cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25403/upresearchdata.23634603.v1
title: Stable isotope analysis of Southern Ocean myctophids using faecal otoliths

description: The tables and figures in this dataset represent the development of a method to estimate the stable isotope values of myctophids when only otoliths from faecal samples of predators (or sedimentary samples) are available. Otoliths from wild myctophids were collected from faecal samples of fur seals at Marion Island in the Southern Ocean, and were cleaned using NaOCl (bleach) before obtaining the stable isotope values of the otoliths' organic component. The cleaning step required correction which was done by establishing a correction curve based on the amount of otolith material to be cleaned.The next step involved establishing an offset value between otoliths and muscle that could be applied to otolith stable isotope values to estimate muscle values in the absence of fresh fish samples (only faecal or sedimentary otoltihs available). The offsets were calculated by measuring paired otolith and muscle values of Antarctic fish, while ensuring control for lipid content in muscle. With the offsets applied to the cleaned and corrected Marion Island samples, the utility of the method is demontrated by comparisons with published counterparts, and used in the stable isotope dietary mixing models of Marion Island predators including various southern elephant seal classes and killer whales. Analyses were done using R software. Juvenile southern elepehant seal diet modelling was improved, first models for sub-adult, adult female and adult male seals are now available, and killer whale modelling remains unresolved.
DOI: 10.11583/dtu.19306886
title: Geomagnetic Virtual Observatories

description: <strong>Description of GVO data product:</strong> Geomagnetic Virtual Observatories (GVO) data are derived from satellite magnetic measurements. The GVO data products consists of magnetic field time series regularly distributed in space and time. GVO time series including all field contributions, and those processed to isolate the core field, are available along with estimated errors. <br> The GVO product are designed to make satellite data more accessible to researchers studying the physics of the core dynamo process, and related phenomenon such are secular variation, geomagnetic jerks and rapid core dynamics. In addition, the GVO product also provide valuable information to those interested in investigating magnetospheric and ionospheric magnetic signals on timescales of months and longer. <br> GVO data products of 1, 4 and 12 month cadance are available for the Ørsted mission (1999-2005), the CHAMP mission (2000-2010), the CryoSat-2 mission (2010-2018), the Grace mission (2007-2018, 12 month only) and the Swarm mission (2014-2022). In addition, a composite GVO dataset derived from all the above missions (with the execption of Grace) are available covering 1999-2022. <br> The GVO data products are given as CDF files providing the magnetic field components, the secular variation field components, and their associated error estimates, all provided in a global grid of 300 Geomagnetic Virtual Observatories. For each satellite zip compressed folders contains the CDFs and a ReadMe.txt file which provides details on the datasets and how these where made. <strong>References:</strong> Hammer, M.D, Finlay, C.C, and Olsen, N. (2021) Applications for CryoSat-2 satellite magnetic data in studies of the Earth's core field variations. Earth, Planets and Space, 73:73. <br> Hammer, M.D., Cox, G. A., Brown, W. J., Beggan, C. D., & Finlay, C. C. (2021). Geomagnetic Virtual Observatories: monitoring geomagnetic secular variation with the Swarm satellites. Earth, Planets and Space, <em>73</em>(1), 1-22.
DOI: 10.11583/dtu.19306886.v1
title: Geomagnetic Virtual Observatories

description: <strong>Description of GVO data product:</strong> Geomagnetic Virtual Observatories (GVO) data are derived from satellite magnetic measurements. The GVO data products consists of magnetic field time series regularly distributed in space and time. GVO time series including all field contributions, and those processed to isolate the core field, are available along with estimated errors. <br> The GVO product are designed to make satellite data more accessible to researchers studying the physics of the core dynamo process, and related phenomenon such are secular variation, geomagnetic jerks and rapid core dynamics. In addition, the GVO product also provide valuable information to those interested in investigating magnetospheric and ionospheric magnetic signals on timescales of months and longer. <br> GVO data products of 1, 4 and 12 month cadance are available for the Ørsted mission (1999-2005), the CHAMP mission (2000-2010), the CryoSat-2 mission (2010-2018), the Grace mission (2007-2018, 12 month only) and the Swarm mission (2014-2022). In addition, a composite GVO dataset derived from all the above missions (with the execption of Grace) are available covering 1999-2022. <br> The GVO data products are given as CDF files providing the magnetic field components, the secular variation field components, and their associated error estimates, all provided in a global grid of 300 Geomagnetic Virtual Observatories. For each satellite zip compressed folders contains the CDFs and a ReadMe.txt file which provides details on the datasets and how these where made. <strong>References:</strong> Hammer, M.D, Finlay, C.C, and Olsen, N. (2021) Applications for CryoSat-2 satellite magnetic data in studies of the Earth's core field variations. Earth, Planets and Space, 73:73. <br> Hammer, M.D., Cox, G. A., Brown, W. J., Beggan, C. D., & Finlay, C. C. (2021). Geomagnetic Virtual Observatories: monitoring geomagnetic secular variation with the Swarm satellites. Earth, Planets and Space, <em>73</em>(1), 1-22.
DOI: 10.34740/kaggle/dsv/6109549
title: Egypt Gold Prices | Daily Updated
DOI: 10.11583/dtu.10322033
title: The DeRisk Database

description: The DeRisk database is an extensive database of long-crested (2D) nonlinear wave kinematics for computation of extreme loads on offshore structures.<br><br>The database was produced by running the nonlinear potential flow model OceanWave3D [1] on a gently sloping domain. The performed computations spanned a large parameter space, defined by nondimensional water depth, significant wave height and spectral peak period. A work containing a thorough description and validation of the current computations and of the database concept is currently under preparation [2].<br><br>The DataBase is openly accessible under the conditions of the present license.<br>Routines for interacting with the database, and usage examples are located at:<i>https://gitlab.windenergy.dtu.dk/fabpi/deriskdatabase.git</i><i><br></i><i>[1] </i>Engsig-Karup, Allan Peter, Harry B. Bingham, and Ole Lindberg.(2009) "An efficient flexible-order model for 3D nonlinear water waves." <i>Journal of computational physics</i> 228.6 : 2100-2118.<i>[2]</i> Pierella, F., Lindberg, O., Bredmose, H., Bingham, H. B., Read, R. W., & Engsig-Karup, A. P. (2021). The derisk database: Extreme design waves for offshore wind turbines. <i>Marine Structures</i>, <i>80</i>, 103046.
DOI: 10.11583/dtu.10322033.v1
title: The DeRisk Database

description: The DeRisk database is an extensive database of long-crested (2D) nonlinear wave kinematics for computation of extreme loads on offshore structures.<br><br>The database was produced by running the nonlinear potential flow model OceanWave3D [1] on a gently sloping domain. The performed computations spanned a large parameter space, defined by nondimensional water depth, significant wave height and spectral peak period. A work containing a thorough description and validation of the current computations and of the database concept is currently under preparation [2].<br><br>The DataBase is openly accessible under the conditions of the present license.<br>Routines for interacting with the database, and usage examples are located at:<i>https://gitlab.windenergy.dtu.dk/fabpi/deriskdatabase.git</i><i><br></i><i>[1] </i>Engsig-Karup, Allan Peter, Harry B. Bingham, and Ole Lindberg. "An efficient flexible-order model for 3D nonlinear water waves." <i>Journal of computational physics</i> 228.6 (2009): 2100-2118.<i>[2] </i>Pierella, F., Lindberg, O., Bredmose, H., Bingham, H.B., Read, R., Engsig-Karup, A.P.<i> The DeRisk database: extreme design waves for Offshore Wind Turbines. </i>Under preparation.<br>
DOI: 10.17909/yq10-rj82
title: Data from 'A Large Population of Faint 8 < z < 16 Galaxies Found in the First JWST NIRCam Observations of the NGDEEP Survey'

description: Data used in published work constructed from the original level 1 products of the observations listed.
DOI: 10.21227/4sgg-2274
title: Rajamangala University of Technology Thanyaburi Dropout Dataset (RDD)

description: Thailand's national development relies on higher education, posing challenges for the government to enhance graduate competence. High dropout rates impact education quality and student welfare, necessitating a comprehensive study. This research collects a dataset on student dropout and utilizes classification models to predict dropout likelihood at Rajamangala University of Technology Thanyaburi (RMUTT), Thailand. The dataset includes 2,137 undergraduate students from 2013 to 2019 and follows the CRISP-DM model, utilizing internal data sources from ARIT. Leveraging this dataset helps understand the challenges universities face in maintaining student quality and well-being, contributing to effective strategies for improving higher education in Thailand. The dataset analysis identifies influential features in the General Education Course, English for Communication, related to dropout cases. Future research aims to develop a predictive model that utilizes these features to accurately determine dropout likelihood. Upon enrollment, new students' individual features will be input into the model, providing personalized advice on whether to take the course in the current semester or delay it to a later semester, considering its flexible timing. This guidance aims to reduce the dropout rate based on the model's predictions. Overall, this research addresses challenges related to student quality and well-being, enhancing the overall quality of higher education in Thailand.
DOI: 10.11583/dtu.13549487
title: Dataset for “Highly structured 3D pyrolytic carbon electrodes derived from additive manufacturing technology”

description: Research data related to publication by Babak Rezaei et al. in Materials & Design 2020. The data includes electrochemical measurements, source files for 3D printing, material characterization by SEM, XPS, Raman and BET. It should allow others to print similar structures and reproduce the results included in the paper. More details about the experimental settings and software used are included in the publication.
DOI: 10.11583/dtu.13549487.v1
title: Dataset for “Highly structured 3D pyrolytic carbon electrodes derived from additive manufacturing technology”

description: Research data related to publication by Babak Rezaei et al. in Materials & Design 2020. The data includes electrochemical measurements, source files for 3D printing, material characterization by SEM, XPS, Raman and BET. It should allow others to print similar structures and reproduce the results included in the paper. More details about the experimental settings and software used are included in the publication.
DOI: 10.11583/dtu.19576042.v1
title: ERA5 atmospheric stability for usage in WAsP 12.8

description: The dataset "ERA5-meso.nc" is obtained by loading the variables needed for calculating the temperature scale from hourly ERA5 files. These files are available in grib format that have been obtained from the Copernicus Data Store (CDS). They are opened using xarray and cfgrib and processed using the functions stability_histogram from the python package PyWAsP. Because a conditional mean based on the 50% highest wind speeds must be calculated, all values are binned according to wind speed at 100 m and this histogram is then used to calculate the mean and root-mean-square of the temperature scale. The boundary layer height scale is calculated in a similar fashion. For more documentation see the accompanying paper. A validation of the WAsP model using these data is available in the references. <br> The file "ERA5-baro.nc" contains the geostrophic wind shear. The mean magnitude and direction is obtained sector-wise in similar fashion as described above. The geostrophic wind shear can be calculated from the pressure level geopotential height. The way to do this is described here: https://orbit.dtu.dk/en/publications/implementation-of-large-scale-average-geostrophic-wind-shear-in-w <br> Please, also refer to the item "ERA5 data for air density calculations in WAsP" https://doi.org/10.11583/DTU.13286294
DOI: 10.11583/dtu.21914916
title: Data for: A catalogue of unusually long thermonuclear bursts on neutron stars

description: This data repository for the catalog of unusually long bursts contains the light curves and time-resolved spectral analysis results discussed in the paper. Downloadable plots and the data itself are available.
DOI: 10.11583/dtu.21914916.v1
title: Data for: A catalogue of unusually long thermonuclear bursts on neutron stars

description: This data repository for the catalog of unusually long bursts contains the light curves and time-resolved spectral analysis results discussed in the paper. Downloadable plots and the data itself are available.
DOI: 10.11583/dtu.7813523
title: Supporting data - The effect of gelation on statically and dynamically freeze-cast structures

description: SEM images obtained of freeze-cast samples.
DOI: 10.11583/dtu.14501574
title: Quality controlled precipitation data from a disdrometer installed at Hvide Sande, Denmark

description: This dataset consists of 1-minute precipitation measurements observed with a OTT Parsivel2 installed on the ground at Hvide Sande, Denmark, a site of the Danish Meteorological Institute (DMI). The period includes around 1.47 years of measurements. See https://doi.org/10.1127/metz/2021/1063 for more details about the station.
DOI: 10.11583/dtu.14501574.v1
title: Quality controlled precipitation data from a disdrometer installed at Hvide Sande, Denmark

description: This dataset consists of 1-minute precipitation measurements observed with a OTT Parsivel2 installed on the ground at Hvide Sande, Denmark, a site of the Danish Meteorological Institute (DMI). The period includes around 1.47 years of measurements. See https://doi.org/10.1127/metz/2021/1063 for more details about the station.
DOI: 10.11583/dtu.14501568
title: Quality controlled precipitation data from a disdrometer installed at Horns Rev 3, Denmark

description: This dataset consists of 1-minute precipitation measurements observed with a OTT Parsivel2 installed at the offshore wind farm Horn Rev 3 in cooperation with the company Vattenfall. The period includes around 0.97 years of measurements. See https://doi.org/10.1127/metz/2021/1063 for more details about the station.
DOI: 10.11583/dtu.14501568.v1
title: Quality controlled precipitation data from a disdrometer installed at Horns Rev 3, Denmark

description: This dataset consists of 1-minute precipitation measurements observed with a OTT Parsivel2 installed at the offshore wind farm Horn Rev 3 in cooperation with the company Vattenfall. The period includes around 0.97 years of measurements. See https://doi.org/10.1127/metz/2021/1063 for more details about the station.
DOI: 10.11583/dtu.14380826
title: Turbulence measurements from the tall CIBA mast

description: This dataset consists of raw time series and 10-minute statistics of wind speed and wind direction measurements from a 100m tall met mast NW to Valladolid, Spain. The period includes more than 400 hours of turbulence profile measurements during the late summer 1988.<br>Detailed site documentation is available here.<br><br>Data files:1) ciba_all.nc2) ciba_concurrent.nc<br>Raw time series, (ascii) each with a duration of 3600 sec, sampled with 5 and 20 Hz:<br>3) Raw time series = ciba.zip<br>
DOI: 10.11583/dtu.14380826.v1
title: Turbulence measurements from the tall CIBA mast

description: This dataset consists of raw time series and 10-minute statistics of wind speed and wind direction measurements from a 100m tall met mast NW to Valladolid, Spain. The period includes more than 400 hours of turbulence profile measurements during the late summer 1988.<br>Detailed site documentation is available here.<br><br>Data files:1) ciba_all.nc2) ciba_concurrent.nc<br>Raw time series, (ascii) each with a duration of 3600 sec, sampled with 5 and 20 Hz:<br>3) Raw time series = ciba.zip<br>
DOI: 10.21227/3zf9-9y90
title: water sample of nitrite

description: This dataset contains 198  nitrite water samples with different concentrations. We used sodium nitrite and distilled water to prepare nitrite water samples with concentrations ranging from 0.010 mg/L to 0.200 mg/L and gradients of 0.010 mg/L, as well as nitrite water samples with concentrations of 0.005 mg/L, then 198 images were taken under different lighting intensities (including natural light with different intensities, natural light with different intensities of light sources, and different intensities of light sources in a darkroom). The content of solid nitrite used in this experiment is 99.05%，and the manufacturer of the nitrite test reagent is SOUTH RANCH, and the product model of the camera is HD1313.
DOI: 10.11583/dtu.19629942
title: Onshore wind generation time series for technology SP199 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) 199 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<br>
DOI: 10.11583/dtu.19629942.v1
title: Onshore wind generation time series for technology SP199 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) 199 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<br>
DOI: 10.6084/m9.figshare.23649786.v2
title: Datasets by figure

description: Here are the original datasets corresponding to each of the figures of the publication. Some datasets are not fully uploaded due to limitations in the size of the storage, but they are available upon request from the authors.
DOI: 10.11583/dtu.19688980
title: Onshore wind generation time series for technology SP199 HH150 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) 199 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 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