cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1594/pangaea.950979
title: Geolocated sea-ice or snow surface elevation point cloud segments from helicopter-borne laser scanner during the MOSAiC expedition flight 20200510_01, version 1

description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data &gt;85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
DOI: 10.1594/pangaea.950980
title: Geolocated sea-ice or snow surface elevation point cloud segments from helicopter-borne laser scanner during the MOSAiC expedition flight 20200615_02, version 1

description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data &gt;85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
DOI: 10.1594/pangaea.950981
title: Geolocated sea-ice or snow surface elevation point cloud segments from helicopter-borne laser scanner during the MOSAiC expedition flight 20200616_01, version 1

description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data &gt;85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
DOI: 10.1594/pangaea.950984
title: Geolocated sea-ice or snow surface elevation point cloud segments from helicopter-borne laser scanner during the MOSAiC expedition flight 20200704_01, version 1

description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data &gt;85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
DOI: 10.1594/pangaea.950994
title: Merged grids of sea-ice or snow freeboard from helicopter-borne laser scanner during the MOSAiC expedition flight 20191020_01, version 1

description: This data set is a higher-processing-level version of Gridded segments of sea-ice or snow surface elevation and freeboard from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Hutter et al., 2022; doi:10.1594/PANGAEA.950339), where the individual 30-second segments of the small scale grid flights have been combined into merged grids. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The merged data are stored in netCDF and geotiff format. The data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data &gt;85°N. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate. The merged grids include all data variables of the gridded 30-s segments: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. Also the calculated elevation offset correction term is provided for each flight as a csv file.
DOI: 10.1594/pangaea.950995
title: Merged grids of sea-ice or snow freeboard from helicopter-borne laser scanner during the MOSAiC expedition flight 20191002_01, version 1

description: This data set is a higher-processing-level version of Gridded segments of sea-ice or snow surface elevation and freeboard from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Hutter et al., 2022; doi:10.1594/PANGAEA.950339), where the individual 30-second segments of the small scale grid flights have been combined into merged grids. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The merged data are stored in netCDF and geotiff format. The data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data &gt;85°N. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate. The merged grids include all data variables of the gridded 30-s segments: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. Also the calculated elevation offset correction term is provided for each flight as a csv file.
DOI: 10.1594/pangaea.950996
title: Merged grids of sea-ice or snow freeboard from helicopter-borne laser scanner during the MOSAiC expedition flight 20191112_02, version 1

description: This data set is a higher-processing-level version of Gridded segments of sea-ice or snow surface elevation and freeboard from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Hutter et al., 2022; doi:10.1594/PANGAEA.950339), where the individual 30-second segments of the small scale grid flights have been combined into merged grids. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The merged data are stored in netCDF and geotiff format. The data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data &gt;85°N. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate. The merged grids include all data variables of the gridded 30-s segments: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. Also the calculated elevation offset correction term is provided for each flight as a csv file.
DOI: 10.1594/pangaea.950997
title: Merged grids of sea-ice or snow freeboard from helicopter-borne laser scanner during the MOSAiC expedition flight 20191119_01, version 1

description: This data set is a higher-processing-level version of Gridded segments of sea-ice or snow surface elevation and freeboard from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Hutter et al., 2022; doi:10.1594/PANGAEA.950339), where the individual 30-second segments of the small scale grid flights have been combined into merged grids. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The merged data are stored in netCDF and geotiff format. The data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data &gt;85°N. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate. The merged grids include all data variables of the gridded 30-s segments: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. Also the calculated elevation offset correction term is provided for each flight as a csv file.
DOI: 10.6084/m9.figshare.23650235.v1
title: Additional file 11 of Identification and characterization of sugar-regulated promoters in Chaetomium thermophilum

description: Additional file 11. Supplementary Data 1. All differentially expressed genesidentified by Illumina deepsequencing.
DOI: 10.6084/m9.figshare.23650238
title: Additional file 12 of Identification and characterization of sugar-regulated promoters in Chaetomium thermophilum

description: Additional file 12. Supplementary Data 2. Gene IDs of the 50 most significant changes between glucose- and xylose treated samples.
DOI: 10.6084/m9.figshare.23650238.v1
title: Additional file 12 of Identification and characterization of sugar-regulated promoters in Chaetomium thermophilum

description: Additional file 12. Supplementary Data 2. Gene IDs of the 50 most significant changes between glucose- and xylose treated samples.
DOI: 10.6084/m9.figshare.23650241
title: Additional file 13 of Identification and characterization of sugar-regulated promoters in Chaetomium thermophilum

description: Additional file 13. Supplementary Data 3. Gene IDs of selected genes involved in lignocellulolytic and catabolic functions.
DOI: 10.6084/m9.figshare.23650241.v1
title: Additional file 13 of Identification and characterization of sugar-regulated promoters in Chaetomium thermophilum

description: Additional file 13. Supplementary Data 3. Gene IDs of selected genes involved in lignocellulolytic and catabolic functions.
DOI: 10.6084/m9.figshare.23650244
title: Additional file 14 of Identification and characterization of sugar-regulated promoters in Chaetomium thermophilum

description: Additional file 14. Supplementary Data 4. qRT measurements of selected genes in comparison to Illumina sequencing.
DOI: 10.6084/m9.figshare.23650244.v1
title: Additional file 14 of Identification and characterization of sugar-regulated promoters in Chaetomium thermophilum

description: Additional file 14. Supplementary Data 4. qRT measurements of selected genes in comparison to Illumina sequencing.
DOI: 10.5519/qd.3xcwmstv
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.6084/m9.figshare.23620521
title: GL4U 2023 CSULA Amplicon Bootcamp materials

description: Materials for the NASA GeneLab GL4U 2023 CSULA Amplicon Sequencing Bootcamp: https://github.com/NASA/GeneLab-Training/wiki/
DOI: 10.6084/m9.figshare.23620521.v4
title: GL4U 2023 CSULA Amplicon Bootcamp materials

description: Materials for the NASA GeneLab GL4U 2023 CSULA Amplicon Sequencing Bootcamp: https://github.com/NASA/GeneLab-Training/wiki/
DOI: 10.5281/zenodo.8127377
title: Data set for Finding Equivalence of Layout Independent Water Distribution Network for Expansion/Reorganization Using Nonlinear Multi-Port Thevenin Theorem

description: This is the dataset for our manuscript titled "Finding Equivalence of Layout Independent Water Distribution Network for Expansion/Reorganization Using Nonlinear Multi-Port Thevenin Theorem".
DOI: 10.5281/zenodo.8127376
title: Data set for Finding Equivalence of Layout Independent Water Distribution Network for Expansion/Reorganization Using Nonlinear Multi-Port Thevenin Theorem

description: This is the dataset for our manuscript titled "Finding Equivalence of Layout Independent Water Distribution Network for Expansion/Reorganization Using Nonlinear Multi-Port Thevenin Theorem".
DOI: 10.5281/zenodo.8126386
title: A tightly coupled river-ocean model for simulating combined flood due to storm surge and river flow in coastal-urban areas

description: Coastal flooding, resulting from storm surges or extreme river flows, causes significant causalities and damage to properties in low-lying areas. The simultaneous occurrence of river flows and storm surges, termed combined/compound events, exacerbates the flood risk compared to independent occurrences. Combined flood events are simulated with the help of hydraulic and hydrodynamic models using a loosely or tightly coupled approach. In the loosely coupled approach, a hydrodynamic model simulates storm surges first, and a hydraulic model then simulates inland flood due to river overflow considering surge as the boundary condition at the river mouth/estuary. Conversely, the tightly coupled approach involves simultaneous simulation of both river flow and storm surge by coding the mathematical representation of river and ocean flow dynamics in the same numerical model. This allows the interaction between river and ocean flows to be simulated anywhere in the combined river-ocean computational domain, making it highly relevant for simulating combined floods. However, existing models based on this approach encounter numerical instability, especially in inland regions where topography variation is steep and highly uneven. Also, such combined models are highly limited for large scale applications. Therefore, this research focuses on developing a tightly coupled 2D finite volume river-ocean model called IROMS-C2D. The developed model intends to address the limitations of the previous models and provide a stable solution framework for the simulation of combined flooding resulting from the interaction of storm surges and river flows in coastal urban areas. Further, it enhances our understanding of flood risks in coastal areas, particularly in urban settings, and facilitates the formulation of effective measures for flood control and adaptation of coastal infrastructure.
DOI: 10.5281/zenodo.8126385
title: A tightly coupled river-ocean model for simulating combined flood due to storm surge and river flow in coastal-urban areas

description: Coastal flooding, resulting from storm surges or extreme river flows, causes significant causalities and damage to properties in low-lying areas. The simultaneous occurrence of river flows and storm surges, termed combined/compound events, exacerbates the flood risk compared to independent occurrences. Combined flood events are simulated with the help of hydraulic and hydrodynamic models using a loosely or tightly coupled approach. In the loosely coupled approach, a hydrodynamic model simulates storm surges first, and a hydraulic model then simulates inland flood due to river overflow considering surge as the boundary condition at the river mouth/estuary. Conversely, the tightly coupled approach involves simultaneous simulation of both river flow and storm surge by coding the mathematical representation of river and ocean flow dynamics in the same numerical model. This allows the interaction between river and ocean flows to be simulated anywhere in the combined river-ocean computational domain, making it highly relevant for simulating combined floods. However, existing models based on this approach encounter numerical instability, especially in inland regions where topography variation is steep and highly uneven. Also, such combined models are highly limited for large scale applications. Therefore, this research focuses on developing a tightly coupled 2D finite volume river-ocean model called IROMS-C2D. The developed model intends to address the limitations of the previous models and provide a stable solution framework for the simulation of combined flooding resulting from the interaction of storm surges and river flows in coastal urban areas. Further, it enhances our understanding of flood risks in coastal areas, particularly in urban settings, and facilitates the formulation of effective measures for flood control and adaptation of coastal infrastructure.
DOI: 10.5281/zenodo.8127387
title: UDRP data used in legal factor discovery paper

description: UDRP metadata and sectionised texts included.
DOI: 10.5281/zenodo.8127386
title: UDRP data used in legal factor discovery paper

description: UDRP metadata and sectionised texts included.
DOI: 10.34740/kaggle/dsv/5333319
title: Computer Science Theory QA Dataset
DOI: 10.15151/esrf-es-1118672906
title: IBS BAG

description: The IBS BAG includes in this proposal IBS teams and external groups from CEA-Cadarache (St Paul lez Durance) and IRBA (Brétigny-sur-Orge). We study structure-function relationships of proteins emphasizing in human health, biotechnology and methodology. Most of the projects are a continuation of the MX-2398 proposal and can be grouped into different topics as follows: host-pathogen interaction, new drug targets, fundamental cellular mechanisms, membrane proteins, biotechnological applications and methodology. Some of these projects use integrative approaches (X-ray crystallography, SAXS, NMR (and cryo-EM), together with other biophysical studies) with the aim to fully understand the complexity of the biological macromolecular systems on study.
DOI: 10.15151/esrf-es-1118703180
title: SAARLAND BAG: ATOMIC MECHANISMS OF ACTION OF MEMBRANE PROTEINS AND BIOSYNTHETIC ENZYMES

description: Projects are focussed primarily within the framework of the DFG-funded excellence network program in Homburg (Saar) on "Ca2+signals: Molecular Mechanisms und Integrative Functions" at Saarland University and a new joint multidisciplinary initiative of the University with the Helmholtz Institute of Pharmaceutical Research Saarland in Saarbrücken on natural bioactive compounds including their interactions with their target proteins. These projects involve the structure determination of membrane proteins and soluble proteins.
DOI: 10.15151/esrf-es-1120148996
title: Structural biology at Umeå University

description: The BAG consists out of 7 groups that are heavily involved in macromolecular crystallography, as well as other structural biology techniques, at Umeå University, Sweden. We work on a diverse set of biological questions, ranging from understanding the structure and function of Gram-positive Type 4 Secretion Systems, mobile genetic elements in Staphylococcus aureus, DNA and RNA polymerases, effector protein in Type 6 Secretion Systems and transcriptional regulators.