cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/rs86jtwfn9.1
title: Merged SAR and Optical

description: The map package files were prepared and can be opened by Arc Gis 10.8.2 and above versions. The data files include the SAR data in HH,HV- polarizations, DEM and IRS LISS III data with the 30m spatial resolution which are download from Bhuvan-NRSC, Geology data from Geological Survey of india (Bhukosh). The resolution merged data of Optical and SAR data has been prepared using Brovey transform in ERDAS 2015 software. Rose diagrams based on the trend of lineaments are prepared with the help of Arc GIS 10.8.2 tools and Rockworks 17 software. Lineaments are the linear geological features can extend from few meters to hundreds of kms. Geologically lineaments are either structural or stratigraphical, typically it will comprise fault, fold axis, bedding contacts, dyke intrusions, shear zone or a straight coast line. Mapping lineaments using remote sensing is economical, faster can act as a preliminary study. Generally lineaments have been mapped using the optical remote sensing data such as Landsat, Resourcesat etc. For India, Lineaments were mapped using the LISS III and LISS IV of Resourcesat-1 & 2 at a scale of 1:50k. However in tropical region like India, limited exposure of ground due to vegetation cover, lineaments may go unnoticed in optical remote sensing data. This problem can be overcome by Synthetic Aperture Radar (SAR) data, which can penetrate ground significantly. With the launch of RISAT-1satelite, data availability of SAR data is immense for Indian region. Aim of this study to explore the SAR data and merged SAR and optical data for lineament mapping.
DOI: 10.17632/rs86jtwfn9.2
title: Merged SAR and Optical

description: The map package files (merged.mpk) were prepared and can be opened by Arc Gis 10.8.2 and above versions. The map package data files include the SAR data (RISAT-1 from ISRO-Bhoonidhi) in HH,HV- polarizations, DEM ( USGS ) and IRS LISS III (Bhuvan-NRSC) data with the 30m spatial resolution were downloaded from the respective websites. Geology data in 1:50,000 scale is downloaded from GSI Bhukosh. The resolution merged data of Optical and SAR data has been prepared using Brovey transform in ERDAS 2015 software. The output file have advantages of both optical and microwave features. Extracted the Lineaments(.shp) from the coupled data of merged SAR and improved and verified with the DEM, Optical, SAR and Geology data sets. All these data generation and Statistical calculation done with the help of ArcGIS software. ArcGIS guide will help to create shape files, Attribute table calculations of length, classification. Azumutal trend calculations of each lineaments done using Split lines and other geometric calculations giving the trend of each lineament and finally export the map (All .jpg files). Rose diagrams was prepared based on the trend of lineaments with the help of Rockworks 17 software. The generated Azimuthal trend data in lineament shape file can be import to linears - utilites - Rose diagram. I was prepared Rose diagram of different class of lineaments using frequency calculation method. Lineaments are the linear geological features can extend from few meters to hundreds of kms. Geologically lineaments are either structural or stratigraphical, typically it will comprise fault, fold axis, bedding contacts, dyke intrusions, shear zone or a straight coast line. Mapping lineaments using remote sensing is economical, faster can act as a preliminary study. Generally lineaments have been mapped using the optical remote sensing data such as Landsat, Resourcesat etc. For India, Lineaments were mapped using the LISS III and LISS IV of Resourcesat-1 & 2 at a scale of 1:50k. However in tropical region like India, limited exposure of ground due to vegetation cover, lineaments may go unnoticed in optical remote sensing data. This problem can be overcome by Synthetic Aperture Radar (SAR) data, which can penetrate ground significantly. With the launch of RISAT-1satelite, data availability of SAR data is immense for Indian region. Aim of this study to explore the SAR data and merged SAR and optical data for lineament mapping.
DOI: 10.17632/rs86jtwfn9.3
title: Merged SAR and Optical

description: The map package files (merged.mpk) were prepared and can be opened by Arc Gis 10.8.2 and above versions. The map package data files include the SAR data (RISAT-1 from ISRO-Bhoonidhi) in HH,HV- polarizations, DEM ( USGS ) and IRS LISS III (Bhuvan-NRSC) data with the 30m spatial resolution were downloaded from the respective websites. Geology data in 1:50,000 scale is downloaded from GSI Bhukosh. The resolution merged data of Optical and SAR data has been prepared using Brovey transform in ERDAS 2015 software. The output file have advantages of both optical and microwave features. Extracted the Lineaments(.shp) from the coupled data of merged SAR and improved and verified with the DEM, Optical, SAR and Geology data sets. All these data generation and Statistical calculation done with the help of ArcGIS software. ArcGIS guide will help to create shape files, Attribute table calculations of length, classification. Azumutal trend calculations of each lineaments done using Split lines and other geometric calculations giving the trend of each lineament and finally export the map (All .jpg files). Rose diagrams was prepared based on the trend of lineaments with the help of Rockworks 17 software. The generated Azimuthal trend data in lineament shape file can be import to linears - utilites - Rose diagram. I was prepared Rose diagram of different class of lineaments using frequency calculation method. Lineaments are the linear geological features can extend from few meters to hundreds of kms. Geologically lineaments are either structural or stratigraphical, typically it will comprise fault, fold axis, bedding contacts, dyke intrusions, shear zone or a straight coast line. Mapping lineaments using remote sensing is economical, faster can act as a preliminary study. Generally lineaments have been mapped using the optical remote sensing data such as Landsat, Resourcesat etc. For India, Lineaments were mapped using the LISS III and LISS IV of Resourcesat-1 & 2 at a scale of 1:50k. However in tropical region like India, limited exposure of ground due to vegetation cover, lineaments may go unnoticed in optical remote sensing data. This problem can be overcome by Synthetic Aperture Radar (SAR) data, which can penetrate ground significantly. With the launch of RISAT-1satelite, data availability of SAR data is immense for Indian region. Aim of this study to explore the SAR data and merged SAR and optical data for lineament mapping.
DOI: 10.17632/rs86jtwfn9.4
title: Merged SAR and Optical

description: The map package files (merged.mpk) were prepared and can be opened by Arc Gis 10.8.2 and above versions. The map package data files include the SAR data (RISAT-1 from ISRO-Bhoonidhi) in HH,HV- polarizations, DEM ( USGS ) and IRS LISS III (Bhuvan-NRSC) data with the 30m spatial resolution were downloaded from the respective websites. Geology data in 1:50,000 scale is downloaded from GSI Bhukosh.
The resolution merged data of Optical and SAR data has been prepared using Brovey transform in ERDAS 2015 software. The output file have advantages of both optical and microwave features. Extracted the Lineaments(.shp) from the coupled data of merged SAR and improved and verified with the DEM, Optical, SAR and Geology data sets. All these data generation and Statistical calculation done with the help of ArcGIS software. ArcGIS guide will help to create shape files, Attribute table calculations of length, classification. Azumutal trend calculations of each lineaments done using Split lines and other geometric calculations giving the trend of each lineament and finally export the map (All .jpg files).
Rose diagrams was prepared based on the trend of lineaments with the help of Rockworks 17 software. The generated Azimuthal trend data in lineament shape file can be import to linears - utilites - Rose diagram. I was prepared Rose diagram of different class of lineaments using frequency calculation method.
Lineaments are the linear geological features can extend from few meters to hundreds of kms. Geologically lineaments are either structural or stratigraphical, typically it will comprise fault, fold axis, bedding contacts, dyke intrusions, shear zone or a straight coast line. Mapping lineaments using remote sensing is economical, faster can act as a preliminary study. Generally lineaments have been mapped using the optical remote sensing data such as Landsat, Resourcesat etc. For India, Lineaments were mapped using the LISS III and LISS IV of Resourcesat-1 & 2 at a scale of 1:50k. However in tropical region like India, limited exposure of ground due to vegetation cover, lineaments may go unnoticed in optical remote sensing data. This problem can be overcome by Synthetic Aperture Radar (SAR) data, which can penetrate ground significantly. With the launch of RISAT-1satelite, data availability of SAR data is immense for Indian region. Aim of this study to explore the SAR data and merged SAR and optical data for lineament mapping.
DOI: 10.6084/m9.figshare.24786873.v1
title: Anhang zum Abschlussbericht BUiLD.DIGITiZED

description: Eine Dokumentation der Inbetriebnahmemaßnahmen, die im Zuge des Forschungsprojekts BUiLD.DIGITiZED, durchgeführt wurden. Sie dient als Anhang des Abschlussberichts.
DOI: 10.6084/m9.figshare.24786873
title: Anhang zum Abschlussbericht BUiLD.DIGITiZED

description: Eine Dokumentation der Inbetriebnahmemaßnahmen, die im Zuge des Forschungsprojekts BUiLD.DIGITiZED, durchgeführt wurden. Sie dient als Anhang des Abschlussberichts.
DOI: 10.5281/zenodo.10280180
title: ABodyBuilder2 predicted structures of paired antibody sequences from Observed Antibody Space.

description: We used ABodyBuilder2 (https://doi.org/10.1038/s42003-023-04927-7) to model ~1.5M paired antibody structures from paired antibody sequences in Observed Antibody Space (https://opig.stats.ox.ac.uk/webapps/oas/oas_paired/). We have save the structures in folders and sub folders that correspond to the OAS files they came from. Parent folders are named according to study. Within each parent folder are sub folders names according to the files (named by SRA ID) containing sequences. Each structure is then named with the parent file followed by the row number from this file.
DOI: 10.5281/zenodo.10280181
title: ABodyBuilder2 predicted structures of paired antibody sequences from Observed Antibody Space.

description: We used ABodyBuilder2 (https://doi.org/10.1038/s42003-023-04927-7) to model ~1.5M paired antibody structures from paired antibody sequences in Observed Antibody Space (https://opig.stats.ox.ac.uk/webapps/oas/oas_paired/). We have save the structures in folders and sub folders that correspond to the OAS files they came from. Parent folders are named according to study. Within each parent folder are sub folders names according to the files (named by SRA ID) containing sequences. Each structure is then named with the parent file followed by the row number from this file.
DOI: 10.6084/m9.figshare.21629583
title: Three-dimensional numerical modelling of floods in river corridor with complex vegetation quantified using airborne LiDAR imagery

description: Excessive flood flow over the historic diversion weir in the vegetated Asahi River in Okayama Prefecture, Japan, was recently recorded for the first time after its renovation work. Fluvial researchers analysed the diversion discharge for flood mitigation measures through laboratory studies and conventional two-dimensional (2-D) depth-averaged simulations. The existing model was insufficient for simulation of certain phenomena such as flow resistance caused by vegetation branches and leaves and vertical flow distribution around the river corridor. Therefore, we developed a three-dimensional (3-D) vegetation resistance porous model by estimating topography, land cover, and vegetation distribution from airborne light detection and ranging (LiDAR) topo-bathymetry (ALB) data. Results show that the water level and flow regime were more reproducible than by referenced 2-D calculations when compared to space-time image velocimetry (STIV) data and field measurements. The diversion discharge designed using the proposed model is feasible with the current riverbed and vegetation conditions.
DOI: 10.6084/m9.figshare.21629583.v1
title: Three-dimensional numerical modelling of floods in river corridor with complex vegetation quantified using airborne LiDAR imagery

description: Excessive flood flow over the historic diversion weir in the vegetated Asahi River in Okayama Prefecture, Japan, was recently recorded for the first time after its renovation work. Fluvial researchers analysed the diversion discharge for flood mitigation measures through laboratory studies and conventional two-dimensional (2-D) depth-averaged simulations. The existing model was insufficient for simulation of certain phenomena such as flow resistance caused by vegetation branches and leaves and vertical flow distribution around the river corridor. Therefore, we developed a three-dimensional (3-D) vegetation resistance porous model by estimating topography, land cover, and vegetation distribution from airborne light detection and ranging (LiDAR) topo-bathymetry (ALB) data. Results show that the water level and flow regime were more reproducible than by referenced 2-D calculations when compared to space-time image velocimetry (STIV) data and field measurements. The diversion discharge designed using the proposed model is feasible with the current riverbed and vegetation conditions.
DOI: 10.6084/m9.figshare.21629583.v2
title: Three-dimensional numerical modelling of floods in river corridor with complex vegetation quantified using airborne LiDAR imagery

description: Excessive flood flow over the historic diversion weir in the vegetated Asahi River in Okayama Prefecture, Japan, was recently recorded for the first time after its renovation work. Fluvial researchers analysed the diversion discharge for flood mitigation measures through laboratory studies and conventional two-dimensional (2-D) depth-averaged simulations. The existing model was insufficient for simulation of certain phenomena such as flow resistance caused by vegetation branches and leaves and vertical flow distribution around the river corridor. Therefore, we developed a three-dimensional (3-D) vegetation resistance porous model by estimating topography, land cover, and vegetation distribution from airborne light detection and ranging (LiDAR) topo-bathymetry (ALB) data. Results show that the water level and flow regime were more reproducible than by referenced 2-D calculations when compared to space-time image velocimetry (STIV) data and field measurements. The diversion discharge designed using the proposed model is feasible with the current riverbed and vegetation conditions.
DOI: 10.48677/cf69f625-cd76-40e0-9e75-3ce05c90f193
title: Digitales Landschaftsmodell - Bauten Stichtag 01.10.2021

description: Der Objektbereich DLM - Bauten umfasst in Form von hochgenauer Vektorgrafik mit Attributen als Basis für Geographische Informationssysteme flächendeckend für Österreich die Elemente:
• Objekte (koordinative Bezugspunkte – POI) aus den Bereichen Kommunale Einrichtungen, Betriebseinrichtungen, Kultur, Freizeit und Sport, Schutzeinrichtungen
• Sonstige linienhafte Objekte (z.B. Bob-, Rodelbahn)
• Anlagen und Bauwerke für Versorgung (Stromleitungen und Strommasten)

Stichtag = Erstellungsdatum:
 Die Daten sind zu diesem Tag der aktuellste Datenbestand des BEV.
 Dies ist nicht ident mit dem Aufnahme- bzw. Vermessungsdatum.
DOI: 10.48677/c8ffe3f7-1cd4-4449-9bd3-511d9f2a9221
title: Digitales Landschaftsmodell - Bauten Stichtag 08.04.2022

description: Der Objektbereich DLM - Bauten umfasst in Form von hochgenauer Vektorgrafik mit Attributen als Basis für Geographische Informationssysteme flächendeckend für Österreich die Elemente:
• Objekte (koordinative Bezugspunkte – POI) aus den Bereichen Kommunale Einrichtungen, Betriebseinrichtungen, Kultur, Freizeit und Sport, Schutzeinrichtungen
• Sonstige linienhafte Objekte (z.B. Bob-, Rodelbahn)
• Anlagen und Bauwerke für Versorgung (Stromleitungen und Strommasten)

Stichtag = Erstellungsdatum:
 Die Daten sind zu diesem Tag der aktuellste Datenbestand des BEV.
 Dies ist nicht ident mit dem Aufnahme- bzw. Vermessungsdatum.
DOI: 10.48677/43d3b1f6-04d4-49ab-be3b-539b6ea71130
title: Digitales Landschaftsmodell - Bauten Stichtag 25.10.2022

description: Der Objektbereich DLM - Bauten umfasst in Form von hochgenauer Vektorgrafik mit Attributen als Basis für Geographische Informationssysteme flächendeckend für Österreich die Elemente:
• Objekte (koordinative Bezugspunkte – POI) aus den Bereichen Kommunale Einrichtungen, Betriebseinrichtungen, Kultur, Freizeit und Sport, Schutzeinrichtungen
• Sonstige linienhafte Objekte (z.B. Bob-, Rodelbahn)
• Anlagen und Bauwerke für Versorgung (Stromleitungen und Strommasten)

Stichtag = Erstellungsdatum:
 Die Daten sind zu diesem Tag der aktuellste Datenbestand des BEV.
 Dies ist nicht ident mit dem Aufnahme- bzw. Vermessungsdatum.
DOI: 10.48677/3d0f31d2-9811-4bf3-b3dd-9f22df7caaa5
title: Digitales Landschaftsmodell - Bauten Stichtag 25.01.2023

description: Der Objektbereich DLM - Bauten umfasst in Form von hochgenauer Vektorgrafik mit Attributen als Basis für Geographische Informationssysteme flächendeckend für Österreich die Elemente:
• Objekte (koordinative Bezugspunkte – POI) aus den Bereichen Kommunale Einrichtungen, Betriebseinrichtungen, Kultur, Freizeit und Sport, Schutzeinrichtungen
• Sonstige linienhafte Objekte (z.B. Bob-, Rodelbahn)
• Anlagen und Bauwerke für Versorgung (Stromleitungen und Strommasten)

Stichtag = Erstellungsdatum:
 Die Daten sind zu diesem Tag der aktuellste Datenbestand des BEV.
 Dies ist nicht ident mit dem Aufnahme- bzw. Vermessungsdatum.
DOI: 10.48677/6a5b702d-fc01-4230-9a70-7fccee20d01a
title: Digitales Landschaftsmodell - Bauten Stichtag 12.09.2023

description: Der Objektbereich DLM - Bauten umfasst in Form von hochgenauer Vektorgrafik mit Attributen als Basis für Geographische Informationssysteme flächendeckend für Österreich die Elemente:
• Objekte (koordinative Bezugspunkte – POI) aus den Bereichen Kommunale Einrichtungen, Betriebseinrichtungen, Kultur, Freizeit und Sport, Schutzeinrichtungen
• Sonstige linienhafte Objekte (z.B. Bob-, Rodelbahn)
• Anlagen und Bauwerke für Versorgung (Stromleitungen und Strommasten)

Stichtag = Erstellungsdatum:
 Die Daten sind zu diesem Tag der aktuellste Datenbestand des BEV.
 Dies ist nicht ident mit dem Aufnahme- bzw. Vermessungsdatum.
DOI: 10.6073/pasta/fd3ae24e57a40c8e8bbe082834e456dc
title: Land-Cover Change Scenarios for Massachusetts 2010-2060

description: Working with a panel of practitioners and regional experts, we developed and analyzed four plausible but divergent land-use scenarios that depict the future of Massachusetts from 2010 to 2060. We simulated the land-use scenarios and their interactions with anticipated climate change by coupling statistical models of land use to the LANDIS-II landscape model and then evaluated the outcomes in terms of the magnitude and spatial distribution of (1) direct human uses of the landscape (residential and commercial development, agricultural, timber harvest), (2) ecosystem services (carbon storage, flood regulation, nutrient retention), and (3) habitat quality (forest tree species composition, interior forest habitat). Across all scenarios, conflicts occurred between dispersed residential development and the supply of ecosystem services and habitat quality. In all but the scenario that envisioned a significant agricultural expansion, forest growth resulted in net increases in aboveground carbon storage, despite the concomitant forest clearing and harvesting. One scenario, called Forests as Infrastructure, showed the potential for synergies between increased forest harvest volume through the sustainable practices that encouraged the maintenance of economically and ecologically important tree species, and carbon storage. This scenario also showed trade-offs between development density and water quantity and quality at the watershed scale. The process of integrated scenario analysis led to important insights for land managers and policymakers in a populated forested region where there are tensions among development, forest harvesting, and land conservation. More broadly, the results emphasize the need to consider the consequences of contrasting land-use regimes that result from the interactions between human decisions and spatially heterogeneous landscape dynamics.
DOI: 10.6084/m9.figshare.24786930
title: Supplementary table I.xlsx

description: Supplementary Table I from the paper with the title: Comparative Immunoprofiling of Peripheral Blood and Cerebrospinal Fluid in a Hellenic Cohort of Multiple Sclerosis Patients
DOI: 10.6084/m9.figshare.24786930.v1
title: Supplementary table I.xlsx

description: Supplementary Table I from the paper with the title: Comparative Immunoprofiling of Peripheral Blood and Cerebrospinal Fluid in a Hellenic Cohort of Multiple Sclerosis Patients
DOI: 10.6073/pasta/9632df53e5161476303819695cecabfc
title: Red Maple Seedling Soil Warming Experiment in Harvard Forest Lath House 2015

description: Microhabitat environmental conditions are an important filter for seedling
 establishment, controlling the availability of optimal recruitment sites.
 Understanding how tree seedlings respond to warming soil temperature is critical
 for predicting population recruitment in the future hardwood forests of
 northeastern North America, particularly as environmental conditions and thus
 optimal microhabitat availabilities change. We examined the effect of 5˚C soil
 warming during the first growing season on germination, survival, phenology,
 growth, and stem and root biomass allocation in Acer rubrum (red maple)
 seedlings. While there was no effect of soil warming on germination or survival,
 seedlings growing in warmer soils demonstrated significantly accelerated leaf
 expansion, delayed autumn leaf senescence, and an extended leaf production
 period. Further, seedlings growing in warmer soils showed larger leaf area, stem
 and root structures at the end of the first growing season, with no evidence of
 biomass allocation tradeoffs. Results suggest A. rubrum seedlings can capitalize
 on soil warming by adjusting leaf phenology and leaf production, resulting in a
 longer period of carbon uptake and leading to higher overall biomass. The
 absence of growth allocation tradeoffs suggests A. rubrum will respond
 positively to increasing soil temperatures in northeastern forests, at least in
 the early life stages.
DOI: 10.48677/c4a07ddc-0855-4e5c-bec6-c35f9372b937
title: Digitales Landschaftsmodell - Bodenbedeckung Stichtag 01.10.2021

description: Der Objektbereich DLM - Bodenbedeckung umfasst in Form von hochgenauer Vektorgrafik mit Attributen als Basis für Geographische Informationssysteme flächendeckend für Österreich die Elemente: 
• die Flächen von Flüssen über 5 m Breite 
• die Flächen aller stehenden Gewässer (Seen und Teiche), Gletscher, Schwimmbecken
• die Flächen von Gletschern
• die Flächen von Klärbecken, Schwimmbecken, Industriebecken

Stichtag = Erstellungsdatum:
 Die Daten sind zu diesem Tag der aktuellste Datenbestand des BEV.
 Dies ist nicht ident mit dem Aufnahme- bzw. Vermessungsdatum.
DOI: 10.48677/4b0ab8ae-c0fa-4142-b744-000b213cb48a
title: Digitales Landschaftsmodell - Bodenbedeckung Stichtag 08.04.2022

description: Der Objektbereich DLM - Bodenbedeckung umfasst in Form von hochgenauer Vektorgrafik mit Attributen als Basis für Geographische Informationssysteme flächendeckend für Österreich die Elemente: 
• die Flächen von Flüssen über 5 m Breite 
• die Flächen aller stehenden Gewässer (Seen und Teiche), Gletscher, Schwimmbecken
• die Flächen von Gletschern
• die Flächen von Klärbecken, Schwimmbecken, Industriebecken

Stichtag = Erstellungsdatum:
 Die Daten sind zu diesem Tag der aktuellste Datenbestand des BEV.
 Dies ist nicht ident mit dem Aufnahme- bzw. Vermessungsdatum.