cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.11583/dtu.15090207.v1
title: Data for "Warm meets fresh: Vertical redistribution of principle water masses on the Northeast Greenland Shelf"

description: Here we present a comprehensive dataset of temperature and practical salinity collected over the Northeast Greenland shelf and fjords from 1923 to 2019 as part of several research cruises and monitoring programs. Prior to 1990, the majority of the profiles were sampled using bottles whereas modern profiles were primarily sampled with CTD instrumentation. Additionally, Airborne Expendable CTD and a few profiling floats contribute to the data set. A total of 4247 profiles are complied.
DOI: 10.11583/dtu.15090207.v2
title: Data for "Vertical redistribution of principle water masses on the Northeast Greenland Shelf"

description: Here we present a comprehensive dataset of temperature and practical salinity collected over the Northeast Greenland shelf and fjords from 1923 to 2019 as part of several research cruises and monitoring programs. Prior to 1990, the majority of the profiles were sampled using bottles whereas modern profiles were primarily sampled with CTD instrumentation. Additionally, Airborne Expendable CTD and a few profiling floats contribute to the data set. A total of 4247 profiles are complied.
DOI: 10.11583/dtu.16691825.v1
title: Ethylene Carbonate Molecular Dynamics

description: This dataset consists of charge densities of individual snapshots from a molecular dynamics trajectory. We insert 8 ethylene carbonate molecules in the simulation box. To quickly explore a large part of the configurational space we put Hookean constraints on the molecular bonds (to maintain molecular identity such that molecules are not torn apart at such high temperature) and run Langevin molecular dynamics with thermostat temperature of 3000 K. The simulation was run for 12380 steps of 0.5 fs. <br> The resulting CHGCAR files have been compressed with lz4 compression and packed in non-compressed tar archives with up to 1000 structures in each. <br> The datasplits json files contain the indices (0-index) of the train, validation and test sets used in the paper "Graph neural networks for fast electron density estimation of molecules, liquids, and solids" <br> To only use equilibrated structures the first 1000 time steps are not used in the paper. Index 0 in the split files corresponds to index 1000 of the CHGCAR files (01000.CHGCAR.lz4).
DOI: 10.34740/kaggle/dsv/6122893
title: Egypt Gold Prices | Daily Updated
DOI: 10.11583/dtu.16716349
title: Data for "Novel flow modulation method for R744 two-phase ejectors – Proof of concept, optimization and first experimental results"

description: Experimental data of CO2 transcritical system with and without ejector.Data belongs the article "Novel flow modulation method for R744 two-phase ejectors – Proof of concept, optimization and first experimental results".<br><br>The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 844924 (Project: ECO<sub>2</sub>-RAPJECT)Contact person for dataset: Paride Gullo, ORCID: 0000-0001-9256-875X
DOI: 10.34740/kaggle/dsv/6122908
title: UNET Lung Segmentation Weights for Chest X Rays
DOI: 10.34740/kaggle/dsv/6122909
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.11583/dtu.12358043
title: Supplementary Materials to Appended Paper 3 - Systematisation of Circular Economy business model archetypes

description: This dataset supports the Appended Paper 3 of the PhD thesis "Circular Economy Business Modelling: decoupling value creation from resource consumption within manufacturing companies". <br> Pieroni, M.P.P., McAloone, T.C. and Pigosso, D.C.A. (2020), “From theory to practice: systematising and testing business model archetypes for Circular Economy”, https://doi.org/10.1016/j.resconrec.2020.105029.
DOI: 10.11583/dtu.12358043.v1
title: Supplementary Materials to Appended Paper 3 - Systematisation of Circular Economy business model archetypes

description: This dataset supports the Appended Paper 3 of the PhD thesis "Circular Economy Business Modelling: decoupling value creation from resource consumption within manufacturing companies". <br> Pieroni, M.P.P., McAloone, T.C. and Pigosso, D.C.A. (2020), “From theory to practice: systematising and testing business model archetypes for Circular Economy”, https://doi.org/10.1016/j.resconrec.2020.105029.
DOI: 10.5519/qd.7qy4ezzt
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5589651 records)
DOI: 10.11583/dtu.20631648
title: Pourbaix References

description: Additional solid and aqueous phase references for Pourbaix analysis adapted from Atlas of electrochemical Equilibria in aqueous solutions given as a Python dictonary. Used to perform an analysis of the aqueous stability of transition metal dichalcogenides <br> ref: M, Pourbaix, Atlas of electrochemical Equilibria in aqueous solutions, NACE International Cebelcor , 2nd edition, 1974
DOI: 10.11583/dtu.20631648.v1
title: Pourbaix References

description: Additional solid and aqueous phase references for Pourbaix analysis adapted from Atlas of electrochemical Equilibria in aqueous solutions given as a Python dictonary. Used to perform an analysis of the aqueous stability of transition metal dichalcogenides <br> ref: M, Pourbaix, Atlas of electrochemical Equilibria in aqueous solutions, NACE International Cebelcor , 2nd edition, 1974
DOI: 10.11583/dtu.14501619
title: Quality controlled precipitation data from a disdrometer installed at Rødsand, Denmark

description: This dataset consists of 1-minute and 20-second precipitation measurements observed with a OTT Parsivel2 installed at the offshore wind farm Rødsand in cooperation with the company RWE. The period includes more than 2 years of measurements. See https://doi.org/10.1127/metz/2021/1063 for more details about the station.
DOI: 10.11583/dtu.14501619.v1
title: Quality controlled precipitation data from a disdrometer installed at Rødsand, Denmark

description: This dataset consists of 1-minute and 20-second precipitation measurements observed with a OTT Parsivel2 installed at the offshore wind farm Rødsand in cooperation with the company RWE. The period includes more than 2 years of measurements. See https://doi.org/10.1127/metz/2021/1063 for more details about the station.
DOI: 10.6084/m9.figshare.23667387
title: Ichioka & Kajimura_foam nest.xlsx

description: This file contains thermal and egg survival data of foam nests those of <em>Zhangixalus arboreus</em> we studied. Additionally, we also publish whole data constitutes all of our figure and table.
DOI: 10.11583/dtu.19689049
title: Onshore wind generation time series for technology SP335 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) 335 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: 10.5281/zenodo.7313926
title: Compressed COBS indexes (XZ) - Full 661k - Part 1

description: <strong>Compressed COBS indexes (XZ) - Full 661k - v0.0.2</strong> – part1, part2, part3 Part 1 of the compressed COBS indexes built from all assemblies of the 661k dataset. More information can be found on https://brinda.eu/mof/. <strong>Citation:</strong> K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <strong>Efficient and robust search of microbial genomes via phylogenetic compression</strong>, <em>bioRxiv</em> 2023.04.15.536996, 2023. <strong>https://doi.org/10.1101/2023.04.15.536996</strong>
DOI: 10.5281/zenodo.7273907
title: Compressed COBS indexes (XZ) - Full 661k - Part 1

description: <strong>Compressed COBS indexes (XZ) - Full 661k - v0.0.2</strong> – part1, part2, part3 Part 1 of the compressed COBS indexes built from all assemblies of the 661k dataset. More information can be found on https://brinda.eu/mof/. <strong>Citation:</strong> K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <strong>Efficient and robust search of microbial genomes via phylogenetic compression</strong>, <em>bioRxiv</em> 2023.04.15.536996, 2023. <strong>https://doi.org/10.1101/2023.04.15.536996</strong>
DOI: 10.25345/c5vh5cv03
title: MassIVE MSV000092411 - Ttttttttttttttttttttttttttttttttttttttteszt
title: teszttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttt
DOI: 10.5281/zenodo.8138701
title: Pre-trained-model LA

description: Pre-trained SSL antispoofing models are available for LA
DOI: 10.5281/zenodo.8138700
title: Pre-trained-model LA

description: Pre-trained SSL antispoofing models are available for LA
DOI: 10.5281/zenodo.7313942
title: Compressed COBS indexes (XZ) - Full 661k - Part 2

description: <strong>Compressed COBS indexes (XZ) - Full 661k - v0.0.2</strong> – part1, part2, part3 Part 2 of the compressed COBS indexes built from all assemblies of the 661k dataset. More information can be found on https://brinda.eu/mof/. <strong>Citation:</strong> K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <strong>Efficient and robust search of microbial genomes via phylogenetic compression</strong>, <em>bioRxiv</em> 2023.04.15.536996, 2023. <strong>https://doi.org/10.1101/2023.04.15.536996</strong>
DOI: 10.5281/zenodo.7313941
title: Compressed COBS indexes (XZ) - Full 661k - Part 2

description: <strong>Compressed COBS indexes (XZ) - Full 661k - v0.0.2</strong> – part1, part2, part3 Part 2 of the compressed COBS indexes built from all assemblies of the 661k dataset. More information can be found on https://brinda.eu/mof/. <strong>Citation:</strong> K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <strong>Efficient and robust search of microbial genomes via phylogenetic compression</strong>, <em>bioRxiv</em> 2023.04.15.536996, 2023. <strong>https://doi.org/10.1101/2023.04.15.536996</strong>
DOI: 10.34965/i10904
title: Reconstruction_of_3D_topographic_landscape
DOI: 10.5281/zenodo.7315499
title: Compressed COBS indexes (XZ) - Full 661k - Part 3

description: <strong>Compressed COBS indexes (XZ) - Full 661k - v0.0.2</strong> – part1, part2, part3 Part 3 of the compressed COBS indexes built from all assemblies of the 661k dataset. More information can be found on https://brinda.eu/mof/. <strong>Citation:</strong> K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <strong>Efficient and robust search of microbial genomes via phylogenetic compression</strong>, <em>bioRxiv</em> 2023.04.15.536996, 2023. <strong>https://doi.org/10.1101/2023.04.15.536996</strong>
DOI: 10.5281/zenodo.7315498
title: Compressed COBS indexes (XZ) - Full 661k - Part 3

description: <strong>Compressed COBS indexes (XZ) - Full 661k - v0.0.2</strong> – part1, part2, part3 Part 3 of the compressed COBS indexes built from all assemblies of the 661k dataset. More information can be found on https://brinda.eu/mof/. <strong>Citation:</strong> K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <strong>Efficient and robust search of microbial genomes via phylogenetic compression</strong>, <em>bioRxiv</em> 2023.04.15.536996, 2023. <strong>https://doi.org/10.1101/2023.04.15.536996</strong>
DOI: 10.5281/zenodo.6845083
title: Compressed COBS indexes (XZ) - HQ 661k - Part 1

description: <strong>Compressed COBS indexes (XZ) - HQ 661k - v1</strong> – part1, part2 Part 1 of the compressed COBS indexes built from the high-quality assemblies of the 661k dataset. More information can be found on https://brinda.eu/mof/. <strong>Citation:</strong> K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <strong>Efficient and robust search of microbial genomes via phylogenetic compression</strong>, <em>bioRxiv</em> 2023.04.15.536996, 2023. <strong>https://doi.org/10.1101/2023.04.15.536996</strong>
DOI: 10.5281/zenodo.6845082
title: Compressed COBS indexes (XZ) - HQ 661k - Part 1

description: <strong>Compressed COBS indexes (XZ) - HQ 661k - v1</strong> – part1, part2 Part 1 of the compressed COBS indexes built from the high-quality assemblies of the 661k dataset. More information can be found on https://brinda.eu/mof/. <strong>Citation:</strong> K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <strong>Efficient and robust search of microbial genomes via phylogenetic compression</strong>, <em>bioRxiv</em> 2023.04.15.536996, 2023. <strong>https://doi.org/10.1101/2023.04.15.536996</strong>
DOI: 10.5281/zenodo.6849657
title: Compressed COBS indexes (XZ) - HQ 661k - Part 2

description: <strong>Compressed COBS indexes (XZ) - HQ 661k - v1</strong> – part1, part2 Part 2 of the compressed COBS indexes built from the high-quality assemblies of the 661k dataset. More information can be found on https://brinda.eu/mof/. <strong>Citation:</strong> K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <strong>Efficient and robust search of microbial genomes via phylogenetic compression</strong>, <em>bioRxiv</em> 2023.04.15.536996, 2023. <strong>https://doi.org/10.1101/2023.04.15.536996</strong>
DOI: 10.5281/zenodo.6845421
title: Compressed COBS indexes (XZ) - HQ 661k - Part 2

description: <strong>Compressed COBS indexes (XZ) - HQ 661k - v1</strong> – part1, part2 Part 2 of the compressed COBS indexes built from the high-quality assemblies of the 661k dataset. More information can be found on https://brinda.eu/mof/. <strong>Citation:</strong> K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <strong>Efficient and robust search of microbial genomes via phylogenetic compression</strong>, <em>bioRxiv</em> 2023.04.15.536996, 2023. <strong>https://doi.org/10.1101/2023.04.15.536996</strong>
DOI: 10.11583/dtu.19689046
title: Onshore wind generation time series for technology SP335 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) 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 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.19689037
title: Onshore wind generation time series for technology SP335 HH200 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 200 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
DOI: 10.11583/dtu.21967226
title: CFSR data for air density calculations in WAsP

description: This is the CFSR dataset that can be used to calculate air density for wind energy purposes using the equations presented in https://doi.org/10.3390/en12112038. The NetCDF file contains mean fields of monthly means from 2001 to 2010 of the variable temperature, surface pressure and specific humidity. The horizontal grid resolution is 0.5 degrees and covers the whole globe. These fields were used in the WAsP software version up to version 12.5 to calculate the air density at specified heights above mean sea level. The WAsP software (www.wasp.dk) is the industry standard method to calculate the annual energy production of wind farms. Saha, S., et al. 2010. <em>NCEP Climate Forecast System Reanalysis (CFSR) Monthly Products, January 1979 to December 2010</em>. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. https://doi.org/10.5065/D69K487J. Accessed 21 dec 2017.
DOI: 10.11583/dtu.21967226.v1
title: CFSR data for air density calculations in WAsP

description: This is the CFSR dataset that can be used to calculate air density for wind energy purposes using the equations presented in https://doi.org/10.3390/en12112038. The NetCDF file contains mean fields of monthly means from 2001 to 2010 of the variable temperature, surface pressure and specific humidity. The horizontal grid resolution is 0.5 degrees and covers the whole globe. These fields were used in the WAsP software version up to version 12.5 to calculate the air density at specified heights above mean sea level. The WAsP software (www.wasp.dk) is the industry standard method to calculate the annual energy production of wind farms. Saha, S., et al. 2010. <em>NCEP Climate Forecast System Reanalysis (CFSR) Monthly Products, January 1979 to December 2010</em>. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. https://doi.org/10.5065/D69K487J. Accessed 21 dec 2017.
DOI: 10.11583/dtu.16887601
title: Indicator for Eastern Baltic cod length structure

description: Time series of indicator for size structure of the Eastern Baltic cod (Gadus morhua) stock in 1991-2021. The index represents length (in mm) at the 95th percentile of the length distribution (L95). The index is calculated using data from Baltic International Bottom Trawl survey in the 1st quarter. Raw data are available from International Council for Exploration of the Sea (https://www.ices.dk/data/data-portals/Pages/DATRAS.aspx)..<br>
DOI: 10.6084/m9.figshare.23667462
title: Annex 2: distribution of reasons for retraction according to the type of male-female collaboration, in whole account and in fractional account

description: Annex 2: distribution of reasons for retraction according to the type of male-female collaboration, in whole account and in fractional account