cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48436/efkq8-4ya14
title: Sentinel-1 Flood Maps Using A Topographic Index As Prior In Bayesian Inference

description: Background
The TUWien flood mapping algorithm is a Sentinel-1 based workflow using Bayes Inference at the pixel level. However, priors in its formulation have, so far, been reduced to non-informative priors (50%-50% probability). We proposed and tested a Height Above Nearest Drainage (HAND) index based prior probability function compared to the baseline non-informed case. We have conducted experiments on six study sites for both flooded and no-flood scenarios. Full description and discussion is found in the paper: Improving Sentinel-1 Flood Maps Using A Topographic Index As Prior In Bayesian Inference.
Methodology

We propose an exponential function defined by a midpoint and steepness parameter for the HAND prior function.
We determine optimal parameterization for the proposed function by iterating the midpoint (5 - 40) values and steepness (5 - 40).
Each flood map is compared with reference CEMS Rapid Mapping reference dataset-- generating validation/confusion maps.
Flood maps were generated using Bayes Inference based SAR Flood mapping algorithm implemented in python using Yeoda software package.
Technical details

Datasets are stored in GeoTiff format using LZW Compression
Files are compressed per dataset/map product
Files are orgnized and tiled following T3 Equi7Grid tilling system at 20m x 20m resolution.

Folder structure: dataset/map product>(continental)subgrid>tile>files.
Files are named follows the Yeoda filenaming convention.
Datasets:

Flood - flood maps generated using different parameterization of HAND prior function and non-informed priors.
No Flood - maps generated using different parameterization of HAND prior function and non-informed priors at no flood scenarios.
Validation - confusion maps generated from the difference of the Flood maps generated and rasterized CEMS Rapid Mapping reference dataset.
HAND - corresponding Height Above Nearest Drainage dataset used in the flood map generation.
DOI: 10.48436/vtfkq-snz36
title: Sentinel-1 Flood Maps Using A Topographic Index As Prior In Bayesian Inference

description: Background
The TUWien flood mapping algorithm is a Sentinel-1 based workflow using Bayes Inference at the pixel level. However, priors in its formulation have, so far, been reduced to non-informative priors (50%-50% probability). We proposed and tested a Height Above Nearest Drainage (HAND) index based prior probability function compared to the baseline non-informed case. We have conducted experiments on six study sites for both flooded and no-flood scenarios. Full description and discussion is found in the paper: Improving Sentinel-1 Flood Maps Using A Topographic Index As Prior In Bayesian Inference.
Methodology

We propose an exponential function defined by a midpoint and steepness parameter for the HAND prior function.
We determine optimal parameterization for the proposed function by iterating the midpoint (5 - 40) values and steepness (5 - 40).
Each flood map is compared with reference CEMS Rapid Mapping reference dataset-- generating validation/confusion maps.
Flood maps were generated using Bayes Inference based SAR Flood mapping algorithm implemented in python using Yeoda software package.
Technical details

Datasets are stored in GeoTiff format using LZW Compression
Files are compressed per dataset/map product
Files are orgnized and tiled following T3 Equi7Grid tilling system at 20m x 20m resolution.

Folder structure: dataset/map product>(continental)subgrid>tile>files.
Files are named follows the Yeoda filenaming convention.
Datasets:

Flood - flood maps generated using different parameterization of HAND prior function and non-informed priors.
No Flood - maps generated using different parameterization of HAND prior function and non-informed priors at no flood scenarios.
Validation - confusion maps generated from the difference of the Flood maps generated and rasterized CEMS Rapid Mapping reference dataset.
HAND - corresponding Height Above Nearest Drainage dataset used in the flood map generation.
DOI: 10.48436/b3drg-3s504
title: Supplimentary Data for "Biomimetic Cooling: Functionalizing Biodegradable Chitosan Films with Saharan Silver Ant Microstructures"

description: This is the supplimentary data for the publication: "Biomimetic Cooling: Functionalizing Biodegradable Chitosan Films with Saharan Silver Ant Microstructures" in Biomimetics

Contents of the folders:

confocal

Measurements done with the Nanofocus usurf explorer of: Silver Ants, shrimp shells (pre and post climate chamber exposure), PVS stamp and chitsoan films

FTIR

averaged data (exported from LabCognition panorama 4.0 to excel) of FTIR measurments (Bruker Lumos) of structured and unstructured chitosan films. Python scripts for generating reflectance diagrams.  Reflectance diagrams.

optical

image of chitosan film with CD structure taken with a digital camera (Canon EOS R10). Two Silver Ant Speciman under an optical microscope.

SEM

SEM Findings include serveral micrographs of shrimp shells as well as micrographs of an investigated Silver Ant gaster. Instrument used: ThermoFisher Scios II

miscellaneous

Graphs and illustrations used in the article

 

Technical Details

The uploaded data uses common file formats (pdf, xlsx, PNG & JPG) which should require no special software to read.

The python files are functional with the following package versions: 

Python 3.12

* matplotlib 3.9.2

* numpy 2.1.0

* pandas 2.2.2

* openpyxl 3.1.5

License

The CC-BY license applies to all the data. All distributed code is under the MIT license.
DOI: 10.48436/q5rca-4tz18
title: Inventory of hazardous substance concentrations in different environmental compartments in the Danube river basin
title: Output of Danube Hazard m³c, an Interreg project in the Danube Transnational Programm

description: The data set contains an SQL-dump of a PostgreSQL data base. This data base contains concentrations of hazardous substances and other water quality parameters in different environmental compartments:

river water (water and suspended sediments)
ground water
waste water (treated and untreated) and sewage sludge
storm water runoff from combined and separate sewer systems
atmospheric deposition
soil
Data from many different data sources were collected, cheked and combined and meta data were harmonized to allow for a combined data evaluation.
The SQL-file was exported from a PostgreSQL 15.2 data base and compressed using 7zip into a zip-file (dhm3cinventoryV2.zip). Text-encoding is UTF-8.
A short documentation (documentation_inventory_db_V2.0.pdf) and a listof known issues with the data which could not be resolved before publication  (List_of_known_issues_V2.0.pdf)  are enclosed as PDF files.
This Version 2.0.0 of the database contains more data as for further data sets a publication agreement was reached and some data were reimported to resolve some errors created during data preparation for import. The database structure was extended and corrected at different points, leading to an improved data model.
DOI: 10.48436/yaecs-dgr27
title: Inventory of hazardous substance concentrations in different environmental compartments in the Danube river basin
title: Output of Danube Hazard m³c, an Interreg project in the Danube Transnational Programm

description: Description of the dataset
The data set contains an SQL-dump of a PostgreSQL data base. This data base contains concentrations of hazardous substances and other water quality parameters in different environmental compartments:

river water (water and suspended sediments)
ground water
waste water (treated and untreated) and sewage sludge
storm water runoff from combined and separate sewer systems
atmospheric deposition
soil
Data from many different data sources were collected, cheked and combined and meta data were harmonized to allow for a combined data evaluation.
Technical specifications
The SQL-file was exported from a PostgreSQL 15.2 data base and compressed using 7zip into a zip-file (dhm3cinventoryV2.zip).
Text-encoding is UTF-8.
Supplementary files
A short documentation (documentation_inventory_db_V2.0.pdf) and a listof known issues with the data which could not be resolved before publication  (List_of_known_issues_V2.0.pdf)  are enclosed as PDF files.
Versions
Version 2.0.1
During creation of Version 1.0.0 and 2.0.0 there was an processing error in the coordinates of atmospheric deposition and soil sampling sites leading to identical values for latitude and longitude for some sites. This was fixed in version 2.0.1.
Version 2.0.0
This Version 2.0.0 of the database contains more data as for further data sets a publication agreement was reached and some data were reimported to resolve some errors created during data preparation for import. The database structure was extended and corrected at different points, leading to an improved data model.
DOI: 10.48436/qf6nz-nzh95
title: Data for "Strawberry post-harvest colour development to improve the colour of strawberry nectars"

description: This data set contains the raw data used to produce the paper "Strawberry post-harvest colour development to improve the colour of strawberry nectars" as a part of the HiStabJuice Project. 
Context and methodology

This excel file contains all the data used to create the statistics, tables and figures for the paper.
Technical details

This excel file contains 3 tabs that each correspond to a sheet in SPSS that can be re-uploaded into SPSS. The Key contains the numerical codes used to denote the different parameters.
DOI: 10.48436/wc8n0-f4d46
title: Data for "Strawberry post-harvest colour development to improve the colour of strawberry nectars"

description: This data set contains the raw data used to produce the paper "Strawberry post-harvest colour development to improve the colour of strawberry nectars" as a part of the HiStabJuice Project. 
Context and methodology

This excel file contains all the data used to create the statistics, tables and figures for the paper.
Technical details

This excel file contains 3 tabs that each correspond to a sheet in SPSS that can be re-uploaded into SPSS. The Key contains the numerical codes used to denote the different parameters.
DOI: 10.48436/972dt-kt839
title: Supplementary data for "Verifying antibacterial properties of nanopillars on cicada wings"

description: AFM Statistical data extraction for insect wing nano topology
Supplementary data for "Verifying antibacterial properties of nanopillars on cicada wings" (1.0.0) [Data set]. TU Wien. https://doi.org/10.1007/s13204-024-03030-5
Extract the zip file, there the files with the extension ".ibw" are the original binary AFM scans the ".txt" variant of the file is the same file saved as the ASCII data matrix from Gwyddiom. The _xy.txt file stores the pillar's top positions from Fiji see https://doi.org/10.34726/hss.2022.99685 for the description of how to export this file from Fiji. The files with the extensions  "_xy.csv", "_xy_normal.txt" and  "_z.csv" are generated with the python script outing the command "python3 histogram.py" in the python directory. 
The last execution was with python3-numpy (1.21.5-1ubuntu22.04.1) with python3 (3.10.6-1~22.04) all provided paths are relative to the locations in the provided zip.
The diagrams themself are generated utilising tikz in latex to generate a pdf containing the diagrams execute "latexmk  figures.tex -pdf -f"  in the latex_table_generation directory. The last execution was with version 4.76-1 on a Ubuntu installation 22.04.1.
Licences
CC License applies to all images and text files
MIT License applies to the python program
DOI: 10.48436/jnne6-3xj46
title: Supplementary data for "Verifying antibacterial properties of nanopillars on cicada wings"

description: AFM Statistical data extraction for insect wing nano topology
Supplementary data for "Verifying antibacterial properties of nanopillars on cicada wings" (1.0.0) [Data set]. TU Wien. https://doi.org/10.1007/s13204-024-03030-5
Extract the zip file, there the files with the extension ".ibw" are the original binary AFM scans the ".txt" variant of the file is the same file saved as the ASCII data matrix from Gwyddiom. The _xy.txt file stores the pillar's top positions from Fiji see https://doi.org/10.34726/hss.2022.99685 for the description of how to export this file from Fiji. The files with the extensions  "_xy.csv", "_xy_normal.txt" and  "_z.csv" are generated with the python script outing the command "python3 histogram.py" in the python directory. 
The last execution was with python3-numpy (1.21.5-1ubuntu22.04.1) with python3 (3.10.6-1~22.04) all provided paths are relative to the locations in the provided zip.
The diagrams themself are generated utilising tikz in latex to generate a pdf containing the diagrams execute "latexmk  figures.tex -pdf -f"  in the latex_table_generation directory. The last execution was with version 4.76-1 on a Ubuntu installation 22.04.1.
Licences
CC License applies to all images and text files
MIT License applies to the python program
DOI: 10.48436/1rb90-abf79
title: Apple pomace as potential source of oxidative stress protecting dihydrochalcones

description: Phloridzin, a natural dihydrochalcone, exhibits a wide range of beneficial effects and even broader applications, spanning from the pharmaceutical to the food industry. It is found in high concentrations in apple fruit. When apples are processed into juice, approximately 30% of their weight remains as a byproduct known as pomace, making pomace an intriguing candidate as a source of phloridzin. 

In this study, 13 apple cultivars were evaluated for their phloridzin content across different apple parts: flesh, peel, and seeds, as well as in apple juice and the byproduct pomace. The physical characteristics of the apples, including weight, size, starch content, and firmness, were measured to ensure their suitability for comparison. Methanol extracts were analyzed using the Folin–Ciocalteu assay for total phenolic content, HPLC for phloridzin quantification, and the FRAP assay for antioxidant activity.Among the solid parts, seeds exhibited the highest phloridzin content, with an average of 7.2 g/kg of apple dry weight, followed by the peel and flesh. Regarding total phenolic content, the flesh and seeds were comparable, with averages of 8.8 g and 7.8 g of gallic acid equivalent per kg of apple dry weight, respectively. For juices and pomace, phloridzin concentrations were similar across all samples, ranging from 0.32 to 0.57 g/kg of apple dry weight. In terms of total phenolic content, untreated juice contained an average of 42 g/kg of apple dry weight. 

Antioxidant activity was highest in the peel for the solid parts, with an average of 11.4 g of ascorbic acid equivalent per kg of apple dry weight, followed by the flesh and seeds. Among the juices, the treated juice exhibited the highest antioxidant activity, with an average of 29.7 g/kg of apple dry weight. Given the substantial amount of pomace produced during apple processing, it presents a promising and sustainable source for phloridzin extraction, particularly when using environmentally friendly extraction methods. The data sheet represents the concentrations of each sample resulting from the three primary tests (HPLC, Folin–Ciocalteu assay, and FRAP). Additionally, each apple cultivar's physical characteristics (weight, size, starch, firmness). This project IT-ETN-956257 is funded by Horizon 2020
DOI: 10.48436/f6x1y-szj30
title: Field observations reveal how plunging mixing and sediment resuspension affect the pathway of a dense river inflow into a deep stratified lake - Dataset

description: This dataset is part of the manuscript "Field observations reveal how plunging mixing and sediment resuspension affect the pathway of a dense river inflow into a deep stratified lake" submitted to Water Resources Research in 2024. It contains measurements of a river intrusion and underflow event in Lake Geneva, Switzerland, from 2016.09.23 to 2017.01.23.
This dataset contains the following files:

Current and echo measurements from an upward and a downward looking Acoustic Doppler Current Profile (ADCP), Teledyne RDI Workhorse 300 kHz.
Bottom water temperature measurements from a line of eight Seabird SBE56 thermistor nodes.
Vertical profiles of depth, temperature and conductivity from a CTD (ME OTS Sonde) obtained on 2016.12.12. The profile numbering corresponds to the positions given in the 20161212_CTD_Deployment_Figure.png file.
DOI: 10.48436/kz5kb-m2a05
title: Field observations reveal how plunging mixing and sediment resuspension affect the pathway of a dense river inflow into a deep stratified lake - Dataset

description: This dataset is part of the manuscript "Field observations reveal how plunging mixing and sediment resuspension affect the pathway of a dense river inflow into a deep stratified lake" submitted to Water Resources Research in 2024. It contains measurements of a river intrusion and underflow event in Lake Geneva, Switzerland, from 2016.09.23 to 2017.01.23.
This dataset contains the following files:

Current and echo measurements from an upward and a downward looking Acoustic Doppler Current Profile (ADCP), Teledyne RDI Workhorse 300 kHz.
Bottom water temperature measurements from a line of eight Seabird SBE56 thermistor nodes.
Vertical profiles of depth, temperature and conductivity from a CTD (ME OTS Sonde) obtained on 2016.12.12. The profile numbering corresponds to the positions given in the 20161212_CTD_Deployment_Figure.png file.
DOI: 10.48436/t2w3k-thq59
title: Work-Based Learning in Manufacturing Industry

description: This dataset has been created by the IALF Working Group of Work-Based Learning. It consists of a meta analysis of work-based learning approaches in manufacturing, classified by type of work-based learning and manufacturing sector after NACE 2.
DOI: 10.48436/tqtym-nyh26
title: Work-Based Learning in Manufacturing Industry

description: This dataset has been created by the IALF Working Group of Work-Based Learning. It consists of a meta analysis of work-based learning approaches in manufacturing, classified by type of work-based learning and manufacturing sector after NACE 2.
DOI: 10.48436/90tq8-5km09
title: Concentrations of major ions in wet precipitation samples in Austria

description: This dataset was compiled by the Research Group for Environmental Analytics - Institute of Chemical Technologies and Analytics, TU Wien. The precipitation data was gathered as part of the Austrian precipitation chemistry network. It contains precipitation amount, concentrations of major ions, pH and conductivity at several sites in Lower Austria, Salzburg, Styria and Tyrol.
The dataset includes the following columns:Datum – date of the precipitation event, sampling takes place at the next dayOrt – sampling site locationNS – precipitation amount in mmLF – conductivity in µS/cmpH – pH valueNH4 – concentration NH4+ in mg/LNa – concentration Na+ in mg/LK – concentration K+ in mg/LCa – concentration Ca2+ in mg/LMg – concentration Mg2+ in mg/LCl – concentration Cl- in mg/LNO3 – concentration NO3- in mg/LSO4 – concentration SO42- in mg/LPb – concentration Pb2+ in µg/LCd – concentration Cd2+ in µg/L
In addition there are flag columns for every analyte. Used flags are:1 – valid4 – contaminated7 – not available
The sampling was performed with WADOS – Wet And Dry Only Samplers by Kroneis. Anions and cations were mostly quantified with chromatographic systems. Lead and Cadmium concentrations are not available for every station and were measured with ICP-OES. pH and conductivity were measured with respective electrodes. For more detailed information on measurement see the annual reports on wet deposition, published by the federal governments of Lower Austria, Salzburg, Styria and Tyrol.
Code Availability
Python code for correct calculation of deposition is available at the Research Group for Environmental Analytics.
Acknowledgements
We would like to thank our partners at the federal governments of Lower Austria, Salzburg, Styria and Tyrol for funding and our former and current colleagues at TU Wien who collaborated to produce this dataset.
DOI: 10.48436/b0g4h-rv840
title: Concentrations of major ions in wet precipitation samples in Austria

description: This dataset was compiled by the Research Group for Environmental Analytics - Institute of Chemical Technologies and Analytics, TU Wien. The precipitation data was gathered as part of the Austrian precipitation chemistry network. It contains precipitation amount, concentrations of major ions, pH and conductivity at several sites in Lower Austria, Salzburg, Styria and Tyrol.The dataset includes the following columns:Datum – date of the precipitation event, sampling takes place at the next dayOrt – sampling site locationNS – precipitation amount in mmLF – conductivity in µS/cmpH – pH valueNH4 – concentration NH4+ in mg/LNa – concentration Na+ in mg/LK – concentration K+ in mg/LCa – concentration Ca2+ in mg/LMg – concentration Mg2+ in mg/LCl – concentration Cl- in mg/LNO3 – concentration NO3- in mg/LSO4 – concentration SO42- in mg/LPb – concentration Pb2+ in µg/LCd – concentration Cd2+ in µg/LIn addition there are flag columns for every analyte. Used flags are:1 – valid4 – contaminated7 – not availableThe sampling was performed with WADOS – Wet And Dry Only Samplers by Kroneis. Anions and cations were mostly quantified with chromatographic systems. Lead and Cadmium concentrations are not available for every station and were measured with ICP-OES. pH and conductivity were measured with respective electrodes. For more detailed information on measurement see the annual reports on wet deposition, published by the federal governments of Lower Austria, Salzburg, Styria and Tyrol.Code AvailabilityPython code for correct calculation of deposition is available at the Research Group for Environmental Analytics.AcknowledgementsWe would like to thank our partners at the federal governments of Lower Austria, Salzburg, Styria and Tyrol for funding and our former and current colleagues at TU Wien who collaborated to produce this dataset.
DOI: 10.48436/m28sd-k1a81
title: IMU Data for different Motorcyclist Behaviour

description: The data sets were collected during motorcycle trips near Vienna in 2021 and 2022. The behavior was split into different classes using videos (not part of the published data due to privacy concerns) and then cut into segments of 10 seconds.
Context and methodology

The data set was collected to show how accurate motorcyclist behavior can be assessed using IMU data
The work follows the ideas published in http://hdl.handle.net/20.500.12708/43982
The authors have a background in geodesy and computer science respectively and work in the field of geoinformation / navigation
Technical details

The data are stored as CSV files
Each file contains data from a unique behavior and has a length of 10 seconds
Each file has a header describing the columns
Units for acceleration are meters per squared second, units for angles are degrees
The files are names AB_Daten_D_C.csv

D: Datum of the trip (as YYYY_MM_DD)
A: Behavior (cruise, fun, overtake, traffic, or wait)
B: Number of the occurrence of this behavior during the trip
C: Number of the segment within the occurrence
The files are grouped by folders named after the corresponding behavior
The IMU used to collect the data was a XSENS MTi