cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.11583/dtu.21731366
title: Supplementary materials for publication "Eliminating OFF-frame clones in randomized gene libraries: An improved split β-lactamase enrichment system" in New Biotechnology

description: ONT sequencing data for manuscript "A split β-lactamase complementation based in-frame selection system for fast and efficient increase of functional diversity of antibody phage display libraries" <br> The data is divided in barcodes 1 to 10, which correspond to: Barcode 1: Mix 1 in unselective plate Barcode 2: Mix 1 in selective plate Barcode 3: Mix 2 in unselective plate Barcode 4: Mix 2 in selective plate Barcode 5: Mix 3 in unselective plate Barcode 6: Mix 3 in selective plate Barcode 7: Mix 4 in unselective plate Barcode 8: Mix 4 in selective plate Barcode 9: Library in unselective plate Barcode 10: Library in selective plate IGV can be used to analyze data in each barcode by comparing to the reference sequence "pTP_AlBla-7B6pRE-OmBla_fix.fa". The ONT sequencing report can also be found.
DOI: 10.11583/dtu.21731366.v1
title: Supplementary materials for publication "Eliminating OFF-frame clones in randomized gene libraries: An improved split β-lactamase enrichment system" in New Biotechnology

description: ONT sequencing data for manuscript "A split β-lactamase complementation based in-frame selection system for fast and efficient increase of functional diversity of antibody phage display libraries" <br> The data is divided in barcodes 1 to 10, which correspond to: Barcode 1: Mix 1 in unselective plate Barcode 2: Mix 1 in selective plate Barcode 3: Mix 2 in unselective plate Barcode 4: Mix 2 in selective plate Barcode 5: Mix 3 in unselective plate Barcode 6: Mix 3 in selective plate Barcode 7: Mix 4 in unselective plate Barcode 8: Mix 4 in selective plate Barcode 9: Library in unselective plate Barcode 10: Library in selective plate IGV can be used to analyze data in each barcode by comparing to the reference sequence "pTP_AlBla-7B6pRE-OmBla_fix.fa". The ONT sequencing report can also be found.
DOI: 10.5281/zenodo.7757034
title: Data set of simulated rimed aggregates for "A riming-dependent parameterization of scattering by snowflakes using the self-similar Rayleigh-Gans approximation"

description: <strong>Simulated rimed aggregates</strong> generated with https://github.com/jleinonen/aggregation in setting "aggregation followed by riming". Aggregates were built from between 10 to 700 monomer crystals of <strong>columns, dendrites, needles, plates or rosettes</strong> with mean sizes of 100 or 200 micrometer. Then they were exposed to ELWP = 2.0 kg m⁻². Monomer crystals are composed of cubical elements with resolution 20 micrometer. Frozen rime droplets are also represented by 20 micrometer cubes. The data set contains folders with <strong>evolution (evol) and shape files for each monomer crystal type</strong>. For each particle one evolution and one corresponding shape file exists. The evolution (evol) file contains particle mass, rime mass, area, size, fall speed (Heymsfield&Westbrook, 2010), fall speed (Khvorostyanov&Curry, 2005) for each step during the aggregation and riming process. The corresponding shape file contains the x,y,z positions of the cubical elements that compose the particle for each step. <strong>For further documentation see readme.</strong>
DOI: 10.5281/zenodo.7757033
title: Data set of simulated rimed aggregates for "A riming-dependent parameterization of scattering by snowflakes using the self-similar Rayleigh-Gans approximation"

description: <strong>Simulated rimed aggregates</strong> generated with https://github.com/jleinonen/aggregation in setting "aggregation followed by riming". Aggregates were built from between 10 to 700 monomer crystals of <strong>columns, dendrites, needles, plates or rosettes</strong> with mean sizes of 100 or 200 micrometer. Then they were exposed to ELWP = 2.0 kg m⁻². Monomer crystals are composed of cubical elements with resolution 20 micrometer. Frozen rime droplets are also represented by 20 micrometer cubes. The data set contains folders with <strong>evolution (evol) and shape files for each monomer crystal type</strong>. For each particle one evolution and one corresponding shape file exists. The evolution (evol) file contains particle mass, rime mass, area, size, fall speed (Heymsfield&Westbrook, 2010), fall speed (Khvorostyanov&Curry, 2005) for each step during the aggregation and riming process. The corresponding shape file contains the x,y,z positions of the cubical elements that compose the particle for each step. <strong>For further documentation see readme.</strong>
DOI: 10.5281/zenodo.5741748
title: Training and test data for retrievals based on MiRAC-P observations during MOSAiC

description: The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of integrated water vapour (prw) from brightness temperatures (tb) measured by the MiRAC-P (microwave radiometer for Arctic clouds, aka. LHUMPRO-243-340). A neural network retrieval has been developed to derive the prw. The trained retrieval is applied on the MiRAC-P observations gathered onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. For the data to be specialized on Arctic conditions they are based on ERA-Interim reanalysis. An IDL-based radiative transfer model has been used to simulate brightness temperatures. The elevation angle (ele) is always 90° because the MiRAC-P performed zenith scans only throughout the MOSAiC campaign.
description: More information can be found in the following publication to which this dataset belongs to: (work in progress)
DOI: 10.5281/zenodo.5741747
title: Training and test data for retrievals based on MiRAC-P observations during MOSAiC

description: The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of integrated water vapour (prw) from brightness temperatures (tb) measured by the MiRAC-P (microwave radiometer for Arctic clouds, aka. LHUMPRO-243-340). A neural network retrieval has been developed to derive the prw. The trained retrieval is applied on the MiRAC-P observations gathered onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. For the data to be specialized on Arctic conditions they are based on ERA-Interim reanalysis. An IDL-based radiative transfer model has been used to simulate brightness temperatures. The elevation angle (ele) is always 90° because the MiRAC-P performed zenith scans only throughout the MOSAiC campaign.
description: More information can be found in the following publication to which this dataset belongs to: (work in progress)
DOI: 10.5281/zenodo.5846394
title: Training and test data for retrievals based on MiRAC-P observations during MOSAiC

description: The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of integrated water vapour (prw) from brightness temperatures (tb) measured by the MiRAC-P (microwave radiometer for Arctic clouds, aka. LHUMPRO-243-340). A neural network retrieval has been developed to derive the prw. The trained retrieval is applied on the MiRAC-P observations gathered onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. For the data to be specialized on Arctic conditions they are based on ERA-Interim reanalysis. An IDL-based radiative transfer model has been used to simulate brightness temperatures. The elevation angle (ele) is always 90° because the MiRAC-P performed zenith scans only throughout the MOSAiC campaign.
description: More information can be found in the following publication to which this dataset belongs to: (work in progress)
DOI: 10.5281/zenodo.6673886
title: Retrieval coefficients for HATPRO observations during MOSAiC

description: Retrieval files required to run the IDL program MWR_PRO <strong>[1]</strong> script 'pl_mk_pol.sh' to process raw HATPRO brightness temperatures and retrieve integrated water vapour (IWV, also called prw), liquid water path (LWP, also called clwvi), zenith humidity profiles (hze), zenith temperature profiles (tze) and boundary layer temperature profiles (tel). The retrieval files contain the coefficients acquired via regression with linear only (tel) or also quadratic terms (prw, clwvi, hze, tze) based on Ny-Alesund radiosonde measurements. The coefficients have been determined by Nomokonova et al. <strong>[2]</strong>.<br> <br> <strong>[1]</strong>: Walbröl, Andreas. (2022). Codes for: Atmospheric temperature, water vapour and liquid water path from two microwave radiometers during MOSAiC (v2.1.0). Zenodo. https://doi.org/10.5281/zenodo.6673957 <strong>[2]</strong>: Nomokonova, T., Ebell, K., Löhnert, U., Maturilli, M., Ritter, C., and O'Connor, E.: Statistics on clouds and their relation to thermodynamic conditions at Ny-Ålesund using ground-based sensor synergy, Atmos. Chem. Phys., 19, 4105–4126, https://doi.org/10.5194/acp-19-4105-2019, 2019.
DOI: 10.5281/zenodo.6673885
title: Retrieval coefficients for HATPRO observations during MOSAiC

description: Retrieval files required to run the IDL program MWR_PRO <strong>[1]</strong> script 'pl_mk_pol.sh' to process raw HATPRO brightness temperatures and retrieve integrated water vapour (IWV, also called prw), liquid water path (LWP, also called clwvi), zenith humidity profiles (hze), zenith temperature profiles (tze) and boundary layer temperature profiles (tel). The retrieval files contain the coefficients acquired via regression with linear only (tel) or also quadratic terms (prw, clwvi, hze, tze) based on Ny-Alesund radiosonde measurements. The coefficients have been determined by Nomokonova et al. <strong>[2]</strong>.<br> <br> <strong>[1]</strong>: Walbröl, Andreas. (2022). Codes for: Atmospheric temperature, water vapour and liquid water path from two microwave radiometers during MOSAiC (v2.1.0). Zenodo. https://doi.org/10.5281/zenodo.6673957 <strong>[2]</strong>: Nomokonova, T., Ebell, K., Löhnert, U., Maturilli, M., Ritter, C., and O'Connor, E.: Statistics on clouds and their relation to thermodynamic conditions at Ny-Ålesund using ground-based sensor synergy, Atmos. Chem. Phys., 19, 4105–4126, https://doi.org/10.5194/acp-19-4105-2019, 2019.
DOI: 10.5281/zenodo.5741350
title: Training and test data for retrievals based on HATPRO observations during MOSAiC

description: The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of temperature (ta) and humidity (hua) profiles, integrated water vapour (prw), and liquid water path (clwvi) from brightness temperatures (tb) measured by a HATPRO (humidity and temperature profiler). A regression with quadratic terms, except for the boundary layer scan which is confined to linear terms, is performed to derive these meteorological variables. The trained retrieval is applied on the HATPRO observations gathered onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. For the data to be specialized on Arctic conditions they are based on Ny-Ålesund radiosonde observations. An IDL-based radiative transfer model has been used to simulate brightness temperatures for each radiosonde. Several elevation angles (ele) are given in the file because the HATPRO radiometer performs elevation scans in between zenith scans to increase the resolution of temperature profiles in the atmospheric boundary layer.
description: More information can be found in the following publication to which this dataset belongs to: (work in progress)
DOI: 10.5281/zenodo.5741349
title: Training and test data for retrievals based on HATPRO observations during MOSAiC

description: The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of temperature (ta) and humidity (hua) profiles, integrated water vapour (prw), and liquid water path (clwvi) from brightness temperatures (tb) measured by a HATPRO (humidity and temperature profiler). A regression with quadratic terms, except for the boundary layer scan which is confined to linear terms, is performed to derive these meteorological variables. The trained retrieval is applied on the HATPRO observations gathered onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. For the data to be specialized on Arctic conditions they are based on Ny-Ålesund radiosonde observations. An IDL-based radiative transfer model has been used to simulate brightness temperatures for each radiosonde. Several elevation angles (ele) are given in the file because the HATPRO radiometer performs elevation scans in between zenith scans to increase the resolution of temperature profiles in the atmospheric boundary layer.
description: More information can be found in the following publication to which this dataset belongs to: (work in progress)
DOI: 10.11583/dtu.21284967.v2
title: Raw AI4Arctic Sea Ice Challenge Dataset

description: The AI4Arctic Sea Ice Challenge Datasets are produced for the AI4EO sea ice competition initiated by the European Space Agency (ESA) ɸ-lab. The purpose of the competition is to develop deep learning models to automatically produce sea ice charts including sea ice concentration, stage-of-development and floe size (form) information. The training datasets contain Sentinel-1 active microwave Synthetic Aperture Radar (SAR) data and corresponding passive MicroWave Radiometer (MWR) data from the AMSR2 satellite sensor. While SAR data has ambiguities between open water and sea ice, it has a high spatial resolution, whereas MWR data has good contrast between open water and ice. However, the coarse resolution of the AMSR2 MWR observations introduces a new set of obstacles, e.g. land spill-over, which can lead to erroneous sea ice predictions along the coastline adjacent to open water. Label data in the challenge datasets are ice charts, that have been produced by the Greenland ice service at the Danish Meteorological Institute (DMI) and the Canadian Ice Service (CIS) for the safety of navigation. The challenge datasets also contain other auxiliary data such as the distance to land and numerical weather prediction model data. The scenes are from the time period from January 8 2018 to December 21 2021. Two versions of the dataset exist, the '<em>raw'</em> and '<em>ready-to-train'-</em>versions with corresponding test datasets<em>. </em>The datasets each consist of the same 513 training and 20 test (without label data) scenes. The ‘<em>ready-to-train’</em>-version has been further prepared for model training, such as downsampled data from 40 to 80 m pixel spacing, standard scaled, converted ice charts (sea ice concentration, stage of development and floe size), removal of nan values, mask alignment etc. This is the '<em>raw'</em>-version<em>. </em>The netCDF files are bundled together in groups ~25 with the filename format corresponding to the Sentinel-1 satellite from which the SAR image was acquired by, followed by the first file acquisition time to the last, i.e. S1(A/B)_FirstDate_LastDate.zip. Further details are described in the common manual that is published together with the datasets; “AI4Arctic_challenge-dataset-manual”. Code with a get-started toolkit for the '<em>ready-to-train</em>' dataset: https://github.com/astokholm/AI4ArcticSeaIceChallenge A quick challenge video overview of the challenge is available at: https://youtu.be/iuXIeLPyKfg This item is part of the Collection https://doi.org/10.11583/DTU.c.6244065 Version 2 has updated two zip files, which contained four corrupted netCDF files. The zip files in question are: S1A_20190419T203541_20190823T114541.zip S1B_20191028T132359_20200714T184241.zip In addition, 20 more scenes have been added in "added_v2.zip".
DOI: 10.11583/dtu.21284967
title: Raw AI4Arctic Sea Ice Challenge Dataset

description: The AI4Arctic Sea Ice Challenge Datasets are produced for the AI4EO sea ice competition initiated by the European Space Agency (ESA) ɸ-lab. The purpose of the competition is to develop deep learning models to automatically produce sea ice charts including sea ice concentration, stage-of-development and floe size (form) information. The training datasets contain Sentinel-1 active microwave Synthetic Aperture Radar (SAR) data and corresponding passive MicroWave Radiometer (MWR) data from the AMSR2 satellite sensor. While SAR data has ambiguities between open water and sea ice, it has a high spatial resolution, whereas MWR data has good contrast between open water and ice. However, the coarse resolution of the AMSR2 MWR observations introduces a new set of obstacles, e.g. land spill-over, which can lead to erroneous sea ice predictions along the coastline adjacent to open water. Label data in the challenge datasets are ice charts, that have been produced by the Greenland ice service at the Danish Meteorological Institute (DMI) and the Canadian Ice Service (CIS) for the safety of navigation. The challenge datasets also contain other auxiliary data such as the distance to land and numerical weather prediction model data. The scenes are from the time period from January 8 2018 to December 21 2021. Two versions of the dataset exist, the '<em>raw'</em> and '<em>ready-to-train'-</em>versions with corresponding test datasets<em>. </em>The datasets each consist of the same 512 training and 20 test (without label data) scenes. The ‘<em>ready-to-train’</em>-version has been further prepared for model training, such as downsampled data from 40 to 80 m pixel spacing, standard scaled, converted ice charts (sea ice concentration, stage of development and floe size), removal of nan values, mask alignment etc. This is the '<em>raw'</em>-version<em>. </em>The netCDF files are bundled together in groups ~25 with the filename format corresponding to the Sentinel-1 satellite from which the SAR image was acquired by, followed by the first file acquisition time to the last, i.e. S1(A/B)_FirstDate_LastDate.zip. Further details are described in the common manual that is published together with the datasets; “AI4Arctic_challenge-dataset-manual”. Code with a get-started toolkit for the '<em>ready-to-train</em>' dataset: https://github.com/astokholm/AI4ArcticSeaIceChallenge A quick challenge video overview of the challenge is available at: https://youtu.be/iuXIeLPyKfg This item is part of the Collection https://doi.org/10.11583/DTU.c.6244065 Version 2 has updated two zip files, which contained four corrupted netCDF files. The zip files in question are: S1A_20190419T203541_20190823T114541.zip S1B_20191028T132359_20200714T184241.zip In addition, 20 more scenes have been added in "added_v2.zip". Version 3 fixes an error with a duplicate zip file starting with "S1A_20190419T203541_".., adds the "S1A_20181018T121002_20190415T211043.zip" file and removed a scene with a faulty ice chart resulting in an updated "S1A_20191201T205227_20200619T122818.zip" file.
DOI: 10.5281/zenodo.8091065
title: An analysis of the impact of domain debt in open-source projects

description: Datasets related to the thesis paper: <em>original_boa.txt</em> contains the intial output given by BOA, while <em>filtered_boa.txt</em> comprises the set of repositories with at most five domain entity packages <em>original_seart.txt</em> included data as returned by SEART <em>database_structure.sql</em> is the SQL file that may be exploited to perform further analysis on the two datasets <em>repos.json</em> contains the final list of repositories, as chosen in the study
DOI: 10.5281/zenodo.8091064
title: An analysis of the impact of domain debt in open-source projects

description: Datasets related to the thesis paper: <em>original_boa.txt</em> contains the intial output given by BOA, while <em>filtered_boa.txt</em> comprises the set of repositories with at most five domain entity packages <em>original_seart.txt</em> included data as returned by SEART <em>database_structure.sql</em> is the SQL file that may be exploited to perform further analysis on the two datasets <em>repos.json</em> contains the final list of repositories, as chosen in the study
DOI: 10.48320/996718dc-423e-414b-8c3b-787dfe281010
title: Interviews used to analyze the digitalization in healthcare of the Republic of Kazakhstan and its implications on access to healthcare services
DOI: 10.5281/zenodo.8145347
title: test data zendo

description: test data
DOI: 10.5281/zenodo.8145348
title: test data zendo

description: test data
DOI: 10.11583/dtu.14564634
title: Data for "Improved dynamic imaging of multiphase flow by constrained tomographic reconstruction” - ground_truth_distribution

description: Histogram of the ground truth. This is needed for comparison when evaluating the performance of the algorithms described in the article 'Improved dynamic imaging of multiphase flow by constrained tomographic reconstruction'. The code can be found at https://gitlab.gbar.dtu.dk/pwra/NumericalExperiments<br>This item is a part of the collection: Data for "Improved dynamic imaging of multiphase flow by constrained tomographic reconstruction”, https://doi.org/10.11583/DTU.c.5448594
DOI: 10.11583/dtu.14564634.v1
title: Data for "Improved dynamic imaging of multiphase flow by constrained tomographic reconstruction” - ground_truth_distribution

description: Histogram of the ground truth. This is needed for comparison when evaluating the performance of the algorithms described in the article 'Improved dynamic imaging of multiphase flow by constrained tomographic reconstruction'. The code can be found at https://gitlab.gbar.dtu.dk/pwra/NumericalExperiments<br>This item is a part of the collection: Data for "Improved dynamic imaging of multiphase flow by constrained tomographic reconstruction”, https://doi.org/10.11583/DTU.c.5448594
DOI: 10.4121/7c37ae88-0215-4803-8403-57e48088ff0f.v3
title: Software and data used in the paper: Stratospheric small-scale disturbance characteristic in China based on round-trip intelligent sounding system

description: The procedures and the data files needed to recreate the figures in the paper. It includes the detection data of the round-trip intelligent sounding system (RTISS) and the process files for drawing.
DOI: 10.6084/m9.figshare.23694540
title: K80T_VUS

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23694540.v1
title: K80T_VUS

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.11583/dtu.9878894
title: Supporting information & data - Functionally graded multi-material freeze-cast structures with continuous microchannels

description: Supporting Information & Data for publication 'Functionally graded multi-material freeze-cast structures with continuous microchannels
DOI: 10.11583/dtu.9878894.v1
title: Supporting information & data - Functionally graded multi-material freeze-cast structures with continuous microchannels

description: Supporting Information & Data for publication 'Functionally graded multi-material freeze-cast structures with continuous microchannels
DOI: 10.6084/m9.figshare.23694585
title: K102Q_VUS

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23694585.v1
title: K102Q_VUS

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.