> The dataset contains wind tunnel test data acquired for the validation of a new flutter test version of the Parametric Flutter Margin (PFM) method, specifically applied to wings undergoing large deflections. The PFM method adds a stabilising parameter, such as a stabilising mass, to the model such that the flutter velocity is increased. To demonstrate the method a wind tunnel test campaign was performed at TU Delft using the Delft Pazy Wing which can exhibit large nonlinear deflection. The wing was equipped with a flutter pod consisting of a shaker and stabilising mass that was placed at the mid-span position at the leading edge of the wing. During the test campaign, three test series were performed. The first identified the flutter boundary through direct flutter tests, with the flutter onset and offset velocities being determined by actually hitting flutter that turned into a Limit Cycle Oscillation (LCO). The second and third test series were the PFM measurements, where both Single Input Single Output (SISO) and Multiple Input Multiple Output (MIMO) PFM tests were applied to obtain the nominal flutter boundaries without actually hitting flutter. <br>
DOI: 10.4121/21656672.v1
title: Delft Parametric Flutter Margin (PFM) Test Data Archive

description: The dataset corresponds to the supplementary data for the paper titled 'Safe Flutter Determination for Wings Undergoing Large Deflections' (link: https://arc.aiaa.org/eprint/HDIFICQ9BW3BMFT2ZAAM/full/10.2514/6.2023-0379) and the MSc thesis entitled 'Safe Flutter Determination for Wings Undergoing Large Deflections' (link: http://resolver.tudelft.nl/uuid:b0e12214-ca2a-4dde-8499-955d2da08937). <br> The dataset contains wind tunnel test data acquired for the validation of a new flutter test version of the Parametric Flutter Margin (PFM) method, specifically applied to wings undergoing large deflections. The PFM method adds a stabilising parameter, such as a stabilising mass, to the model such that the flutter velocity is increased. To demonstrate the method a wind tunnel test campaign was performed at TU Delft using the Delft Pazy Wing which can exhibit large nonlinear deflection. The wing was equipped with a flutter pod consisting of a shaker and stabilising mass that was placed at the mid-span position at the leading edge of the wing. During the test campaign, three test series were performed. The first identified the flutter boundary through direct flutter tests, with the flutter onset and offset velocities being determined by actually hitting flutter that turned into a Limit Cycle Oscillation (LCO). The second and third test series were the PFM measurements, where both Single Input Single Output (SISO) and Multiple Input Multiple Output (MIMO) PFM tests were applied to obtain the nominal flutter boundaries without actually hitting flutter. <br>
DOI: 10.4121/19487078
title: Data underlying the publication: The embodied carbon emissions of lettuce production in vertical farming, greenhouse horticulture, and open-field farming in the Netherlands

description: This research evaluates the current carbon footprint of vertical farming systems by comparing the impact of one kilogram of butterhead lettuce produced in an operational vertical farm to that of conventional open-field farming (OF), soil-based greenhouse cultivation (GH(s)) and hydroponic greenhouse cultivation (GH(h)) in the Netherlands. A typical farm is defined for OF, GH(s) and GH(h) in the Netherlands, based on existing databases. It is not yet possible to define a typical vertical farm (VF) due to the breadth of approaches. Therefore, an operational vertical farm was used as a case study. Within the dataset the baseline activity data has been collected within the tabs ‘OF’, ‘GH(s)’, GH(h) and ‘VF’. The carbon footprint of each case study was calculated by accounting for all the GHG emissions from activities within the system boundaries, from cradle-to-grave, for the life cycle of both the crop and the farm. These GHG emissions were calculated as follows: CO₂<sub>-eq</sub> = activity data x EF, where CO<sub>2-eq </sub>is the carbon footprint of the activity in kg CO<sub>2-eq</sub>, and EF is the emission factor of the activity in kg CO₂<sub>-eq</sub> per unit of the activity data, assessed by referring to the IPCC GWP100a characterisation method in SimaPro 9.0.0, which is based on the Ecoinvent 3.6 database. Tab ‘EF’ provides an overview of the emissions factor sources used for activities within the study. Country-specific emission factors for the Netherlands were used for natural gas and electricity consumption to reflect the correct energy mix. By combining the collected activity data with the EFs, the carbon footprints of each farming system were calculated within the tabs ‘FIG04’ and ‘FIG05’. Three scenarios were created to improve the comparability of the baseline data as well as present potential carbon savings as a result of transitioning to renewable energy, which included: the lost carbon sequestration potential as a result of land-use change (FIG06), assuming identical sales packaging for all farming systems (FIG07, FIG08, FIG09), and a transition to renewable energy (FIG10). These scenarios, when considered collectively, greatly reduced the disparity between the carbon footprints of the three farming systems (FIG11 and FIG12). Electricity use represented the largest share in the carbon fooptrint of both baseline and alternative scenario, therefore, the electricity consumption of the VF was compared to that of other VF from literature in the tabs ‘FIG13’ and ‘FIG14’.
DOI: 10.4121/19487078.v1
title: Data underlying the publication: The embodied carbon emissions of lettuce production in vertical farming, greenhouse horticulture, and open-field farming in the Netherlands

description: This research evaluates the current carbon footprint of vertical farming systems by comparing the impact of one kilogram of butterhead lettuce produced in an operational vertical farm to that of conventional open-field farming (OF), soil-based greenhouse cultivation (GH(s)) and hydroponic greenhouse cultivation (GH(h)) in the Netherlands. A typical farm is defined for OF, GH(s) and GH(h) in the Netherlands, based on existing databases. It is not yet possible to define a typical vertical farm (VF) due to the breadth of approaches. Therefore, an operational vertical farm was used as a case study. Within the dataset the baseline activity data has been collected within the tabs ‘OF’, ‘GH(s)’, GH(h) and ‘VF’. The carbon footprint of each case study was calculated by accounting for all the GHG emissions from activities within the system boundaries, from cradle-to-grave, for the life cycle of both the crop and the farm. These GHG emissions were calculated as follows: CO₂<sub>-eq</sub> = activity data x EF, where CO<sub>2-eq </sub>is the carbon footprint of the activity in kg CO<sub>2-eq</sub>, and EF is the emission factor of the activity in kg CO₂<sub>-eq</sub> per unit of the activity data, assessed by referring to the IPCC GWP100a characterisation method in SimaPro 9.0.0, which is based on the Ecoinvent 3.6 database. Tab ‘EF’ provides an overview of the emissions factor sources used for activities within the study. Country-specific emission factors for the Netherlands were used for natural gas and electricity consumption to reflect the correct energy mix. By combining the collected activity data with the EFs, the carbon footprints of each farming system were calculated within the tabs ‘FIG04’ and ‘FIG05’. Three scenarios were created to improve the comparability of the baseline data as well as present potential carbon savings as a result of transitioning to renewable energy, which included: the lost carbon sequestration potential as a result of land-use change (FIG06), assuming identical sales packaging for all farming systems (FIG07, FIG08, FIG09), and a transition to renewable energy (FIG10). These scenarios, when considered collectively, greatly reduced the disparity between the carbon footprints of the three farming systems (FIG11 and FIG12). Electricity use represented the largest share in the carbon fooptrint of both baseline and alternative scenario, therefore, the electricity consumption of the VF was compared to that of other VF from literature in the tabs ‘FIG13’ and ‘FIG14’.
DOI: 10.4121/21541416.v1
title: Dataset proteomics: Analysis of change in protein expression in Saccharomyces cerevisiae upon shift from chemostat to feast/famine regime for different sugar substrates

description: This proteomics dataset was generated to analyse the changes in protein expression between a chemostat and feast/famine regime culture of <em>Saccharomyces cerevisiae</em> CEN.PK113-7D grown on sucrose, maltose and fructose at D = 0.1 h<sup>-1</sup>. Proteome composition of the taken samples was subsequently measured by DSM, and the obtained dataset was analysed for significant changes in protein expression between chemostat and feast/famine conditions. <br> Files in this item:<br> 1) Materials and Methods:<br> Materials_methods_proteomics_sugar_substrates_chemostat_FF_shift_Verhagen.docx<br> <br> Describes the methodology of the reactor experiment, sampling and proteomics data analysis.<br> <br> 2) Proteomics data:<br> Data_proteomics_sugar_substrates_shift_chemostat_FF_Verhagen.xlsx<br> <br> Contains raw data received from DSM's proteomics measurements facility and subsequent statistical analysis.<br> Read sheet 'Description' for explanation of other available sheets in the file
DOI: 10.4121/21541416
title: Dataset proteomics: Analysis of change in protein expression in Saccharomyces cerevisiae upon shift from chemostat to feast/famine regime for different sugar substrates

description: This proteomics dataset was generated to analyse the changes in protein expression between a chemostat and feast/famine regime culture of <em>Saccharomyces cerevisiae</em> CEN.PK113-7D grown on sucrose, maltose and fructose at D = 0.1 h<sup>-1</sup>. Proteome composition of the taken samples was subsequently measured by DSM, and the obtained dataset was analysed for significant changes in protein expression between chemostat and feast/famine conditions. <br> Files in this item:<br> 1) Materials and Methods:<br> Materials_methods_proteomics_sugar_substrates_chemostat_FF_shift_Verhagen.docx<br> <br> Describes the methodology of the reactor experiment, sampling and proteomics data analysis.<br> <br> 2) Proteomics data:<br> Data_proteomics_sugar_substrates_shift_chemostat_FF_Verhagen.xlsx<br> <br> Contains raw data received from DSM's proteomics measurements facility and subsequent statistical analysis.<br> Read sheet 'Description' for explanation of other available sheets in the file
DOI: 10.4121/21692057
title: Dataset metabolomics: Measurements of extracellular sugars and intracellular central carbon metabolites in Saccharomyces cerevisiae under steady-state chemostat and feast/famine regimes with different sugar substrates

description: This metabolomics dataset was generated to analyse the changes in the intracellular metabolome between both steady-state chemostat and feast/famine regime cultivation of <em>Saccharomyces cerevisiae</em> CEN.PK113-7D grown on sucrose, maltose and fructose at D = 0.1 h<sup>-1</sup>. <br> Files in this item: 1) Materials and Methods: Materials_methods_metabolomics_sugar_substrates_chemostat_FF_Verhagen.docx Describes the methodology of the reactor experiment, sampling and metabolomics analysis. 2) Metabolomics data: Metabolomics_data_steady-state_and_feast-famine_sugar_substrates_Verhagen.xlsx Contains data of extracellular sugar concentrations and intracellular central carbon metabolite concentrations, measured under both steady state chemostat conditions and feast/famine conditions with respectively fructose, sucrose and maltose as substrates.
DOI: 10.4121/22180438.v1
title: FreeRef-1 verification, inter-observer and user tests 2023

description: Photographs, measurements done in photographs and calculated measurement errors from verification tests, inter-observer tests and user tests of the FreeRef-1 contactless size-reference system for evidence photography. Data for S- and L-mode of the FreeRef-1 system.
DOI: 10.4121/22180438
title: FreeRef-1 verification, inter-observer and user tests 2023

description: Photographs, measurements done in photographs and calculated measurement errors from verification tests, inter-observer tests and user tests of the FreeRef-1 contactless size-reference system for evidence photography. Data for S- and L-mode of the FreeRef-1 system.
DOI: 10.4121/22182325
title: Data underlying the publication: The potential impact of human interventions at different scales in offshore wind farms to promote flat oyster (Ostrea edulis) reef development in the southern North Sea.

description: Stepwise procedure to quantify the effect of interventions that stimulate oyster reef development in offshore windfarms. An assessment was made in the southern North Sea, of all offshore wind farms and the infrastructure therein, present up to the date 31 December 2020, and of areas designated for future wind farms. Wind farm data was obtained from wind farm owners, wind farm websites, and from web-based sources www.4coffshore.com and www.emodnet.ec.europe.eu. Data on physical conditions (i.e. shear stress from Kamermans et al. (2018) and suspended particle matter from Gayer (2020)) were determined for the wind farm locations using using GoogleEarth. The effects of various interventions on oyster reef development were estimated quantitatively from assumptions based upon various previous studies. For further information see manuscript: https:/doi.org/10.1051/alr/2023001
DOI: 10.4121/22182325.v1
title: Data underlying the publication: The potential impact of human interventions at different scales in offshore wind farms to promote flat oyster (Ostrea edulis) reef development in the southern North Sea.

description: Stepwise procedure to quantify the effect of interventions that stimulate oyster reef development in offshore windfarms. An assessment was made in the southern North Sea, of all offshore wind farms and the infrastructure therein, present up to the date 31 December 2020, and of areas designated for future wind farms. Wind farm data was obtained from wind farm owners, wind farm websites, and from web-based sources www.4coffshore.com and www.emodnet.ec.europe.eu. Data on physical conditions (i.e. shear stress from Kamermans et al. (2018) and suspended particle matter from Gayer (2020)) were determined for the wind farm locations using using GoogleEarth. The effects of various interventions on oyster reef development were estimated quantitatively from assumptions based upon various previous studies. For further information see manuscript: https:/doi.org/10.1051/alr/2023001
DOI: 10.4121/22182280.v1
title: Data underlying the publication: Offshore wind farms contribute to epibenthic biodiversity in the North Sea.

description: Video footage of epibenthic organisms at and around the scour protection in offshore wind farms was collected using a Remotely Operated Vehicle. The epibenthic community structure was assessed for species abundance and species diversity (species richness (<em>S</em>), species evenness (<em>E</em>) and Shannon diversity index (<em>H</em>)). Species density for individual species were calculated as the number of individuals per m2 in a video frame; species density of clustering species was calculated in percentage as covered area per video frame. These different types of densities were combined by transforming them to the ordinal Marine Nature Conservation Review (MNCR) SACFOR scale. Statistical analyses were performed using the software package R version 3.6.3 with several functions from the ‘vegan package’. Before statistical analyses, species with only 1 observation in the dataset were removed to minimize the influence of rare species in multivariate analyses. To obtain a balanced dataset, the Monte Carlo resampling strategy was applied (by 100 randomized repetitions). <br> For further information see manuscript.
DOI: 10.4121/22182280
title: Data underlying the publication: Offshore wind farms contribute to epibenthic biodiversity in the North Sea.

description: Video footage of epibenthic organisms at and around the scour protection in offshore wind farms was collected using a Remotely Operated Vehicle. The epibenthic community structure was assessed for species abundance and species diversity (species richness (<em>S</em>), species evenness (<em>E</em>) and Shannon diversity index (<em>H</em>)). Species density for individual species were calculated as the number of individuals per m2 in a video frame; species density of clustering species was calculated in percentage as covered area per video frame. These different types of densities were combined by transforming them to the ordinal Marine Nature Conservation Review (MNCR) SACFOR scale. Statistical analyses were performed using the software package R version 3.6.3 with several functions from the ‘vegan package’. Before statistical analyses, species with only 1 observation in the dataset were removed to minimize the influence of rare species in multivariate analyses. To obtain a balanced dataset, the Monte Carlo resampling strategy was applied (by 100 randomized repetitions). <br> For further information see manuscript.
DOI: 10.6084/m9.figshare.22212127
title: Data_FFioroni_et_al_2023a.xlsx

description: In this research we evaluated the influence of: soil origin (native forest/pine plantation), type of culture (mono-specific/mixed), and application of a commercial ectomycorrhizal inoculant, over morphological measure-ments; shoot and root biomass; ectomycorrhizas, arbuscular mycorrhizas, and root endophytic fungi abundance; and ectomicorrhizal diversity of Nothofagus obliqua and Pinus ponderosa seedlings in a greenhouse experiment.
DOI: 10.3886/e152141v1
title: How Fertility Behavior Responds to Trade Shock: Evidence from China

description: This paper examines the fertility effect of a trade shock induced by China’s accession to the WTO by exploiting the variation in regional exposure to the shock. We find that trade uncertainty decline restrains fertility by both delaying childbearing and reducing lifetime fertility. Overall, the negative effect is larger in urban areas and for couples that already have children, especially sons. In addition, consistent with Becker’s model, the shock to females decreases fertility, but the shock to males has an opposite impact. Finally, with son preference, higher opportunity cost raises the probability of the first child being a boy.
description: whole population in China.
description: This data contains information on fertility behavior and trade uncertainty in China. We rely on China's 2005 population census to measure the fertility behavior of individuals. We link children to their parents and then trace the fertility history of each woman according to the ages of her children. In addition, we measure the decline in product-level trade uncertainty following Pierce and Schott (2016), which is defined as the gap between NTR tariff rates that are locked in by PNTR and non-NTR rates. We compute the NTR gap using ad valorem equivalent NTR and non-NTR tariff rates in 1999 (the year before the passage of PNTR in the US) provided by Feenstra et al. (2002).
DOI: 10.6084/m9.figshare.22216951
title: Genes' sequence annotations in paper named "Serotonin/GABA receptors modulate odour input to olfactory receptor neuron in locust".xlsx

description: New genes' sequence annotations in paper named "Serotonin/GABA receptors modulate odour input to olfactory receptor neuron in locust"
DOI: 10.6084/m9.figshare.22216951.v1
title: Genes' sequence annotations in paper named "Serotonin/GABA receptors modulate odour input to olfactory receptor neuron in locust".xlsx

description: New genes' sequence annotations in paper named "Serotonin/GABA receptors modulate odour input to olfactory receptor neuron in locust"
DOI: 10.6084/m9.figshare.18866726
title: Henan

description: The sustainable development of specialized villages
DOI: 10.6084/m9.figshare.18866726.v3
title: Henan

description: The sustainable development of specialized villages
DOI: 10.6084/m9.figshare.18866726.v2
title: Understanding the Spatial Patterns of Sustainable Development of Specialized Villages and Influencing Factors

description: The sustainable development of specialized villages
DOI: 10.6084/m9.figshare.22213894
title: Supplementary Table Jan 21.xlsx

description: cheminformatics data for benzylic C-H coupling products (PCA, PMI, Box Plots, etc.)
DOI: 10.6084/m9.figshare.22213894.v2
title: Supplementary Table Jan 21.xlsx

description: cheminformatics data for benzylic C-H coupling products (PCA, PMI, Box Plots, etc.)
DOI: 10.6084/m9.figshare.22213894.v1
title: Supplementary Table Jan 21.xlsx

description: cheminformatics data for benzylic C-H coupling products (PCA, PMI, Box Plots, etc.)
DOI: 10.6084/m9.figshare.21430371
title: Increased heat risk in wet climate induced by urban humid heat

description: Data and code in support of paper "Increased heat risk in wet climate induced by urban humid heat".
DOI: 10.34740/kaggle/dsv/5110481
title: Dataset, Images of symbols
DOI: 10.5281/zenodo.7699893
title: Raw Data for manuscript published at Nanomaterials, entitled: Asymmetrical Plasmon Distribution in Hybrid AuAg Hollow/Solid Coded Nanotubes

description: .dm3 TEM, STEM and EELS raw data
DOI: 10.5281/zenodo.7699892
title: Raw Data for manuscript published at Nanomaterials, entitled: Asymmetrical Plasmon Distribution in Hybrid AuAg Hollow/Solid Coded Nanotubes

description: .dm3 TEM, STEM and EELS raw data