cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8150033
title: Extensions to Mining Framework Annotation Rules

description: Framework usage is challenging because the requirements for the correctness are often implicit. We focus on making such requirements more explicit by association rule mining on the data from client projects that use a framework. We present an extension to an existing baseline method that does this. In particular, we examine alternative rule quality measures used in the ranking of association rules mined, and alternatives in the selection of client projects. Such alternatives are novel and have not been explored in the context of the baseline method. We evaluate the alternatives by comparing their results to those produced by the baseline method. More concretely, we base the comparison on their ranking of incorrect rules, and on their measurements for the Area Under Curve metric. We conclude that some of the evaluated quality measures outperform the baseline for the ranking and selection of rules. We also show that the selection of secondary client projects, adding some clients that do not directly use the framework of interest, matters.
DOI: 10.6084/m9.figshare.23694408
title: IDD Raw Data.xlsx

description: Demographic data for revision of digtal technologies in the construction industry
DOI: 10.6084/m9.figshare.23694408.v1
title: IDD Raw Data.xlsx

description: Demographic data for revision of digtal technologies in the construction industry
DOI: 10.11583/dtu.13332497
title: Data set for the manuscript: North Sea region energy system towards 2050: integrated offshore grid and sector coupling drive offshore wind power installations

description: This source contains the input data in gdx format that was used in the paper "North Sea region energy system towards 2050: integrated offshore grid and sector coupling drive offshore wind turbine installations" to perform the energy system optimizations in the model Balmorel. The gdx file can be opened in GAMS.<br>The Balmorel model branch used for this paper can be found in this link:https://github.com/balmorelcommunity/Balmorel/tree/f298bf31c1a2a81d559cdae85f4788fc2fb723a2<br>
DOI: 10.11583/dtu.13332497.v1
title: Data set for the manuscript: North Sea region energy system towards 2050: integrated offshore grid and sector coupling drive offshore wind power installations

description: This source contains the input data in gdx format that was used in the paper "North Sea region energy system towards 2050: integrated offshore grid and sector coupling drive offshore wind turbine installations" to perform the energy system optimizations in the model Balmorel. The gdx file can be opened in GAMS.<br>The Balmorel model branch used for this paper can be found in this link:https://github.com/balmorelcommunity/Balmorel/tree/f298bf31c1a2a81d559cdae85f4788fc2fb723a2<br>
DOI: 10.11583/dtu.10264988
title: Data for "Computational Framework for a Systematic Investigation of Anionic Redox Process in Li-rich Compounds"

description: Data for the paper "Computational Framework for a Systematic Investigation of Anionic Redox Process in Li-rich Compounds". DOI: 10.26434/chemrxiv.10272647.<br>Initial structure set (initial.db) and the corresponding relaxed structures (results.db).<br>As mentioned in the paper, all results have been obtained using the VASP code, and the data is managed through the Atomic Simulation Environment (ASE).<br>The files are in an ASE database format.<br>The database can be inspected by using the ASE command-line interface (CLI), or through a python interface.An example of the CLI, which shows all of the available data in the database:<br>ase db results.db -c ++ -L 0 <br>Note, that the Bader charges are stored in the `data` keyword, which is not shown by default, but can be accesses through the Python scripting API.<br>The ASE software can be found at https://wiki.fysik.dtu.dk/ase/<br>For more information on the ASE database format, see https://wiki.fysik.dtu.dk/ase/ase/db/db.html
DOI: 10.11583/dtu.10264988.v1
title: Data for "Computational Framework for a Systematic Investigation of Anionic Redox Process in Li-rich Compounds"

description: Data for the paper "Computational Framework for a Systematic Investigation of Anionic Redox Process in Li-rich Compounds". DOI: 10.26434/chemrxiv.10272647.<br>Initial structure set (initial.db) and the corresponding relaxed structures (results.db).<br>As mentioned in the paper, all results have been obtained using the VASP code, and the data is managed through the Atomic Simulation Environment (ASE).<br>The files are in an ASE database format.<br>The database can be inspected by using the ASE command-line interface (CLI), or through a python interface.An example of the CLI, which shows all of the available data in the database:<br>ase db results.db -c ++ -L 0 <br>Note, that the Bader charges are stored in the `data` keyword, which is not shown by default, but can be accesses through the Python scripting API.<br>The ASE software can be found at https://wiki.fysik.dtu.dk/ase/<br>For more information on the ASE database format, see https://wiki.fysik.dtu.dk/ase/ase/db/db.html
DOI: 10.25934/pr00009100
title: Trend analysis of AMR in Kenya

description: There is a tremendous and collaborative effort against Antimicrobial Resistance (AMR) globally. However, sub-Saharan Africa (SSA) is under-studied and data on AMR is sparse.

The proposed trend analysis will assess the prevalence of clinically relevant resistance microorganisms in Kenya. The aim of the study is to compare country data from Pfizer with antibiotic susceptibility tests data retrieved from a tertiary hospital in Nairobi to assess variability between country-level and institutional-level AMR.

The findings from this study will have the following benefits:
- adding to the body of knowledge on AMR in Kenya and consequently in SSA
- elucidate the similarities and differences between the tertiary hospital and the country AMR profiles
- inform prescribing practices in the tertiary hospital
- provide an evidence base for the tertiary hospital to implement antimicrobial stewardship programs
DOI: 10.5281/zenodo.8153604
title: TFAP2A upregulates SKA3 to promote glycolysis and reduce the sensitivity of lung adenocarcinoma cells to cisplatin

description: Supplementary data for the article
DOI: 10.5281/zenodo.8153603
title: TFAP2A upregulates SKA3 to promote glycolysis and reduce the sensitivity of lung adenocarcinoma cells to cisplatin

description: Supplementary data for the article
DOI: 10.18712/nsd-nsd0516-v4
title: Consumer Expenditure Survey 1991

description: Statistics Norway has used periodic consumption surveys as a basis for preparing and updating the weight basis for the consumer price index. Detailed overviews of consumption in private households have also formed an important basis for the living conditions statistics, and for research on income, living conditions and e.g. effects of the tax system's use of direct and indirect taxes. The material is also so detailed that it provides an interesting basis for dietary analyses. Data can also be used for demand studies, in economic planning models or for specification of private consumption in the national accounts.

Up to and including 1973, Statistics Norway carried out consumption surveys at intervals of several years, in 1974 Statistics Norway switched to a system of annual surveys, with a somewhat smaller sample. From 1974-1976, data is published for 3 years at a time, and the annual gross sample is approximately 1,500 households, net sample 1,100-1,200 households. The aim was still to build up a detailed overview of consumption in private households, as a basis for updating the weight basis in the consumer price index. One also wants to survey consumption in different types of households. The households are classified according to characteristics such as household type, income, main employee's occupational status and age, area of residence, size of total consumption expenditure, etc. Data is organized in several parts and contains background information about the household (collected through an introductory interview that maps the household composition), detailed consumption measured in value and quantity (registered by keeping a diary, respectively for the household and individuals), the household's purchase of durable consumer goods (durable capital goods, collected at a comprehensive closing interview), consumption of self-produced goods and income and tax.

Gross sample: 6330 households for the 3-year period 1989-1991.
A household has shared housing and shared meals.
Net sample: 1991: 1284 households. Somewhat varying for different parts of the data collection.
For the 3-year period 1989-1991, the net sample is 3,657 households (Response rate 57.8%).
DOI: 10.18712/nsd-nsd0690-v4
title: Consumer Expenditure Survey 2000

description: Statistics Norway has used periodic consumption surveys as a basis for preparing and updating the weight basis for the consumer price index. Detailed overviews of consumption in private households have also gradually formed an important basis for living conditions statistics, and for research on income, living conditions and e.g. effects of the tax system's use of direct and indirect taxes. The material is also so detailed that it provides an interesting basis for dietary analyses. Data can also be used for demand studies, in economic planning models or for specification of private consumption in the national accounts.

In 1974, Statistics Norway switched to a system of annual surveys, with a somewhat smaller sample. From 1974-1976, data is published for 3 years at a time, and the annual gross sample is approximately 1,500 households, net sample 1,100-1,200 households. The aim was still to build up a detailed overview of consumption in private households, as a basis for updating the weight basis in the consumer price index. One also wants to survey consumption in different types of households. The households are classified according to characteristics such as household type, income, main employee's occupational status and age, area of residence, size of total consumption expenditure, etc. Data is organized in several parts and contains background information about the household (collected through an introductory interview which maps the household composition), detailed consumption measured in value and quantity (registered by keeping a diary, respectively for the household and individuals), the household's purchase of durable consumer goods (durable capital goods, collected by a comprehensive closing interview), consumption of self-produced goods and income and tax.
DOI: 10.6084/m9.figshare.23666796
title: Nonstationarity Standardized Precipitation Evapotranspiration Index (NSPEI) dataset

description: The NSPEI dataset contains three types of data, one is based on meteorological station observation data, the second is based on ERA5 grid data, and the third is based on CMIP6 grid data. The dataset with timescales of 1–12 months has been archived in the Network Common Data Form (NetCDF) format. The monthly NSPEI has a spatial resolution of 0.25◦ from 1980 to 2020.
DOI: 10.6084/m9.figshare.23666796.v2
title: Nonstationarity Standardized Precipitation Evapotranspiration Index (NSPEI) dataset

description: The NSPEI dataset contains two types of data, one is based on meteorological station observation data, and the second is based on ERA5 grid data. The dataset with timescales of 1–12 months has been archived in the Network Common Data Form (NetCDF) format. The monthly NSPEI has a spatial resolution of 0.25◦ from 1980 to 2020.
DOI: 10.34658/rdb.hptscb/hzuvok
title: p_vsB M250-35-50Hz.dat

description: Measured data
DOI: 10.34658/rdb.hptscb/jynizp
title: Fig7.jpg

description: :unav
DOI: 10.34658/rdb.hptscb/klh3bi
title: Fig2a.jpg

description: :unav
DOI: 10.34658/rdb.hptscb/pylrhi
title: Fig5.jpg

description: :unav
DOI: 10.34658/rdb.hptscb/quofvv
title: B_vs_H M250-35.xlsx

description: Measured data
DOI: 10.34658/rdb.hptscb/vtwdio
title: Fig2b.jpg

description: :unav
DOI: 10.34658/rdb.hptscb
title: Impact of a Punching Process on the SyRM Iron Loss: SPICE Model as an Effective Tool for Iron Loss Modeling

description: Data linked to the article published in Energies journal (https://doi.org/ 10.3390/en14217185), 2 November 2021
DOI: 10.34658/rdb.hptscb/bcr1ug
title: B_vs_H M250-35.tab

description: Measured data
DOI: 10.34658/rdb.hptscb/cxv39b
title: p_vs_B M250-35-50Hz.tab

description: Measured data
DOI: 10.34658/rdb.hptscb/gzs7hu
title: Fig4.jpg

description: :unav
DOI: 10.34658/rdb.hptscb/sslxe0
title: Fig8.jpg

description: :unav
DOI: 10.34658/rdb.hptscb/u8c1is
title: Fig6.jpg

description: :unav
DOI: 10.11583/dtu.14178707
title: 3D Landmark data on grey seal skull measured using a Microscribe 3D, and on 3D surface models based on structured light scans

description: 3D Landmark data on grey seal skulls for the article "Measurement error using a SeeMaLab structured light 3D scanner against a Microscribe 3D digitizer".<br>The landmarks were measured in two different ways:1) Directly on the skulls using a Microscribe 3D digitizer2) On the 3D surface models of the skulls based on structured light scans using the Stratovan Checkpoint software. The 3D models are available on Morphosource: https://www.morphosource.org/projects/000355763.
DOI: 10.11583/dtu.19688986
title: Onshore wind generation time series for technology SP277 HH100 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) 277 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 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: 10.11583/dtu.19688986.v1
title: Onshore wind generation time series for technology SP277 HH100 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) 277 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 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.23694300
title: Arctic_surface_data_all_pub.xlsx

description: Data for all samples
DOI: 10.11583/dtu.19704883
title: Offshore wind fields in near-real-time

description: An archive of wind fields retrieved from satellite Synthetic Aperture Radar (SAR) observations over the ocean by the Technical University of Denmark (DTU, https://ror.org/04qtj9h94). The wind fields are instantaneous ocean backscatter measurements converted to ocean winds at 10m above the sea surface, through the implementation of dedicated retrieval algorithms and input wind directions from numerical models. The archive is updated daily and users can browse and download the wind fields though a web interface. The maps are available in .nc and .png format. The archive of wind fields is generated with the SAR Ocean Products System (SAROPS) developed by the Johns Hopkins University, Applied Physics Laboratory (JHU/APL, https://ror.org/029pp9z10) and the US National Atmospheric and Oceanographic Administration (NOAA, https://ror.org/02z5nhe81). The system uses input data from the following sources: <br> Envisat [2002-12] and Copernicus Sentinel-1 [2014-present] satellite SAR data provided by the European Space Agency (ESA, https://ror.org/03wd9za21). <br> Wind directions [2002-10] from the Climate Forecast System Reanalysis (CFSR) by the National Centers for Environmental Prediction (NCEP, https://ror.org/00ndyev54). <br> Wind directions [2011-present] from the Global Forecast System (GFS) by the National Centers for Environmental Prediction (NCEP, https://ror.org/00ndyev54). <br> Land surface topography data from the Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG).<sup>1</sup> <br> Sea ice data [2002-present] from IMS Daily Northern Hemisphere Snow and Ice Analysis.<sup>2</sup> <br> <sup>1 </sup>Wessel, P., and W. H. F. Smith, 1996. A Global Self-consistent, Hierarchical, High-resolution Shoreline Database, J. Geophys. Res., 101, 8741-8743. https://doi.org/10.1029/96JB00104 <sup>2</sup> U.S. National Ice Center. 2008, updated daily. <em>IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions, Version 1</em>. [2002-present]. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. https://doi.org/10.7265/N52R3PMC.
DOI: 10.34740/kaggle/dsv/6147809
title: AutoXGB
DOI: 10.34740/kaggle/dsv/6147810
title: Health Workforce Shortage Areas
DOI: 10.11583/dtu.10259936
title: EllipSys3D large eddy simulation data of single wind turbine wakes in neutral atmospheric conditions

description: This data set represents transient velocity fields obtained by large eddy simulations (LES) of a wind turbine wake, for two different ambient turbulence intensities (4% and 12.8%). EllipSy3D [1-3], the in-house flow solver of DTU Wind Energy, is used to compute the LES data. The complete setup and post processed data are described in:<br><br>http://onlinelibrary.wiley.com/doi/10.1002/we.1736/pdf<br>http://iopscience.iop.org/article/10.1088/1742-6596/1037/7/072001/pdf<br><br>The average flow field is published as Cases 5 and 6 in the first article, corresponding to a low and high ambient turbulence intensity. Two additional LES data sets are added (*_NoAD_*.nc), where the wind turbine model is switched off so one can isolate the wake effect.<br><br>More specific info:<br>- The NREL-5MW rotor is modeled as an actuator disk with a fixed force distribution that does not change with time. The force distribution is based on a rotor resolved detached eddy simulation. The rotor diameter D is 126 m.<br>- The coordinate system is: x=streamwise, y=lateral and z=height.<br>- The LES data is extracted from a plane at hub height (z=90 m) with:<br>a) Spatial dimensions x=-1D,8.5D and y=-2D,2D (actuator disk is located at (x,y,z) = (0, 0, 90) m,<br>b) Spatial sample resolution is D/30 (4.2 m). The numerical grid is twice as fine (D/60).<br>c) Temporal sample resolution is 0.24 s.<br>d) Each plane has 286x121 = 34606 spatial points.<br>e) 15500 planes are stored per case.<br>- The averaged freestream hub height velocity is close to 8 m/s.<br>- Inflow turbulence is modeled by the Mann model.- Each case corresponds to 1 hour LES. This duration is long enough to get converged mean velocity fields for the low ambient turbulence case. However, the high ambient turbulence case could be improved by running a longer LES because the mean streamwise velocity field in the far wake is still asymmetric, where a symmetric mean streamwise velocity field would be expected.- The filenames *_UWV_* should have been called *_UVW_*.<br><br>An example Python script, read_netcfd_and_plot.py, is provided which can read and plot the LES data. <br><br>For questions, please contact: Paul van der Laan, plaa@dtu.dk<br>EllipSys3D references:<br>1: Michelsen, J. A., <em>Basis3D - a Platform for Development of Multiblock PDE Solvers</em>, AFM 92-05, Department of Fluid Mechanics, Technical University of Denmark, December 1994.<br> 2: Michelsen, J. A., Block structured Multigrid solution of 2D and 3D Elliptic PDEs, AFM 94-06, Department of Fluid Mechanics, Technical University of Denmark, May 1994.<br> 3: Sørensen, N. N., <em>General Purpose Flow Solver Applied to Flow Over Hills</em>, Risø-R-827, Risø National Laboratory, Roskilde, Denmark, 1995.