cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14483400
title: Supplementary Material for: Difference in Insulin Resistance Assessment between European Union and Non-European Union Obesity Treatment Centers (ESPE Obesity Working Group Insulin Resistance Project)

description: <b><i>Introduction:</i></b> The obesity epidemic has become one of the most important public health issues of modern times. Impaired insulin sensitivity seems to be the cornerstone of multiple obesity related comorbidities. However, there is no accepted definition of impaired insulin sensitivity. <b><i>Objective:</i></b> We hypothesize that assessment of insulin resistance differs between centers. <b><i>Methods:</i></b> The ESPE Obesity Working Group (ESPE ObWG) Scientific Committee developed a questionnaire with a focus on the routine practices of assessment of hyperinsulinemia and insulin resistance, which was distributed through Google Docs platform to the clinicians and researchers from the current ESPE ObWG database (<i>n</i> = 73). Sixty-one complete responses (84% response rate) from clinicians and researchers were analyzed: 32 from European Union (EU) centers (representatives of 14 countries) and 29 from Non-EU centers (representatives from 10 countries). Standard statistics were used for the data analysis. <b><i>Results:</i></b> The majority of respondents considered insulin resistance (IR) as a clinical tool (85.2%) rather than a research instrument. For the purpose of IR assessment EU specialists prefer analysis of the oral glucose tolerance test (OGTT) results, whereas non-EU ones mainly use Homeostatic Model Assessment of Insulin Resistance (HOMA-IR; <i>p</i> = 0.032). There was no exact cutoff for the HOMA-IR in either EU or non-EU centers. A variety of OGTT time points and substances measured per local protocol were reported. Clinicians normally analyzed blood glucose (88.52% of centers) and insulin (67.21%, mainly in EU centers, <i>p</i> = 0.0051). Furthermore, most participants (70.5%) considered OGTT insulin levels as a more sensitive parameter of IR than glucose. Meanwhile, approximately two-thirds (63.9%) of the centers did not use any cutoffs for the insulin response to the glucose load. <b><i>Conclusions:</i></b> Since there is no standard for the IR evaluation and uniform accepted indication of performing, an OGTT the assessment of insulin sensitivity varies between EU and non-EU centers. A widely accepted standardized protocol is needed to allow comparison between centers.
DOI: 10.6084/m9.figshare.14483379
title: Supplementary Material for: The Effects of the Lockdown during the COVID-19 Pandemic on Alcohol and Tobacco Consumption Behavior in Germany

description: <b><i>Background:</i></b> Similar to other countries, the government of Germany has implemented various restrictions of social life in March 2020 to slow the spread of COVID-19 pandemic. This results in millions of people being isolated for long periods, which may increase feelings of worry and anxiety. As the consumption of alcohol and tobacco is an often used dysfunctional strategy to cope with such feelings, these restrictions might cause an increase of consumption. Already at the beginning of the COVID-19 pandemic, the World Health Organization (WHO) warned that increased alcohol consumption during the lockdown can increase the prevalence of alcohol use disorders in the future. However, up to now little is known about the changes in alcohol-drinking behavior and tobacco smoking in the general population during the COVID-19 pandemic. <b><i>Methods:</i></b> To address this theme, we investigated the changes in alcohol and tobacco consumption in the German population aged between 18 and 80 years via an online survey. <b><i>Results:</i></b> In total, 3,245 persons participated in the survey; 35.5% of them reported an increase in drinking during the lockdown (42.9% did not change their drinking behavior, 21.3% drank less, and 0.3% started drinking). The odds of consuming more alcohol during lockdown were associated with middle age, higher subjective stress due to the COVID-19 pandemic, a lower agreement with the importance of the restrictions, and consuming alcohol more than once per week before the lockdown. Also, 45.8% of the participants increased their smoking during the lockdown. The odds of smoking more during lockdown were associated with higher subjective stress due to the COVID-19 pandemic. <b><i>Conclusion:</i></b> These findings suggest that it is important to start campaigns to inform the general population about potential long-term effects of increased alcohol and tobacco consumption and to raise the health-care professionals’ awareness of this topic.
DOI: 10.21227/ry8q-sj65
title: 9/11 Hijackers

description: · 9/11 hijackers network dataset [20]: The 9/11 hijackers network incorporates 61 nodes (each node is a terrorist involved in 9/11 bombing at World Trade Centers in 2011). Dataset was prepared based on some news report, and ties range from ‘at school with’ to ‘on the same plane’. The Data consists of a mode matrix with 19*19 terrorist by terrorist having trusted prior contacts with 1 mode matrix of 61 edges of other involved associates. A Directed binary matrix shows whole Ties with 199 edges; some relations are given as a mix of prior contacts such as together trained and financial transactions and together lived and on the same plane. There are some annotations which are used as attribute nodes, and attribute code.
DOI: 10.15129/53eb0335-2257-4532-a261-edaab2356ff2
title: Data for: "A novel integrated workflow for isolation solvent selection using prediction and modelling"

description: In the ESI there are two folders: gPROMS prediction and solubility prediction. In gPROMS prediction folder there are the model B and the different simulations of model B, with the data analysis. I solubility prediction folder there are the COSMOtherm simulations. Binary plot prediction solvent selection paper excel file contains the solubility binary plots used in model B. PCM-Dry excel files contain the particle size analysis of the two paracetamol grades. The solvent selection paper and the visio solvent selection paper file contain the workflow diagram. In solvent selection paper calculation from gPROM there is the data analysis of model A and model B simulations. The remaining pfd and excel files are the HPLC analysis of the different samples used for the validation.
DOI: 10.34808/76ep-fp83
title: High-resolution wind wave parameters  in the area of the Gulf of Gdańsk during 21 extreme storms (GIS dataset)

description: This GIS dataset contains the results of wind-wave parameter modelling in the area of the Gulf of Gdańsk (Southern Baltic). For the simulations, a high resolution SWAN model was used. The dataset consists of the significant wave height, the direction of the wave approaching the shore and the wave period during 21 historical, extreme storms (rasters). The storms were selected by an automatic search over the 44-year-long significant wave height time series.
DOI: 10.15454/gyqeuw/rx24r0
title: RepereE.stl

description: :unav
DOI: 10.3886/e114027v1-84920
title: Replication data for: Rising Government Debt: Causes and Solutions for a Decades-Old Trend

description: Over the past four decades, government debt as a fraction of GDP has been on an upward trajectory in advanced economies, approaching levels not reached since World War II. While normative macroeconomic theories can explain the increase in the level of debt in certain periods as a response to macroeconomic shocks, they cannot explain the broad-based long-run trend in debt accumulation. In contrast, political economy theories can explain the long-run trend as resulting from an aging population, rising political polarization, and rising electoral uncertainty across advanced economies. These theories emphasize the time-inconsistency in government policymaking, and thus the need for fiscal rules that restrict policymakers. Fiscal rules trade off commitment to not overspend and flexibility to react to shocks. This tradeoff guides design features of optimal rules, such as information dependence, enforcement, cross-country coordination, escape clauses, and instrument versus target criteria.
DOI: 10.15454/gyqeuw/ckknt8
title: Slider.stl

description: :unav
DOI: 10.15454/gyqeuw/voz086
title: Ventricules.vtk

description: :unav
DOI: 10.5281/zenodo.4719516
title: From spherical compartments to polymer films: exploiting vesicle fusion to generate solid supported thin polymer membranes

description: Data underlying the figures in the publication “From spherical compartments to polymer films: exploiting vesicle fusion to generate solid supported thin polymer membranes” published in <em>Nanoscale </em>2021, 13, 6944-6952. Table of contents: 1. Fig 1 Size and Morphology Characterization 1.1 Fig 1a DLS Graphs 1.2 Fig 1b TEM Micrographs 2. Fig 2 QCM-D Frequency and Dissipation Shifts 2.1 Fig 2a Polysomers A<sub>6</sub>B<sub>34</sub>A<sub>6</sub> 2.2 Fig 2b Polymersomes A<sub>6</sub>B<sub>65</sub>A<sub>6</sub> 3. Fig 3 Changes of Viscoelastic Properties 3.1 Fig 3a Upper Layer Thickness (QCM-D Determination) 3.1.1 Polysomers A<sub>3</sub>B<sub>22</sub>A<sub>3</sub> 3.1.2 Polysomers A<sub>6</sub>B<sub>34</sub>A<sub>6</sub> 3.1.3 Polysomers A<sub>7</sub>B<sub>42</sub>A<sub>7</sub> 3.1.4 Polysomers A<sub>6</sub>B<sub>44</sub>A<sub>6</sub> 3.1.5 Polysomers A<sub>6</sub>B<sub>65</sub>A<sub>6</sub> 3.2 Fig 3b Shear Elastic Modulus 3.2.1 Polysomers A<sub>3</sub>B<sub>22</sub>A<sub>3</sub> 3.2.2 Polysomers A<sub>6</sub>B<sub>34</sub>A<sub>6</sub> 3.2.3 Polysomers A<sub>7</sub>B<sub>42</sub>A<sub>7</sub> 3.2.4 Polysomers A<sub>6</sub>B<sub>44</sub>A<sub>6</sub> 3.2.5 Polysomers A<sub>6</sub>B<sub>65</sub>A<sub>6</sub> 3.3 Fig 3c Upper Layer Thickness (SE Determination) 3.3.1 Polysomers A<sub>3</sub>B<sub>22</sub>A<sub>3</sub> 3.3.2 Polysomers A<sub>6</sub>B<sub>34</sub>A<sub>6</sub> 3.3.3 Polysomers A<sub>7</sub>B<sub>42</sub>A<sub>7</sub> 3.3.4 Polysomers A<sub>6</sub>B<sub>44</sub>A<sub>6</sub> 3.3.5 Polysomers A<sub>6</sub>B<sub>65</sub>A<sub>6</sub> 3.4 Fig 3d Differences of Thickness 3.4.1 Polysomers A<sub>3</sub>B<sub>22</sub>A<sub>3</sub> 3.4.2 Polysomers A<sub>6</sub>B<sub>34</sub>A<sub>6</sub> 3.4.3 Polysomers A<sub>7</sub>B<sub>42</sub>A<sub>7</sub> 3.4.4 Polysomers A<sub>6</sub>B<sub>44</sub>A<sub>6</sub> 3.4.5 Polysomers A<sub>6</sub>B<sub>65</sub>A<sub>6</sub> 4. Fig 4 Membrane Thickness Comparison 4.1 Polysomers A<sub>3</sub>B<sub>22</sub>A<sub>3</sub> 4.2 Polysomers A<sub>6</sub>B<sub>34</sub>A<sub>6</sub> 4.3 Polysomers A<sub>7</sub>B<sub>42</sub>A<sub>7</sub> 4.4 Polysomers A<sub>6</sub>B<sub>44</sub>A<sub>6</sub> 4.5 Polysomers A<sub>6</sub>B<sub>65</sub>A<sub>6</sub> 5. Fig 5 AFM Micrographs 5.1 Fig 5a Immobilized Thiol-Bearing A<sub>3</sub>B<sub>22</sub>A<sub>3</sub> 5.2 Fig 5b <em>left</em> A<sub>6</sub>B<sub>34</sub>A<sub>6</sub> Planar Membrane Formation 5.3 Fig 5b <em>middle</em> A<sub>7</sub>B<sub>42</sub>A<sub>7</sub> Membrane “Patches” Formation 5.4 Fig 5b <em>right</em> A<sub>6</sub>B<sub>34</sub>A<sub>6 </sub>Deformed Polymersomes
DOI: 10.5281/zenodo.4719515
title: From spherical compartments to polymer films: exploiting vesicle fusion to generate solid supported thin polymer membranes

description: Data underlying the figures in the publication “From spherical compartments to polymer films: exploiting vesicle fusion to generate solid supported thin polymer membranes” published in <em>Nanoscale </em>2021, 13, 6944-6952. Table of contents: 1. Fig 1 Size and Morphology Characterization 1.1 Fig 1a DLS Graphs 1.2 Fig 1b TEM Micrographs 2. Fig 2 QCM-D Frequency and Dissipation Shifts 2.1 Fig 2a Polysomers A<sub>6</sub>B<sub>34</sub>A<sub>6</sub> 2.2 Fig 2b Polymersomes A<sub>6</sub>B<sub>65</sub>A<sub>6</sub> 3. Fig 3 Changes of Viscoelastic Properties 3.1 Fig 3a Upper Layer Thickness (QCM-D Determination) 3.1.1 Polysomers A<sub>3</sub>B<sub>22</sub>A<sub>3</sub> 3.1.2 Polysomers A<sub>6</sub>B<sub>34</sub>A<sub>6</sub> 3.1.3 Polysomers A<sub>7</sub>B<sub>42</sub>A<sub>7</sub> 3.1.4 Polysomers A<sub>6</sub>B<sub>44</sub>A<sub>6</sub> 3.1.5 Polysomers A<sub>6</sub>B<sub>65</sub>A<sub>6</sub> 3.2 Fig 3b Shear Elastic Modulus 3.2.1 Polysomers A<sub>3</sub>B<sub>22</sub>A<sub>3</sub> 3.2.2 Polysomers A<sub>6</sub>B<sub>34</sub>A<sub>6</sub> 3.2.3 Polysomers A<sub>7</sub>B<sub>42</sub>A<sub>7</sub> 3.2.4 Polysomers A<sub>6</sub>B<sub>44</sub>A<sub>6</sub> 3.2.5 Polysomers A<sub>6</sub>B<sub>65</sub>A<sub>6</sub> 3.3 Fig 3c Upper Layer Thickness (SE Determination) 3.3.1 Polysomers A<sub>3</sub>B<sub>22</sub>A<sub>3</sub> 3.3.2 Polysomers A<sub>6</sub>B<sub>34</sub>A<sub>6</sub> 3.3.3 Polysomers A<sub>7</sub>B<sub>42</sub>A<sub>7</sub> 3.3.4 Polysomers A<sub>6</sub>B<sub>44</sub>A<sub>6</sub> 3.3.5 Polysomers A<sub>6</sub>B<sub>65</sub>A<sub>6</sub> 3.4 Fig 3d Differences of Thickness 3.4.1 Polysomers A<sub>3</sub>B<sub>22</sub>A<sub>3</sub> 3.4.2 Polysomers A<sub>6</sub>B<sub>34</sub>A<sub>6</sub> 3.4.3 Polysomers A<sub>7</sub>B<sub>42</sub>A<sub>7</sub> 3.4.4 Polysomers A<sub>6</sub>B<sub>44</sub>A<sub>6</sub> 3.4.5 Polysomers A<sub>6</sub>B<sub>65</sub>A<sub>6</sub> 4. Fig 4 Membrane Thickness Comparison 4.1 Polysomers A<sub>3</sub>B<sub>22</sub>A<sub>3</sub> 4.2 Polysomers A<sub>6</sub>B<sub>34</sub>A<sub>6</sub> 4.3 Polysomers A<sub>7</sub>B<sub>42</sub>A<sub>7</sub> 4.4 Polysomers A<sub>6</sub>B<sub>44</sub>A<sub>6</sub> 4.5 Polysomers A<sub>6</sub>B<sub>65</sub>A<sub>6</sub> 5. Fig 5 AFM Micrographs 5.1 Fig 5a Immobilized Thiol-Bearing A<sub>3</sub>B<sub>22</sub>A<sub>3</sub> 5.2 Fig 5b <em>left</em> A<sub>6</sub>B<sub>34</sub>A<sub>6</sub> Planar Membrane Formation 5.3 Fig 5b <em>middle</em> A<sub>7</sub>B<sub>42</sub>A<sub>7</sub> Membrane “Patches” Formation 5.4 Fig 5b <em>right</em> A<sub>6</sub>B<sub>34</sub>A<sub>6 </sub>Deformed Polymersomes
DOI: 10.15454/gyqeuw/tf7spe
title: Frame V2.stl

description: :unav
DOI: 10.15454/gyqeuw/6fw1om
title: RepereA.stl

description: :unav
DOI: 10.15454/gyqeuw/6qjgmb
title: Screw.stl

description: :unav
DOI: 10.15161/oar.it/73746
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.4719468
title: ImmunoCluster: Development and testing datasets

description: Here you can find links to download datasets assosciated with ImmunoCluster: an R package that provides a complete toolkit to carry out immune profiling from both liquid and imaging high-dimensional mass (CyTOF) and flow cytometry. https://github.com/kordastilab/ImmunoCluster Description of files: *Github walkthrough data* -fcs_data.zip: .fcs data files that have undergone gating cleanup -panel_metadata.rds: CyTOF staining panel metadata rds object -panel_metadata.xlsx: CyTOF staining panel metadata excel file -sample_metadata.rds: CyTOF patient metadata rds object -sample_metadata.xlsx: CyTOF patient metadata excel file -sce_gvhd.rds: Initialised ImmunoCluster SingleCellExperiment object *Full processed dataset* -FR-FCM-Z244_gated.zip: Complete gated dataset of .fcs files that have undergone gating cleanup and metadata excel files
DOI: 10.5281/zenodo.3801881
title: ImmunoCluster: Development and testing datasets

description: Here you can find links to download datasets assosciated with ImmunoCluster: an R package that provides a complete toolkit to carry out immune profiling from both liquid and imaging high-dimensional mass (CyTOF) and flow cytometry. https://github.com/kordastilab/ImmunoCluster Description of files: *Github walkthrough data* -fcs_data.zip: .fcs data files that have undergone gating cleanup -panel_metadata.rds: CyTOF staining panel metadata rds object -panel_metadata.xlsx: CyTOF staining panel metadata excel file -sample_metadata.rds: CyTOF patient metadata rds object -sample_metadata.xlsx: CyTOF patient metadata excel file -sce_gvhd.rds: Initialised ImmunoCluster SingleCellExperiment object *Full processed dataset* -FR-FCM-Z244_gated.zip: Complete gated dataset of .fcs files that have undergone gating cleanup and metadata excel files
DOI: 10.6084/m9.figshare.14400872.v1
title: Medical costs of and changes in glaucoma treatment among patients newly starting glaucoma care

description: <b>Purpose</b>: To use a medical claim database to investigate medical costs and treatment patterns among patients newly starting glaucoma care. <b>Subjects and methods</b>: Subjects registered in the Japan Medical Database Center (JMDC) from January 2005 to March 2016 who were newly diagnosed with glaucoma, started glaucoma treatment, and had treatment records covering more than five years were included in the analysis. All direct medical costs were collected for a period of up to ten years. Factors affecting medical costs were analyzed. Changes in hypotensive eyedrops and choices related to glaucoma surgery were also analyzed. <b>Results</b>: Out of approximately 1.42 million subjects, 2,393 satisfied the inclusion and exclusion criteria. The average total medical cost incurred per patient over a period of ten years was US$9,030, including US$1,214 during the initial year. The proportion of the total cost represented by the cost of hypotensive eyedrops increased from 5.2% to 10.6% over the ten-year period. Medical costs were higher in patients younger than ten years old than in patients of all other age groups. The number of ocular hypotensive eyedrops increased from 0.9 to 1.5 over the ten-year period. Medical costs were higher for subjects with secondary glaucoma than for other subjects. Sixty-three patients underwent trabeculotomy or trabeculectomy, and trabeculectomy was the preferred choice in later years. <b>Conclusions</b>: The total direct medical cost associated with glaucoma was US$9,030 for the first ten years. Drug costs gradually increased with treatment duration and patient age and varied by glaucoma type.
DOI: 10.5517/ccdc.csd.cc2570hy
title: CCDC 2002739: Experimental Crystal Structure Determination

description: Related Article: Burak Ay, Ghodrat Mahmoudi, Ali Akbar Khandar, Farhad Akbari Afkhami, Atakan Toprak, Fedor I. Zubkov, Jonathan White, Julia Kłak, Damir A. Safin|2021|Inorg.Chim.Acta|522|120335|doi:10.1016/j.ica.2021.120335
DOI: 10.5517/ccdc.csd.cc2685l8
title: CCDC 2033649: Experimental Crystal Structure Determination

description: Related Article: Qi‐Rui Shui, Hong‐Xin Tang, Rui‐Biao Fu, Yuan‐Bin Fang, Zu‐Ju Ma, Xin‐Tao Wu|2021|Angew.Chem.,Int.Ed.|60|2116|doi:10.1002/anie.202013088
DOI: 10.5517/ccdc.csd.cc2685m9
title: CCDC 2033650: Experimental Crystal Structure Determination

description: Related Article: Qi‐Rui Shui, Hong‐Xin Tang, Rui‐Biao Fu, Yuan‐Bin Fang, Zu‐Ju Ma, Xin‐Tao Wu|2021|Angew.Chem.,Int.Ed.|60|2116|doi:10.1002/anie.202013088
DOI: 10.5281/zenodo.4719481
title: R scirpt and data for the paper untitled "No Evidence for Long-range Male Sex Pheromones in Two Malaria Mosquitoes"

description: This is an R script and data sets to replicate the analyses from the article entitled "No Evidence for Long-range Male Sex Pheromones in Two Malaria Mosquitoes" by Serge Bèwadéyir Poda, Bruno Buatois, Benoit Lapeyre, Laurent Dormont, Abdoulaye Diabaté, Olivier Gnankiné, Roch K. Dabiré & Olivier Roux (2021). Code developed by Olivier Roux : oliver.roux@ird.fr
DOI: 10.6084/m9.figshare.12909815
title: DATASET - Physics-based earthquake simulations in slow-moving faults: a case study from the Eastern Betic Shear Zone (SE Spain)

description: This dataset contains input and output files used to run the RSQSim earthquake simulations performed in our study: Herrero‐Barbero, P., Álvarez‐Gómez, J. A., Williams, C., Villamor, P., Insua‐Arévalo, J. M., Alonso‐Henar, J., & Martínez‐Díaz, J. J. <b>Physics‐based earthquake simulations in slow‐moving faults: a case study from the Eastern Betic Shear Zone (SE Iberian Peninsula).</b> <i>Journal of Geophysical Research: Solid Earth</i>, e2020JB021133. https://doi.org/10.1029/2020JB021133
DOI: 10.5281/zenodo.4719584
title: Maternal effects and fitness consequences of individual variation in bottlenose dolphins' ecological niche

description: Data needed to replicate analyses in "Maternal effects and fitness consequences of individual variation in bottlenose dolphins’ ecological niche", Strickland et al, JAE 2021 "nichedata.csv" will be needed to replicate repeatability and maternal effect analyses and results. "fitness.csv" will be needed to replicate analyses of calving success and habitat use.
DOI: 10.5281/zenodo.4719583
title: Maternal effects and fitness consequences of individual variation in bottlenose dolphins' ecological niche

description: Data needed to replicate analyses in "Maternal effects and fitness consequences of individual variation in bottlenose dolphins’ ecological niche", Strickland et al, JAE 2021 "nichedata.csv" will be needed to replicate repeatability and maternal effect analyses and results. "fitness.csv" will be needed to replicate analyses of calving success and habitat use.
DOI: 10.6084/m9.figshare.14483514
title: Reynolds, Smith, Macniven. Comparison of free‐living physical activity data obtained from a Fitbit Zip, the Apple iPhone Health app and a modified Bouchard Activity Record

description: SPSS sav file
DOI: 10.5281/zenodo.4719582
title: Quantification of the uncertainty within a SAS-SST simulation caused by the unknown high-wavenumber damping factor

description: This paper aims to quantify the uncertainty in the SAS-SST simulation of a prism bluff-body flow due to varying the higher-wavenumber damping factor (). Instead of performing the uncertainty quantification on the CFD simulation directly, a surrogate modelling approach is adopted. The mesh sensitivity is first studied and the numerical error due to the mesh is approximated accordingly. The Gaussian processes/Kriging method is used to generate surrogate models for quantities of interest (QoIs). The suitability of the surrogate models is assessed using the leave-one-out cross-validation tests (LOO-CV). The stochastic tests are then performed using the cross-validated surrogate models to quantify the uncertainty of QoIs by varying Cs. Four prior probability density functions (such as U(0,1), N(0.5, 0.1^2), Beta (2,2) and Beta (5,1.5)) of Cs are considered. It is demonstrated in this study that the uncertainty of a predicted QoI due to varying Cs is regionally dependent. The flow statistics in the near wake of the prism body are subject to larger variance due to the uncertainty in Cs. The influence of Cs rapidly decays as the location moves downstream. The response of different QoIs to the changing Cs varies greatly. Therefore, the calibration of Cs only using observations of one variable may bias the results. Last but not least, it is important to consider different sources of uncertainties within the numerical model when scrutinising a turbulence model, as ignoring the contributions to the total error may lead to biased conclusions.
DOI: 10.21957/epyv-3t61
title: CY46R1 wave hindcast based on ERA5 forcing 2019-2020
DOI: 10.5519/qd.lr6vyuha
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (350 records)
DOI: 10.5281/zenodo.4719639
title: prism_meta + supporting files

description: Prism meta files for DDH project + supporting files: prism_meta.Rds compound_mesh.Rds compound_descriptions.Rds
DOI: 10.5281/zenodo.4661825
title: prism_meta + supporting files

description: Prism meta files for DDH project + supporting files: prism_meta.Rds compound_mesh.Rds compound_descriptions.Rds