cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25625/8e7f2s
title: A05_net N cycling rate and soil mineral N stocks

description: monthly soil net N cycling rate in OPMX from July 2019 to June 2020. soil mineral N stocks from 0-50 cm, 50-100 cm, and 100-150 cm soil in OPMX in March 2021
DOI: 10.25625/khngmw
title: A05_element leaching (OPMX)_N, DOC, Al, Ca, K, Mg, Na, S

description: Element leaching losses including N, DOC, Al, Ca, K, Mg, Na, S in OPMX from November 2019 to October 2020
DOI: 10.25625/khngmw/mgqqzi
title: A05_element leaching (OPMX).tab

description: :unav
DOI: 10.57745/eizale
title: 167indiv_Ventoux_genotypes_18192174SNPs_final.vcf.gz

description: Set of 18,192,174 SNPs identified in 167 F. sylvatica individuals.
DOI: 10.5281/zenodo.8134510
title: 134

description: 1
DOI: 10.5281/zenodo.8134509
title: 134

description: 1
DOI: 10.6084/m9.figshare.23096465
title: Soose et al. 2023 STOTEN Data.csv

description: We present behavioral raw-data related to Soose et al. 2023.
DOI: 10.6084/m9.figshare.23096465.v1
title: Soose et al. 2023 STOTEN Data.csv

description: We present behavioral raw-data related to Soose et al. 2023.
DOI: 10.5517/ccdc.csd.cc25yr30
title: CCDC 2024582: Experimental Crystal Structure Determination

description: Related Article: Ramanaiah Chennuru, Ramesh Devarapalli, Prathap Rengaraj, P. L. Srinivas, Somnath Dey, C. Malla Reddy|2020|Cryst.Growth Des.|20|5018|doi:10.1021/acs.cgd.0c00153
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fk09b
title: CCDC 2250671: Experimental Crystal Structure Determination

description: Related Article: Ibrahim I. Ozturk, Kadriye Turk, Anita M. Grześkiewicz, Maciej Kubicki, Christina N. Banti, Sotiris K. Hadjikakou|2023|New J.Chem.|47|12779|doi:10.1039/D3NJ01411H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fk3sx
title: CCDC 2250779: Experimental Crystal Structure Determination

description: Related Article: Arghyadeep Bhattacharyya, Viki Bhakta, Nikhil Guchhait|2023|Phys.Chem.Chem.Phys.(PCCP)|25|17482|doi:10.1039/D3CP01703F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fk089
title: CCDC 2250670: Experimental Crystal Structure Determination

description: Related Article: Ibrahim I. Ozturk, Kadriye Turk, Anita M. Grześkiewicz, Maciej Kubicki, Christina N. Banti, Sotiris K. Hadjikakou|2023|New J.Chem.|47|12779|doi:10.1039/D3NJ01411H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fk0bc
title: CCDC 2250672: Experimental Crystal Structure Determination

description: Related Article: Ibrahim I. Ozturk, Kadriye Turk, Anita M. Grześkiewicz, Maciej Kubicki, Christina N. Banti, Sotiris K. Hadjikakou|2023|New J.Chem.|47|12779|doi:10.1039/D3NJ01411H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.23660223
title: ONT sequencing data (23S rRNA reads from E.coli)

description: The A2030 site is a known methylated site on the 23S rRNA of <em>E. coli</em>, and we aimed to observe the electrical signal changes caused by this modification. Firstly, 2000 reads aligned on the 23S rRNA from the wild-type sample and in vitro transcription (IVT) sample were extracted respectively.
DOI: 10.6084/m9.figshare.23660223.v1
title: ONT sequencing data (23S rRNA reads from E.coli)

description: The A2030 site is a known methylated site on the 23S rRNA of <em>E. coli</em>, and we aimed to observe the electrical signal changes caused by this modification. Firstly, 2000 reads aligned on the 23S rRNA from the wild-type sample and in vitro transcription (IVT) sample were extracted respectively.
DOI: 10.11583/dtu.19689004
title: Onshore wind generation time series for technology SP277 HH150 at resource grade B (PECD 2021 update)

description: This data (csv file) provides simulated hourly time series of onshore wind generation with specific power (SP) 277 W/m2 turbines at hub height (HH) of 150 m for the regions shown in the attached map. The analysed wind power plants are sited at the 10...50 % highest mean wind speed locations in each region, i.e., in resource grade (RG) B. The map shows the resulting capacity factors (annual mean). The Excel file gives a rough indication if this wind technology is suitable for the different regions for this RG or not. The available land considers all onshore land area of a region, except lakes, cities, and very high elevation locations. The possible impact of any existing onshore wind installations in the region is not considered. Wake losses are modeled, with additional 5 % of other losses and unavailability considered. The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00 is the aggregated onshore wind generation of all the UK regions (weighted by regional installed capacities). The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. The linked journal paper (1st link) describes the simulation methodology (combination of ERA5 and GWA data is used). It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the concept of resource grades and how they can be applied in energy system analyses. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581
DOI: 10.11583/dtu.19689004.v1
title: Onshore wind generation time series for technology SP277 HH150 at resource grade B (PECD 2021 update)

description: This data (csv file) provides simulated hourly time series of onshore wind generation with specific power (SP) 277 W/m2 turbines at hub height (HH) of 150 m for the regions shown in the attached map. The analysed wind power plants are sited at the 10...50 % highest mean wind speed locations in each region, i.e., in resource grade (RG) B. The map shows the resulting capacity factors (annual mean). The Excel file gives a rough indication if this wind technology is suitable for the different regions for this RG or not. The available land considers all onshore land area of a region, except lakes, cities, and very high elevation locations. The possible impact of any existing onshore wind installations in the region is not considered. Wake losses are modeled, with additional 5 % of other losses and unavailability considered. The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00 is the aggregated onshore wind generation of all the UK regions (weighted by regional installed capacities). The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. The linked journal paper (1st link) describes the simulation methodology (combination of ERA5 and GWA data is used). It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the concept of resource grades and how they can be applied in energy system analyses. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581
DOI: 10.11588/data/a4kgyj/ulztbw
title: MPCA_Plot3.txt

description: :unav
DOI: 10.11588/data/a4kgyj/ed1e56
title: points_dem3.txt

description: :unav
DOI: 10.11588/data/a4kgyj/hyeybh
title: gully_multi_profile.py

description: :unav
DOI: 10.11588/data/a4kgyj/swyivr
title: MetaData_multiprofile_gully.txt

description: :unav
DOI: 10.11583/dtu.11611689
title: Supporting data - Impact of hysteresis on cooling performance of caloric materials

description: Supporting data for publication 'Impact of hysteresis on caloric cooling performance" submitted in the International Journal of Refrigeration (DOI 10.1016/j.ijrefrig.2020.10.012) <br><br>The data comprise:1) An Excel file presenting the modeling results from the 1D regenerator model with hysteresis term (Qhys) to predict how modeled materials with a range of hysteresis values affect the cooling performance, including COP and cooling performance (Pcool). The Excel file presents the results for the six model materials, which have different physical (and thermodynamic) properties, i.e., specific heat capacity at zero field (c) and isothermal entropy change (s). For example, the material 'LoCLoS' has both a low specific heat capacitiy and a low isothermal entropy change. <br><br>2) An MS Word document with the additional Figs. S1 and S2 showing the modeled COPs for five model materials for a design cooling power of 50 W as a function of cycle frequency for different hysteresis values. The document also comprises a Table S1 presenting the modeled COPs for a design cooling power of 300 W as a function of cycle frequency for different hysteresis values.<br><br><br><br><br>
DOI: 10.11583/dtu.11611689.v1
title: Supporting data - Impact of hysteresis on cooling performance of caloric materials

description: Supporting data for publication 'Impact of hysteresis on caloric cooling performance" submitted in the International Journal of Refrigeration (DOI 10.1016/j.ijrefrig.2020.10.012) <br><br>The data comprise:1) An Excel file presenting the modeling results from the 1D regenerator model with hysteresis term (Qhys) to predict how modeled materials with a range of hysteresis values affect the cooling performance, including COP and cooling performance (Pcool). The Excel file presents the results for the six model materials, which have different physical (and thermodynamic) properties, i.e., specific heat capacity at zero field (c) and isothermal entropy change (s). For example, the material 'LoCLoS' has both a low specific heat capacitiy and a low isothermal entropy change. <br><br>2) An MS Word document with the additional Figs. S1 and S2 showing the modeled COPs for five model materials for a design cooling power of 50 W as a function of cycle frequency for different hysteresis values. The document also comprises a Table S1 presenting the modeled COPs for a design cooling power of 300 W as a function of cycle frequency for different hysteresis values.<br><br><br><br><br>
DOI: 10.11583/dtu.19299899
title: Supplementary files for: ”Hygrothermal assessment of solid masonry walls internally insulated with bio-based insulation materials”

description: Supplementary data for: Nickolaj Feldt Jensen, Eva B. Møller, Kurt Kielsgaard Hansen and Carsten Rode, 2022. Hygrothermal assessment of solid masonry walls internally insulated with bio-based insulation materials. <br>The article is submitted for review for the 5th Central European Symposium on Building Physics 2022. The authors are awaiting link/DOI for the article.<br>Abstract: This study investigated the hygrothermal performance and risk of mould growth in two bio-based thermal insulation systems for internal retrofitting purposes; loose-fill cellulose insulation and hemp fibre insulation. The experimental set-up comprised a 40-feet (12.2 m) insulated reefer container with controlled indoor climate, reconfigured with several holes (1x2 m each) containing solid masonry walls with embedded wooden elements on the interior side and different internal insulation systems, with and without exterior hydrophobisation. The container was placed in Denmark. Throughout 1 year and 2 months, temperature and relative humidity were measured in the interface between the masonry and the internal insulation and in the embedded wooden elements. The effect of exterior hydrophobisation was also investigated. The VTT mould growth model was used to evaluate the mould risk. Lastly, the hygrothermal performance of the bio-based systems was compared with that of previously examined inorganic internal insulation systems. Findings for the two bio-based thermal insulation systems showed that exposed walls experienced high relative humidity. The combination with exterior hydrophobisation to lower intrusion of driving rain had a positive effect on the moisture balance for the masonry walls. However, the insulated walls with hydrophobisation still showed high relative humidity levels during specific periods of the year in some of the examined masonry walls. The mould risk evaluation showed a high risk of mould growth in masonry/insulation interface, as well as in the wooden wall plates in some test walls. <br>The dataset comprises an Excel file containing measurement data from the experimental set-up (1 hour data and 96 hour running average) and mould growth predictions calculated using the VTT model by Hukka and Viitanen.
DOI: 10.11583/dtu.19299899.v1
title: Supplementary files for: ”Hygrothermal assessment of solid masonry walls internally insulated with bio-based insulation materials”

description: Supplementary data for: Nickolaj Feldt Jensen, Eva B. Møller, Kurt Kielsgaard Hansen and Carsten Rode, 2022. Hygrothermal assessment of solid masonry walls internally insulated with bio-based insulation materials. <br>The article is submitted for review for the 5th Central European Symposium on Building Physics 2022. The authors are awaiting link/DOI for the article.<br>Abstract: This study investigated the hygrothermal performance and risk of mould growth in two bio-based thermal insulation systems for internal retrofitting purposes; loose-fill cellulose insulation and hemp fibre insulation. The experimental set-up comprised a 40-feet (12.2 m) insulated reefer container with controlled indoor climate, reconfigured with several holes (1x2 m each) containing solid masonry walls with embedded wooden elements on the interior side and different internal insulation systems, with and without exterior hydrophobisation. The container was placed in Denmark. Throughout 1 year and 2 months, temperature and relative humidity were measured in the interface between the masonry and the internal insulation and in the embedded wooden elements. The effect of exterior hydrophobisation was also investigated. The VTT mould growth model was used to evaluate the mould risk. Lastly, the hygrothermal performance of the bio-based systems was compared with that of previously examined inorganic internal insulation systems. Findings for the two bio-based thermal insulation systems showed that exposed walls experienced high relative humidity. The combination with exterior hydrophobisation to lower intrusion of driving rain had a positive effect on the moisture balance for the masonry walls. However, the insulated walls with hydrophobisation still showed high relative humidity levels during specific periods of the year in some of the examined masonry walls. The mould risk evaluation showed a high risk of mould growth in masonry/insulation interface, as well as in the wooden wall plates in some test walls. <br>The dataset comprises an Excel file containing measurement data from the experimental set-up (1 hour data and 96 hour running average) and mould growth predictions calculated using the VTT model by Hukka and Viitanen.
DOI: 10.5281/zenodo.8134866
title: Three-dimensional video recordings: Accuracy, reliability, clinical and research guidelines.

description: The datasets include positional differences between consecutive frames of three-dimensional video recordings performed by two independent operators.
DOI: 10.5281/zenodo.8134865
title: Three-dimensional video recordings: Accuracy, reliability, clinical and research guidelines.

description: The datasets include positional differences between consecutive frames of three-dimensional video recordings performed by two independent operators.
DOI: 10.11583/dtu.9963614
title: Data for "Particle-in-cell simulations of parametric decay instabilities at the upper hybrid layer of fusion plasmas to determine their primary threshold"

description: The data needed to generate figures 5-18 of the article Particle-in-cell simulations of parametric decay instabilities at the upper hybrid layer of fusion plasmas to determine their primary threshold. / Senstius, Mads Givskov; Nielsen, Stefan Kragh; Vann, R. G.; Hansen, Søren Kjer.In: Plasma Physics and Controlled Fusion, 2019. https://doi.org/10.1088/1361-6587/ab49ca<br>
DOI: 10.5281/zenodo.7787426
title: HIV RT RBFE calculation results

description: # unzip the results folders using: tar -xf hiv_rt_rbfe_results.tar.gz # Four folders containing R-modification and Core-hopping series RBFE calculations using <br> Pilot and Adaptive lambda scheme, as stated in the folder names. # In every folder, there is a Juptyer notebook "fep_analysis.ipynb", <br> which is used to obtain ddg values, overlapping matrix plots, and dg_i,0 vs lambda window plots.<br> Users who want to use the notebook need to install all the packages listed in the first cell,<br> and the most important two packages are alchemlyb and pymbar.<br> Users only need to modify the perturbation folders name in the inputs of the "get_ddg" function <br> located in the last cell and run the entire notebook to obtain the results. # In every pilot calculations folder, there is a Juptyer notebook "adaptive_lambda.ipynb",<br> which is primarily used to obtain the adaptive lambda scheme based on the pilot calculation results.<br> Users only need to modify the perturbation folders name in the "system" variable <br> located at the 1st line of the last cell and run the entire notebook to obtain the results.
DOI: 10.34740/kaggle/dsv/6117461
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.11583/dtu.10082312
title: Voyage simulation data for the paper ” The impact of propulsion factors on vessel performance in waves”

description: Route simulations for 4 different ships. It consists of wave conditions, ship speed, resistance, power, rpm, propulsion efficiency and propulsion factors at multiple points along the route.<br>All the data of voyage simulations on a route from Los Angeles to Osaka performed in the paper "The impact of propulsion factors on vessel performance in waves". The dataset is a detailed version of plots in Figure 5 and Figure 6 presented in the paper.
DOI: 10.11583/dtu.10082312.v1
title: Voyage simulation data for the paper ” The impact of propulsion factors on vessel performance in waves”

description: Route simulations for 4 different ships. It consists of wave conditions, ship speed, resistance, power, rpm, propulsion efficiency and propulsion factors at multiple points along the route.<br>All the data of voyage simulations on a route from Los Angeles to Osaka performed in the paper "The impact of propulsion factors on vessel performance in waves". The dataset is a detailed version of plots in Figure 5 and Figure 6 presented in the paper.
DOI: 10.11583/dtu.19689028
title: Onshore wind generation time series for technology SP335 HH100 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 100 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 <br>