cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.11583/dtu.21762830.v2
title: Ready-To-Train AI4Arctic Sea Ice Challenge Test Dataset

description: The AI4Arctic Sea Ice Challenge Datasets are produced for the AI4EO sea ice competition initiated by the European Space Agency (ESA) ɸ-lab. The purpose of the competition is to develop deep learning models to automatically produce sea ice charts including sea ice concentration, stage-of-development and floe size (form) information. The training datasets contain Sentinel-1 active microwave Synthetic Aperture Radar (SAR) data and corresponding passive MicroWave Radiometer (MWR) data from the AMSR2 satellite sensor. While SAR data has ambiguities between open water and sea ice, it has a high spatial resolution, whereas MWR data has good contrast between open water and ice. However, the coarse resolution of the AMSR2 MWR observations introduces a new set of obstacles, e.g. land spill-over, which can lead to erroneous sea ice predictions along the coastline adjacent to open water. Label data in the challenge datasets are ice charts, that have been produced by the Greenland ice service at the Danish Meteorological Institute (DMI) and the Canadian Ice Service (CIS) for the safety of navigation. The challenge datasets also contain other auxiliary data such as the distance to land and numerical weather prediction model data. The scenes are from the time period from January 8 2018 to December 21 2021. Two versions of the dataset exist, the '<em>raw'</em> and '<em>ready-to-train'-</em>versions with corresponding test datasets<em>. </em>The datasets each consist of the same 513 training and 20 test (without label data) scenes. The ‘<em>ready-to-train’</em>-version has been further prepared for model training, such as downsampled data from 40 to 80 m pixel spacing, standard scaled, converted ice charts (sea ice concentration, stage of development and floe size), removal of nan values, mask alignment etc. This is the Test data for the Ready-To-Train version. Reference data is not included. Further details are described in the common manual that is published together with the datasets; “AI4Arctic_challenge-dataset-manual”. Code with a get-started toolkit for the '<em>ready-to-train</em>' dataset: https://github.com/astokholm/AI4ArcticSeaIceChallenge A quick challenge video overview of the challenge is available at: https://youtu.be/iuXIeLPyKfg <br> Version 2 includes the reference sea ice charts (previously absent) as the AutoICE Challenge has been finalised. The ice charts are both included in numerical format in the netCDF files and in quicklook images containing the SIC, SOD and FLOE for each scene in png format. <br> This item is part of the Collection https://doi.org/10.11583/DTU.c.6244065
DOI: 10.5281/zenodo.8153763
title: Lagrangian trajectories tracking the origin and fate of a hydrothermal plume observed in the southeastern Pacific sector of the Southern Ocean

description: A dataset of Lagrangian trajectories which were used to estimate the origin and fate of a hydrothermal plume in the southeastern Pacific sector of the Southern Ocean observed during the DY111 CUSTARD cruise. All variables have long names and units. These files have been used for the analysis in Birchill and Baker et al. ‘Pathways and timescales of Southern Ocean hydrothermal iron and manganese transport’ with further information about the methodology available in the paper. There are five forward-tracking experiments from suspected vent sites (CUSTARD_NEPR.nc, CUSTARD_SEPR.nc, CUSTARD_PAR.nc, CUSTARD_WCR.nc and CUSTARD_ECR.nc) included in the paper and one forward-tracking experiment that was not included in the paper (CUSTARD_EM.nc) as no trajectories crossed our sampling site. There was one backward-tracking experiment undertaken from the Ocean Observatories Initiative (OOI) station at which the hydrothermal signal was observed (CUSTARD_OOI_backward.nc).
DOI: 10.5281/zenodo.8153762
title: Lagrangian trajectories tracking the origin and fate of a hydrothermal plume observed in the southeastern Pacific sector of the Southern Ocean

description: A dataset of Lagrangian trajectories which were used to estimate the origin and fate of a hydrothermal plume in the southeastern Pacific sector of the Southern Ocean observed during the DY111 CUSTARD cruise. All variables have long names and units. These files have been used for the analysis in Birchill and Baker et al. ‘Pathways and timescales of Southern Ocean hydrothermal iron and manganese transport’ with further information about the methodology available in the paper. There are five forward-tracking experiments from suspected vent sites (CUSTARD_NEPR.nc, CUSTARD_SEPR.nc, CUSTARD_PAR.nc, CUSTARD_WCR.nc and CUSTARD_ECR.nc) included in the paper and one forward-tracking experiment that was not included in the paper (CUSTARD_EM.nc) as no trajectories crossed our sampling site. There was one backward-tracking experiment undertaken from the Ocean Observatories Initiative (OOI) station at which the hydrothermal signal was observed (CUSTARD_OOI_backward.nc).
DOI: 10.11583/dtu.18549719
title: Global characterization factors for quantifying water consumption impacts on freshwater biodiversity in life cycle assessment - Support Material

description: Support material 1 : supplementary methods, figures, and tables (pdf file). <br> Support material 2: effect, and characterization factors at the basin scale and aggregated values (excel file). <br> basins_5min_pcrglobwb.gpkg : geopackage containing the river basin delineation (n=20317) at 5 arcmin spatial resolution.
DOI: 10.11583/dtu.18549719.v1
title: Global characterization factors for quantifying water consumption impacts on freshwater biodiversity in life cycle assessment - Support Material

description: Support material 1 : supplementary methods, figures, and tables (pdf file). <br> Support material 2: effect, and characterization factors at the basin scale and aggregated values (excel file). <br> basins_5min_pcrglobwb.gpkg : geopackage containing the river basin delineation (n=20317) at 5 arcmin spatial resolution.
DOI: 10.14473/csda/sar0k6/rre6w7
title: TI_ISSP00027.pdf

description: Technical information
DOI: 10.14473/csda/sar0k6/jrz39i
title: ISSP 2019_FINAL.tab

description: Data
DOI: 10.14473/csda/sar0k6/fdclkv
title: D_ISSP00027.pdf

description: Questionnaire
DOI: 10.14473/csda/sar0k6/yypbjk
title: V11_19_ISSP karty_v5.pdf

description: Cards
DOI: 10.14473/csda/ylvjc4/affukp
title: D_ISSP00027.pdf

description: Dotazník
DOI: 10.14473/csda/ylvjc4/le1eer
title: TI_ISSP00027.pdf

description: Technická informace AJ
DOI: 10.14473/csda/ylvjc4/iylbxd
title: K_ISSP00027.pdf

description: Karty
DOI: 10.14473/csda/ylvjc4/yolrlz
title: ISSP 2019_FINAL-1.tab

description: Data
DOI: 10.11583/dtu.19689040
title: Onshore wind generation time series for technology SP335 HH100 at resource grade C (PECD 2021 update)

description: This data (csv file) provides simulated hourly time series of onshore wind generation with specific power (SP) 335 W/m2 turbines at hub height (HH) of 100 m for the regions shown in the attached map. The analysed wind power plants are sited at the 50 % lowest mean wind speed locations in each region, i.e., in resource grade (RG) C. The map shows the resulting capacity factors (annual mean). The Excel file gives a rough indication if this wind technology is suitable for the different regions for this RG or not. The available land considers all onshore land area of a region, except lakes, cities, and very high elevation locations. The possible impact of any existing onshore wind installations in the region is not considered. Wake losses are modeled, with additional 5 % of other losses and unavailability considered. The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00 is the aggregated onshore wind generation of all the UK regions (weighted by regional installed capacities). The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. The linked journal paper (1st link) describes the simulation methodology (combination of ERA5 and GWA data is used). It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the concept of resource grades and how they can be applied in energy system analyses. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581
DOI: 10.5281/zenodo.8154675
title: An over-dominant effect associated with an SNP in a TP63 regulatory region is implicated in susceptibility to non-syndromic orofacial clefts.

description: <strong>Background</strong>: Non-syndromic orofacial clefts (NSOC) are complex phenotypes, involving multiple low penetrance genetic and environmental factors. Using a candidate gene approach, we aimed to analyze the role of four single nucleotide polymorphisms (SNPs) in the susceptibility to NSOC. <strong>Methods</strong>: A total of 254 individuals, 120 patients with NSOC, and 134 controls, all of Portuguese origin and non-consanguineous, were recruited. About 92% of patients had cleft lip with or without cleft palate (NSCL/P) and 8% had cleft palate (NSCP). Four SNPs in the <em>MTHFR</em>, <em>IRF6</em>, <em>PAX7,</em> and <em>TP63</em> genes were studied, using a real-time approach with TaqMan probes. Statistical analysis was performed with IBM SPSS Statistics and included chi-squared, Cochran test for trend and binomial logistic regression. Bonferroni correction was applied. Statistical significance was set to 0.05. <strong>Results: </strong>All SNPs were in Hardy-Weinberg equilibrium. A significant statistical association was found for <em>TP63</em> rs9332461 in an over-dominant model (<em>p</em> = 0.016; OR 1.897 (1.144 - 3.147)). <strong>Discussion and conclusion</strong>: Few SNPs have been so far identified in <em>TP63</em> in population association studies. In our sample, a significant association was only highlighted for rs9332461, localized in upstream regulatory region. Mechanisms involved in heterozygous advantage may underlie the association.
DOI: 10.5281/zenodo.8154674
title: An over-dominant effect associated with an SNP in a TP63 regulatory region is implicated in susceptibility to non-syndromic orofacial clefts.

description: <strong>Background</strong>: Non-syndromic orofacial clefts (NSOC) are complex phenotypes, involving multiple low penetrance genetic and environmental factors. Using a candidate gene approach, we aimed to analyze the role of four single nucleotide polymorphisms (SNPs) in the susceptibility to NSOC. <strong>Methods</strong>: A total of 254 individuals, 120 patients with NSOC, and 134 controls, all of Portuguese origin and non-consanguineous, were recruited. About 92% of patients had cleft lip with or without cleft palate (NSCL/P) and 8% had cleft palate (NSCP). Four SNPs in the <em>MTHFR</em>, <em>IRF6</em>, <em>PAX7,</em> and <em>TP63</em> genes were studied, using a real-time approach with TaqMan probes. Statistical analysis was performed with IBM SPSS Statistics and included chi-squared, Cochran test for trend and binomial logistic regression. Bonferroni correction was applied. Statistical significance was set to 0.05. <strong>Results: </strong>All SNPs were in Hardy-Weinberg equilibrium. A significant statistical association was found for <em>TP63</em> rs9332461 in an over-dominant model (<em>p</em> = 0.016; OR 1.897 (1.144 - 3.147)). <strong>Discussion and conclusion</strong>: Few SNPs have been so far identified in <em>TP63</em> in population association studies. In our sample, a significant association was only highlighted for rs9332461, localized in upstream regulatory region. Mechanisms involved in heterozygous advantage may underlie the association.
DOI: 10.11583/dtu.19691002
title: Existing offshore wind generation time series (PECD 2021 update)

description: This data (csv file) provides simulated hourly time series of existing offshore wind generation for the regions shown in the attached map. Only regions with existing (by the time of modeling) offshore wind power plants are simulated (otherwise the data are NaN). The map shows the resulting capacity factors (annual mean). Wake losses are modeled, with additional 5 % of other losses and unavailability considered. The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00_OFF is the aggregated offshore wind generation of all the UK regions (weighted by regional installed capacities). The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. The linked journal paper (1st link) describes the ERA5-based simulation methodology. It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the modeling of wake losses and storm shutdown behaviour for the offshore wind power plants. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581
DOI: 10.11583/dtu.20156714
title: Virtual nacelle lidar measurements in wake through aeroelastic simulations

description: The data files include results from aeroelastic simulations performed with HAWC2 and combined with a lidar simulator. Virtual nacelle lidars scan the inflow to the rotor in order to generate a dataset of virtual power performance tests, where the power output is retrieved from HAWC2 and the wind speed measurements are given by the virtual lidars. Both wake-free and waked conditions are simulated and the Wake Dynamic Meandering model (https://doi.org/10.1115/1.4002555) is used to simulate the wakes.
DOI: 10.11583/dtu.20156714.v1
title: Virtual nacelle lidar measurements in wake through aeroelastic simulations

description: The data files include results from aeroelastic simulations performed with HAWC2 and combined with a lidar simulator. Virtual nacelle lidars scan the inflow to the rotor in order to generate a dataset of virtual power performance tests, where the power output is retrieved from HAWC2 and the wind speed measurements are given by the virtual lidars. Both wake-free and waked conditions are simulated and the Wake Dynamic Meandering model (https://doi.org/10.1115/1.4002555) is used to simulate the wakes.
DOI: 10.34740/kaggle/dsv/6148644
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.11583/dtu.19689016
title: Onshore wind generation time series for technology SP277 HH200 at resource grade C (PECD 2021 update)

description: This data (csv file) provides simulated hourly time series of onshore wind generation with specific power (SP) 277 W/m2 turbines at hub height (HH) of 200 m for the regions shown in the attached map. The analysed wind power plants are sited at the 50 % lowest mean wind speed locations in each region, i.e., in resource grade (RG) C. The map shows the resulting capacity factors (annual mean). The Excel file gives a rough indication if this wind technology is suitable for the different regions for this RG or not. The available land considers all onshore land area of a region, except lakes, cities, and very high elevation locations. The possible impact of any existing onshore wind installations in the region is not considered. Wake losses are modeled, with additional 5 % of other losses and unavailability considered. The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00 is the aggregated onshore wind generation of all the UK regions (weighted by regional installed capacities). The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. The linked journal paper (1st link) describes the simulation methodology (combination of ERA5 and GWA data is used). It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the concept of resource grades and how they can be applied in energy system analyses. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581
DOI: 10.11583/dtu.19689022
title: Onshore wind generation time series for technology SP335 HH150 at resource grade A (PECD 2021 update)

description: This data (csv file) provides simulated hourly time series of onshore wind generation with specific power (SP) 335 W/m2 turbines at hub height (HH) of 150 m for the regions shown in the attached map. The analysed wind power plants are sited at the best 10 % of locations in each region, i.e., in resource grade (RG) A. The map shows the resulting capacity factors (annual mean). The Excel file gives a rough indication if this wind technology is suitable for the different regions for this RG or not. The available land considers all onshore land area of a region, except lakes, cities, and very high elevation locations. The possible impact of any existing onshore wind installations in the region is not considered. Wake losses are modeled, with additional 5 % of other losses and unavailability considered. The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00 is the aggregated onshore wind generation of all the UK regions (weighted by regional installed capacities). The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. The linked journal paper (1st link) describes the simulation methodology (combination of ERA5 and GWA data is used). It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the concept of resource grades and how they can be applied in energy system analyses. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581
DOI: 10.6084/m9.figshare.23214551.v1
title: Supplementary Material for: Phacoemulsification with and without iStent: a systematic review and meta-analysis of comparative studies

description: Background: The iStent (Glaukos Corporation, Laguna Hills, CA, USA) is one of the minimally invasive glaucoma devices. It can be inserted at the time of phacoemulsification or as a stand-alone procedure to lower the intraocular pressure. Objective: Our aim is to conduct a systematic review and meta-analysis comparing the effect of iStent insertion at the time of phacoemulsification with phacoemulsification alone in patients with ocular hypertension or open-angle glaucoma. Methods We searched EMBASE, MEDLINE (OVID and Pubmed), CINAHL and Cochrane Library for articles published between 2008 and June 2022(PRISMA 2020 for checklist). Studies comparing the IOP lowering effect of iStent with phacoemulsification versus phacoemulsification alone were included. The endpoints were the reduction in IOP (IOPR) and the mean reduction in number of glaucoma drops. A quality-effects model was used to compare both surgical groups. Results 10 studies were included, reporting on 1,453 eyes. 853 eyes had the combined iStent and phacoemulsification, and 600 eyes underwent phacoemulsification alone. IOPR was higher in the combined surgery at of 4.7 ± 2. mmHg compared to 2.8 ± 1.9 mmHg in phacoemulsification alone. A greater decrease in post-operative eye drops was noted in the combined group having a decrease of 1.2 ± 0.3 eye drops vs of 0.6 ± 0.6 drops in isolated phacoemulsification. Quality effect model showed an IOPR weighted mean difference (WMD) of 1.22 mmHg (CI: [-0.43, 2.87]; Q=315.64; P&lt;0.01; I2=97%) and decreased eye drops WMD 0.42 drops (CI: [0.22, 0.62]; Q=42.6; P&lt;0.01; I2=84%) between both surgical groups. Subgroup analysis shows that the new generation iStent may be more effective in reducing IOP. Conclusion iStent has a synergetic effect with phacoemulsification. The reduction of IOP and glaucoma eye drops was higher when iStent is combined with phacoemulsification compared with isolated phacoemulsification.
DOI: 10.11583/dtu.19689034
title: Onshore wind generation time series for technology SP335 HH150 at resource grade B (PECD 2021 update)

description: This data (csv file) provides simulated hourly time series of onshore wind generation with specific power (SP) 335 W/m2 turbines at hub height (HH) of 150 m for the regions shown in the attached map. The analysed wind power plants are sited at the 10...50 % highest mean wind speed locations in each region, i.e., in resource grade (RG) B. The map shows the resulting capacity factors (annual mean). The Excel file gives a rough indication if this wind technology is suitable for the different regions for this RG or not. The available land considers all onshore land area of a region, except lakes, cities, and very high elevation locations. The possible impact of any existing onshore wind installations in the region is not considered. Wake losses are modeled, with additional 5 % of other losses and unavailability considered. The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00 is the aggregated onshore wind generation of all the UK regions (weighted by regional installed capacities). The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. The linked journal paper (1st link) describes the simulation methodology (combination of ERA5 and GWA data is used). It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the concept of resource grades and how they can be applied in energy system analyses. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581