cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.11583/dtu.12999642
title: IASI dataset v1

description: The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series measures the infrared spectrum with high resolution. The ground footprint resolution of the instruments is 12 km at nadir, and a spectral resolution of 0.25cm −1 in the spectrum between 645 cm −1 and 2760 cm −1 . This results in 8461 spectral samples covering 2200km scan-swath with 60 points per line. IASI is an ideal instrument for monitoring different physical/chemical parameters in the atmosphere e.g. temperature, humidity and trace gases such as ozone. Energy from different altitudes returns a different spectral shift. In this way atmospheric profiles can be obtained and these provides important information for e.g. Numerical Weather Models. In the IASI dataset, channel selection has been performed reducing spectral components from its 8461 channels to 4699, before any further processing. For statistical modelling of atmospheric parameters, forecast models are used to provide dense target values for every point observed by IASI. The IASI dataset has been matched with forecasts by the Medium-Range Weather Forecasts (ECMWF) model. This includes temperatures, humidity, and ozone concentrations for 137 altitudes through the atmosphere. EUMETSAT which operates the MetOp satellites uses forecasting data together with IASI measurements to provide derived products. These retrievals are validated both on the forecasts and in-situ measurements from e.g. radiosondes. Temperatures can be derived down to 1K accuracy, humidity with 10% and ozone with 5%, all at 25km ground resolution. <br>The time of the samples for this dataset is the august 16 2013 and august 17 2013 with one orbit and 13 orbits respectively. The data is stored in blocks of 60x60 spatial pixels with 4699 spectral channels. The filename specifier,<br><br>20130817210256_NN_XXXX.npy<br>refers first to the date-time stamp. the NN refers to the block number in the orbit which starts and ends near the north pole. A full orbit is 1260 pixels along track and 60 pixels across track, so one orbit is split in 1260/60=21 files. Recordings shorter or longer than one full orbit can occur.<br><br>XXXX can be "data" (the IASI recording), "TP" (Temperature profiles), "WP" (Water Pressure profiles), "OC" (Ozone concentration profiles), hPa (atm. air pressure profiles), "['C_F', 'ZA', 'lat', 'lon', 'SP', 'SCG', 'L_F']" (Cloud Fraction, Zenith Angle, Lattitude, Longitude, Surface Pressure, ..., Land Fraction)<br><br>More instructions and code for using this dataset can be found on the github page link below.<br>
DOI: 10.11583/dtu.12999642.v1
title: IASI dataset v1

description: The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series measures the infrared spectrum with high resolution. The ground footprint resolution of the instruments is 12 km at nadir, and a spectral resolution of 0.25cm −1 in the spectrum between 645 cm −1 and 2760 cm −1 . This results in 8461 spectral samples covering 2200km scan-swath with 60 points per line. IASI is an ideal instrument for monitoring different physical/chemical parameters in the atmosphere e.g. temperature, humidity and trace gases such as ozone. Energy from different altitudes returns a different spectral shift. In this way atmospheric profiles can be obtained and these provides important information for e.g. Numerical Weather Models. In the IASI dataset, channel selection has been performed reducing spectral components from its 8461 channels to 4699, before any further processing. For statistical modelling of atmospheric parameters, forecast models are used to provide dense target values for every point observed by IASI. The IASI dataset has been matched with forecasts by the Medium-Range Weather Forecasts (ECMWF) model. This includes temperatures, humidity, and ozone concentrations for 137 altitudes through the atmosphere. EUMETSAT which operates the MetOp satellites uses forecasting data together with IASI measurements to provide derived products. These retrievals are validated both on the forecasts and in-situ measurements from e.g. radiosondes. Temperatures can be derived down to 1K accuracy, humidity with 10% and ozone with 5%, all at 25km ground resolution. <br>The time of the samples for this dataset is the august 16 2013 and august 17 2013 with one orbit and 13 orbits respectively. The data is stored in blocks of 60x60 spatial pixels with 4699 spectral channels. The filename specifier,<br><br>20130817210256_NN_XXXX.npy<br>refers first to the date-time stamp. the NN refers to the block number in the orbit which starts and ends near the north pole. A full orbit is 1260 pixels along track and 60 pixels across track, so one orbit is split in 1260/60=21 files. Recordings shorter or longer than one full orbit can occur.<br><br>XXXX can be "data" (the IASI recording), "TP" (Temperature profiles), "WP" (Water Pressure profiles), "OC" (Ozone concentration profiles), hPa (atm. air pressure profiles), "['C_F', 'ZA', 'lat', 'lon', 'SP', 'SCG', 'L_F']" (Cloud Fraction, Zenith Angle, Lattitude, Longitude, Surface Pressure, ..., Land Fraction)<br><br>More instructions and code for using this dataset can be found on the github page link below.<br>
DOI: 10.5281/zenodo.8074715
title: SEM Data Base Microvilli Semantic Segmentation in Microscopic Images Using a Visual Learning Pipeline

description: SEM images raw data X1, X1, Y1, Y2, Z1 and Z2
DOI: 10.5281/zenodo.8154719
title: SEM Data Base Microvilli Semantic Segmentation in Microscopic Images Using a Visual Learning Pipeline

description: SEM images raw data X1, X1, Y1, Y2, Z1 and Z2
DOI: 10.5281/zenodo.8074714
title: SEM Data Base Microvilli Semantic Segmentation in Microscopic Images Using a Visual Learning Pipeline

description: SEM images raw data X1, X1, Y1, Y2, Z1 and Z2
DOI: 10.11583/dtu.20222331
title: Datasets for replicating the paper "Raman Spectrum Matching with Contrastive Representation Learning"

description: Datasets Mineral and Organic for replicating the paper "Raman spectrum matching with contrastive learning". <br> The detailed instruction about how to use the dataset, please visit our Github Repository. <br> To use these two datasets, please cite: <br> @inproceedings{Lafuente2016ThePO, title={The power of databases: The RRUFF project}, author={B. Lafuente and R. Downs and Hexiong Yang and N. Stone}, booktitle = {Highlights in Mineralogical Crystallography}, year={2016} } <br> @article{organic_dataset, author = {Zhang, Rui and Xie, Huimin and Cai, Shuning and Hu, Yong and Liu, Guo-kun and Hong, Wenjing and Tian, Zhong-qun}, title = {Transfer-learning-based Raman spectra identification}, journal = {Journal of Raman Spectroscopy}, volume = {51}, number = {1}, pages = {176-186}, keywords = {deep learning, Raman spectroscopy, transfer learning}, year = {2020} } <br> @Article{D2AN00403H, author ="Li, Bo and Schmidt, Mikkel N. and Alstrøm, Tommy S.", title ="Raman spectrum matching with contrastive representation learning", journal ="Analyst", year ="2022", volume ="147", issue ="10", pages ="2238-2246", publisher ="The Royal Society of Chemistry", doi ="https://doi.org/10.1039/d2an00403h", url ="http://dx.doi.org/10.1039/D2AN00403H", }
DOI: 10.11583/dtu.20222331.v1
title: Datasets for replicating the paper "Raman Spectrum Matching with Contrastive Representation Learning"

description: Datasets Mineral and Organic for replicating the paper "Raman spectrum matching with contrastive learning". <br> The detailed instruction about how to use the dataset, please visit our Github Repository. <br> To use these two datasets, please cite: <br> @inproceedings{Lafuente2016ThePO, title={The power of databases: The RRUFF project}, author={B. Lafuente and R. Downs and Hexiong Yang and N. Stone}, booktitle = {Highlights in Mineralogical Crystallography}, year={2016} } <br> @article{organic_dataset, author = {Zhang, Rui and Xie, Huimin and Cai, Shuning and Hu, Yong and Liu, Guo-kun and Hong, Wenjing and Tian, Zhong-qun}, title = {Transfer-learning-based Raman spectra identification}, journal = {Journal of Raman Spectroscopy}, volume = {51}, number = {1}, pages = {176-186}, keywords = {deep learning, Raman spectroscopy, transfer learning}, year = {2020} } <br> @Article{D2AN00403H, author ="Li, Bo and Schmidt, Mikkel N. and Alstrøm, Tommy S.", title ="Raman spectrum matching with contrastive representation learning", journal ="Analyst", year ="2022", volume ="147", issue ="10", pages ="2238-2246", publisher ="The Royal Society of Chemistry", doi ="https://doi.org/10.1039/d2an00403h", url ="http://dx.doi.org/10.1039/D2AN00403H", }
DOI: 10.34740/kaggle/dsv/6148543
title: Github Bipartite graph dataset(developers,repos)
DOI: 10.11583/dtu.13739593
title: Supplementary data for: "Hygrothermal assessment of internally insulated historic solid masonry walls with focus on the thermal bridge due to internal partition walls"

description: Supplementary data for: N.F. Jensen,<sup> </sup>C. Rode, E.B. Møller, 2021, "Hygrothermal assessment of internally insulated historic solid masonry walls with focus on the thermal bridge due to internal partition walls"<br>This study investigated the hygrothermal performance of five thermal insulation systems for internal retrofitting purposes. Focus was on the hygrothermal performance near partition brick walls compared to the middle of the wall. The setup consisted of two 40-feet (12.2 m) insulated reefer containers with controlled indoor climate, reconfigured with several holes (1x2 m each) containing solid masonry walls with embedded wooden elements on the interior side, an internal brick partition wall and different internal insulation systems, with and without exterior hydrophobisation. Relative humidity and temperature were measured over five years in the masonry/insulation interface and near the interior surface, in the centre of the test field and near the internal partition wall. In addition, calibrated numerical simulations were performed for further investigation of the thermal bridge effect. <br>The datasets comprises:1) Measurements from the experimental setup (hourly and 96-hour average values)2) Visual representations of the measurements from the experimental setup 3) VTT mould growth modelling (calculation files and results)4) Calibrated Delphin simulation models (including Output JPG files)<br>
DOI: 10.11583/dtu.13739593.v1
title: Supplementary data for: "Hygrothermal assessment of internally insulated historic solid masonry walls with focus on the thermal bridge due to internal partition walls"

description: Supplementary data for: N.F. Jensen,<sup> </sup>C. Rode, E.B. Møller, 2021, "Hygrothermal assessment of internally insulated historic solid masonry walls with focus on the thermal bridge due to internal partition walls"<br>This study investigated the hygrothermal performance of five thermal insulation systems for internal retrofitting purposes. Focus was on the hygrothermal performance near partition brick walls compared to the middle of the wall. The setup consisted of two 40-feet (12.2 m) insulated reefer containers with controlled indoor climate, reconfigured with several holes (1x2 m each) containing solid masonry walls with embedded wooden elements on the interior side, an internal brick partition wall and different internal insulation systems, with and without exterior hydrophobisation. Relative humidity and temperature were measured over five years in the masonry/insulation interface and near the interior surface, in the centre of the test field and near the internal partition wall. In addition, calibrated numerical simulations were performed for further investigation of the thermal bridge effect. <br>The datasets comprises:1) Measurements from the experimental setup (hourly and 96-hour average values)2) Visual representations of the measurements from the experimental setup 3) VTT mould growth modelling (calculation files and results)4) Calibrated Delphin simulation models (including Output JPG files)<br>
DOI: 10.11583/dtu.16782598
title: Turbulence and turbine measurements from the Nordtank turbine

description: This dataset consists of raw time series from a mast, turbine power and load measurements (appr. 35 Hz) including 10-minute statistics of these measurements. The 36m mast and the 41/550kW turbine is located at DTU Risø campus, Denmark. The period includes more than 1900 hours of mast and turbine measurements recorded during the period Oct04 - Apr06.<br>Detailed site documentation is available here.<br><br><b>Public data</b><br> Run statistics stored as NetCDF format 1) Nordtank_all.nc2) Nordtank_concurrent.nc<br> Raw time series (ascii), with a duration of 600 sec, sampled with 35 Hz.:3) Nordtank_2006.zip4) Nordtank_2007.zip5) Nordtank_2008.zip<br> Re-sampled wind speed and power (1 Hz, csv files)6) Nordtank_1Hz.zip<br>
DOI: 10.11583/dtu.16782598.v1
title: Turbulence and turbine measurements from the Nordtank turbine

description: This dataset consists of raw time series from a mast, turbine power and load measurements (appr. 35 Hz) including 10-minute statistics of these measurements. The 36m mast and the 41/550kW turbine is located at DTU Risø campus, Denmark. The period includes more than 1900 hours of mast and turbine measurements recorded during the period Oct04 - Apr06.<br>Detailed site documentation is available here.<br><br><b>Public data</b><br> Run statistics stored as NetCDF format 1) Nordtank_all.nc2) Nordtank_concurrent.nc<br> Raw time series (ascii), with a duration of 600 sec, sampled with 35 Hz.:3) Nordtank_2006.zip4) Nordtank_2007.zip5) Nordtank_2008.zip<br> Re-sampled wind speed and power (1 Hz, csv files)6) Nordtank_1Hz.zip<br>
DOI: 10.11583/dtu.21333291
title: Data for "Observing the near-surface properties of the Greenland Ice sheet"

description: File containing the underlying data presented the manuscript "Observing the near-surface properties of the Greenland ice sheet" by K.M. Scanlan, A. Rutishauser, and S.B. Simonsen. DOI:10.1029/2022GL101702. <br> Data in the single netCDF data file are organized into three parallel groups; one for each base satellite altimetry dataset (i.e., SARAL, CryoSat-2 LRM, and CryoSat-2 SARIn). Goups for indiviudal years (2013-2018) are included within each base sateliitle data group. The actual data are then organized in monthly groups within each year. Note that CryoSat-2 LRM and CryoSat-2 SARIn data span the entire time range (January 2013 - December 2018) while SARAL data files only cover May 2013 through December 2018 as the SARAL satellite was lauched in February 2018. <br> Data have been subject the quality control procedures described in the manuscript. Data at any grid node not meeting the qualiy control standards has been assigned NaN. Each netCDF file contains the following variables; <br> X: Grid node EPSG:3413 X coordinate [meters] Y: Grid node EPSG:3413 Y coordinate [meters] Pc: Uncalibrated RSR coherent power [dB] Pn: Uncalibrated RSR incoherent power [dB] Pt: Uncalibrated RSR total power [dB] crl: RSR statistical fit correlation coefficient [-] R: RSR search radius [meters] sig_h: Calibrated surface RMS height [meters] eps: Calibrated surface relative dielectric permittivity [-] rho: Calibrated surface density [gram per cubic centimeter]
DOI: 10.11583/dtu.21333291.v1
title: Data for "Observing the near-surface properties of the Greenland Ice sheet"

description: File containing the underlying data presented the manuscript "Observing the near-surface properties of the Greenland ice sheet" by K.M. Scanlan, A. Rutishauser, and S.B. Simonsen. DOI:10.1029/2022GL101702. <br> Data in the single netCDF data file are organized into three parallel groups; one for each base satellite altimetry dataset (i.e., SARAL, CryoSat-2 LRM, and CryoSat-2 SARIn). Goups for indiviudal years (2013-2018) are included within each base sateliitle data group. The actual data are then organized in monthly groups within each year. Note that CryoSat-2 LRM and CryoSat-2 SARIn data span the entire time range (January 2013 - December 2018) while SARAL data files only cover May 2013 through December 2018 as the SARAL satellite was lauched in February 2018. <br> Data have been subject the quality control procedures described in the manuscript. Data at any grid node not meeting the qualiy control standards has been assigned NaN. Each netCDF file contains the following variables; <br> X: Grid node EPSG:3413 X coordinate [meters] Y: Grid node EPSG:3413 Y coordinate [meters] Pc: Uncalibrated RSR coherent power [dB] Pn: Uncalibrated RSR incoherent power [dB] Pt: Uncalibrated RSR total power [dB] crl: RSR statistical fit correlation coefficient [-] R: RSR search radius [meters] sig_h: Calibrated surface RMS height [meters] eps: Calibrated surface relative dielectric permittivity [-] rho: Calibrated surface density [gram per cubic centimeter]
DOI: 10.11583/dtu.19771423
title: Quantum Point Defects in 2D Materials (QPOD) Database

description: The database contains structural, thermodynamic, electronic, magneto-optical properties of 500 distinct intrinsic point defects in 2D Materials. The properties are calculated by density functional theory (DFT) as implemented in the GPAW electronic structure code. The workflow was constructed using the Atomic Simulation Recipes (ASR) and executed with the MyQueue task manager. For each defect, different charge states are available resulting in more than 2000 rows for this database. A detailed description of how to access the data and how to work with it in practice is described on the webpages of the QPOD database.
DOI: 10.11583/dtu.19771423.v1
title: Quantum Point Defects in 2D Materials (QPOD) Database

description: The database contains structural, thermodynamic, electronic, magneto-optical properties of 500 distinct intrinsic point defects in 2D Materials. The properties are calculated by density functional theory (DFT) as implemented in the GPAW electronic structure code. The workflow was constructed using the Atomic Simulation Recipes (ASR) and executed with the MyQueue task manager. For each defect, different charge states are available resulting in more than 2000 rows for this database. A detailed description of how to access the data and how to work with it in practice is described on the webpages of the QPOD database.
DOI: 10.25934/pr00004294.2
title: Available IPD datapackage for study 'A Repeat-dose Study in Subjects With Type 2 Diabetes Mellitus to Assess the Efficacy, Safety, Tolerability and Pharmacodynamics, of Albiglutide Liquid Drug Product'

description: This is a phase III, randomized, double-blind, multicenter, parallel group, repeat-dose, study of 26 weeks duration to evaluate the efficacy, safety, tolerability and pharmacodynamic response of albiglutide liquid drug product relative to the commercial lyophilized drug product. The study will specifically evaluate the potential for immunogenicity (example [e.g.] incidences of anti-drug antibodies [ADA]) and injection site reactions (ISRs). Albiglutide is a novel analogue of glucagon-like peptide-1 (GLP-1) with a sufficiently long half-life to permit once a week injection. Currently, lyophilized albiglutide and the diluent are provided in a dual chamber cartridge (DCC), single-dose pen injector, requiring reconstitution prior to use. A liquid formulation of albiglutide will enable the commercialization of a liquid product in a single dose, ready-to-use prefilled syringe in an auto-injector. The primary hypothesis of this study is to test that liquid drug product will provide glycemic control (as measured by HbA1c change from baseline) non-inferior to lyophilized drug product for a period of 26 weeks of treatment in subjects with T2DM. This study will comprise of 3 study periods : screening (2 weeks), treatment (26 weeks) and for those subjects not entering the extension study a follow-up period (8 weeks). Approximately 300 subjects will be randomized in a 1:1 ratio to either Albiglutide active liquid auto-injector (LAI) plus Placebo lyophilized DCC pen injector (lyophilized DCC PI); or, Albiglutide lyophilized DCC PI plus Placebo LAI.
DOI: 10.11583/dtu.13353062
title: Greenland Ice Sheet mass balance (1992-2020) from calibrated radar altimetry

description: Supporting datasets for Simonsen et al. (2021): Greenland Ice Sheet mass balance (1992-2020) from calibrated radar altimetry, Geophysical Research Letters, https://doi.org/10.1029/2020GL091216<br>Data Set S1 - Radar-VMB for the Greenland ice sheet (1992-2020) for hydrological years (GrIS-RA-VMB-1992-2020.nc): Tabulated mass balance for the Greenland ice sheet and major basins. <br>Data Set S2 - Mapped radar-VMB for the Greenland ice sheet (1992-2020) asannual average (GrIS-Annual-RA-VMB-1992-2020.nc): Maps of annual averaged mass change. <br><br>
DOI: 10.6084/m9.figshare.23674506.v1
title: Supplementary Date 7.12.docx

description: High fat diet.
DOI: 10.5281/zenodo.8154748
title: Accessible Chemical Space for Metal Nitride Perovskites

description: Supporting data to manuscript "Accessible Chemical Space for Metal Nitride Perovskites" The folders contain respectively: 25_Candidates_HSE06: The structures of the 25 candidates presented in the manuscript and relaxed with HSE06 Competing_phases: The structures of the 86 competing phases considered for thermodynamic stability and relaxed with HSE06 Json_summaries: Json files containing all the relevant information. This includes structures and energies for: the 279 candidates in 15 possible tilts the lowest energy tilted structure for all 279 candidates the intermediate structures found during the phonon mapping process the ten lowest energy structures for each of the 25 candidates obtained with AIRSS the summary of all lowest-energy structures identified in both workflows the materials already reported in literature and recomputed in this work the energy above the hull for all considered structures calculated with HSE06 and PBEsol Switching_paths: The structures along the switching path and the Born effective charges of the material calculated in the initial configuration
DOI: 10.5281/zenodo.8154749
title: Accessible Chemical Space for Metal Nitride Perovskites

description: Supporting data to manuscript "Accessible Chemical Space for Metal Nitride Perovskites" The folders contain respectively: 25_Candidates_HSE06: The structures of the 25 candidates presented in the manuscript and relaxed with HSE06 Competing_phases: The structures of the 86 competing phases considered for thermodynamic stability and relaxed with HSE06 Json_summaries: Json files containing all the relevant information. This includes structures and energies for: the 279 candidates in 15 possible tilts the lowest energy tilted structure for all 279 candidates the intermediate structures found during the phonon mapping process the ten lowest energy structures for each of the 25 candidates obtained with AIRSS the summary of all lowest-energy structures identified in both workflows the materials already reported in literature and recomputed in this work the energy above the hull for all considered structures calculated with HSE06 and PBEsol Switching_paths: The structures along the switching path and the Born effective charges of the material calculated in the initial configuration
DOI: 10.34740/kaggle/dsv/6148779
title: Inverter Load Analysis Dataset